text:
Shots in the city centre – London Bridge evacuated
On London Bridge in the British capital, it came to British media, according to a violence incident. Several shots should be like. This, among others, reporte
29 November 2019 Friday 16:04 - 42 reads.
On London Bridge in the British capital, it came to British media, according to a violence incident. Several shots should be like.
This, among others, reported the BBC television on Friday afternoon. The bridge had widely been disabled. A BBC Reporter according to who was at the scene of the crime, had tried several people to push a man to the ground. Then, shots had fallen.
Scotland Yard announced via Twitter that it was the early stage of an incident. Passers-by were asked to follow the instructions of the security forces. In the Videos that circulated in social networks course, were to be seen fleeing people.
Shooting at London Bridge #London bridge pic.twitter.com/RB0489E0mw
— Clare (@Clarerobbo1000) November 29, 2019
confirmed to The police that the incident had claimed five victims, including the Suspect, had shot at the police.
Created: 29.11.2019, 15:40 Uhr


text:
Dunmow's Earl brings home county golf title
Rebecca Earl the Herts Girls Golf Champion 2015
Teenage Dunmow golfer Rebecca Earl has claimed the Hertfordshire Girls' County Championship title.
The 15-year-old, a member of the Girls' England U16 team and Felsted School pupil, swept aside the competition at the Old Fold Manor Golf Club to win by five shots.
Rebecca also represented Felsted and Essex in the South East Regional Schools Golf Championship at the John O'Gaunt Golf Club, finishing third.
On a cold and wet morning Rebecca shot five over par scoring 79, the second best girls score.
In the afternoon she fired 81 which confirmed her third place overall securing a spot in the National Schools Golf Finals at Peterborough Milton GC on June 22.
Rebecca is a sports scholar at Felsted School and receives a tailored sporting programme to help her reach her potential.
Rebecca's mother Julia said: "We are incredibly proud of her achievements last week, and her dedication to her sport and academic work. Also thanks to Felsted for giving her the opportunity to be able to train weekly."

redpajama_set_name: RedPajamaCommonCrawl


As the project to build new Gresley class P2 steam locomotive No. 2007 Prince of Wales prepares for the wheeling of the engine in the autumn, only four years after its launch, a new batch of components has been released for sponsorship in time for Father's Day on Sunday 18th June 2017.
The class P2 2-8-2 'Mikado' locomotives were the most powerful passenger steam locomotives to operate in the UK, designed by Sir Nigel Gresley to haul 600 ton trains on the arduous Edinburgh to Aberdeen route. Sadly, the design was never fully developed and they were rebuilt in 1943/44 and scrapped by 1961. The A1 Steam Locomotive Trust (registered charity and builders of famous new 100mph steam locomotive No. 60163 Tornado) is building the seventh member of this class over seven years at an estimated cost of £5m. The project will demonstrate how the design can be fully realised through use of modern computer design techniques, enabling the new locomotive to deliver its full potential hauling passenger trains at high speed across today's national network.
Public interest in seeing a new Gresley class P2 become a reality sooner rather than later is high and well over 800 people have already signed up to the 'P2 for the price of a pint of beer per week' (£10 per month or more) covenant scheme since its launch three years ago. In addition, funds have been raised through The Founders Club (over 360 people donated £1,000 each – target 100 people), The Boiler Club (over 115 people have pledged £2,000 each – target of 300 people), The Mikado Club (just launched and over 145 people have pledged £1,000 each – target 160 people) and Dedicated Donations (over £200,000 from existing supporters sponsoring a variety of components). This means that the project has already received pledges of over £2.3m (including Gift Aid) of the £5m needed.
Dedicated Donations are normally only available to existing supporters but are being made generally available in the run up to Father's Day. In addition to being able to sponsor one of the '18 spokes of the 6ft 2in driving wheels' for £600 (or £25pm for 24 months), other components now available for sponsorship range from a 'Hornblock Driven Bolt & Nut' at £25, a 'Pony Truck Bearer Spring' at £125, 'Cartazzi wheelset, axlebox & bearing assembly' at £700 (or £28 per month for 25 months), 'Driving coupled wheelset roller bearing' at £1,050 (or £52 per month for 25 months), a 'Cartazzi axlebox casting' at £1,300 (or from £50 per month for 26 months), a Crank axle crank sweep forging LH at £3600 (or £100 per month for 36 months), the 'Pony truck axle' at £4,440 (or £70 per month for 60 months), to a '6ft 2in Driving Wheel Casting and Proof Machining' at £12,000 (or £200pm for 60 months). People who subscribe to the scheme will have their names (or the name of the person the gift is for) inscribed on the official roll of honour at Darlington Locomotive Works listing the components sponsored, receive a certificate recording the sponsorship and copy of the drawing of the component.
"We are delighted with the support the project to build Britain's most powerful steam locomotive has received since its launch three years ago, with over £2.3m of the required £5m pledged to-date. We are confident that we will have the engine on its wheels this autumn and remain on-track for completion in 2021. However, to maintain this progress we need to raise £700,000 per year. We are therefore taking the opportunity of Father's Day to give the Dedicated Donation scheme a boost.
Please contact dedicated.donations@p2steam.com for further information. New components available for sponsorship will become available as construction progresses. Please note that monthly payment options are one of the benefits made available to existing regular supporters only.


The GMOA has been the most vocal component of the medical establishment in its opposition towards expanding medical education opportunities through non-State options. The one and only non-State option available locally is none other than the much-maligned medical degree offered by SAITM (or the South Asian Institute of Technology and Medicine).
The exorbitant cost of a SAITM medical degree education has fuelled sentiments against the institution by the public, though the very same public is relying on non-State options in every other sector. Catchy slogans against SAITM have drowned out the fact that fees are higher than necessary at SAITM because of the costs incurred by the institution to fight a powerful medical monopoly made up of at least two other actors with vested interests.
The judgment of the Sri Lanka Appellate Court in favour of a medical graduate of SAITM who sought justice against SLMC (or the Sri Lanka Medical Council) has exposed the essential role of that institution in this monopoly. The Appellate Court judgment should be required reading for anybody who has a hard time believing the duplicity and/or the incompetence of the supposedly premier regulatory body in the medical sector.
Now the deans of the faculties of medicine at public universities have joined the fray, making contradictory arguments to pass judgment on a peer private institution, exposing their own weaknesses in the process. The trio of GMOA, SLMC and the Medical Deans is what I would call the iron triangle of a medical monopoly.
An iron triangle is a situation where three key stakeholders in a sector are working together to stymie outside efforts to reform the sector (Gordon Adams, 1981). I have used the term before in the context of higher education in general, but its application to the medical sector with SLMC, GMOA and Medical Deans as the key stakeholder might be just the explanation we need to explain the current bizarre situations in our medical sector.
Yet, SLMC has given recognition to a medical degree program at the Kotelawala Defence University, with only two pages of review relying on the promise of a hospital to be built. In contrast, they have conducted lengthy inspections of the medical degree program at SAITM according some unspecified standard and judged the program as unworthy (See pages 27-28 of the judgment). As the Court Order notes, the observations in the report are favourable and includes suggestions for improvement, but "[the] observations do not match with the final recommendation made." The recommendation being to deny provisional registration to SAITM graduates.
As Dr. Pradeep de Silva, an educator at the Ragama Medical Faculty noted in an article published in Lankadeepa on 13 February, the Sri Lanka Medical Council is an archaic institution with archaic and inefficient practices. According to him, the council essentially consists more or less of the medical deans of public universities and the dean of Faculty of Medicine at the Kotelawela Defence University.
Other categories of doctors are elected to the council but the election procedures are not conducive to participation by the full membership. Apparently, the council does not even have a proper list of medical specialists registered in Sri Lanka. It is time the Government considered amendments to the SLMC Act to shape up the organisation by mandating, for example, that one-half of the council should consist of lay persons with a wider range of expertise and experience.
The GMOA has been vicious in its opposition to SAITM calling it an illegal institution and demanding nothing short of its abolishment. While SLMC is suggesting more clinical training for students at SAITM, the GMOA is making everything possible to block the students from access to Government hospitals.
Fighting for better working conditions and better pay is well and good, but the present actions of the GMOA obstructing entry to the profession should receive scrutiny by experts for its legality or ethicality.
It is rather presumptuous of these medical deans to reject a peer medical faculty in this manner. Have the public medical faculties headed by these deans been evaluated by SLMC and issued similar certificates of compliance? As Dr. Pradeep de Silva notes in his column in Lankdeepa, just because students in these medical faculties get access to patients in teaching hospitals does not mean that they get a proper training. Apparently, clinical teams in some faculties now may consist of 40 students in one group. Has the SLMC looked into those practices and barred the registration of graduates from such programs?
In fact, teachers at our public institutions seem to think that by virtue of being public employees, we have to accept them as being of good quality. I have written in the Sinhala media about quality issues in Faculties of Arts in the public sector based on a survey of faculty quality that I carried out as a consultant to the University Grants Commission in 2003. I am not aware of any external assessment of our Faculties of Medicine.
The medical deans rightly ask for "regulations prescribing minimum standards for medical education in all HEIs that are empowered to award medical degrees" in their joint statement, but in the same document they pass judgment on SAITM based on an improper assessment by the SLMC. These same deans are members of SLMC we should note.
Sri Lanka leads in health and sanitation indicators in South Asia and the unsung heroes in these achievements are the public health inspectors and public health nurses and midwives. The doctors and support staff at most public hospitals too are doing an adequate job with limited resource.
To face these challenges we need doctors who can spend more time discussing prevention of diseases and lifestyle changes, but Sri Lanka has a shortage of doctors, in rural areas in particular. In urban areas too, patients wait long hours, particularly when they are seeking channelling services or private consultations with Government doctors. What we experience as patients is hastiness to prescribe drugs where perhaps a good listen by the doctor followed by lifestyle advice and continuing care by the same doctor would have been the right thing to do.
Talking to Sandeshaya, the Sinhala service of BBC on 11 July 2016, Dr. Palitha Mahipala, the Director General of the Health Services Department then had said that there is a shortage of 2,500 doctors, particularly in rural areas. As the country develops we can expect the demand for doctors to increase in parallel, he said.
While there is a dire need for more doctors, we have thousands of able and willing youngsters who miss opportunity for a free-of-charge medical education by a thousandth fraction of the Z-score. If the SLMC and the deans have their say, these youth will have to wait till the SLMC gets its act together. If the GMOA and the ultra-left political groups with their captive audience of submissive university students have their way, it is free-of-charge of education or nothing for the rest.
The behaviour of SLMC, GMOA and Deans of the public medical faculties makes one wonder whether, knowingly or otherwise, these institutions are colluding to keep competition at bay.
GMOA is a trade union, skating too close to 'closed-shop' behaviour which is now illegal for trade unions in most countries. As for SLMC apparently it has to go back to kindergarten to learn about regulating. The Deans of Medical Colleges seem to be viewing the medical profession from the comforts of the public sector where students are brought to their doorsteps and all expenses are paid by the Government.
nRemind the SLMC that they are mandated to develop standards for medical education and apply them to all higher education institutions consistently and fairly.
nAward no-interest loans to at least 100 students who missed a free medical education in 2017 by a few decimal points in their Z-scores; Allow them to use the funds for a medical education anywhere with the loans being payable by service in rural areas after their graduation.
nRestructure the SLMC to become a more modern and efficient institution able to fulfil its obligations; Appointing half of the members of the Council out of eminent personnel outside of the medical profession as in the General Medical Council of UK, would be a good start.
nSet up a system to recover costs from medical graduates who study at public expense and leave the country or join the private sector; Channel those funds, and more, to public medical education.
nAssist the private medical institutions with land, low-interest loans and training facilities so that they can offer a medical education at lower costs; Stop harassing them; Make the best of these private investments through reasonable mandates that direct the investors towards public interest objectives.
nIn the long term, establish a Higher Education Funding Agency to award grants and/or loans for higher education areas of national interest.
Note: The statements in this column are not meant to denigrate those good doctors in the public sector who provide medical services under difficult conditions and are striving to reform the sector.


With a name like "gorgeous," who wouldn't want to slip into this bra? Sleek cups work well under even your thinnest tops, while scalloped lace adorns the band, allowing you to feel truly feminine. Bows between the cups and on the convertible straps add to the appeal.
Please refer to "Bra Fitting - Where to start"


text:
\section{Introduction}

\input{introduction.tex}

\section{Related Work}

In our work, to achieve fast adaptation, we utilize human feedback and use it under the framework of reinforcement learning (RL).
RL has been used since the early days of research in social HRI. One of the early works was conducted by Bozinovski et al.~\cite{bozinovski1996emotion}, who considered the concept of emotion in its learning and behavioural scheme. Later, several researchers in HRI investigated the effect of RL algorithms like Exp3~\cite{leite2014empathic, yuan2018when, ahmad2018emotion} or Q-learning~\cite{mataric1997learning, tsiakas2018task}. With the development of deep learning~\cite{lecun2015deep}, several methods were proposed to understand different modalities in HRI, for example, ResNet~\cite{he2016deep} for image processing and Transformer~\cite{vaswani2017attention} based solutions for text processing. Naturally, the same trend can be observed regarding the problem of adaptation in HRI. One of the pioneer works was conducted by Qureshi in 2017~\cite{qureshi2016robot} where a Deep Q-Network~\cite{mnih2015human} was used to learn a mapping from visual input to one of the several predefined actions for greeting people.

However, for deep RL methods, one of the drawbacks is that the algorithms need a lot of training data to converge~\cite{mnih2015human}. This makes deep learning methods' applicability in HRI limited, even for the most basic adaptation problem.
Thus, the dilemma is clear: on the one hand, if statistical methods are used, the robot may not be able to capture the details of the interactive process~\cite{yuan2018when}. On the other hand, if deep learning methods are used, a lot of training data is needed to optimize the algorithms~\cite{mnih2015human}. One potential solution to the dilemma may be to use  meta-learning. Using meta-learning for pre-training may address the problem of limited training data~\cite{duan2016rl,finn2017model}. Examples of this have been shown for image recognition~\cite{zoph2018learning} and imitation learning~\cite{duan2017one}. However, to the best of our knowledge, there is no research on applying meta-learning in HRI.

Trust as a social phenomenon has been widely studied in psychology, sociology, and economics, however, the definition of trust is not commonly agreed upon across the disciplines.
It was shown in numerous studies that trust is essential for successful human-human interactions \cite{mcallister1995}. In regards to relationships with robots, trust plays a major role in human's willingness to accept information from a robot \cite{hancock2011meta} and to cooperate \cite{freedy2007measurement}.
Modelling trust can help us to build more intelligent socially assistive robots.

According to Marsh's formalization, trust consists of three components, namely, basic, general and situational trust of an agent in another agent  \cite{marsh1994formalising}. The basic trust is the value derived and updated from the previous experience, and it helps the agent to make decisions about the unknown agents in future situations. The general trust is agent-specific, representing a bias towards another particular agent, while the situational trust is dependent on external conditions. In our case, we focus on modelling the basic trust, which can be viewed as a general disposition of the robot to be more trustworthy towards a human during an interaction. As a consequence, this can increase the speed of adaptation in a particular task. From the sociological standpoint, the convergence of the meta-reinforcement learning process can be considered as a part of trust modelling.

\subsection{Trust evaluation in HRI}

There is no commonly used procedure to measure perceived trust in HRI. As stated in Hancock's overview of the field, one of the most influential factors on perceived trust is a robot's performance, its reliability and understandability \cite{hancock2011meta}. Another study showed that human-related subjective measures, such as personality traits and level of expertise, also have an effect on how people perceive the robot's trustworthiness \cite{salem2015would}. Moreover, in human-computer interaction literature, cooperation is defined as a "behavioural outcome of trust" \cite{wilson2006all}. We combine all previously mentioned metrics together to measure perceived trust during our human study. In the majority of works, trust was measured after the interaction (e.g. \cite{schaefer2013perception} and \cite{aroyo2018trust}). However, trust is dynamic in nature and can be influenced by many different factors throughout an interaction \cite{BLOMQVIST1997271}. To capture the changing dynamics of trust, it is advised to measure it multiple times during an interaction \cite{mcallister1995, schaefer2016measuring}.

Another thing to consider in trust evaluation is the HRI scenario. A common scenario to evaluate trust involves economic games, where a participant is asked to gamble for a monetary gain \cite{lee2013computationally}. Some works, on the other hand, present a more general task, where a robot also asks participants to perform unusual requests \cite{salem2015would}. In both cases, only one-directional trust towards the robot is measured. However, to our knowledge, there is no study that measures the bi-directional perceived trust in HRI.
In this work, we develop an escape room scenario that allows variety in robot's and human's behaviours and is suitable for testing bi-directional perceived trust.
The escape room scenario has been found to be helpful for investigating human's behaviour in diverse collaborative tasks \cite{pan2017collaboration}. Recently, a similar scenario has been used to study the effects of robot's failure in human-robot collaboration~\cite{van2019take}. In an escape room scenario, players are locked in a room and they need to solve a series of puzzles in order to escape it under a time constraint. The main advantage of this scenario is its flexibility, which means that specific puzzles can be easily added to study different behaviours.

\section{Interaction Modelling}\label{sec:Method}
In this section, we describe our approach to model the human-robot interaction process. In Section~\ref{method:preliminaries}, we first introduce the general RL problem mathematically. Then in Section~\ref{method:our_model}, we describe the details of our model implementation.
\subsection{Preliminaries}\label{method:preliminaries}
We consider a general interaction process, modelled using $\mathbf{s}_t$ and $\mathbf{a}_t$ as the state and action of the robot agent at time $t$ during the interaction. The interaction could be viewed as maximization of the expected cumulative reward $E_{\tau\sim\pi}[\mathcal{R}(\tau)]$ over trajectories
\begin{align}
\tau = \{\mathbf{s}_1,\mathbf{a}_1,\dots, \mathbf{s}_T, \mathbf{a}_T\},
\end{align}
where $T$ is the final time step of the interaction, $\mathcal{R}(\tau)=\sum_{t=1}^T\mathcal{R}(\mathbf{s}_t, \mathbf{a}_t)$ is the cumulative reward over $\tau$. The expectation is under a distribution,
 \begin{align}p(\tau) = p(\mathbf{s}_1)\prod_{t=1}^{T}p(\mathbf{s}_{t+1}|\mathbf{s}_t,\mathbf{a}_t)p(\mathbf{a}_t|\mathbf{s}_t),
 \end{align}
 where $\pi(\mathbf{s}_t) = p(\mathbf{a}_t|\mathbf{s}_t)$ is the policy we would like to train and $p(\mathbf{s}_{t+1}|\mathbf{s}_t,\mathbf{a}_t)$ is the forward model determined by the interaction dynamics. We now define a meta-learning technique $M \equiv \{\zeta_p, \zeta_r\}$ consisting of a pre-training method $\zeta_p$ and a refinement method $\zeta_r$. Here, $\zeta_p$ takes policy $\pi$ and auxiliary environments $\mathcal{E}_x$ to receive a meta-policy

\begin{align}
\pi_{meta} = \zeta_p(\mathcal{E}_x, \pi).
\end{align}
Intuitively, the meta-policy is a policy that learned prior knowledge about the tasks it is going to solve. For more detailed explanation, interested readers refer to this survey paper~\cite{vanschoren2018meta}.

We then consider a task-specific environment $\mathcal{E}_t$ for policy refinement. The final outcome of the system, that we would like to have, is the sampled trajectories $\tau^\star_i$ from all possible trajectories generated under the optimized policy $\pi^\star$ using $\zeta_r$. Mathematically, $\tau^\star_i$ is defined as $\tau^\star_i \sim \pi^\star$, where $\pi^\star = \zeta_t(\mathcal{E}_t, \pi_{meta})$.

\subsection{Proposed model}\label{method:our_model}
In order to model the interaction in our scenario, we loosely follow the assumption that a human-like robot should have the tripartite mental activities, namely conation $\mathcal{T}$, cognition $\mathcal{G}$ and affection $\mathcal{A}$. The assumption is inspired by Hilgard's tripartite classification of mental activities of human personality and intelligence in modern behaviour psychology  \cite{hilgard1980trilogy}. Here we define the interactive space $\mathcal{C}$ as $\mathcal{C} \equiv \{\mathcal{T}, \mathcal{G}, \mathcal{A}\}$. For our particular escape room scenario, each instance of the mental functionality, $\mathcal{E}^{c \in \{\mathcal{T}, \mathcal{G}, \mathcal{A}\}}$, is then modelled and implemented as an environment of adversarial MAB problem. All of the three instances operate independently throughout the interaction process. We define different meta-learning processes $M^{c \in \{\mathcal{T}, \mathcal{G}, \mathcal{A}\}}$ for different functionality in $\mathcal{C}$. Each meta-learning strategy contains $\{\zeta_p^{c}, \zeta_r^{c}\}$.

During the interaction, the robot needs to optimize its all policies $(\pi^c)^*$ to learn the most preferred action for each MAB environment. In order to keep the generality of the concept of trust, we also assume the observational states of each category $\mathbf{s}^c$ to be fixed for each category $c$.

Our methods involve two training processes. Firstly, we model the human feedback of each action of MAB as a Gaussian distributions $r^c \sim \mathcal{N}(\mu^c,(\sigma^c)^{2})$ for all the auxiliary environments. This modelling is based on the fact that signal of emotion recognition normally follows Gaussian distributions~\cite{kragel2016decoding}.  Simultaneously, we use $\zeta_p^{c}$ to train initial random $\pi^c$ in order to get a meta policy $\pi_{meta}^c$. $\pi_{meta}^c$ learns the inner structure of the problem which makes the adaptation during the interactive session much faster and data-efficient. Finally, we conduct human experiments and study different subjective measures along with interaction. Mathematically, the training steps can be summarized as follows:
\begin{align}
    \mathcal{R}^c(\mathbf{s}, \mathbf{a}) &\equiv r^c \sim \mathcal{N}(\mu_\mathbf{a}^c,(\sigma_\mathbf{a}^c)^{2})\quad\forall \mathcal{E}_x^c\\
    \pi_{meta}^c &= \zeta_p^{c}(\mathcal{E}_x^c, \pi^c)\\
    (\pi^c)^* &= \zeta_p^{r}(\mathcal{E}_t^c, \pi_{meta}^c),
\end{align}
Where $c\in\{\mathcal{T}, \mathcal{G}, \mathcal{A}\}$, $\mu_\mathbf{a}^c$ and $\sigma_\mathbf{a}^c$ are mean and standard deviation associated with action distribution $\mathbf{a}$, $\mathcal{E}_x^c$ are auxiliary environments for pre-training and $\mathcal{E}_t^c$ are real interactive environments. We use MAML ~\cite{finn2017model} as the meta-learning algorithms $M^c$ and trust region policy optimization (TRPO)~\cite{schulman2015trust} as optimization algorithm for all policies. Interested readers can refer to Appendix~\ref{ramdom_meta} to see the differences in convergence between meta policy and randomly initialized policy for MAB problems with different number of actions using a neural network based policy.

\textbf{Implementation details:} We used a PyTorch based MAML implementation\footnote{https://github.com/tristandeleu/pytorch-maml-rl} and built a customized MAB environment using OpenAI Gym~\cite{brockman2016openai}. For each instance of mental functionality, an auxiliary environment is used as the pre-training method. For all instances, the same policy network structure is used, namely, one hidden layer with 100 neurons. MR engine Unity is used to animate the changes in the Hololens environment\footnote{https://unity.com/}. The communication between Hololens and the algorithm in the policy refinement step is carried out via a UDP based communication method.

\section{Methodology}\label{sec:Experiment}

\subsection{Scenario}

We built an escape room scenario to conduct a between-subjects study, in which participants interacted with a Pepper robot\footnote{https://www.softbankrobotics.com/emea/en/pepper}. The escape room was created in augmented reality and participants were required to wear a Mixed Reality headset HoloLens\footnote{https://www.microsoft.com/en-us/hololens} to see walls of the virtual maze, triggers, keys, and the exit door (see Fig~\ref{setup}). The interaction consists of three parts: an instance of conation, an instance of affection, and an instance of cognition. Each instance is triggered by the participant's position in the virtual maze, recorded from the HoloLens. For each instance, the robot chooses one out of four actions, according to a probability distribution provided by the algorithm. Here, an action is implemented as a verbal question (i.e. \textit{``Did you come here to bring me something?"}, see Table \ref{tab:questions}). After the participant answers, the robot updates the probability associated with the previous question and gives feedback accordingly (i.e. for the low probability ``\textit{I do not believe it, but fine.}", see Table \ref{tab:replies}). After completing all the interaction steps, the participant can escape the room and a run is over.
\begin{figure}[h]
\includegraphics[width=0.46\textwidth]{setup.png}
\caption{This figure shows the setup of our study. During a run, the participant starts from the dotted line circle to approach the red button trigger. Then it goes to the solid line circle to go out of the room. During the run, three instances of mental activities will each be triggered once. }\label{setup}
\end{figure}

For the control group, a statistical MAB algorithms Exp3 (\textbf{C1}), implemented as in the previous studies, was used. The experimental group interacted under our proposed model, a policy gradient based solution for MAB problem, together with meta-learning (\textbf{C2}).

\begin{table}[t]
\centering
\resizebox{\columnwidth}{!}{\begin{tabular}{ll}
\hline
Mental activity & Examples of implementation \\ \hline
Conation                 & \begin{tabular}[t]{@{}l@{}}``Do you want to escape the room?''\\ ``Do you come here to stay with me?"\\ ``Did you come here to bring me something?"\\ ``Did you come here to look for your friends?"\end{tabular} \\
Affection                & \begin{tabular}[t]{@{}l@{}}``Be careful with the walls. You should avoid them."\\ ``I can help you to do whatever you want to do here."\\ ``Hey, be relaxed, no matter what you do,\\you have no fault in this game."\\ ``Are you worried? Don't worry! I am here with you."\end{tabular} \\
Cognition                & \begin{tabular}[t]{@{}l@{}}``Here are the keys. Do you need the first key?"\\ ``Here are the keys. Do you need the second key?"\\ ``Here are the keys. Do you need the third key?"\\ ``Here are the keys. Do you need the fourth key?"\end{tabular}\\ \hline
\end{tabular}}
\caption{Examples of questions the robot asks during an interaction based on the mental activity classification \cite{hilgard1980trilogy}.}\label{tab:questions}
\end{table}

\subsection{Procedure}

Upon arrival, participants received a brief description of the experiment. Then they were asked to sign a consent form and fill in a pre-study questionnaire about their demographic background, prior experience with robots and technology, a personality assessment \cite{gosling2003very}, and negative attitude towards robots  \cite{syrdal2009negative}. In order for the algorithms to converge, the participant's behaviour has to be consistent during several runs. Thus, the participants are asked to act according to the following rules during the interaction:

\begin{itemize}
    \item Your goal is to escape the room;
    \item You need to get further information regarding the walls of the maze;
    \item You need the second key;
    \item You can answer only ``yes" or ``no";
\end{itemize}

The first attempt at escaping the room is treated as a test run to familiarize the participant with the setup and is not taken into account in an adaptation algorithm. Each participant has to go through four sessions, while each session consists of three full runs, during which the robot gradually adapts to the participant's requests. Overall, each participant goes through twelve iterations of algorithm adaptation. After each session, the participant fills in a short questionnaire evaluating perceived bi-directional trust. By the end of the fourth session, the participant fills in a questionnaire regarding the overall experience and the quality of interaction.

\subsection{Hypotheses}

By comparing the effects of the two conditions we aim to test whether the \textit{overall perceived bi-directional trust} is affected by different adaptation algorithms. We propose to define \textit{bi-directional trust} as how trustworthy the participant perceives the robot and how much, in their opinion, the robot trusts them in return. Moreover, we hypothesize that the \textit{dynamics} of how the bi-directional trust changes throughout the interaction sessions vary in two conditions. More formally,

\begin{itemize}
    \item \textbf{H1}: Perceived trust of a participant towards the robot is \textit{higher} in the \textbf{C2}.
    \item \textbf{H2}: The dynamics of perceived trust of a participant towards the robot differs in two conditions.
    \item \textbf{H3}: Perceived trust of a robot towards the participant is \textit{higher} in \textbf{C2}.
    \item \textbf{H4}: The dynamics of perceived trust of a robot towards the participant differs in two conditions.
\end{itemize}

Based on the simulation results (see Fig.~\ref{simu_exp3_meta}), we assume that our proposed model (condition \textbf{C2}) ,with faster adaptation and its specific adaptation dynamic, will influence participant's perception of bi-directional trust in a positive way. Additionally, we expect that slower adaptation (condition \textbf{C1}) will cause fewer changes in subjective measures between four sessions, in comparison to the meta-learning based approach.

\begin{table}[t]
\centering
\begin{tabular}{ll}
\hline
Confidence level & Examples of robot's replies         \\ \hline
Low ($p<0.5$)             & ``I do not believe it but fine."     \\
Medium-low ($p<0.65$)      & ``Is that really so? I am not sure." \\
Medium ($p<0.8$)          & ``I understand now."                 \\
High ($p\geq$0.8)             & ``Awesome, I knew you would say so." \\ \hline
\end{tabular}
\caption{Examples of robot's replies to participants' answers based on the confidence level, where $p$ denotes probability associated with the action.}\label{tab:replies}
\end{table}

\subsection{Measures}

\subsubsection{Objective measures}

In order to analyze the performance of the algorithms in our setup, we tested our method using simulated data. Like what we described in the previous section~\ref{method:our_model}, we assume user's feedback is a continuous signal distributed with Gaussian function with mean $\mu=1$ and variance $\sigma^2=0.1$. Based on this simulated feedback, we compare the adaptation speed between the Exp3 algorithms in the control group and the meta-policy in the experimental group. In this simulation, we choose the number of actions in MAB settings to be four, similar to previous studies~\cite{yuan2018when,leite2014empathic}.

\subsubsection{Subjective measures}
Bi-directional perceived trust is measured from the participant's point of view, how trustworthy they perceive the robot and how much they think the robot trusts them in return. We evaluate the bi-directional perceived trust by following Salem et al.'s work on trust evaluation in HRI \cite{salem2015would}. Single items were extracted and adjusted to suit our scenario.

Subjective measures were collected in a form of a questionnaire, given to the participants after each session during the interaction. The participants were asked to evaluate their answers on the 5 point Likert scale (1 = ``\textit{strongly disagree}", 5 = ``\textit{strongly agree}").

We selected single modified items from \cite{salem2015would}: ``I perceive the robot as trustworthy", ``The robot perceives me as trustworthy". Single modified item was added from the ``Propensity to Trust survey" \cite{evans2008survey}: ``The robot anticipates my needs".
We further examined participant's perception of the robot's trust towards them with counteractive items: ``The robot believed my answers", ``The robot questioned my answers".

\subsection{Participants}

A total of 24 subjects (11 female, 13 male), with ages ranging between 22 and 48 ($M=27.04$, $SD=5.42$), were recruited for this experiment. On a Likert scale from 1 to 5 (with 1 representing very little and 5 - very much), participants were found to have moderate experience interacting with robots ($M=3.04$, $SD=1.338$), negligible familiarity with Virtual or Augmented reality ($M=2.32$, $SD=1.376$), and major skills regarding digital technologies ($M=4.76$, $SD=.436$). Participants were randomly assigned to one of the two conditions, resulting in two groups of 12 subjects each.

\section{Results}\label{sec:Results}

\subsection{Objective Measures}
 Fig.~\ref{simu_exp3_meta} shows the comparison between the overall adaptation speed between the control group and the experimental group over all the instances using simulated feedbacks. The x-axis indicates the average probability of all the right answers over all the instances throughout the four sessions. The y-axis shows the number of iterations that the algorithms has optimized. The result shows that our method has on average higher adaptation speed.

\begin{figure}[h]
\includegraphics[width=0.46\textwidth]{algs_compare.png}
\caption{This figure shows the simulated adaptation results of the escape room scenario for two different algorithms after each interaction. The results of Exp3 algorithms is shifted to the right in order to show the difference clearly.}\label{simu_exp3_meta}
\end{figure}

\subsection{Subjective Measures}
The bi-directional perceived trust was analyzed using a mixed design repeated measures one-way ANOVA at significance level $\alpha=.05$. The two conditions were viewed as between-subjects factors $(N_1=12, N_2=12)$, while sessions numbers corresponded to within-subjects factor on the dependent variables of perceived trust towards the robot and perceived robot's trust towards the participant. The overall measurements were compared based on the results of the tests of the between-subjects effects. Neither of the two measures violated the sphericity assumption. Dynamics analysis was carried out on the basis of the interactions' statistical significance.

\subsubsection{Perceived trust towards the robot}

For the condition \textbf{C1} (see Fig.~\ref{results:robot-trust}), the first session resulted into moderate scores ($M=$ 3.03, $SD=$ 1.24), then the perceived trust towards the robot dropped in the second session ($M=$ 2.42, $SD=$ .996). After that, it stabilized during the third ($M=$ 2.83, $SD=$ 1.267) and the fourth ($M=$ 2.67, $SD=$ .888) sessions. In the condition \textbf{C2}, at the beginning participants evaluated their perceived trust towards the robot at a similar moderate score as the C1 ($M=$ 2.92, $SD=$ .793). In the consecutive sessions, we can observe a gradual increase of the scores, specifically after the second session ($M=$ 3.17, $SD=$ .835), the third ($M=$ 3.75, $SD=$ .754), and finally the fourth ($M=$ 3.92, $SD=$ .515).

We found a statistically  significant effect of condition on participant's perception of the robot's trustworthiness, $F(1,22)= 4.794, p < .05, \eta^{2}=  .179$, such that the average score of the \textbf{C1} ($M=$ 2.75, $SD=$ .222) is lower than the \textbf{C2} condition score ($M=$ 3.437, $SD=$ .222). Thereby, \textbf{H1} \textit{was supported} and the participants perceived the robot's trustworthiness higher in the meta-learning based adaptation.
A statistically significant effect of the session number was found, $F(3,66)= 1.427, p < .02, \eta^{2}= .142 $, regardless of condition.

There was a statistically significant effect of the session number and condition interaction, $F(3, 66)= 5.629, p < .002, \eta^{2}=  .204$. This means that the dynamics of how the participants gain trust in the robot is significantly different in two conditions and \textbf{H2} \textit{was supported}.

\begin{figure}[t]
\includegraphics[width=0.46\textwidth]{figures/trust-robot-labelled.png}
\caption{The average value (5-point Likert scale) of the participant's perceived trust towards a robot by session number and condition with 95\% CI errors. The condition \textbf{C1} is shifted to the right to show the difference clearly.}\label{results:robot-trust}
\end{figure}

\begin{figure}[t]
\includegraphics[width=0.46\textwidth]{figures/trust-human-labelled.png}
\caption{The average value (5-point Likert scale) of the robot's perceived trust towards a participant from a robot by session number and condition with 95\% CI errors. The condition \textbf{C1} is shifted to the right to show the difference clearly. }\label{results:human-trust}
\end{figure}

\subsubsection{Perceived robot's trust towards the participant}

From Fig.~\ref{results:human-trust}, we can observe that in the beginning, the participants in the condition \textbf{C1} evaluated moderately-low ($M=$ 2.08, $SD=$ .996) on how trustworthy the robot perceives them. In the second session, the average score decreased ($M=$ 1.67, $SD=$ .778) and then increased in the third session ($M=$ 2.5, $SD=$ 1.168). The scores in the final session decreased ($M=$ 2.17, $SD=$ .937). The results of \textbf{C1} show fluctuations between sessions, but also have a positive trend overall. In comparison to the condition \textbf{C1}, the first session in condition \textbf{C2} has higher average score ($M=$ 3.08, $SD=$ 1.379), which increased after the second session ($M=$ 3.58, $SD=$ 1.24), and flattened out afterwords in the both third ($M=$ 3.67, $SD=$ 1.155) and fourth ($M=$ 3.75, $SD=$ .965) sessions.

A statistically significant effect of condition was found on perceived trust towards the participant, $F(1,22)= 16.44, p < .001, \eta^{2}= .428$, such that the \textbf{C2} has higher average score of perceived trust towards the participant ($M=$ 3.521, $SD=$ .247) compared to the \textbf{C1} ($M=$ 2.104, $SD=$ .247). This denotes that the participants perceived the robot as more trustworthy towards them in the \textbf{C2} and \textbf{H3} \textit{was supported}.

Even though the overall robot's trust towards the participant depends on condition, the differences in dynamics of how the perceived robot's trust towards the participant changes in two conditions are not statistically significant, $F(3,66)= 1.673, p = .181, \eta^{2}= .071$, \textbf{H4} \textit{was rejected}. Additionally, no statistical significance of session number was found, $F(3,66)= 2.374, p = .078, \eta^{2}= .097$.

\section{Discussion}\label{sec:Discussion}

Despite \textbf{H3} \textit{being  supported} and participants perceiving the robot as more trusting towards them in the \textbf{C1}, the dynamics of the perceived robot's trust towards the participant was not significantly different between groups. As a consequence, \textbf{H4} \textit{was not supported}. This can be explained by the meta-learning based adaptation algorithm converging on at least one of the actions during the first session. In other words, during the first session the robot might have gone from explicitly saying ``I don't believe you" to a more neutral ``I understand". Thus, the results show a significant difference between the first sessions in two conditions, but for the subsequent sessions, the perceived robot's trust towards the participant fluctuates within the 95\% confidence interval. We can hypothesize that we observe this due to too fast adaptation with the meta-learning pre-trained policy gradient algorithm and too slow adaptation with Exp3.

In contrast, the convergence rate of the algorithms did not have the same effect on how trustworthy the participants perceived the robot. The estimated marginal means in the second condition were significantly higher, meaning that the participants found the robot more trustworthy in the second group. Thus, we can conclude that \textbf{H1} was supported. However, we also found a significant effect of interaction, which can be interpreted as a difference in trends for two conditions and supports \textbf{H2}. A noteworthy distinction between the dynamics of  the robot's trust towards the participant and the trust towards the robot appears in the first session.

The discrepancies of how two algorithms influenced the dynamics of perceived bi-directional trust can be explained by the explicit nature of how the robot expressed its trust towards the participants, while human trust is a complex social construct. Further investigation is required in order to examine this phenomenon.

Compared to \cite{yuan2018when}, we established an alternative method to model the interactive adaptive process and show that our model is able to learn a prior from modelled auxiliary environments. This helps the algorithm to deal with the real-time requirement in human-robot interaction.

We think this work could be extended in two directions, both from the algorithmic and the human study point of view. From the algorithmic perspective,
we want to incorporate deep learning perceptual modules for processing multimodal human input to enrich the interaction process.
Second, trust modelling can be expanded to include implementation of general and situational trust, as well as reaction to trust violation, according to Marsh's formalization \cite{marsh1994formalising}. Finally, the developed system can be tested further for how different approaches of trust modelling influence the participants' perception of trust, robot's intelligence, and quality of interaction.

We hypothesize that the proposed models can help us to achieve a richer socially interactive process for real-time HRI.

\section{Conclusion and Future Work}
In this work, we proposed to use a meta-learning based policy gradient method for addressing the problem of fast adaptation in HRI. Compared to the statistical model, it can be pre-trained in auxiliary environments and then adapted faster in the real HRI scenario.

We designed an escape room scenario in mixed reality to evaluate the proposed method and investigate its potential effects on the perceived bi-directional trust. Our results show that not only the algorithm adopted a higher learning rate after the meta-learning process but also has increased the participant's perception on how trustworthy the robot perceives them.

In the future, we will combine this modelling with different neural network-based perception modules and examine their influences on the interactive process. From the trust modelling side, we will investigate the possible cause of the differences in the dynamics of perceived bi-directional trust. Moreover, we will address trust violation and examine human's perception of different approaches to modelling it.
\section*{Acknowledgement}
This work was supported by the COIN project (RIT15-0133) funded by the Swedish Foundation for Strategic Research and by the Swedish Research
Council (grant n. 2015-04378)

redpajama_set_name: RedPajamaArXiv


"The crematoria ovens had a width of ten metres and a length of about five to six metres. They were not protruding from the ground. They had no chimneys. They tapered down towards the bottom where grates were fastened. These grates consisted of railroad tracks; the shorter rails are the grates. The longer ones, however, served the purpose of camouflaging the crematoria ovens from airplanes. These rails were placed over the pits and covered up with metal sheet. On top of each layer of dead bodies, a layer of wood logs was placed. As far as I can remember, the oven was lit from the bottom. Whoever was lighting the fire had to make his way through the opening, the ash opening to get underneath the grates. I would like to remark that an underground corridor led to the ash box serving as an air supply channel as well as for the purpose of removing the ashes. The corpses burned quickly. New corpses were constantly being thrown on top."
"If a gassing was to be carried out, Roth ordered one of the camp inmates under his command, usually the witness Kanduth, to heat up a brick inside the crematoria. Often Roth carried the hot brick on a shovel into a gas cell and placed it into a gas input device consisting of an iron box with a removable lid which could be closed airtight by using wing screws and a gasket. The introduced hot brick served the purpose of vapourizing the subsequently filled-in poison gas which was bound to paper scraps using the uprising heat."


Everyone from Margot Robbie to Mary J Blige gathered in Los Angeles on Monday for the traditional Oscars "class photo".
This year's Academy Awards contenders rubbed shoulders at the annual nominees' luncheon, which took place four weeks before they will regroup for ceremony itself.
While the catering team at the Academy are probably getting sick of the sight of Meryl Streep, we certainly aren't.
At 89 years old, Agnes Varda has already become the oldest nominee in Oscars history.
Sadly, Agnes couldn't make it to the luncheon in person. So her co-director JR held up a cardboard cut-out of her instead.
We don't often use the words "Hollyoaks" and "Oscars" in the same sentence.
The Silent Child, written by Shenton and directed by Chris Overton (also former actor from the British soap) is nominated for best live action short film.
"I don't class myself as a writer so I felt grossly underqualified doing this," she said last month. But she certainly isn't out of place in this year's class photo.
"Oh this? Oh it's just my bicep. I'd totally forgotten it was there! Goodness gracious, what am I like, eh?" That's Dunkirk cinematographer Hoyte van Hoytema in the back row.
The co-chairman of Working Title films might have managed to stay behind the scenes as a producer of Victoria & Abdul but had less success going undetected here, peering out from behind the giant statuette.
After last year's trouser-gate, we really thought 2018 would be different.
We don't care if they don't win any actual trophies – they have already won this year's photo.
There's Common in the back row, nominated for best original song alongside Diane Warren for Stand Up For Something from the film Marshall.
The rapper was looking so dapper you probably wouldn't notice he was actually trying to hide a black eye.
We've all been Mudbound director Dee Rees at some point and managed to blink at the precise moment a photo is being taken.
9. Quite a lot of men aren't there?
It's an issue Academy president John Bailey is aware of, making reference to the gender imbalance when he spoke at the luncheon.
Which is a very sensible idea, given that he's six-and-a-half feet tall.
They probably could have sat him on the floor and he'd still tower over half the people there.
He wasn't the only Phantom Thread star who didn't make it though – best supporting actress nominee Lesley Manville is nowhere to be seen either.
Perhaps one of the most notable absentees is best actress favourite Frances McDormand, who's nominated for Three Billboards Outside Ebbing, Missouri.
As is tradition, the photographer also took a "fun" version of the photo, where the nominees were encouraged to pull funny faces or different poses.


text:
Disney releases full trailer for Star Wars: Rise of Skywalker
The full trailer for Star Wars: The Rise of Skywalker — the final chapter in the Skywalker saga — has finally dropped, and it's out of this world.
Lexie Cartwright@LexieCartwright
news.com.auOctober 22, 20192:35pm
Star Wars, The Rise of Skywalker - Final Trailer
Star Wars, The Rise of Skywalker - Final Trailer.
The full trailer for the final Star Wars movie is here.Source:YouTube
Two months out from its hugely anticipated release, Disney has dropped the final full-length trailer for Star Wars: The Rise of Skywalker and, boy, do we have chills.
Directed by JJ Abrams and produced by Lucasfilm, The Rise of Skywalker is the final chapter of the nine-part Skywalker saga that began in 1977, and the third instalment of the Star Wars sequel trilogy after The Force Awakens (2015) and The Last Jedi (2017).
There was a lot to take from the two minute trailer but here are the key moments.
Among the wild montage of epic cinematography are scenes of Rey and Kylo Ren fighting. A voiceover hints the pair will band together, despite being on opposing sides of The Force.
"Long have I waited, and now, your coming together is your undoing."
Rey appears to team up with Kylo.Source:YouTube
An unlikely duo.Source:YouTube
Fans suggest the voice is Emperor Palpatine, who was thought to be dead at the end of the 1983 movie Return of the Jedi. Palpatine's voice has been used in previous teasers, but the trailer strongly hints at a possible return.
Elsewhere, and in what will no doubt spark furious tears among diehard supporters, it looks as though C-3PO could be on his last legs, with the rest of the gang going on a mission without him.
"(I'm) taking one last look sir, at my friends," he says to Poe. And to all of our souls.
The trailer ends with a close up of Rey gripping her lightsaber, with Luke's voice whispering, "the Force will be with you," followed with Leia saying, "always."
Sobbing. Uncontrollably.
Leia!Source:YouTube
The film, which will be 155 minutes long (the longest Star Wars movie ever), will see the Resistance face the First Order one more time, and the end of the ancient conflict between the Jedi and the Sith.
The Last Jedi saw the controversial death of Luke Skywalker (Mark Hamill) after a battle with Kylo Ren (Adam Driver), leaving diehard fans distraught.
And while there were many fan theories Luke didn't actually die, Hamill himself, who is returning for Episode IX, seemed to reveal he would be a Force ghost.
For those who don't know, a Force ghost, or Jedi ghost, is someone who has died but hasn't merged with the Force so they can still be visible to whomever they choose.
Hamill actually seemed a little salty when asked about his return for the final chapter.
"The fact that I'm involved in any capacity is only because of that peculiar aspect of the Star Wars mythology in that, as a Jedi, you're allowed to come back and make a curtain call as a force ghost," he toldAP.
RELATED: Disney releases trailer for Star Wars series The Mandalorian
Luke Skywalker died in The Last Jedi.Source:Supplied
The Rise of Skywalker will hit cinemas on December 20.
It was originally supposed to be directed by Colin Trevorrow, who made Jurassic World, but he was fired in the early stages and replaced by Abrams, the man behind The Force Awakens, which is the fourth highest grossing film of all time.
Tickets were made available for pre-purchase in the US yesterday, and ticket company Atom Tickets reportedly sold 45 per cent more in the first hour than Marvel's April blockbuster Avengers: Endgame, which eclipsed Avatar to become the highest grossing movie ever.
Though it would be a huge feat, this holds The Rise of Skywalker in good stead to knock Endgame off its perch for the coveted title.
Disney last year announced returning cast members including Daisy Ridley, Adam Driver, John Boyega, Oscar Isaac, Lupita Nyong'o, Domhnall Gleeson, Kelly Marie Tran, Joonas Suotamo, and Billie Lourd.
Daisy Ridley plays Rey. Picture: YouTubeSource:YouTube
Veteran Star Wars actors Hamill, Anthony Daniels, and Billy Dee Williams, who will reprise his role as Lando Calrissian, also star in the film.
Newcomers to the cast include Naomi Ackie and Richard E Grant.
The trailer release coincides with original Star Wars star Carrie Fisher's 63rd birthday.
Fisher died suddenly in 2016 at age 60, but her character, Princess Leia, will appear in the movie.
As is customary in the era of social media, the Twitter fandom are losing their minds about the whole thing.
pic.twitter.com/kNrEQwf9gV
— Bruce Crawford (@BruceCrawford1) October 22, 2019
pic.twitter.com/KNpusIfT8V
— LL McKinney 🔜 Texas Book Festival (@ElleOnWords) October 22, 2019
"This force will be with you. Always" pic.twitter.com/A0DSwsgSG7
— 💜💜Alivia💪🏿 (@Alivia0988) October 22, 2019
Star Wars fans the minute #TheRiseofSkywalker tickets went on salepic.twitter.com/0gj25gKzTd
— Hammie (@balancedpadawan) October 22, 2019
Me, watching C-3PO in that #TheRiseOfSkywalker trailer. pic.twitter.com/XeGCAGhFg3
— Chris Pirillo (@ChrisPirillo) October 22, 2019
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Chilling moment in new Star Wars trailer


The Crossword Solver found 21 answers to the Brought forth crossword clue. The Crossword Solver finds answers to American-style crosswords, British-style crosswords, general knowledge crosswords and cryptic crossword puzzles. Enter the answer length or the answer pattern to get better results. Click the answer to find similar crossword clues.
AGO "... ___ our fathers brought forth ..."
NATION "... brought forth on this continent a new ___..."
ASON "...and shall bring forth ___": Matt.


text:
Driver, Passenger Injured in Early Morning Car to Car Shooting on Northbound 101 Freeway
A male driver was injured by gunfire in a car to car shooting, then collided into center divider wall on the Northbound 101 Freeway early Wednesday morning.
Los Angeles: California Highway Patrol responded to a shooting call on the Northbound 101 Freeway near the Santa Monica Boulevard exit around 1:54 a.m. Wednesday, May 26, 2021. Upon arrival, officers discovered it was a car to car shooting where a dark SUV possibly fired multiple rounds at a Chevrolet pick-up truck with a 38-year-old driver and 25 year-old passenger, according to Sergeant Jeff Lane with the CHP Central Los Angeles area office at the scene of the incident.
As a result of the shooting, the driver of the pick-up truck sustained a leg injury and then subsequently collided with the center divider wall. He was transported to Cedars-Sinai Hospital with moderate injuries, said Sergeant Lane. The passenger suffered minor injuries and walked off the freeway on his own. Sergeant Lane said it looks to be an isolated incident.
Anyone with information regarding this incident may contact CHP at (800) 835-5247.
Andrew Gonzalez Photojournalist, Key News Network


The Miami Travel Guide - Florida FL creates custom itineraries in Miami - Florida based on your preferences! The guide calculates the best routes, suggests places for you to visit, considers schedules of places, helps you to find hotels, restaurants, resort, beaches, weather, nightlife, sawgrass, tours and maps. Explore MIA - Florida as if you were a local!Features:★ The Miami Travel Guide Florida creates itineraries for your trips in seconds, based on your preferences! You can change and adapt it as you wish★ Explore Miami Beach, South Beach, Ocean Drive, Premium Outlets, food and more! You can find attractions, restaurants, hotels, nightlife, maps, hostels and city tours in Miami Florida★ Nativoo Miami MIA Florida FL Travel Guide learns your preferences, you can also set a quick profile to help the guide suggest the best routes and places for you.


The holidays are usually a time for splurging. We know this. But healthy holiday party foods are possible. Give your guests a little surprise, with familiar holiday flavors served up a little "healthier" for their sake.
Add one of these delicious recipes to your holiday party menu, and celebrate a Know. Better. Holiday. this year.


Chandigarh – A 23-year-old Punjabi singer was found dead in Dera Bassi near here, police said on Monday. Navjot Singh's body, bearing four to five bullet injury marks, was found a few metres away from his car at village Rampur Sainian in Dera Bassi, Mohali, at around 1am, said Inspector Mahinder Singh, police station, Dera Bassi. The singer's family, living in Mohali, was frantically searching for him after he did not reach home on Sunday night, police said. "Investigations are underway," the Inspector said, adding, that a murder case has been registered in this regard and the body has been sent to the civil hospital for post mortem.


text:
Originally published: 2001-02-23 19:03:43
Last updated: 2001-02-23 19:03:43
Author: Gisle Aas

The pyperl dist provide an interface to the Perl DBI (dbi.py).
This allow direct access to all the perl supported databases from
python. Full documentation of the API from 'perldoc DBI' or
the ORA "Programming the Perl DBI" book, by A. Descartes
and T. Bunce.

redpajama_set_name: RedPajamaGithub


text:
Russell L. Robinson
Emeritus Professor of Music Educaton
School of Music/Music Education
Dr. Russell L. Robinson is Emeritus Professor of Music Education at the University of Florida, where he was the Area Head of Music Education and taught graduate and undergraduate courses from 1984-2016. He holds the Bachelor of Music Education degree from Drury University, Springfield, Missouri, and the Masters and Ph.D. in Music Education degrees from the University of Miami. Well-known for his innovative and practical teaching and conducting techniques, Dr. Robinson has made over 300 appearances as a conductor, speaker and presenter at festivals, workshops, state, regional and national conferences of the National Association for Music Education (NAfME) and the American Choral Directors Association (ACDA), and internationally, including Canada, Mexico, Costa Rica, Panama, Dominican Republic, Nicaragua, Brazil, Colombia, Argentina, Thailand, Vietnam, the Philippines, Italy, France, Germany, the Czech Republic, Australia, Kenya, Tanzania, Ghana, Cameroon, Zimbabwe, Ethiopia, South Africa, Bulgaria, Poland, Qatar, Austria, and China. His invited conducting appearances have included Carnegie Hall (1997, 2006, 2014), Lincoln Center, the Kennedy Center, and Boston's Symphony Hall.
Robinson is past President of the Florida Music Educators Association (FMEA), Interim Associate Dean for the UF College of the Arts, and National Collegiate Chair for NAfME. After several professional trips to Africa, Dr. Robinson created an "institutional linkage" program with Kenyatta University (Nairobi, Kenya), that brought Duncan Miano Wambugu to UF to pursue his Ph.D. with Robinson, and together, they founded the UF Africa Choir in 2009. Now, under its third director from Kenyatta University, the Africa Choir continues at UF to this day, presenting the "real" music of Eastern and Southern Africa. From 2010-2016, he and his wife, Brenda, were the Directors of the Teach the World Study Abroad program that they founded at UF. The annual program took more than 100 education interns to international schools in Italy and France. Dr. Robinson has been an exclusive clinician/adjudicator for Festival Disney Choral Festivals at Walt Disneyworld since 2004, and he has conducted the classical choral studio recordings for Warner Bros. Publications, Lawson-Gould and Alfred Publishing.
Professor Robinson has been the recipient of numerous awards, including: the UF College of the Arts Teacher of the Year, the Doctoral Adviser/Mentor Award, the Outstanding Alumnus Award for Career Achievement from Drury University, the UF Research Foundation Professorship, the UF College of Education's Scholarship of Engagement award, and the FMEA Leadership Award. In 2016, Dr. Robinson was inducted into the Florida Music Educators Hall of Fame, and was the first College of the Arts recipient of the University of Florida Provost's Preeminent Faculty award, presented to him by Provost Joseph Glover. In 2018, he and his wife, Brenda, established an annual Russell Robinson Music Education Scholarship for a graduating senior at Cassville (MO) High School where Robinson began his music teaching career (1974-1981).
Dr. Robinson is a published author, composer and arranger with over 450 publications in the catalogues of Warner Bros., Shawnee Press, Hal Leonard, Mark Foster, Carl Fischer, Heritage Music Press, Colla Voce, Walton, Alliance Music, Alfred Publishing, Bri-Lee Music, and Belwin Publications, and he has been the recipient of numerous ASCAP composer/arranger awards. Dr. Robinson's instructional DVD's Creative Rehearsal Techniques for Today's Choral Classroom, Jazz Style and Improvisation for Choirs, Middle School Singers: Turning Their Energy Into Wonderful Choirs! and his books, The Complete Choral Warm-up Book, co-authored with composer, Jay Althouse, and Songwriting Made Easy!, are published by Alfred Publications. A book and CD for classroom teachers, Creating Poetry and Songs to Teach ANYTHING! is published by Superior Educational Publishing, and his books, I Know Sousa, Not Sopranos! A Survival Guide for the Band Director Teaching Choirs!, Quick Start Choral Warm-Ups (2017) and Sing a Merry Christmas (2018) a collection of 8 holiday songs for middle school mixed voices, are published by Heritage Music Press. Dr. Robinson's website is: www.RussellRobinson.com
rlrob@ufl.edu
Russell Robinson's Website


text:
K2'S Tales Of True Tech Crimes - Ripped From The Headlines
Total Credits: 2 including 2 Information Technology - Technical
ACPEN Industry Institute | Industry
Brian F. Tankersley, CPA.CITP, CGMA
Tue, Feb 01, 2022 - 09:00am to 10:52am CST Fri, Feb 11, 2022 - 11:00am to 12:52pm CST Tue, Mar 01, 2022 - 01:00pm to 02:52pm CST Fri, Mar 11, 2022 - 03:00pm to 04:52pm CST Tue, Apr 05, 2022 - 09:00am to 10:52am CDT Fri, Apr 15, 2022 - 11:00am to 12:52pm CDT Tue, May 03, 2022 - 01:00pm to 02:52pm CDT Fri, May 20, 2022 - 03:00pm to 04:52pm CDT Tue, Jun 07, 2022 - 09:00am to 10:52am CDT Fri, Jun 17, 2022 - 11:00am to 12:52pm CDT
$79.00 - Member $109.00 - Non-Member
2020 was an exceptional year for everyone, and like everyone, the hackers, scammers, and other technology criminals took advantage of new opportunities brought by COVID. Criminals reportedly used innovative approaches to exploit weaknesses created by the pandemic. You will learn tips and techniques from these events to help protect you, your company, and your data.
Attend this session and learn more about how high-profile control failures occurred so you can be more effective at preventing crimes such as theft, malware, ransomware, phishing, and hacking.
List at least three major security incidents reported in the headlines in the last year and explain at least one primary control design or operation flaws that allowed the hack to occur
Select the correct definitions from a list of standard security terms such as attack surface, vulnerability, exploit, social engineering, phishing, malware, heuristics, biometrics, and multi-factor authentication (MFA)
Explain why home-grade computer hardware and applications are sometimes less secure than business and enterprise-grade hardware and applications
List at least three best practices learned because of reviewing the incidents cited in the case studies
Common security weaknesses that occur with hardware and software at home and in the office
Malware, ransomware, data breach, and incident response tips
Internal control failures resulting in the theft of assets or unauthorized manipulation of data
User authentication and security awareness training
Important CPE Credit Instructions_READ BEFORE WEBCAST UPDATED (0.47 MB) Available after Purchase
Handout (4.2 MB) Available after Purchase
Brian F. Tankersley, CPA.CITP, CGMA Related seminars and products
Director, Strategic Relationships
K2 Enterprises, LLC
Brian Tankersley is a consultant who advises US firms and companies on accounting technology issues. Mr. Tankersley is a frequent speaker at continuing education courses for K2 Enterprises, writes for multiple accounting media outlets, and publishes a nationally recognized blog on accounting and technology (www.cpatechblog.com). Mr. Tankersley has over 25 years of professional experience, including accounting, auditing, technology, and education, has been with K2 Enterprises since 2005. Brian started teaching CPA review courses in 1997, and currently works with Yaeger CPA Review.
Tankersley has been recognized eight times as one of the "Top 25 Thought Leaders in Public Accounting Technology" by Cygnus Business Media. Brian has made presentations in 48 of the 50 US states and has served as a guest speaker for many professional accounting organizations across Canada. He has received numerous awards for his writing and speaking from state CPA societies. Brian has presented sessions at most major national accounting technology conferences, including AICPA TECH+/Practitioners Conference, The Sleeter Group's Solutions Conference, Sage Summit, CCH Connections, and Thomson Reuters Synergy User Conference.
Brian is a certified public accountant (Tennessee), a certified information technology professional (CITP), and a Chartered Global Management Accountant (CGMA). He holds bachelor's degrees in both Accounting and Finance, cum laude, from the University of Tennessee. Tankersley also maintains numerous vendor software certifications, and is a member of the Tennessee Society of CPAs and the American Institute of CPAs.
Brian and his family make their home in Farragut, Tennessee.
Tue, Feb 01, 2022 - 09:00am to 10:52am CST
Fri, Feb 11, 2022 - 11:00am to 12:52pm CST
Tue, Mar 01, 2022 - 01:00pm to 02:52pm CST
Fri, Mar 11, 2022 - 03:00pm to 04:52pm CST
Tue, Apr 05, 2022 - 09:00am to 10:52am CDT
Fri, Apr 15, 2022 - 11:00am to 12:52pm CDT
Tue, May 03, 2022 - 01:00pm to 02:52pm CDT
Fri, May 20, 2022 - 03:00pm to 04:52pm CDT
Tue, Jun 07, 2022 - 09:00am to 10:52am CDT
Original Recording Date 2021
Designed For Business professionals desiring to improve information security by learning from recent technology crimes and data breaches
Course Developer K2 Enterprises
Yellow Book No
Concesa C
"Great examples - very helpful to know."
J. Harold W
"Very knowledgeable speaker!"


The Omaha Education Association has established four major focus areas for the association and strategic priorities under each focus area. The OEA Board of Directors continues to focus the efforts of the Association toward attaining these strategic priorities.
The mission of the Omaha Education Association is to advance the professional interests of our members in order to make public education the best it can be for students, staff, and our community.
Expand and continue to promote community partnerships in the Omaha community where educators and community members share a common vision, mission and responsibility to support quality teaching and learning for all.
Increase communication to members and potential members about the benefits of Association membership.
Increase level of member's involvement in, and support for, Association activities.
Recruit, retain and support teachers of ethnic minority status to become active leaders in OEA.
Ensure that classroom teachers play a central role in district educational improvement through collaboration with the district involving the design and implementation of curriculum, instruction and assessment.
Advocate for teacher ownership of our professional growth and development.
Advance the professional and economic interests of OPS educators by exploring new venues to discuss improvement of teaching and learning.
Utilize every opportunity for interest-based collective bargaining and collaborative problem-solving approaches.
Strengthen OEA's advocacy for public education by monitoring, supporting and communicating a prioritized legislative and policy agenda.
Recruit, cultivate, elect and maintain positive working relationships with pro-public education candidates for public office and public officials.
Advocate for adequate and equitable policies, practices and resources that drive the OEA vision of raising achievement and closing gaps for all students.


The most expansive map yet, offering the opportunity for you to spend untold hours just exploring the environment around you.
Meeting other players is a strange experience; most will either want to fight you or else they'll ignore you entirely. Hopefully this will change as more players enter the gameworld.
The most ambitious Fallout yet… only time will tell if it is the most successful.
Despite the size of the game, the environment seems sparsely populated, and at times the lack of other players make this a lonely game to play.
The lack of NPC characters really grates, and they are sorely missed as you advance through the quests.
Frequent crashes are only one annoying aspect of what is a very glitchy and buggy game. More playtesting needed.
Graphics are very much 'last-generation' quality. And the less said about the animation, the better.
Pip-boy's clunky UI is annoying to use.
Bugs hinder this game, and until updates are issues that paper over the cracks, it is very hard to recommend Fallout 76. Having said that, the more time you invest, the greater the reward. Don't write this off as a missed opportunity just yet, Bethesda may be on the cusp of something great.


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2018 Primary Endorsements
Endorsements Info Voter Guide
July 4, 2018 August 11, 2018 36th District Democrats
Dear neighbor,
When deciding how you will vote in the August 7th Primary Election, I hope you will consider the endorsements of the 36th District Democrats. We carefully screen the candidates for experience, effectiveness, and adherence to the Democratic Party platform.
Our endorsement process is extensive and involves hundreds of hours by dozens of volunteers to help vet the candidates. For more information about our endorsements, read on below! Join the 36th District Democrats here.
Jeff Manson, Chair of the 36th District Democrats
Maria Cantwell for US Senate
Maria Cantwell currently serves as a United States Senator for the State of Washington. As a respected leader, Senator Cantwell has always embraced the values she first learned growing up in a strong working-class family. Senator Cantwell was elected to the U.S. Senate in 2000, 2006, and again in 2012, pledging to honor the hard work, aspirations, and faith of the people of Washington State. She is working to create affordable opportunities for consumers, businesses, and families, to make our nation more secure today, to foster innovation for tomorrow, and to stand with parents as they educate and care for their children.
Maria gets results. She cut taxes for the middle-class by ensuring that Washington taxpayers can deduct state and local taxes from their federal returns. When bankrupt Enron officials tried to charge Washington ratepayers for millions of dollars in undelivered electricity, Maria led the effort that successfully stopped them. Maria has protected countless jobs in Washington's aerospace industry by cracking down on foreign companies' unfair trade practices, and has worked to create still more well-paying jobs through effective investments in new technology and valuable job training. Maria successfully led the fight to stop drilling in the Arctic National Wildlife Refuge and is leading efforts in the Senate to make America more energy independent.
Pramila Jayapal for US Representative, District 7
Congressperson Pramila Jayapal came to the United States by herself when she was 16-years-old. Using their savings, her parents sent her to this country from her birth country of India. Here, she attended college at Georgetown University and went on to earn her MBA at Northwestern University. Congressperson Jayapal has a long history of grassroots activism before she was elected to Congress: She founded OneAmerica (formerly Hate Free Zone), in response to hate crimes and discrimination targeting the Arab, Muslim, and South Asian communities following the tragic 9/11 terrorist attacks; she successfully sued the Bush administration over Somali deportations, blocking 2,700 illegal deportations; she led an initiative to train 12,000 students and 1,000 teachers to engage in politics, through OneAmerica's Youth Program; and helped lead the largest voter registration drive in Washington's history, registering 23,000 voters in 2008.
As a congressperson, Pramila has been leading the charge to defend immigrants from the Trump administration, and was one of the first members of congress to go inside a detention center to visit detainees and learn their stories. We strongly recommend that you vote for Congressperson Pramila Jayapal.
You can watch Pramila Jayapal's campaign manager, Sarah Brady, speak on her behalf on our YouTube channel.
Reuven Carlyle for State Senator
First elected in 2008, Senator Reuven Carlyle chairs the Energy, Environment, and Technology Committee. He also serves on Ways and Means, Human Services, and Higher Education committees. As committee chair, Carlyle has been lead sponsor of sweeping carbon pricing and energy investment legislation. Carlyle was named by Governing Magazine as "one of 12 legislators to watch" and as one of the nation's most "tech savvy" legislators by GovTech. He has been a leading voice in the Legislature for meaningful tax transparency and accountability, responsible gun safety laws, higher education, foster youth, and environmental and energy strategies.
Senator Carlyle recognizes that, in the years to come, we will have millions of new residents coming to the Puget Sound region and that we are not ready for this population growth. He sees that we face an affordability anxiety that is off the charts and need our elected officials to focus on the education, infrastructure, transportation, human services, and financial integrity of our state so we can ensure the quality of life for both those who already live here and those who are coming to our state. We recommend voting for Reuven Carlyle for State Senate.
You can watch Senator Reuven Carlyle's endorsement interview on our YouTube channel.
Noel Frame for Representative Pos. 1
Representative Noel Frame has served the 36th District in the Washington State House of Representatives since January 2016, and during that time she has tackled the biggest challenges Washington State has ever faced: fixing our regressive and inequitable tax structure and our broken school funding system. To do this, Frame recruits and supports candidates across the state, helping to build progressive coalitions in Olympia by traveling to swing districts to campaign for Democrats.
Frame was the prime sponsor of HB 2895, the sister bill of which (SB 6160) was passed by the legislature and was signed into law this year. That bill limited the number of crimes for which children can be tried as an adult for, preventing many young people in the criminal justice system from staying in that system for the rest of their lives and increasing their chances at rehabilitation and a more promising future. Given that 70% of the youth convicted as adults in this state are people of color, this legislation will help to reduce racial inequity in our adult criminal justice system in the long-run.
Representative Frame is working to change the laws that hold poor, sick, people of color, disabled, LGBTQ, or female people, so that all Washingtonians can thrive. This includes improving access to mental health services, reforming the criminal justice system, strengthening and expanding foster care and bringing a racial equity lens to everything we do, from climate justice to reproductive freedom to voter access.
You can watch Representative Noel Frame's endorsement interview on our YouTube channel.
Gael Tarleton for Representative Pos. 2
Elected in 2012, Representative Gael Tarleton has been a tireless advocate to bring better transit service to the 36th district. She was able to add $10 million to the 2015 Transportation Revenue Package, to expand bus transit on north-south and east-west transit corridors throughout the 36th and 43rd districts, especially to expand access to the highest concentration of low-income housing units along the Greenwood Ave corridor. These routes serve more than 2.3 million riders per year.
Representative Tarleton believes that we must transition the transportation sector and our electric grid from dependence on fossil fuels to clean fuels. Through legislation, budget provisos, and the public hearings process, she has launched a sustained legislative agenda to tackle climate impacts on all these fronts
Representative Tarleton is a healthcare advocate and co-sponsored legislation and voted to expand Medicaid health and adult dental coverage in 2013, guaranteeing access to healthcare for an estimated 1 million Washingtonians under the Affordable Care and Patient Protection Act. Since October 2013, nearly 500,000 previously uninsured residents of our state now have healthcare.
You can watch Representative Gael Tarleton's endorsement interview on our YouTube channel.
Tagged Endorsements Voter Guide
2018 July Membership Meeting
August 15, 2018 Membership Meeting
36th Reorganization & Bylaws Meeting
November 18, 2020 February 15, 2021 Nicole Gomez
Endorsements & Voter Guide for the 2015 General Election!
October 17, 2015 November 8, 2015 36th District Democrats
COVID-19 Resource List
March 21, 2020 May 23, 2021 Brittney Bush Bollay