text:
Brigitte Kepplinger (* 1952) ist eine österreichische Soziologin und Historikerin.

Leben
Brigitte Kepplinger begann 1980 mit ihrer Lehrtätigkeit sowohl an der Universität Linz als auch im Bereich der Erwachsenenbildung. 1986 bis 1987 arbeitete sie an der Oberösterreichischen Landesausstellung "Arbeit, Mensch, Maschine" im Museum Arbeitswelt Steyr mit. Bis zu ihrer Pensionierung war sie wissenschaftliche Mitarbeiterin am Institut für Gesellschafts- und Sozialpolitik der Universität Linz. Sie ist Gründungsmitglied des Vereins "Schloss Hartheim", dessen Obfrau sie ist.

Am 24. Oktober 2012 erhielt Brigitte Kepplinger von Landeshauptmann Josef Pühringer den Titel "Konsulentin für Wissenschaft des Landes Oberösterreich".

Publikationen (Auswahl)

Monographien

 Brigitte Kepplinger, Josef Weidenholzer: Die Rekonstruktion der Sozialdemokratie in Linz 1945–1950. In: Entnazifizierung und Wiederaufbau in Linz (= Historisches Jahrbuch der Stadt Linz 1995). Linz 1996, S. 13–67 ().
 Brigitte Kepplinger, Reinhard Kannonier (Hrsg.): Irritationen. Die Wehrmachtsausstellung in Linz. Sandkorn science, Grünbach 1997, 240 Seiten.
 Brigitte Kepplinger, Gerhart Marckhgott, Hartmut Reese: Tötungsanstalt Hartheim (= Oberösterreich in der Zeit des Nationalsozialismus. Band 3). 3. Auflage, Linz 2013.
 Brigitte Kepplinger, Josef Weidenholzer: Februar 1934 in Oberösterreich. "Es wird nicht mehr verhandelt …" Verlag Bibliothek der Provinz, 2009.

Beiträge in Zeitschriften und Sammelbänden

 ... nie kämpft es sich schlecht für Freiheit und Recht. Der 12. Februar 1934 in Oberösterreich. In: Die Pflicht zum Widerstand. Festschrift Peter Kammerstätter (= Veröffentlichungen des Ludwig Boltzmann Institurs für Geschichte der Arbeiterbewegung). Wien/München/Zürich 1986, S. 61–109.
 Die Entwicklung des sozialen Wohnbaus in Oberösterreich bis 1945. In: Arbeit / Mensch / Maschine. OÖ. Landesausstellung Steyr. Linz 1987, S. 205–212.
 Arbeiterwohnbau in Linz 1850–1945. Ein historischer Überblick. In: Brigitte Kepplinger (Hrsg.): Wohnen in Linz. Zur Geschichte des Linzer Arbeiterwohnbaues von den Anfängen bis 1945 (= Kulturstudien. Bibliothek der Kulturgeschichte. Sonderband 5). Böhlau, Wien/Köln/Graz 1989, S. 1–101.
 Brigitte Kepplinger (Hrsg.): Der Aufstieg zur Massenpartei. Ein Lesebuch zur österreichischen Sozialdemokratie 1889–1918. Löcker, Wien 1990, 255 Seiten.
 Nationalsozialistische Wohnbaupolitik in Oberösterreich. In memoriam Karl R. Stadler. In: Rudolf G. Ardelt, Hans Hautmann (Hrsg.): Arbeiterschaft und Nationalsozialismus (= Veröffentlichungen des Ludwig Boltzmann Instituts für Geschichte der Arbeiterbewegung). Wien/Zürich 1990, S. 265–287.
 Hartheim in der oberösterreichischen Erinnerung. Eine Spurensuche. In: Zeitgeschichte im Wandel. 3. Österreichischer Zeitgeschichtetag 1997. Innsbruck/Wien 1998, S. 346–352.
 Fürsorgeakten als historische Quelle. Die Betreuungsakten des Linzer Jugendamtes (1918–1950). In: Walter Schuster, Maximilian Schimböck, Anneliese Schweiger (Hrsg.): Stadtarchiv und Stadtgeschichte. Forschungen und Innovationen. Festschrift für Fritz Mayrhofer zur Vollendung seines 60. Lebensjahres (= Historisches Jahrbuch der Stadt Linz 2003/2004). Linz 2004, S. 303–309 ().
 Oberösterreich vom Ersten Weltkrieg bis zur Gegenwart. Innsbruck 2011, 160 Seiten.
 Die Europäische Kulturhauptstadt Linz 2009 und ihr Zeitgeschichteprogramm. In: Ferdinand Opll, Walter Schuster (Hrsg.): Stadtkultur – Kultur(haupt)stadt (= Beiträge zur Geschichte der Städte Mitteleuropas 23). Wien 2012, S. 221–274.

Weblinks
 .

Einzelnachweise

Zeithistoriker
Soziologe (20. Jahrhundert)
Soziologe (21. Jahrhundert)
Person (Oberösterreich)
Österreicher
Geboren 1952
Frau


Hotels and Resorts in Dubai can be found in all price ranges from budget to luxury. Everywhere in Dubai you can stay in comfortable hotels and resorts. There are many good, cheap and simple hotels and guesthouses but also worldclass luxury hotels.
The top class luxury 5 star hotels in Dubai are not aimed at budget travelers but travelers who want luxury, worldclass service and facilities. You can find luxury 5 star hotels in Dubai from all the renowned international hotel chains like Intercontinental, Marriot, Hilton, Accor and Hyatt. These hotels cater foremost to the luxury travelers.
In this segment you will find the hotels which are often owned and run by the local hotel industry. You get a significantly lower price than the 5 star international category but often still the same standard of luxury. Definitely the best hotels in Dubai from the 4-star segment can compete with the more expensive international chains. If you would like to enjoy an international standard, but somewhat mind your budget, you can choose a hotel from the 4-star segment.
In the 3-star segment you will often find good hotels with exceptional low prices. Many 3 star hotels in Dubai are in possession of the smaller local hoteliers. Most hotels in Dubai in the 3 star segment have a swimming pool, rooms equipped with air conditioning, refrigerator, television etc. In Dubai you will find a very wide range of 3 star hotels.
Looking for a cheap hotel in Dubai to just sleep in a clean room and no extra features than you can go to the smaller cheap budget hotels. But even in this segment can be good hotels found if you are looking for a nice cheap hotel that have facilities available including a swimming pool and rooms airconditing.
Throughout Dubai, you can find hotels for every budget..
When you are traveling to Dubai you need to book a place where you will be staying. When you search through our booking engine, you will find a long list of hotels and resorts in Dubai from which you can make your own choice. To choose the best suitable place to stay it is handy to know the difference between a resort and a hotel.
Hotels as well as resorts offer lodging for stay for a short period of time, sometimes a few days or a few weeks. In both resorts and hotels the rooms provide a standard list of amenities and facilities that will make your stay comfortable. The prices and rates of the rooms in a resort or a hotel are often determined by how many stars the hotel or resort has. The amount of stars is based on surveys and evaluations made by third party hotelsurveyors.
The basic purpose of hotels is to provide rooms and a place to stay for people that mostly travel for a short period of time. Hotels often offer more than only a place to sleep. Most hotels have facilities such as a restaurant, a swimming pool, a tour desk, business facilities, conference rooms and much more.
Resorts however often provide more than a place to sleep for a short few days. Resorts are often placed near natural surroundings so they can offer much more facilities than hotels. Mostly, resorts have nice gardens or beach terraces, more than 1 swimmingpool, outdoor activities, more than 1 restaurant and much more in order to provide a variety of different activities for the people who stay there. This means that resorts often cater to travelers who stay longer than a few days.
When you book a hotel or resort in Dubai please check the facilities offered by the hotel or resort so you can determine yourself which hotel or resort suits the best with your own specific wishes.


Huang Ching-Wen, the architect of the highlighted project Residential villa:Sculpture White by Huang Ching-Wen illustrates, This is a house sitting on a corner that the designer hoped would become a sculpture in itself. Granite slabs of different thicknesses, the same size as the windows, are hung in carefully calculated positions on the outer walls. During the day they are undisturbed, but light from the windows at night spills across them and breathes life into the sculptural architecture. The white sculptural architecture illustrates spatial aesthetics. The white sculptural architecture illustrates spatial aesthetics. Considering the relationship between man and nature, the art of light and space..
End of the article, click here to go back to previous page, or alternatively go back to Design to Do homepage.


D&G Development, LLC in partnership with PM Communities is offering unique individual styled European townhomes at the heart of downtown Alpharetta. All homes will feature covered roof top terraces. Each will be stubbed for outdoor fireplace & grill. This luxury townhome's main level features a front 10x28 flagstone entrance. Elevator is included, full overlay cabinets through out. Also features qauartz countertops through out, SS appliance package, 5" hardwood floors, lighting and fireplace allowances.


text:
\section{Introduction}

The growing complexity of Integrated Circuits (IC), time-to-market pressure, and expensive design and manufacturing processes have promoted the globalization of the semiconductor industry. Outsourcing the fabrication and depending on third-party hardware Intellectual Property (IP) blocks and Electronic design automation (EDA) tools raise the risk of intentional and malicious manipulation of the circuit, known as Hardware Trojan (HT). Figure \ref{fig:supply-chain} demonstrates the IC supply chain and the involving parties which are vulnerable points of HT insertion.
Currently, HT is a significant hardware security concern with devastating consequences such as denial of service, malfunctioning, data leakage, and performance degradation in the chip. The attackers usually design HT to be a tiny circuit hidden inside the main design, normally inactive with minimal effect on the chip's functionality and specification. The HT often gets triggered under rare circumstances, and consequently, it can escape detection by routine simulation and functional testing.

Trojan detection is crucial to ascertain the authenticity of 3PIPs and prevent the negative consequences of HTs. However, HT detection does not suffice to ensure the fabrication of a trustworthy chip, and HT localization is the next essential step. The hardware IPs fall into three classes based on their format and level of abstraction; Soft IP (i.e., synthesizable Verilog or VHDL source code), Firm IP (i.e., placed RTL block and netlist), and  Hard IP (i.e., physical layout and GDSII). Soft IP is the most popular IP core, and IP trust revolves around it. However, it has the most vulnerability against HT insertion because the flexibility and high level of abstraction in Register Transfer Level (RTL) codes facilitate the HT design and implementation for the attacker \cite{tehranipoor2014integrated}.

Manual review of hardware design to pinpoint HT is very time-consuming and error-prone, especially for an industrial-level large design. Due to the paramount importance of the HT threat, numerous defense mechanisms are proposed in the literature to determine if the design is infested with HT. Still, they fail to locate it in the IC design.
Some works analyze parameters, and side-channel data of circuits such as polynomials of gate-level implementation \cite{farahmandi2017trojan}, thermal map \cite{tang2019activity}, or path delays \cite{sabri2021sat} to pinpoint the disturbance introduced by HT. They have the premise that a trusted Trojan-free reference design called golden reference exists to compare against the parameters of the circuit under test, which is an unrealistic assumption. In order to obtain the golden reference, the whole process of IC design, test, and fabrication should be performed by in-house trusted teams, EDA tools, and manufacturing facilities that would be very expensive and infeasible in practice.

\begin{figure*}[t]
\centering
\includegraphics[width=0.96\textwidth]{figures/supply-chain.png}
\vspace{-1.5em}
\caption{IC supply chain which is vulnerable to HT insertion in different stages.}
\vspace{-1.5em}
\label{fig:supply-chain}
\end{figure*}

HT defense methods based on formal verification and code analysis define some properties for HT, analyze the hardware design, and mark the areas satisfying the predefined properties. For example, \cite{islam2020framework} examines RTL codes using word-level statistics of the inputs and tags the arithmetic blocks with rare nets as vulnerable to HT. \cite{sturton2011defeating} flags the unused portion of the circuit as malicious. \cite{waksman2013fanci} measures the degree of control that an input signal has on the operation and outputs of the circuit and marks weakly-affecting inputs as possible HT triggers.
These works narrow down the search space for HT. However, they still burden the designer to manually review the suspicious areas, which can be a large circuit due to low localization resolution. Moreover, most of the existing solutions require manual property definition or feature extraction, and they fail to outline a comprehensive set of properties or features representing all kinds of HTs. Consequently, they are effective only for particular HTs.

There is an increasing trend to explore the graph representation of hardware for security purposes \cite{fyrbiak2019graph} because hardware design is a non-Eulicidian structural data that shares similar properties with a graph. The graph is a mathematical structure that represents the relation between pairs of objects. It preserves the topological information that makes it the best match for modeling the fundamental objects in the hardware design process \cite{ma2020understanding}. For instance, the graph is leveraged to represent the hardware design in \cite{piccolboni2017efficient, fyrbiak2019graph} for HT mitigation. Still, Graph matching algorithms fail to recognize unknown HTs and are not scalable to large designs due to high complexity.

Deep learning has introduced potent techniques that revolutionized many fields of study \cite{yasaei2020iot}, but it operates on Euclidean data and cannot be directly used for hardware design. The current deep learning models for HT mitigation examine the side-channel emissions of the fabricated chip, which are time-series data \cite{htnet}. To fill the gap and apply machine learning to hardware design, we convert the design from the textual format of HDL code to a graph and leverage GCN, which is like deep learning operating on graphs. A recent work \cite{gnn4tj} proposes a graph classification model based on a graph neural network to find whether the circuit is infested with HT or not, but it fails to locate the Trojan.

In order to overcome the shortcomings of current approaches, we propose a novel, golden reference-free HT localization method in the pre-silicon stage.  We generate a graph representation for the hardware design,  assign attributes to nodes in the graph, classify nodes, and locate the Trojan in HDL code based on the graph node's class. Our node classification model is based on GCN that automatically aggregates the features in graph nodes through the graph convolution operation.
We create a dataset of hardware designs by inserting HT benchmarks from TrustHub \cite{trusthub} to different circuits. Our methodology is trained on this dataset to learn the behavior and features of Trojan nodes. Then, the trained model can locate the Trojan nodes based on their malicious abnormal behavior in even new and unknown Trojans in a fully automated process without any need for manual review.

\subsection{Research Challenges}

HT detection and localization is a difficult problem and the current solutions suffer from the following shortcomings:
\begin{itemize}

\item  \textbf{Reliance on golden reference}: A Trojan-free circuit called golden reference for comparison with the circuit under test is not available in the real world, and the golden reference-dependent methods are not practical.

\item  \textbf{Unable to generalize}: Various types of HTs are discovered so far, and new HT designs are continuously introduced. Due to the variety in HT design and specification, defining a template or some properties that describe all HTs is challenging. Consequently, many countermeasures fail to generalize and are limited to known HTs or only HTs with a specific trigger or payload.

\item  \textbf{Low localization resolution}: Some works output the areas of the circuit that are vulnerable to HT insertion and due to low localization resolution, they burden the designer with an exhaustive review of suspicious regions.

\item  \textbf{Manual feature extraction}: Algorithmic and classic machine learning approaches rely on an expert to define properties and extract features from the circuit, which is error-prone and exhausting.

\item \textbf{Scalability}: With the increased complexity of ICs, scalability has become an essential characteristic of any circuit analysis tool, but complex algorithms fail to scale for large designs.

\end{itemize}
\vspace{-1.5em}
\subsection{Contributions}

In this paper, we surmount the aforementioned research challenges and propose a novel, golden reference-free approach for HT localization that is fully automated with no need for manual revision by experts. To the best of our knowledge, this is the first work to apply GCN for HT localization. Our contributions can be summarized as:

\begin{itemize}
    \item The hardware design HDL code is converted to data-flow graph using hardware design toolkit \cite{pyverilog}. We develop an algorithm to extract the attributes of nodes and assign an attribute vector to each one.

    \item We construct a node classification model based on GCN that automatically aggregates the features for each node in the graph representation of hardware design, learns their behavior, and marks the malicious nodes.

    \item We develop a Trojan labeling algorithm that provides a mapping from HDL code to its graph and labels the nodes in the graph as Trojan or benign. This algorithm determines the HT label vector of the training dataset, which is deployed by the GCN model for training and calculating classification loss.

    \item We survey the existing pre-silicon HT detection and localization methods and their shortcomings to picture the current state and challenges of this research area as well as the potential of graph learning for advancement in hardware security.

\end{itemize}

\section{Related Works and Backgrounds}
\label{sec:related-works}

\subsection{Hardware Trojan Detection}

Due to the severity of the HT threat, its detection is extensively studied in the literature, while the importance of HT localization is overlooked. In this section, we survey the approaches that answer whether the design is contaminated with HT but cannot locate the Trojan.

\subsubsection{\textbf{Graph similarity algorithm}}: Graph is shown to be a natural and potent representation of hardware design \cite{ma2020understanding}. In this regard, \cite{piccolboni2017efficient} attempt to detect HT by examination of the similarity between data/control flow graphs of the Trojan and the circuit under test.  \cite{fyrbiak2019graph} further advances the graph matching algorithm by creating a new graph similarity heuristic for hardware security applications. In addition to the lack of scalability and high complexity of graph matching algorithm (NP-complete), this approach is challenged with new types of HT as their detection range is limited to known HTs and fails to generalize.

\subsubsection{\textbf{Machine learning}}: Machine learning introduces powerful data-driven models that, through the iterative process of training on a dataset, perform optimization to minimize the prediction error and learn to infer correct prediction for a test subject. Since most machine learning models work with Euclidean data, they are mainly used in the side-channel analysis for post-silicon hardware security applications \cite{htm, htnet, ashraf2018towards}. Machine learning models for HT detection in pre-silicon are proposed that are trained on the features extracted from the circuit or its graph representation. For instance, Trojan-net features derived from gate-level netlist are exploited by support vector machine \cite{hasegawa2016hardware} and multi-layer neural network \cite{hasegawa2017hardware}.  Others apply gradient boosting \cite{han2019hardware}, probabilistic neural network \cite{demrozi2017exploiting},  or high-level behavior classification model of artificial immune system \cite{zareen2018detecting} on the features extracted from abstract syntax tree, control flow graph, and data/control-flow graphs of RTL design, respectively. Reliance on a golden reference is one of the shortcomings of many classification models.
Moreover, another common problem with classic machine learning models is the manual feature extraction which is error-prone and highly depends on the developer's knowledge of hardware design. Deep learning models eliminate this problem with automated feature extraction, which is recently employed by \cite{gnn4tj}. It classifies the data-flow graph of RTL design using a graph neural network.

\subsubsection{\textbf{Formal verification}}: Formal verification translates the circuit to proof checking format and proves it satisfies the pre-defined security properties. The limited detection range is the main weakness of this method as it is difficult to define a set of properties that generalizes to the various types of HT. For example, \cite{rajendran2015detecting} and even its advanced version \cite{rajendran2016formal} can only expose HT payloads that leak data.
Information flow tracking is a formal method for security verification, deployed in \cite{ardeshiricham2017register, nahiyan2017hardware, hu2018property}. The model checking used in formal verification approaches encounters state explosion and cannot scale for large designs.

\subsubsection{\textbf{Test pattern generation}}: HT evades detection by the routine verification test and simulations because it is a tiny circuit with negligible effect on circuit functionality and specification. Thus, test pattern generation methods produce a set of test vectors more likely to activate the HT \cite{saha2015improved, zhao2018hardware}. The stand-alone method is usually insufficient, and it is bundled with other techniques such as side-channel analysis \cite{lyu2020automated}.

\subsection{Hardware Trojan Localization}

The majority of defense mechanisms against HT focus on its detection, and there are inadequate works with the capability to locate the Trojan circuit. For example, \cite{sabri2021sat, tang2019activity} perform HT localization with the assumptions that the design pipeline is trusted, and the attacker resides in the foundry. Both works have the unrealistic premise that a golden reference is available.
\cite{sabri2021sat} proposes an SAT-based test pattern generation scheme that detects and locates the Trojan inserted by foundry by comparing the timing and path delays of the suspicious IC with a golden IC.
\cite{tang2019activity} extracts Trojan activity factor from the redundant thermal map and performs HT localization by comparing the thermal side-channel of the target chip with the golden reference.

Code analysis is one of the conventional pre-silicon HT defense mechanisms that inspects the HDL code to ascertain suspicious signals in the circuit, and it is mainly restricted to combinational logic. In this technique, the code is scanned based on coverage metrics (toggle, line, state, etc.) to find the potential areas of HT presence. Different methods propose various definitions for a suspicious area, such as unused circuit identification \cite{hicks2010overcoming}, weakly affecting inputs \cite{waksman2013fanci}, and low dependence on functional inputs \cite{zhang2015veritrust}. Due to exclusive definition of HT, later \cite{sturton2011defeating} defeats \cite{hicks2010overcoming} and  \cite{zhang2015veritrust} and \cite{waksman2013fanci} get bypassed by the new HT attack \cite{zhang2014detrust}. \cite{islam2020framework} proposes a framework to analyze RTL codes using word-level statistics of the inputs. It locates the arithmetic blocks with rare nets to be reviewed as candidates vulnerable to HT and can only identify HTs that are always on or triggered by current inputs. Code analysis suggests the circuit areas susceptible to HT insertion and cannot actually locate the HT or even guarantee its detection.

\cite{farahmandi2017trojan} introduces a formal method based on symbolic algebra by extracting polynomials from the gate-level implementation of the untrustworthy IP and comparing them with the golden reference polynomials.
\cite{islam2019socio} leverages principal features of social network analysis to outline the relation between design properties and locate HT. This approach applies only to combinational Trojans.
\begin{figure*}[t]
\centering
\includegraphics[width=0.96\textwidth]{figures/train-test.png}
\vspace{-1em}
\caption{Overview of our HT localization methodology in training and inference phases.}
\vspace{-1.5em}
\label{fig:overview}
\end{figure*}
\subsection{Graph in Hardware Applications}

Hardware design is non-Eulicidian structural data that shares similar properties with a graph. The graph is a mathematical structure that represents the relation between pairs of objects. It preserves the topological information that makes it the best match for modeling the fundamental objects in the hardware design process. Thus, the graph is leveraged to represent the hardware in numerous Electronic Design Automation (EDA) problems which shift the problem to choosing the appropriate algorithm from the many well-known graph algorithms and apply it directly or with a slight change to solve the problem. However, developing an effective approach for each problem is still challenging.
Furthermore, many problems are NP-hard with large sizes which makes efficiency a major concern and leads to scalability issue. To tackle the complexity issue, data-driven learning techniques have grabbed much attention. The classical machine learning models include an initial step of manual feature extraction which is followed by model training based on a large set of data instances \cite{ma2020understanding}.

The next generation of machine learning models leverages convolutional layers in deep learning models, making the feature extraction process automated through learning. Recently, deep learning models are developed with high resiliency against adversarial attacks \cite{ashraf2020r2ad}. Although that deep learning has improved the performance in various applications, it cannot be directly applied to graphs because it is originally developed for Euclidean data, and notable extra endeavors are needed to extract features from graphs and encode the structural information. In response, the graph learning method is introduced, which defines the convolutional operation on graphs and automates the feature extraction from graphs. There have been a few works investigating the advantages of graph learning for the hardware security \cite{gnn4tj, gnn4ip, hw2vec} and hardware design automation such as test point insertion \cite{ma2019high}, and power estimation in simulation \cite{zhang2020grannite}. In this work, we leverage a state-of-the-art machine learning model, GCN, to model the hardware for security purposes.
\section{Methodology}

In this paper, we propose an automated pipeline to locate Trojan circuit at RTL that includes several steps, as depicted in Figure~\ref{fig:overview}; i) converting hardware design to graph, ii) extracting the node attributes, iii) labeling Trojan nodes, iv) node classification, and v) HT localization in HDL. In the following sections, we define our problem formulation and threat model and then, we elaborate on the aforementioned steps for localization.
\subsection{Problem Formulation and Threat Model}

The main target of our methodology is to locate the Trojan circuit inside hardware design at RTL. The model's input is an HDL code which is later converted to a graph. The graph representation is further processed, and the graph learning model classifies the graph nodes as Trojan or benign. Eventually, the model outputs a list of malicious signals and operations in the HDL code corresponding to Trojan nodes in the graph.

The graph learning model, GCN, is trained on a dataset of graphs derived from HT benchmarks in which the labels of the nodes are known. Our dataset only includes the HT-infested designs, not any Trojan-free design. Our approach is golden reference-free and able to perform HT localization on unknown HTs. To demonstrate these characteristics, we train our model on a set of circuits and test it on the circuits not observed by the model before in the training stage. Therefore, the model locates Trojan nodes in the circuit under test while it has not seen its golden reference or HT benchmark. Moreover, we make no assumption about the HT payload or trigger type, and the fundamental features of Trojan nodes are automatically aggregated and learned by convolutional layers in our GCN.

An attacker may manipulate the hardware design at any pre-silicon stage of the IC supply chain in our threat model (refer to Figure \ref{fig:supply-chain}), but eventually, the HDL code should be available for our methodology to perform HT localization. Therefore, multiple attack scenarios are feasible. The attacker can be a rogue in-house designer, an untrusted 3PIP design company, or a 3P-EDA tool provider who tampers with the HDL code. The adversary may alter the design in the low level of abstractions, such as netlist and physical layout.  In this case, we assume that the RTL code is obtained by reverse engineering.
\subsection{Hardware Design Conversion to Graph}

A circuit is described using Hardware Description Languages (HDL) at the design stage, such as Verilog and VHDL. The HDL code has a textual format with predetermined syntax and cannot be directly used as data for machine learning. Thus, we convert the HDL code to a graph that embeds the design features and preserves the topological information.

HDL code comprises modules, signals, and operations. Modules are used to cluster parts of circuits and better express the hierarchy in the hardware design, but they do not affect the design specification. On the other hand, signals and operations fundamentally describe the hardware design. A  signal can be a register or wire in HDL code, and it carries a value that is changed through an operation or assignment. For instance, the Verilog code for the AES-T1800 HT benchmark is shown in Figure~\ref{fig:TJcode} with its corresponding data-flow graph in Figure~\ref{fig:TJgraph}.

To convert HDL to graph, we combine the modules to have a single HDL code for the whole design. Afterward, we parse it and extract the data dependency subgraphs for all signals in HDL code using a hardware design tool called PyVerilog \cite{pyverilog}. Each signal subgraph is a tree that expresses how the value of root signals depends on the operations and other signals in the design. We connect the common nodes in the subgraphs to construct one data-flow graph per hardware. The extracted graph $G = (V, E)$ is a directed homogeneous graph in which each node is named after its corresponding signal/operation in the HDL code, and an edge $e_{ij}$ indicates that node $v_i$ depends on node $v_j$. Lastly, the graph is processed to remove standalone nodes such as \textit{clk} node in the data-flow graph of AES-T1800 HT benchmark, depicted in Figure~\ref{fig:TJgraph}.
\begin{figure}[t]
\centering
\includegraphics[width=0.46\textwidth]{figures/TJcode.png}
\vspace{-1em}
\caption{The Verilog code of AES-T1800 Trojan benchmark.}
\vspace{-1.5em}
\label{fig:TJcode}
\end{figure}
\begin{figure}[t]
\centering
\includegraphics[width=0.46\textwidth]{figures/graph.png}
\vspace{-1em}
\caption{The data-flow graph and node attributes of AES-T1800 Trojan benchmark, shown in Figure~\ref{fig:TJcode}.}
\vspace{-1.5em}
\label{fig:TJgraph}

\end{figure}
\begin{figure*}[t]
\centering
\includegraphics[width=0.90\textwidth]{figures/gcn.png}
\vspace{-1em}
\caption{The architecture of our GCN model for node classification.}
\vspace{-1.5em}
\label{fig:gcn}

\end{figure*}
\subsection{Node Attribute Extraction}

The initial data-flow graph $G = (V, E)$ expresses the flow of information and connections between components in the circuit, but it does not differentiate between the nodes. Therefore, we develop a node analyzer to extract the type of nodes from their name, which Pyverilog generates during graph generation. Then, the analyzer assigns an attribute vector to each node which is further used as an input feature to GCN. Nodes can be categorized as \textit{constant}, \textit{signal}, or \textit{operation}. The \textit{constant} nodes represent numbers and are tagged as numeric regardless of their value. The \textit{signal} nodes are derived from a wire or register in the HDL code and tagged as input, output, or signal based on their position in the circuit. The \textit{operation} nodes are related to the operands and conditional statements in the HDL code. They have a wide variety, including gates (not, and, or, xor, etc.), branch, conditional operands (equal, less than, greater than, etc.), Part select, and concatenation. We have detected 28 different types of \textit{operation} nodes which sums up the total numbers of the node types to 32. The tags are independent of the circuit design and they represent all the possible types of nodes that can be generated by our graph generation pipeline for any HDL code. Some examples of node tags are demonstrated in Figure~\ref{fig:TJgraph}. After tagging nodes, we generate an attribute vector for each node by performing one-hot encoding on tags. Therefore, the new directed graph with $N$ nodes and $F$ different tags is defined by $A \in R^{N \times N}$ and $X \in R^{N \times F}$ where $A$ is an asymmetric adjacency matrix, and $X$ is the matrix of node attributes.
\subsection{Trojan Labeling Algorithm}

Although HT circuit is known in HDL codes used for training the GCN, the graph representation of the circuit does not have any notion of Trojan. Therefore, we develop an algorithm to determine the HT nodes in the graph representation of HT benchmarks. The algorithm has an HDL processing step in which a keyword is added to the signals and modules of the HT circuit. This keyword will be visible in the name of \textit{signal} nodes of the HT circuit, but the \textit{constant} and \textit{operation} nodes will not be affected. So, after graph generation, the labeling algorithm iterates among the nodes and flags the \textit{operation} and \textit{constant} nodes as 2 (can be Trojan), the \textit{signal} node with the keyword as 1 (definitely Trojan), and the rest of the \textit{signal} nodes as 0 (not Trojan). Thus, the flag of \textit{signal} nodes is known to be Trojan, or benign. The flag of \textit{constant} and \textit{operation} nodes are modified based on the rules that the \textit{operation} nodes applied to Trojan nodes are part of the HT circuit and the \textit{constant} nodes inherit the flag of their parent \textit{operation} node.
Algorithm~\ref{alg:TJlabeling} and Figure~\ref{fig:flowchart} show how the algorithm traverses the graph starting from root nodes and modifies the number 2 flags based on these rules until there is no flag of 2 left and all nodes are marked either as Trojan or benign. The algorithm results in a Trojan label vector $Y \in {[0, 1]}^N $ for each graph with $N$ nodes in which the malicious nodes are marked as 1. This label vector is further used as the classification label of the training dataset to train the GCN model. Note that this step is only once performed for the training dataset in the training stage, and it is not required in the inference stage when the model is already trained and ready to locate HT in a circuit.
\begin{algorithm}[h]
\normalsize
\SetAlgoLined
\SetAlgoVlined
\DontPrintSemicolon
\textbf{Input:} Nodes name list $name$, Nodes type list $type$. \\
\textbf{Input:} Signal nodes tag $SIG$, Operation nodes tag $OP$, Numeric node tag $NUM$. \\
\textbf{Output:} Trojan label vector $lbl$. \\
Initialize $queue$ = a list of root node. \\
Initialize $left$ = a set of all nodes. \\
Initialize $visited$ = an empty set. \\
\ForEach{node $n_i$ in $left$ } {
    \If{ ($type$[$n_i$] is $OP$ or $NUM$)} {
        $lbl$[$n_i$] = 2  \\
    } \ElseIf { ($type$[$n_i$] is $SIG$) and (TJ in $name$[$n_i$]) } {
        $lbl$[$n_i$] = 1  \\
    } \Else {
        $lbl$[$n_i$] = 0   \\
    }
}
\While{ $left$ is not empty}{
    \If {$queue$ is empty } {
        $node$ = $left$.pop()  \\
        add $node$ to $queue$  \\
    } \Else {
        $parent$ = $queue$.pop() \\
        \If {$lbl$[$parent$] != 2 } {
            remove $parent$ from $left$  \\
            add $parent$ to $visited$   \\
            \ForEach{ child $c_i$ of $parent$} {
                \If {$c_i$ not in $visited$}{
                    \If {$lbl$[$c_i$] == 2} {
                        $lbl$[$c_i$] = $lbl$[$parent$] \\
                    }
                    add $c_i$ to $visited$ \\
                    add $c_i$ to $queue$ \\
                }
            }
        }
        \Else {
            add $parent$ to $left$  \\
        }
    }
}
\Return Trojan label vector $lbl$
\caption{Trojan Nodes Labeling Algorithm}
\label{alg:TJlabeling}
\end{algorithm}

\subsection{Graph Convolutional Networks}

Traditionally, deep learning models often use an array/stack of trainable filters such as convolutional neural networks to extract meaningful features for grid-like structured data. Inspired by those works, we adopt the GCN layer as our trainable filter from \cite{kipf2016semi}.  GCN is devised to embed nodes with different features while taking the topological information in non-euclidean data into account. The input of the GCN model is a graph $G = (V,E)$ is represented by adjacency matrix $A \in N \times N$ and node attribute matrix $X \in N \times F$ where $N$ is the number of nodes. $F$ is the length of each node attribute vector in our model.
Each graph convolution layer aggregates information from immediate node neighbors and update nodes through a process called \textit{message passing} based on the following formula:
\begin{equation}
    {H^{(l+1)}} = \sigma(\Tilde{A} {H^{(l)}} W^{(l)})
\end{equation}
Here, $l$ denotes the layer number, and $H^{(0)}$ is the initial node features that equal to $X$, the node attributes matrix. $W^{(l)}$ is a layer-specific trainable weight matrix. $\sigma(.)$ denotes the activation function that is Rectified Linear Unit (ReLU) in our model.
To perform graph convolution, the normalized adjacency matrix $\Tilde{A}$ is computed by:
\begin{equation}
     \Tilde{A} = \widehat{D}^{-\frac{1}{2}} \widehat{A} \widehat{D}^{-\frac{1}{2}}
\vspace{-0.4em}
\end{equation}
where $\widehat{D}$ is the diagonal degree matrix to solve the problem of scale change of the feature vectors after multiplication by the matrix $A$. It is calculated by $\widehat{D} = \sum_{j}\widehat{A}_{ij}$ and $\widehat{A}$ is derived from $\widehat{A} = A + I_N$ where $I_N$ is the identity matrix that adds self-loop connection to $A$, adjacency matrix of graph $G$, to make sure each node embeds its previous value from last iteration as well as new data from its neighbors.

Stacking the graph convolution layers, we create a GCN that is able to integrate information from a larger set of neighbors. Our model architecture is illustrated in Figure~\ref{fig:gcn}. It comprises three convolution layers with a ReLU activation function and one last convolution layer connected to a layer of Softmax units to classify each node as Trojan or benign and generate the predicted node label $Y$. It concludes the computations of our GCN model as below:
\begin{equation}
    Z = Softmax(\Tilde{A} \sigma(\Tilde{A} \sigma(\Tilde{A} \sigma(\Tilde{A} X W^{(0)}) W^{(1)}) W^{(2)} ) W^{(3)} )
\end{equation}
where $Z \in {[0,1]}^2$ indicates the predicted node labels in which $[1,0]$ denotes Trojan and $[0,1]$ denotes benign node.

\textbf{Training}: Figure~\ref{fig:overview} summarize the training process and units. The first step in training is preparing a training dataset based on the GCN model requirement. Thus, the HDL codes are converted to data-flow graphs in which the nodes have been assigned an attribute vector and a label (Trojan/benign). The attribute vectors feed information about each node's characteristic to the model, and the labels are used to calculate the error due to the node misclassification.
Training is an iterative optimization process that modifies the weights in GCN to minimize its classification error, anointed as the loss. We use Adam optimizer \cite{kingma2014adam}, a conventional optimization technique for efficient gradient descent to minimize the loss $L$. We utilize the cross-entropy loss function to calculate the error over all  nodes in a graph using this formula:
\vspace{-0.4em}
\begin{equation}
    L = - \sum_{i \in V} \sum_{j=0}^{C} Y_{ij} ln(Z_{ij})
\end{equation}
\vspace{-0.2em}
where $C$ is the number of classes which is two (Trojan and benign), and the $j$ indicated the dimension of the output vector. $V$ is the set of nodes in a graph, and $i$ iterates over them. $Y$ is the actual label of nodes obtained from the Trojan node labeling algorithm, and $Z$ is the predicted label. Note that $L$ is node classification loss for one graph, and total loss is the summation of all graphs loss.

\textbf{Inference}: At the inference stage that the model is trained, we use it to test new hardware design, as shown in Figure~\ref{fig:overview}. After label prediction, the node labels are passed to HT localization in the HDL unit. It employs the mapping between graph nodes and HDL signals to mark malicious signals in HDL code based on Trojan nodes in the graph. We also perform HT detection by counting the number of nodes predicted to be Trojan and label the circuit as Trojan-free if the number of Trojan nodes is lower than a threshold and, basically, negligible compared to the size of the design. The user can set the threshold depending on their target sensitivity.

\begin{figure}[t]
\centering
\includegraphics[width=0.46\textwidth]{figures/flowchart.png}
\vspace{-1em}
\caption{ The flowchart of Trojan labeling algorithm. }
\vspace{-1.5em}
\label{fig:flowchart}
\end{figure}
\begin{table*}[]
\caption{ Comparing the HT localization methods in the literature. }
\vspace{-1em}
\label{tab:survey}
\begin{tabular}{|l|c|c|c|c|c|l|}
\hline
\rowcolor[HTML]{AED09D}
\multicolumn{1}{|c|}{\cellcolor[HTML]{AED09D}Method} & Stage & \begin{tabular}[c]{@{}c@{}}Golden\\ chip-Free\end{tabular} & Automated & \begin{tabular}[c]{@{}c@{}}Localization\\ Resolution\end{tabular} & \begin{tabular}[c]{@{}c@{}}HT\\ diversity\end{tabular} & \multicolumn{1}{c|}{\cellcolor[HTML]{AED09D}Performance} \\ \hline
\rowcolor[HTML]{EBF1E9}
GCN (ours) & Pre-S$^2$ & Yes & Yes & High & High &  HT localization with 99.6\% accuracy and 98.9\% precision  \\ \hline
Social network \cite{islam2019socio} & Pre-S & Yes & No & High & Low &  HT localization with 97.3\% accuracy and less than 2\% false positive  \\ \hline
\rowcolor[HTML]{EBF1E9}
Code analysis \cite{islam2020framework} & Pre-S & Yes & No & Low & Low &  Activity estimation with less than 2\% error to flag low-activity as HT  \\ \hline
VeriTrust \cite{zhang2015veritrust} & Pre-S & Yes & No & Low & Low &  HT localization with 100\% recall and 11.5\% precision  \\ \hline
\rowcolor[HTML]{EBF1E9}
FANCI \cite{waksman2013fanci} & Pre-S & Yes & No & Low & Low &  HT localization with 100\% recall and less than 8\% false positive  \\ \hline
UCI \cite{hicks2010overcoming} & Pre-S & Yes & No & Low & Low &  HT localization with 100\% recall and 7.5\% precision  \\ \hline
\rowcolor[HTML]{EBF1E9}
Symbolic algebra\cite{farahmandi2017trojan} & Pre-S & No & No & High & High &  HT localization with 100\% recall and 74\% precision  \\ \hline
Thermal map \cite{tang2019activity} & Post-S$^3$ & No & No & Low & High &  Successfully locates the HTs with less than 20 gates  \\ \hline
\rowcolor[HTML]{EBF1E9}
Path delay \cite{sabri2021sat} & Post-S & No & No & High & High &  HT localization with 100\% recall and 0.56\% false positive rate  \\ \hline
\end{tabular}
\vspace{-2.5em}
\end{table*}
\vspace{-0.5em}
\section{Evaluation}

\subsection{Experimental Setup}

We construct and assess our GCN model on the graph representation of a dataset, consisted of 49 Trojan-infested RTL codes that are listed in Table~\ref{tab:resultPerHW}. The limited number of graphs in our dataset is not problematic since our machine learning model is for node classification, and each graph contains thousands of nodes, refer to Table~\ref{tab:ave-result}.
 An extensive dataset enhances the model's performance and capability to learn a generic knowledge of HT and learning-based model is easily adaptable by adding new circuits and Trojans to training for further generalization.
Our dataset comprises three base circuits that contain various HTs. AES, DES, and RC5 are encryption cores with different algorithms that get an input number as plaintext along with a secret key and output the encrypted number known as ciphertext. The AES samples are derived from the TrustHub benchmark \cite{trusthub} which is the most popular open hardware Trojan datasets used in the literature. The RC5 and DES are open-source designs in which we insert the Trojan circuits extracted from AES-Txx benchmarks. However, some of TrustHub HT benchmarks are specific to AES and cannot be inserted in RC5 or DES circuits due to dependency on the internal signals of AES. In Table~\ref{tab:ave-result}, the first part of benchmark name represents the base circuit and the second part, shows the type of Trojan inserted in the base circuit. For example, DES-T100 shows DES circuit infected with T100 Trojan from TrustHub dataset.
All the algorithms and models are implemented in the Python language. We use PyTorch and the Geometric extension library to build the graph learning model. The GCN model training and testing are performed by NVIDIA GeForce GTX 1080 graphics card.

We use the leave-one-out approach for evaluation. We report test results on a circuit infected with a HT benchmark while the model is trained on other circuits and HTs. We change the test circuit and repeat training on the rest again. The process is repeated until all samples are tested. In this scenario, the circuit under test and its HT are not seen by the model in training which indicates the capability of model to locate HT in unknown circuits and HTs.
In all evaluations, we define the positive sample as Trojan node class and the negative sample as benign node class. For example, true positive represents the Trojan nodes that are correctly classified as Trojan.

\begin{table}[t]
\centering
\caption{The performance of HT detection.}
\vspace{-1em}
\label{tab:detction}
\begin{tabular}{|c|c|c|c|}
\hline
\rowcolor[HTML]{AED09D}
Circuit                    & AES  & DES  & RC5  \\ \hline
Classified as Trojan node  & 1             & 2             & 0             \\ \hline
\rowcolor[HTML]{EBF1E9}
Classified as benign node  & 13437         & 10210         & 2106          \\ \hline
Total nodes                & 13438         & 10212         & 2106          \\ \hline
\rowcolor[HTML]{EBF1E9}
Classified as Trojan/total & 7.44e-5       & 1.95e-4       & 0             \\ \hline
HT detection accuracy      & \multicolumn{3}{c|}{100\%}                    \\ \hline
\end{tabular}
\vspace{-1.5em}
\end{table}
\begin{table}[t]
\centering
\caption{The summary of dataset and HT localization performance.}
\vspace{-1em}
\label{tab:ave-result}
\begin{tabular}{|c|c|c|c|c|}
\hline
\rowcolor[HTML]{AED09D}
Benchmark                   & All & AES-Txx    & DES-Txx    & RC5-Txx    \\ \hline
Accuracy                  & 99.6\%       & 99.8\% & 99.8\% & 99.2\% \\ \hline
\rowcolor[HTML]{EBF1E9}
F1-score                  & 93.1\%       & 93.2\% & 92.2\% & 93.1\% \\ \hline
Precision                 & 99.0\%       & 99.8\% & 97.5\% & 99.4\% \\ \hline
\rowcolor[HTML]{EBF1E9}
Recall                    & 88.0\%       & 88.0\% & 87.9\% & 88.0\% \\ \hline
\# of nodes  & 2000-14000   & 13438  & 10212  & 2106   \\ \hline
\rowcolor[HTML]{EBF1E9}
Time & $<$ 500ms      & 222ms & 162ms & 37ms       \\ \hline
\end{tabular}
\vspace{-2em}
\end{table}
\vspace{-0.5em}
\subsection{Comparing HT Localization Methods}

In this section, we compare our model with other HT localization methods in the literature that are elaborated in Section \ref{sec:related-works}. u A quantitative comparison is challenging due to a couple of reasons. Firstly, the dataset and experiment conditions are very varied among different works. For example, \cite{hicks2010overcoming, waksman2013fanci, zhang2015veritrust, farahmandi2017trojan} papers propose various ideas to locate the HT nodes in the circuit, and they demonstrate promising results on their limited sets of benchmarks. However, each one reveals the shortcomings of the former method against distinct Trojans. Secondly, diverse techniques are used for localization with varied evaluation metrics that are not comparable. For example, \cite{islam2020framework} reports the error in activity estimation, which is further used for marking low-activity regions as vulnerable to HT. On the other hand, \cite{islam2019socio} and our approach demonstrate accuracy in finding the Trojan nodes. Although the direct quantitative comparison is not feasible, we provide numeric evaluation of different methods performance in terms of how successful they were to locate HT (using accuracy, recall, and error metrics), how many benign nodes were mislabled as HT (using false positive rate and precision metrics) in Table~\ref{tab:survey}. The comparison shows the superior perfomance of our model in sucessfully detecting HT nodes with very low false positive. The metrics definition are elaborated in Section~\ref{sec:exp-results}.

We study them from qualitative aspects such as pre-silicon or post-silicon HT localization, golden reference-free, automated feature extraction/property definition, localization resolution, and the ability to detect various types of Trojans. According to Table~\ref{tab:survey}, our compelling model surmounts the shortcomings of the state-of-the-art. The post-silicon techniques postpone the HT localization until after fabrications, when the HT removal is very time-consuming and expensive. Therefore, it is crucial to locate and remove Trojans inserted in the design stage early before manufacturing. On the other hand, pre-silicon HT localization approaches mostly suffer from low resolutions because they cannot detect the Trojan nodes specifically. Instead, they mark the suspicious areas that are prone to HT insertion. Thus, they require further manual revision of circuit partitions to check for Trojan nodes, and their localization process is not automated.

\vspace{-1em}
\subsection{HT Detection and Localization Performance}  \label{sec:exp-results}

After finding the best model and architecture which is elaborated in Sections \ref{best-arch} and \ref{best-weight}, we construct our final model.The evaluation results per benchmark are reported in Table~\ref{tab:resultPerHW}. We also include the number of Trojan nodes and the ratio of Trojan nodes to total nodes in the graph to reflect the effect of HT size. We consider several evaluation metrics to assess the performance from different perspectives. The most common metric for classification is accuracy which expresses the correctly classified nodes over all nodes. Accuracy is intuitive but does not suffice since class distribution between nodes is not uniform. Thus, we look into the F1-score, the weighted average of recall and precision.

Recall expresses the ability to find all Trojan nodes in a design. On the other hand, precision is an indicator of False Positive (FP) and expresses the proportion of the nodes our model labels as Trojan, actually are Trojan.The combination of precision and recall metrics examines the model's performance in detecting Trojan nodes while avoiding mislabeling benign nodes as Trojan. We count True Positive (TP), False Negative (FN), and FP and calculate these metrics as follows:

\begin{equation}
    P=\frac{TP}{TP + FP} , \text{   } R=\frac{TP}{TP + FN}
\end{equation}
\begin{equation}
    F_{\beta}\-score=\frac{(1+\beta^2)*P*R}{\beta^2*P + R}
\end{equation}
\begin{equation}
    Accuracy =\frac{TP + TN}{TP + TN + FN + FP}
\end{equation}
\begin{equation}
    \text{HT nodes} = TP + FN
\end{equation}
\begin{equation}
    \text{HT/total ratio} = \frac{TP + FN}{TP + TN + FN + FP}
\end{equation}

We provide a summary of results in Table~\ref{tab:ave-result} in which the average of metrics are calculated for each circuit as well as average node classification time. It can be observed that high accuracy and F1-score in HT localization are maintained for all circuits regardless of size.  The computation and timing of HT localization depend on the size of the circuit. Studying the timing in diverse designs, it is observed that HT localization time scales linearly with the number of nodes in the graph representation of the circuit, which makes it scalable for large designs. In conclusion, our GCN model exhibits high performance in locating the HT nodes with low false positives (below 0.009\%) in less than 1 second.
Further, we study the performance of our model in HT detection by testing it for the HT-free circuits of AES, DES, and RC5. The number of nodes classified as Trojan/benign is mentioned in Table~\ref{tab:detction}. These results show that our model can determine if the design is healthy as it finds only few false Trojan nodes in a design graph with thousands of nodes that is negligible.
\begin{figure}[t]
\centering
\includegraphics[width=0.48\textwidth]{figures/best-model2.png}
\vspace{-1em}
\caption{Performance of various graph neural network models and architectures.}
\label{fig:models}
\vspace{-1em}
\end{figure}
\begin{table}[t]
\centering
\caption{HT localization performance, number of Trojan nodes, and their ratio to total nodes for all benchmarks.}
\vspace{-1em}
\label{tab:resultPerHW}
\begin{tabular}{|C{1.4cm}|C{0.8cm}|C{0.8cm}|C{0.8cm}|C{0.8cm}|C{0.7cm}|C{0.9cm}|}
\hline
\rowcolor[HTML]{AED09D}
  Benchmark &
  Acc $^1$ &
  \begin{tabular}[c]{@{}c@{}}F1\\ score\end{tabular} &
  Prec $^2$ &
  Recall &
  \begin{tabular}[c]{@{}c@{}}HT\\ nodes\end{tabular} &
  \begin{tabular}[c]{@{}c@{}}HT/total\\ ratio\end{tabular} \\ \hline
AES-T100  & 100\% & 99.4\% & 100\% & 98.8\% & 481 & 3.46\%  \\ \hline
\rowcolor[HTML]{EBF1E9}
AES-T200  & 100\% & 99.4\% & 100\% & 98.8\% & 486 & 3.40\%  \\ \hline
AES-T300  & 99.5\%  & 94.7\% & 98.6\%  & 91.0\% & 635 & 5.33\%  \\ \hline
\rowcolor[HTML]{EBF1E9}
AES-T400  & 99.9\%  & 94.2\% & 100\% & 89.1\% & 110 & 0.71\%  \\ \hline
AES-T500  & 99.8\%  & 85.4\% & 100\% & 74.5\% & 94  & 0.69\%  \\ \hline
\rowcolor[HTML]{EBF1E9}
AES-T600  & 99.9\%  & 89.9\% & 100\% & 81.6\% & 87  & 0.64\%  \\ \hline
AES-T700  & 99.8\%  & 97.8\% & 100\% & 95.7\% & 562 & 3.98\%  \\ \hline
\rowcolor[HTML]{EBF1E9}
AES-T800  & 99.8\%  & 97.6\% & 100\% & 95.2\% & 628 & 4.42\%  \\ \hline
AES-T900  & 99.8\%  & 97.8\% & 99.8\%  & 96.0\% & 569 & 4.03\%  \\ \hline
\rowcolor[HTML]{EBF1E9}
AES-T1000 & 99.9\%  & 98.4\% & 100\% & 96.8\% & 503 & 3.73\%  \\ \hline
AES-T1100 & 99.9\%  & 97.7\% & 100\% & 95.6\% & 568 & 3.21\%  \\ \hline
\rowcolor[HTML]{EBF1E9}
AES-T1200 & 99.9\%  & 98.3\% & 100\% & 96.7\% & 509 & 3.51\%  \\ \hline
AES-T1300 & 99.1\%  & 87.5\% & 100\% & 77.8\% & 688 & 3.87\%  \\ \hline
\rowcolor[HTML]{EBF1E9}
AES-T1400 & 99.5\%  & 94.1\% & 98.8\%  & 89.9\% & 723 & 4.05\%  \\ \hline
AES-T1500 & 99.5\%  & 92.1\% & 98.6\%  & 86.4\% & 664 & 3.15\%  \\ \hline
\rowcolor[HTML]{EBF1E9}
AES-T1600 & 99.9\%  & 92.2\% & 100\% & 85.5\% & 179 & 0.82\%  \\ \hline
AES-T1700 & 99.9\%  & 89.0\% & 100\% & 80.2\% & 86  & 0.45\%  \\ \hline
\rowcolor[HTML]{EBF1E9}
AES-T1800 & 99.9\%  & 83.3\% & 100\% & 71.4\% & 27  & 0.17\%  \\ \hline
AES-T1900 & 100\% & 82.8\% & 100\% & 70.6\% & 34  & 0.17\%  \\ \hline
DES-T100  & 99.9\%  & 99.2\% & 99.8\%  & 98.5\% & 481 & 4.50\%  \\ \hline
\rowcolor[HTML]{EBF1E9}
DES-T200  & 99.9\%  & 99.2\% & 99.8\%  & 98.6\% & 486 & 4.54\%  \\ \hline
DES-T400  & 99.9\%  & 94.3\% & 98.0\%  & 90.9\% & 110 & 1.07\%  \\ \hline
\rowcolor[HTML]{EBF1E9}
DES-T500  & 99.8\%  & 84.8\% & 98.6\%  & 74.5\% & 94  & 0.91\%  \\ \hline
DES-T600  & 99.8\%  & 90.0\% & 98.6\%  & 82.8\% & 87  & 0.84\%  \\ \hline
\rowcolor[HTML]{EBF1E9}
DES-T700  & 99.8\%  & 97.6\% & 99.8\%  & 95.6\% & 562 & 5.22\%  \\ \hline
DES-T800  & 99.6\%  & 96.7\% & 98.2\%  & 95.2\% & 628 & 5.79\%  \\ \hline
\rowcolor[HTML]{EBF1E9}
DES-T900  & 99.7\%  & 97.4\% & 99.6\%  & 95.3\% & 569 & 5.28\%  \\ \hline
DES-T1000 & 99.8\%  & 98.1\% & 99.6\%  & 96.6\% & 503 & 4.69\%  \\ \hline
\rowcolor[HTML]{EBF1E9}
DES-T1100 & 99.7\%  & 96.9\% & 98.2\%  & 95.6\% & 568 & 5.27\%  \\ \hline
DES-T1200 & 99.8\%  & 98.2\% & 99.6\%  & 96.9\% & 509 & 4.75\%  \\ \hline
\rowcolor[HTML]{EBF1E9}
DES-T1600 & 99.7\%  & 91.6\% & 98.7\%  & 85.5\% & 179 & 1.72\%  \\ \hline
DES-T1700 & 99.7\%  & 83.6\% & 87.3\%  & 80.2\% & 86  & 0.84\%  \\ \hline
\rowcolor[HTML]{EBF1E9}
DES-T1800 & 99.9\%  & 80.2\% & 90.0\%  & 72.3\% & 27  & 0.26\%  \\ \hline
DES-T1900 & 99.9\%  & 81.4\% & 96.0\%  & 70.6\% & 34  & 0.33\%  \\ \hline
RC5-T100  & 99.8\%  & 99.4\% & 100\% & 98.8\% & 481 & 18.59\% \\ \hline
\rowcolor[HTML]{EBF1E9}
RC5-T200  & 99.8\%  & 99.4\% & 100\% & 98.8\% & 486 & 18.76\% \\ \hline
RC5-T400  & 99.5\%  & 94.2\% & 100\% & 89.1\% & 110 & 4.96\%  \\ \hline
\rowcolor[HTML]{EBF1E9}
RC5-T500  & 98.9\%  & 85.4\% & 100\% & 74.5\% & 94  & 4.27\%  \\ \hline
RC5-T600  & 99.3\%  & 90.0\% & 98.6\%  & 82.8\% & 87  & 3.97\%  \\ \hline
\rowcolor[HTML]{EBF1E9}
RC5-T700  & 99.1\%  & 97.9\% & 100\% & 95.9\% & 562 & 21.06\% \\ \hline
RC5-T800  & 98.9\%  & 97.5\% & 99.7\%  & 95.4\% & 628 & 22.96\% \\ \hline
\rowcolor[HTML]{EBF1E9}
RC5-T900  & 99.0\%  & 97.5\% & 99.6\%  & 95.4\% & 569 & 21.27\% \\ \hline
RC5-T1000 & 99.3\%  & 98.2\% & 100\% & 96.4\% & 503 & 19.28\% \\ \hline
\rowcolor[HTML]{EBF1E9}
RC5-T1100 & 99.1\%  & 97.7\% & 100\% & 95.6\% & 568 & 21.24\% \\ \hline
RC5-T1200 & 99.3\%  & 98.1\% & 99.8\%  & 96.5\% & 509 & 19.46\% \\ \hline
\rowcolor[HTML]{EBF1E9}
RC5-T1600 & 98.8\%  & 91.9\% & 99.4\%  & 85.5\% & 179 & 7.83\%  \\ \hline
RC5-T1700 & 99.2\%  & 88.5\% & 98.6\%  & 80.2\% & 86  & 3.93\%  \\ \hline
\rowcolor[HTML]{EBF1E9}
RC5-T1800 & 99.6\%  & 83.3\% & 95.2\%  & 74.1\% & 27  & 1.27\%  \\ \hline
RC5-T1900 & 99.5\%  & 82.8\% & 100\% & 70.6\% & 34  & 1.59\%  \\ \hline
\rowcolor[HTML]{EBF1E9}
 Average & 99.6\%  & 93.1\% & 98.9\%  & 88.4\% & 356  & 5.84\%  \\ \hline
\end{tabular}
\footnotesize{}{$^1$Accuracy  $^2$Precision}
\vspace{-2em}
\end{table}
\begin{figure}[t]
\centering
\includegraphics[width=0.48\textwidth]{figures/class-weight.png}
\vspace{-1em}
\caption{Performance of GCN model with different class weights. }
\label{fig:weight}
\vspace{-1.5em}
\end{figure}
\vspace{-0.5em}
\subsection {The Best Graph Neural Network Architecture} \label{best-arch}

There are plenty of graph neural networks candidates with various hyper-parameters to choose as our node classification model. Thus, we devise an experiment in which we construct and test different models to find the best model and architecture for our application. In this experiment, we implement 3 different graph learning model including GCN \cite{kipf2016semi}, graph attention network (GAT) \cite{velivckovic2017graph}, and local extrema convolution (LEC) \cite{ranjan2020asap} with different architectures (2-layer to 4-layer). The evaluation results are illustrated in Figure~\ref{fig:models}. F1-score is the main evaluation metrics for comparison because it is the average of precision and recall and represents the two key expected qualities; detecting all Trojan nodes and having low false positives. The LEC model shows the worst performance, and by increasing the number of layers, its performance drops. On the contrary, the GCN and GAT models are improved by stacking more layers while GCN relatively exhibits better performance. Therefore, The GCN model with four layers is chosen for node classification.

\vspace{-0.5em}
\subsection {Compensation for Unbalanced Dataset} \label{best-weight}

A standard step of developing machine learning models is to find the best settings for the model based on the problem. One of the challenges of Trojan localization is the small size of the HT circuit that results in an unbalanced dataset for machine learning. In our dataset, the ratio of HT nodes to total nodes is between 0.001-0.020 (refer to Table~\ref{tab:resultPerHW}), which means the distribution of node classes is not uniform, and one class of nodes is more common. The unbalanced dataset can affect the model's performance and push it to label all nodes as the dominant class, the benign node class. To tackle this problem, we assign a higher weight to the Trojan class in loss calculation that compensates for the minority of Trojan nodes and forces the model to label more nodes as Trojan. We devise an experiment to find the optimum value for class weight by altering the relative weight of Trojan class to benign class among these values: 1:1 (none), 3:1 (low), 6:1 (high), and 21:1 (super). In the evaluation results in Figure~\ref{fig:weight}, we notice that increasing the weight of Trojan continuously increases the recall as more Trojan nodes are found. Still, after some point, it deteriorates the overall performance (F1-score) as the false positive sample increases, and consequently, the precision drops. Therefore, The best class weight with the highest F1-score is 6:1 and we use this value for further evaluations.
\vspace{-1em}
\section{Conclusion}

In this paper, we create a novel, golden reference-free HT localization methodology that converts the hardware design to a graph,  performs node classification on it using GCN, and outputs the malicious circuit corresponding to Trojan nodes. Our methodology is fully automated without any need for manual feature extraction or code inspection. Our evaluation demonstrate that it locate Trojan with 99.6\% accuracy, 93.1\% F1-score, and false positive rate below 0.009\%.
\vspace{-1em}
\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


Our interviewers caught up with one of Issue 14's contributors, John Bolland, to find out more about the skills required to have works published in the likes of POTB.
What skills and qualifications, if any, do you need?
The only qualification you need is to be able to put pen to paper and tell a story. Writers give readers a snippet of their thoughts – be that an experience or something entirely from imagination.
It's true to say that if you never write anything you'll never publish. Lots of people spend their time planning to write but never quite finishing a piece which is obviously the most important step.
Obviously, your writing has to be of a certain quality which captures a mix of style, voice, a distinctive point of view, interesting narrative and topicality but there's no one formula. Each publication has different interests and values and it's generally down to the editor or judging panel. Spelling and grammar helps!
There are many creative writing courses available, which can be useful in the provision of structure and deadlines as well as offering support in the creation of individual networks and some basic marketing skills.
Are there any groups you'd suggest joining?
The writing community in Scotland is supportive and welcoming to writers of all levels who are committed to their work and to helping others on a reciprocal basis.
There are several groups in Aberdeen and the North East and a thriving network of interlocking writing groups, including Lemon Tree Writers. For poets, there are also established and emerging events at venues such as Books and Beans and new spoken word events at Underdog and the Blue Lamp.
More widely, the Federation of Scottish Writers is a good online network and the Society of Authors provides a range of useful resources as well as the POTB website, which has a useful links section.
Creative Learning has also worked hard these past few years to develop and create opportunities for emerging and established writers.
How do you start contacting publishers?
• Buy, subscribe to or read magazines and publications you feel may feature your work.
• Think about how your work matches its submission guidelines: word count limits, typical length of poems, formats and subject matter. It's not that editors aren't on the lookout for compelling, original work, but each has a 'house-style' and you'll waste less time by targeting.
• Read and comply with the submission guidelines – otherwise you're likely to be filtered out at the first stage.
• Polish your piece before submission, then ask another to check it.
• Post or upload it alongside a polite, informative covering letter. Don't hide your light under a bushel but don't compare yourself to established literary superstars!
• Record when you think you'll hear back, and wait.
• If you don't hear back, or your work is rejected, look at the piece again. How could it be improved? If it can, edit accordingly.
What can you do to boost your presence in the writing community?
Participate in writing groups, attend festivals and readings – open mics too, if the opportunity arises! Be generous and supportive of other writers, review and promote their work if you feel it has merit – what goes around comes around.
Follow others and enhance your presence on platforms such as Twitter, it's a fantastic way of finding out what's going on.
Why are publications such as POTB so important for new, and experienced, writers alike?
It's a stable, high quality outlet for writing which has an established profile and presence. POTB prides itself on its 'blind' selection process which ensures work is accepted on the quality of the submission rather than 'who you are', giving new writers the opportunity to have their work published on merit.
The team is dedicated to producing a widely promoted, high-quality magazine. Many pamphlets or transient magazines get 'lost in the shelves' whereas POTB has persisted as one of several key magazines on the Scottish literary scene, within which I'm immensely proud to feature.


text:
Posted November 10, 2021 | 3:42 PM
Share Share on Facebook Share on Twitter Share on Linked In
Hillsborough County Offers COVID-19 Pfizer Pediatric Vaccine for Children Ages 5 to 11
COVID-19 coronavirus Update No. 341
This release has been updated, view the latest version.
Hillsborough County, Fla. (Nov. 10, 2021) - Hillsborough County is offering free COVID-19 Pfizer pediatric vaccines for children ages 5 to 11.
Children ages 5 to 11 who are accompanied by their parent or legal guardian can now receive their first dose of the COVID-19 Pfizer pediatric vaccine at Progress Village Park, 8701 Progress Blvd., Tampa, FL 33619. The site is open from 8 a.m. to 5 p.m. Monday through Friday, and it will be open regular hours on Veterans Day, Nov. 11.
The child's parent or legal guardian needs to be at the site to verbally confirm the child's age. While no appointment is required, the second-dose appointment will be made during the first-dose visit. If a parent or legal guardian would like to make an appointment for the child's first dose, they can visit patientportalfl.com. For assistance, call (844) 770-8548.
The Pfizer pediatric vaccine dosage is one-third of the adolescent and adult dose. Children 5 to 11 years old will be vaccinated with two 10-microgram doses administered 21 days apart.
COVID-19 vaccinations are free at the Progress Village Park site. Proof of medical insurance is recommended and should be presented at the time of the COVID-19 vaccination. People without medical insurance will still be vaccinated for free.
The Progress Village Park site continues to offer free COVID-19 testing. Free vaccinations and booster shots for people ages 12 and older are also offered at the site during regular business hours.
There are local retail pharmacies throughout the county that are offering COVID-19 Pfizer pediatric vaccines. Those locations are also offering vaccinations and boosters for residents 12 and older. Visit HCFLGov.net/COVID19, Florida Vaccine Locator, or Vaccines.gov  to locate pharmacies and other sites offering vaccinations.
For more information on COVID-19, and any other potential emergency in the county, visit HCFLGov.net/StaySafe and sign up for the HCFL Alert system. Additionally, you can follow Hillsborough County on social media at Facebook, Twitter, and Nextdoor for updates. Residents without digital access are encouraged to call (813) 272-5900, the County's main information line, for information on the Progress Village Park COVID-19 testing and vaccination site.

redpajama_set_name: RedPajamaCommonCrawl


Rotterdam home-based dj/producer Bas Mooy (1976) has been active on many fronts in the techno scene since 1999. Heavily influenced by a city that breaths industrial strength twenty-four-seven, he gradually found his way into creating an unique dark sound. For many years he has been a member of the Rotterdam underground collective 'Strictly Techno', creators of over a hundred legendary parties since 1995.
Over the years Bas has been a hard working and devoted producer, spending many hours in the studio and putting out music on respected labels such as Luke Slater's Mote Evolver, Perc Trax, Gynoid, Sleaze Records, Planet Rhythm and of course his own imprints Audio Assault and Arms. With the start of his new label 'Mord' in 2013, Bas shows no sign of slowing down.


Brooklyn gentrification is real. On Thursday August 11th, community leader, entrepreneur and proud Brooklyn boricua Georges Torres, aka el "Urban Jibaro" shot a Facebook Video Live that went viral for days. The theme of the video? The gentrification of Brooklyn and its devastating impact on Puerto Rican small businesses.
The video starts with a shot of Graham Avenue | Avenue of Puerto Rico in Brooklyn.
Torres starts off by referencing a 2013 article stating that newcomers of the neighborhood were in favor of a "rebrand" of the iconic avenue endangering the loss of tradition and diminishing the cultural contribution of the Puerto Rican community in Brooklyn.
Then, Torres enters to a store called San Germán Records, an institution that has been operating for over 50 years in the neighborhood and that is now struggling to stay in business. As Torres peruses around the store to show live what the store looks like, and to urge his community to visit the business, he explains that San Germán Records is a jewel in Brooklyn. They have been organizing the Three King Day Parade for 20 years and, for the first time, they are struggling to put it together. George interviews the store owner who tells his powerful story of commitment to the community through the parade. As the video progresses, numerous people react. Some express outrage, other promise their support to the store through shopping visits, donations and exposure of the story.
Make sure to follow Sofrito for your Soul for updates.
Get in touch with George Torres and San Germán Records.
There needs to be a movement to identify and respect other cultures when making arguments such as the one being made here. One cannot make an argument regarding gentrification without disrespecting the cultures and ideals of others. Sure, we might not be in agreement with the manner by-which other cultures live, or govern, but we should, at the very least, respect their right to live amongst all people as we do and as we have for many years. To refer to Asians as "Chinitos" and Caucasians as "Blankitos," is clearly taking a position that many could argue as racist. The very second that this type of indifference enters a conversation, you have already lost your power to make that argument.
Personally, I will not say that these phrases have crossed the line into racism, but words like these are definitely concerning and we need to rise above these words. The time for moving beyond these words is long overdue. Now, the elder gentlemen could simply be someone who is set in his ways, but there is no excuse for the gentleman filming and narrating this piece.
Secondly, on the subject of gentrification itself, I would argue that no people are indigenous to any area and that all people have committed gentrification at one time or another. Perhaps we should look at a neighborhood's change as an evolution. Why not? Is the term "ever-growing" mean meant to showcase exclusivity to one person or group of people?
I don't have all the answers, but I am pretty certain that exclusion is something that is not right and it is certainly not the way I was raised. By the same token, a new population must learn to respect what is already in place and acknowledge and respect their new surroundings as sacred.
Look, I am not anti-Puerto Rican, or anti-record shop, but what I am is pro-human, and pro-existence. There are Puerto Rican traditions in New York that must not die, but we must also keep in mind that these traditions extend far beyond Brooklyn and far beyonda single record store. As Puerto Ricans we should keep traditions alive, but as humans we should always consider all possibilities.
I say respectfully and in my humble opinion.
Does San Germán Records have a Facebook page or other social media that can be shared and boosted other than this video?
Hello Pam, yes San German Records does have a Facebook page but nothing else for the moment.
The stores facebook page says the store is permanently closed. I so hope that isn't true.


text:
email Created with Sketch.
Globe Local
Bold Types
Top Places to Work 2020
The Emancipator
A Beautiful Resistance
Theater/Dance
Mr. 80 Percent
STAT: The Readout Loud
The Globe Investigates
The Big To Do
This week in Weed
Today in Opinion
Weekend Reads from Ideas
Globe's Most Popular
Metro Headlines
108 Stitches
Rhode Map
Point After
Innovation Beat
CoronavirusMetroSportsBusinessTechnologyOpinionSpotlightRhode IslandPoliticsClimateEducationLifestyleArtsGlobe MagazineCarsReal EstateEvents
Marijuana entrepreneur says her outbursts aimed at cannabis commissioners brought results
By Naomi Martin Globe Staff,January 15, 2020, 2:34 p.m.
Alchemy League owner Leah Cooke Daniels, left, and her wife, Jacinth Cooke Daniels, say they are frustrated with the slow pace of marijuana licensing for the economic empowerment program, which was meant to fast-track the applications of people from communities disproportionately harmed by pot criminalization.Jacinth Cooke Daniels
A Boston entrepreneur said she waited 610 days in limbo with no answers as to why her application to open a marijuana shop was delayed — even though she was in a program meant to fast-track groups hit hardest by the war on drugs.
To vent her frustration, Leah Cooke Daniels interrupted two recent meetings of the Cannabis Control Commission, at times shouting at officials. On Tuesday, the commission said Cooke Daniels's proposed Holyoke marijuana store, Alchemy League, would be recommended for a provisional license Thursday, a major step toward opening.
She said she believes that speaking out paid off.
"If the Cannabis Commission could've continued to ignore me and disregard me and my civil liberties and my opportunities to do business in the Commonwealth, they would've continued to do so," Cooke Daniels said. "This should've been a very proud, happy, joyous moment and it's sad that it had to be played out to this capacity."
The dispute highlights long-simmering frustrations among some applicants who say the state has not kept its promises to give them a fair chance at earning a piece of the nearly $400 million-a-year industry.
A commission spokeswoman declined to comment on Cooke Daniels's assertions Tuesday, saying she would not discuss an application before the commission voted on it. She shared a staff summary of Daniels's application that described it being reopened more than four times as reviewers sought additional needed information.
According to the commission summary, Cooke Daniels completed necessary steps in her application in the summer, signing a host-community agreement in July and a community outreach meeting in August. The commission received the needed response from Holyoke in October.
"The time it takes to complete the initial review process depends on the completeness of the applicant's submitted packets, the time it takes to complete the background check, and the time it takes for a municipality to" verify that the business complies with local rules, the spokeswoman. She added the commission planned to improve transparency around its licensing process with new technology and guidance documents.
The commission's chairman, Steven Hoffman, previously told the Globe that he felt sympathetic to Cooke Daniels's complaints but could not allow people to interrupt the panel's official meetings, where licenses must be approved. In response to the interruptions, the commission scheduled a Jan. 23 forum to hear applicants' concerns.
Cooke Daniels is among 122 people in the state's economic empowerment program. The program is one of two in Massachusetts aimed at helping entrepreneurs from communities — particularly black, Hispanic, and low-income ones — that were disproportionately harmed by pot criminalization.
Economic empowerment offers faster licensing reviews, though some participants say the pace and results have been disappointing. A separate social equity program run by the commission offers training on business development and legal, tax, and regulatory compliance.
Cooke Daniels's outbursts occurred at the commission meetings Dec. 20 and Jan. 9. At the meeting last week, Cooke Daniels was joined by about 20 other people who shouted: "No approvals until economic empowerment approvals."
"We didn't expect it to be easy, but we didn't expect it to be like murder-suicide," said Jacinth Cooke Daniels, Leah's wife.
So far, just six of the state's 258 licenses have gone to people in the economic empowerment or social equity programs. Another four applicants from those programs — including Cooke Daniels — are up for consideration for a provisional license on Thursday. Commissioners typically approve licenses recommended by staff.
It takes 170 days for a typical retailer to move from a provisional license to opening, according to a Globe review of commission data.
Massachusetts was the first state to mandate that its cannabis regulators foster an industry that includes people from neighborhoods with high rates of marijuana arrests. Since the state's 2016 ballot initiative passed, other states such as Illinois and Michigan have followed suit.
Cooke Daniels, who is black, said she experienced the devastation of drugs and their criminalization growing up in a Roxbury public housing development, raised by an uncle with a heroin addiction. Her mother died when Cooke Daniels was 3 and her father was incarcerated for 27 years, she said. She was surrounded by drugs but managed to avoid them herself and navigate toward a positive life, which included serving in the US Army as a medic.
Cooke Daniels said being part of the economic empowerment program felt more like a handicap than an advantage because she believes applicants were subjected to stricter scrutiny than other businesses.
"You've got to be the scum of the earth" in society's eyes, coming from poor, neglected, and high-crime areas, she said. "All it did was give us a target to be disenfranchised from the Cannabis Commission."
She said the commission requested new information seven times, and each time she felt it was for items that other businesses were allowed to address later in the process. She said she watched other businesses breeze through the licensing process far faster than her business, and the delays cost her hundreds of thousands of dollars.
The commission said that all applicants are held to the same standards and that those in the empowerment program receive priority review at all times, including after a request for more information.
In response to complaints about applicants' wait times, commissioners have pointed to many issues outside their control, including opaque and slow municipal approvals, challenges in finding and securing real estate, and struggles obtaining financing.
But the commission does have control over processing times for applicants who meet all the requirements and transparency around their status, said Jim Borghesani, a cannabis consultant who helped lead the legalization campaign.
"They're not simply ignoring anybody's application," Borghesani said. "It's simply too slow all across the board."
He acknowledged that well-funded applicants can hire consultants and lawyers to polish their applications and ensure their files are complete, while smaller businesses would likely lack that advantage.
Cooke Daniels and her wife declined to say whether they believed the interruptions would continue. "We don't speak for the community," Jacinth Cooke Daniels said.
Naomi Martin can be reached at naomi.martin@globe.com. Follow her on Twitter at @NaomiMartin.
Black News Hour presented by The Boston Globe
Run by Black journalists at The Boston Globe, "Black News Hour," a new radio program, delivers reliable news that connects with our community and expands on deeper issues impacting our city.
Boston Globe video
Share on TwitterShare on Facebook Share on Instagram
View the ePaper
Work at Boston Globe Media
©2022 Boston Globe Media Partners, LLC


"With Rwandan Sign Language, Everyone is included" is the theme observed during the celebration of International Deaf Awareness Week 2018. On this 28th September 2018, the event took place in the City of Kigali Car Free Zone.
The ceremony gathered together various guests including the Committee of World Federation for the Deaf, the ambassador of America in Rwanda, and the Vice Mayor in charge of Social Affairs in the City of Kigali who was also the guest of Honor representing the Minister of Local Government. Among others, there were also representatives of DFID, UNDP, VSO, HI, UPHLS, NUDOR, RUB to mention few.
The President of World Federation informed that this federation has 135 country members including Rwanda. He also said that there are more than 300 Sign languages being used across the world and then encouraged Deaf people in Rwanda to continue advocating for Rwanda Sign Language recognition.
It was really encouraging to see the Ambassador of America in Rwanda giving his speech in Sign language and then in Kinyarwanda. He put much emphasis on rights for deaf people to live with others and being given chance to enjoy these rights in education, health, work, etc.
The President of NCPD ensured that considering the provision of Rwandan Constitution, there is hope that Rwanda Sign Language will be recognized because the country has a strong political will to live no one behind. He took this opportunity to share the dream he has that one day there will be a Rwanda without any barriers for Persons with disability.
The Vice mayor in charge of Social affairs in the City of Kigali who was also the Guest of honor thanked Deaf Community for their effort to advocate for their rights. She pledged to approach them and in return she requested them to be confident to communicate any challenge they may face so that they should be solved on time.
The celebration was marked by various kind of entertainment including Rwandan traditional dance and sketch performed by deaf youth. The use of loud speakers during the peaceful procession from "Sainte Famille" Catholic Cathedral to Car Free Zone through "Quartier Commercial" was a good opportunity to give messages about Sign language and right of deaf people.


text:
Sprinkling of Borders players in World Cup training squad
Darcy Graham in action against France in Paris in this year's Six Nations (picture by ANNE-CHRISTINE POUJOULAT/AFP/Getty Images).
A familiar cluster of Borders-linked players has been included in a 42-man Scottish training squad as preparations begin for September's rugby World Cup in Japan.
Scotland head coach Gregor Townsend has kept faith so far with some of the best-known local players who have figured in recent internationals, including Darcy Graham, Stuart Hogg, Greig Laidlaw, Jamie Bhatti and Gary Graham.
A further two spaces in the squad remain unfulfilled (one forward and one back) with Townsend keeping the door open for those operating at the business end of the season to play their way into the group.
Just under half (14) of those named featured in the 31-man squad that reached the quarter-final of the 2015 tournament in England and have been given the chance to surpass their efforts this autumn.
"The selection is a good indication of the depth and level of competition we currently have in Scotland," said ex-Gala and Scotland star Townsend.
"We made a conscious decision to keep squad numbers low, which enables us to do more work with those most likely to be on the plane to Japan. We're really excited to work with this group of players, who will now compete hard for a place in the final 31-man group going to the World Cup.
"There are, of course, a number of very good players who have missed out – players who have been unlucky with injuries this year or haven't hit form at the right time – while others are unlucky to lose out on some very close decisions."
The first block of Scotland's preparations will see the squad divided into smaller skills and strength and conditioning groups to accommodate post-season breaks for those in the knockout stages of domestic or European Cup competition.
The majority of players will then be given three weeks off and will return from mid-June to intensify pre-season preparations at Oriam, Scotland's National Sports Performance Centre, on the outskirts of Edinburgh.
The pre-season campaign will be punctuated by three residential camps in Scotland and a hot-weather camp in Portugal, before four home-and-away Summer Test matches against Georgia and France begin in August to complete the squad's pre-departure preparations.
Townsend added: "Our goal is to deliver our best rugby on September 22 (when Scotland face Ireland in the opening pool round) and throughout the following few weeks, so there will be a big conditioning element driving us towards that.
"We've got to be ready to play with energy, huge effort and be fitter than every team we come up against. That will enable us to attack and defend at a high level and put pressure on the opposition.
"There are a number of challenges that await us in the tournament, starting with facing some quality teams in our pool as well as adapting to the Japan's unique environment.
Kelso swimmer Andrew strikes silver
"As it's likely to be hot and humid during the tournament, our warm-weather training camps in Portugal and Nagasaki, as well two Test matches in the heat of Nice and Tbilisi, should be invaluable.
"We look forward to building closer relationships with each other and improving as a squad during our training camps, while this opportunity to be together for longer means we can add a couple of things to our game to help us get closer to reaching our potential."
Scotland training squad:
Forwards – John Barclay (Edinburgh), Simon Berghan (Edinburgh), Jamie Bhatti (Glasgow Warriors), Magnus Bradbury (Edinburgh), Fraser Brown (Glasgow Warriors), Allan Dell (Edinburgh), Matt Fagerson (Glasgow Warriors), Zander Fagerson (Glasgow Warriors), Grant Gilchrist (Edinburgh), Gary Graham (Newcastle Falcons), Jonny Gray (Glasgow Warriors), Stuart McInally (Edinburgh), Willem Nel (Edinburgh), Gordon Reid (London Irish), Jamie Ritchie (Edinburgh), Sam Skinner (Exeter Chiefs), Grant Stewart (Glasgow Warriors), Josh Strauss (Sale Sharks), Blade Thomson (Scarlets), Ben Toolis (Edinburgh), George Turner (Glasgow Warriors), Hamish Watson (Edinburgh), Ryan Wilson (Glasgow Warriors).
Backs – Darcy Graham (Edinburgh), Nick Grigg (Glasgow Warriors), Chris Harris (Newcastle Falcons), Adam Hastings (Glasgow Warriors), Stuart Hogg (Glasgow Warriors), George Horne (Glasgow Warriors), Pete Horne (Glasgow Warriors), Rory Hutchinson (Northampton Saints), Huw Jones (Glasgow Warriors), Sam Johnson (Glasgow Warriors), Blair Kinghorn (Edinburgh), Greig Laidlaw (Clermont Auvergne), Sean Maitland (Saracens), Byron McGuigan (Sale Sharks), Ali Price (Glasgow Warriors), Henry Pyrgos (Edinburgh), Finn Russell (Racing 92), Tommy Seymour (Glasgow Warriors), Duncan Taylor (Saracens).
Summer Tests:
Saturday, August 17 – France v Scotland (Allianz Stadium, 9pm). Saturday, August 24 – Scotland v France (BT Murrayfield). Saturday, August 31 – Georgia v Scotland (Dinamo Arena). Friday, September 6 – Scotland v Georgia (BT Murrayfield). Kick-off times in last three matches to be confirmed).
Scotland's Pool A fixtures, Rugby World Cup 2019 (kick-off times local):
Sunday, September 22 – Ireland v Scotland (Yokohama, 4.45pm). Monday, September 30 – Scotland v Samoa (Kobe, 7.15pm). Wednesday, October 9 – Scotland v Russia (Shizuoka, 4.15pm). Sunday, October 13 – Japan v Scotland (Yokohama, 7.45pm.
Snakes alive! Regulars at long-shut Galashiels pub planning reunion


text:
Park Moo-jong
Choi Sung-jin
Troy Stangarone
Tong Kim
Lee Seong-hyon
John Burton
Jason Lim
Kim Ji-myung
Hyon O'Brien
Deauwand Myers
Bernard Rowan
Casey Lartigue, Jr.
Stephen Costello
Semoon Chang
Rejecting a dream job - and loving it
By Casey Lartigue, Jr.
Dearest Casey,
During your current trip to the USA to give a series of speeches in New York, D.C., Tennessee and California, you were delightfully blindsided by a job offer that would pay you more than three times as much as you are now making in South Korea.
A few years ago, after a health scare, you began reflecting on your life and decided that you would only do the things that you wanted to do. People who try to pressure you to do things you don't want to do have a 100% chance of failing. As you tell such people: "I don't have to eat everything put on my plate."
People who give advice you reject are told: "I promise, I won't stop you from taking your own advice." You value every moment that youare alive, and will enjoy the rest of your life on your own terms.
For more than two years, you have been focused on helping North Korean refugees, typically using your own funds. You are lucky that Freedom Factory and the Atlas Network both came through with support last year, but the support was more honorary than monetary.
When you received that generous job offer, you had another moment to reflect on the things you do. You could return to America, make much more money than you are making now, helping as a donor rather than a busy-bee and organizer. You love the organizations that you are associated with, but the reality is that you are a one-man think tank with a desk at a start-up think tank. You raise the money to pay your own salary; you areyour own editor; you do all of yourown media; you areyour own supervisor and employee; you go out of your way to praise the many volunteers who have joined up with you, knowing that you rely on them more than they could ever know.
You realized that the job you were being offered would give you the opportunity to have an organization support you fully. Your main task would be to become a talking head on TV, debating and discussing issues of the day. It would be more glamorous than the things you are doing now. As you often tell friends: "Being alive is the only thing more important than being on TV." Instead of helping feature North Korean refugees and to provide them with assistance to help them find their own paths in this world, you would be featured, groomed to become a talking head. You would have an entire media team, production team, editors, probably a full-time assistant, and other (research, financial, structural) support.
The job offer that you received would pay you enough that you could easily do some of the activities you are engaged in, but do them first class with colleagues tasked with supporting you rather than what you are doing now in relying on donations, minimal financial support, and the many volunteers who have come into your life in the last few years. When you say that you are engaged in NK activism because you want to do it, you mean that. It is out of joy. You have turned down other great job opportunities. When people ask why you are doing it, why do you spend so much time helping North Korean refugees, you usually answer, "Because it should be done. And when I think something should be done, I either do it or find someone else to get it done."
The ''interview" you suddenly had was certainly unconventional ― you were interviewing and examining them more than they were interviewing you. Job applicants are usually passive, trying not to trip up to eliminate themselves. Not in your case. You wanted to make sure it would be a good fit for you, and for them ― in that order of importance. Yes, they are the ones with the money and the job, but it is about your life and how you are going to live it.
Before the unexpected interview was over, you turned down the job offer. You will probably be more reckless and active when you return to South Korea. Next month, you will be starting your second year with Atlas and Freedom Factory, with the challenge again of raising your own salary and getting things done with people who happen to come into your life and answer your call for help. You will know that you could have returned to America, coasting as a TV political talking head, but you are at peace with that decision now and sure you will be fine with it a year from now when your latest one-year contract will be up.
The writer is the Director for International Relations at Freedom Factory Co. in Seoul and the Asia Outreach Fellow with the Atlas Network in Washington, D.C. He can be reached atcjl@post.harvard.edu.


Dimensions: 35cm diameter Material: 100% wool felt not to be mistaken with polyester. Made by hands. Pods cushions are a perfect addition to your living or bedroom. You will appreciate its beautiful felt texture, fresh colours and how easily you can mix and match it.


text:
Male Bonding
Initial contact with Endless Now triggers a quandary rather like that faced by the wise owl in those old Saturday morning Tootsie Roll Pop commercials: how many listens does it take before you start singing along with these new Male Bonding songs? One time? Two? If you make it to three, you have either a will of iron or an ear of tin. These are eleven selections- plus a wee reprise- imbued with the fierce urgency of now, and catchy as all get-out.
Life has moved at a fast clip for this trio from London's Dalston neighborhood since the 2010 release of their debut album, Nothing Hurts. The rest of the year was filled with relentless touring across Europe and the UK, miles and miles of American blacktop, and a volcano-mandated sojourn in the unfamiliar climes of Los Angeles. A few of the new tunes were written and road-tested during this period. "I wrote the lyrics to 'Carrying' on a flight back from New York to London, then wrote the music as soon as I got back to my room," recalls guitarist John Arthur Webb. "It has dual citizenship."
As the winter holidays finally arrived, the boys stopped touring-but kept working. "I remember sitting in my room a lot, holding either a cup of tea or a guitar, with the TV on in the background," continues John. "Sometimes I would hold both." Songs poured forth as Christmas came and went. "When we reconvened in the new year we suddenly had more than an album's worth of songs that John brought to us," bassist Kevin Hendrick remembers. They knocked the best of the lot into shape with customary haste, then packed their bags and returned to the United States again.
Endless Now was recorded at Dreamland Recording Studio in Woodstock, NY, the converted 19th century church that birthed such classics as the B-52's "Love Shack" and Dinosaur Jr.'s 1993 full-length Where You Been. Male Bonding took special delight in later learning that Dreamland was also where the 1993 single "'74-'75" by North Carolina jangle-pop ensemble the Connells was made. "We used to come onstage to that song, so it was fate," says drummer Robin Silas Christian. With deep snow everywhere, and sunshine streaming in through the stained glass windows in the morning, the trio worked with producer John Agnello (Kurt Vile, Thurston Moore, Dinosaur Jr.) installed behind the altar. After a week in Woodstock they relocated to Headgear Recording in Brooklyn to mix.
"That's the most time we've ever had in a studio," says Kevin of those two weeks. "We exploited every minute of that luxury. We've never had so much time and every single hour of it was filled." Except for the breaks for three family-style meals daily, and a nightcap, and playing with the studio dogs- Ralphie and Artemis – at Dreamland, it was music non-stop. "We were working quickly, all the time."
Musically, Endless Now is everything fans already love about Male Bonding… and much more. "We wanted to push ourselves as much as possible, which is something we've always done, but we did it even more this time," explains Robin. "We used the studio as a tool much more, whereas I don't feel that we did that last time." New timbres like piano, cello, and the Mellotron all factor into the sound, and there are more guitars than ever before. In fact, there are so many overdubs they've had to enlist their pal Nathan Cheetahs to share guitar duties in recent live shows. Another friend loaned Robin a ride cymbal… for the very first time! "I'd never used one before and absolutely loved it. It's all over the record." There are abundant vocal harmonies, the female ones supplied by Frankie Rose of Crystal Stilts/Dum Dum Girls/Vivian Girls renown.
The end result is 36 minutes of songs tailor-made to anchor mix tapes and playlists-if you can stand to separate them from the whole album. The enthusiastic "Tame The Sun" heralds the arrival of Endless Now with the first notes its plucky bass line. And "heralds" is the right word: Male Bonding's music is animated by an infectious, evangelical zeal that reflects their roots in the D.I.Y. indie rock community. "Bones" opens with the giddy rush one associates with classic hardcore or the best of the Wedding Present, then stretches out the same four-chord progression for six minutes and 24 seconds-all the while retaining its succinct air of breathless excitement. With its hazy vocals, hissing cymbals, and just a hint of vintage Beach Boys, the spacey "Can't Dream" reconciles the Punk and the Hippy in all of us. And then there's Male Bonding's take on Phil Spector-style '60s pop, "What's That Scene," which wraps its gritty, grungy center in a sugar-sweet wash of handclaps and wah-ooh backing vocals.
Hear that? You're singing along already.
Robin Silas Christian—drums
Kevin Hendrick—bass and vocals
John Arthur Webb—guitar and vocals
Nathan Hewitt- guitar
Watch The Homesick's "Male Bonding" a new song from 'The Big Exercise,' the group's forthcoming Sub Pop debut
Tue, Jan 14, 2020 at 8:57 AM
The Final Countdown: Endless Now is Here
Mon, Aug 29, 2011 at 2:41 AM
The not at all endless countdown to Male Bonding's Endless Now: Installment # 4
Thu, Aug 25, 2011 at 9:52 AM
Tame The Sun Tame The Sun
Bones Bones
Year's Not Long Year's Not Long
Weird Feelings Weird Feelings
Endless Now
Male Bonding Items For Sale
Male Bonding Endless Now CD / LP / Digital
Male Bonding Nothing Hurts CD / LP / Digital
See more Male Bonding merch