text:
Ramón Risueño Catalán (Valencia, 1900-Madrid, 11 de agosto de 1975) fue un político y notario español. «Camisa vieja» de Falange, ejerció como gobernador civil de Córdoba entre 1942 y 1943.

Biografía
Nació en Valencia en 1900. Realizó estudios de derecho por la Universidad Central de Madrid. «Camisa vieja» de Falange, tras el estallido de la Guerra civil se unió al bando sublevado y durante la contienda llegó a combatir integrado en una bandera de Falange. Tras la guerra ejerció como notario en Málaga, hasta 1942.

En noviembre de 1942 fue nombrado gobernador civil de Córdoba. Durante su mandato fue un personaje popular y llegó disfrutar de una imagen positiva entre la población local. Sería cesado de todos sus cargos en octubre de 1943, al parecer tras un incidente ocurrido durante la visita de Franco a la ciudad. No volvería a desempeñar ningún cargo de carácter político, volviendo a su anterior trabajo como notario. Falleció en Madrid el 11 de agosto de 1975.

Referencias

Bibliografía

Alumnado de la Universidad Central de Madrid
Miembros de FET y de las JONS
Gobernadores civiles de Córdoba
Militares de la guerra civil española sublevados
Nacidos en Valencia
Fallecidos en Madrid

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text:
William Kimberley
Formula E teams can gain competitive advantage with Cruden's fully functional steering wheel controls
New system for driving simulators can alter the electric powertrain's characteristics while the car is on the move and is already being used by AOTECH's Formula E customers at its simulator facility, near Paris
Cruden has developed a fully functional Season 3 Formula E steering wheel for use on its driving simulators. Teams in the FIA Formula E Championship for electric-powered racing cars can accelerate data gathering and improve their race strategies with the steering wheel, which has switches and buttons capable of altering the electric powertrain's characteristics while the car is on the move.
The XAP-made Formula E steering wheel has an integrated dashboard display, five rotary controls, six paddles, and 10 buttons. The driver uses these to monitor and adjust the car's energy consumption, energy regeneration, battery temperature and optimum engine speed for gear shifts.
The first customer of the fully functional steering wheel is AOTECH, a technical supplier to Formula E teams. The system has been retrofitted to AOTECH's simulator, located near Paris, which can be hired by teams for driver training or race car development in any motorsport formula. The new steering wheel technology is available to anyone using Cruden's Panthera ePhyse open architecture simulator software, which will communicate effortlessly with any vehicle model.
"Attaching a fully functional steering wheel to a simulator can help drivers learn these control processes, but the simulator's greatest value is in enabling engineers to test various settings and strategies to extract the maximum performance from their car's limited energy reserves by trying out different race scenarios. These scenarios are made all the more realistic because they are driver-in-the-loop simulations," says Cruden CEO, Maarten van Donselaar.
In keeping with Formula E's experimental spirit, teams and drivers are encouraged to develop individual ways of managing energy consumption by having the freedom to develop the vehicle's powertrain, including the possibility to customise the CAN communications protocol between the powertrain's electronic control unit (ECU) and the steering wheel. This means each team can determine what information is sent to the dashboard display and what functions are assigned to the steering wheel's rotary controls, paddles and buttons. Although drivers are permitted to talk with their teams by radio during a race, team-to-car telemetry is banned, meaning that all on-the-go changes to car set-up must be made via the steering wheel.
"The way that dashboard information is interpreted and reacted to can make a small but critical difference during a Formula E race," says van Donselaar. "Now that Cruden offers a fully functional steering wheel display and controls, teams can use their simulators with a driver as part of the tool chain to experiment with differing energy management strategies. Far more than just driver training, this is giving teams the kind of information that can give their car a genuinely competitive edge."
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Balancing Between Nuclear Deterrence and Disarmament: Views from the Next Generation presents policy briefs exploring Japan's complex balancing act between its short-term need for nuclear deterrence and long-term desire for nuclear disarmament. In order to meet its national security need for nuclear deterrence while continuing to ensure room for its nuclear disarmament diplomacy, Japan has taken a carefully-balanced approach towards the policy issues related to nuclear nonproliferation and nuclear deterrence. Within the framework of maintaining a strong U.S.-Japan alliance, Japan has sought to ensure the reliability of U.S. extended nuclear deterrence by measures that include close cooperation on ballistic missile defense and active engagement in bilateral dialogue on extended deterrence. At the same time, despite increasing its dependence on nuclear power, Japan has maintained its status as a responsible generator and user of civil nuclear power by becoming a leading example of how a country that is technically capable of pursuing nuclear weapons can remain a strong advocate for nuclear nonproliferation and disarmament as a non-nuclear state, including having accepted the most stringent inspection by the International Atomic Energy Agency. Through these policy choices, Japan has tried to bridge the gap between nuclear weapon states and non-nuclear weapon states by consistently attempting to lead the effort for practical steps towards nuclear disarmament.
This project, conceptualized and led by Senior Associate Yuki Tatsumi, examined the factors impacting Japan's policies on nuclear deterrence and disarmament with five policy briefs written by Masahiro Kurita, Fellow at the National Institute for Defense Studies in Japan's Ministry of Defense; Wakana Mukai, Assistant Professor at Asia University; Masashi Murano, Research Fellow at the Okazaki Institute; Masahiro Okuda, Ph.D. candidate at Takushoku University; and Heigo Sato, Vice President of the Institute for World Studies at Takushoku University. The project concluded with a publication launch event at the Stimson Center on March 19, 2018. The report was edited by Yuki Tatsumi and Research Associate Pamela Kennedy, assisted by Japan Program research intern Eunseo Kim.


The National Drug and Alcohol Research Centre (NDARC) at UNSW has received two Project Grants in NHMRC funding announced this week.
The parental supply of alcohol study led by Professor Richard Mattick will follow 1,810 children, who were first recruited at age 13, and their parents and monitor them to age 20-23 when risky drinking behaviours and harms become obvious.
"We know through monitoring these children and their families to age 18 that parental supply increases risky drinking and harms," said Professor Mattick.
Opioid prescribing has increased 15-fold in Australia in the past two decades. Professor Louisa Degenhardt will lead a study in collaboration with UNSW's Centre for Big Data Research in Health to measure population level harms associated with this increase.
The study will assess the risks for all people prescribed opioids in NSW since 2002 using routine linked de-identified data containing information on health, social and health service utilisation in this cohort.
"This will be the largest post-marketing surveillance of prescribed opioids undertaken in Australia, linking exposure and outcomes and examining risk factors for adverse outcomes of prescribed opioids," said Professor Degenhardt.
Two further NDARC NHMRC grants were announced last week by the Federal Health Minister Greg Hunt as part of a mental health research funding initiative. Professor Maree Teesson and her colleagues at the Centre of Research Excellence in Mental Health and Substance Use (CREMS) were awarded a $1.47 million to study prevention and early intervention for mental illness and substance use during the critical transition period from adolescence to young adulthood.
Professor Teesson will also lead a $1.19 million grant to continue the landmark study of long term heroin use: the Australian Treatment Outcome Study. The study will follow up 615 heroin users 18 to 20 years after they were first recruited by NDARC in 2001, to examine long-term mortality, heroin use and remission, psychiatric health, physical health, and health service utilisation.
In October, NDARC won more than $4.5 million in national health and medical research grants for five projects spanning a range of topics. This included $2.5 million for a new Centre of Research Excellence, the PRevention & Early intervention in Mental Illness and Substance usE (PREMISE CRE).


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Iconic Portland hotel opens rooms to house homeless people with symptoms of COVID-19 | Local News
PORTLAND, OR (KPTV) – The Jupiter Hotel in Burnside is offering all 81 rooms on its original 1960s side so Multnomah County can continue to implement social distancing among its homeless shelter populations .
"As we've always said to Jupiter, everyone is welcome," Jupiter general manager Nick Pearson said at a press conference Thursday.
Multnomah County President Deborah Kafoury said homeless people are among the most vulnerable in the county.
Kafoury issued an order last week that gave the county the power to use hotels during the pandemic, but said she has yet to have to use that power as places like The Jupiter escalate. .
"Throughout my life, I have seen amazing things happen when people come together for a single purpose. It's one of those moments, "Kafoury said.
Thursday's announcement comes as the county's other two temporary socially distanced homeless shelters – the Charles Jordan Community Center and the Oregon Convention Center – reach a capacity of 120 each.
"The last 20 people are moving from one of our couples' shelters, so starting today these two programs will be fully occupied," said Marc Jolin, director of the Joint Homeless Services Office.
Jolin said Jupiter's rooms will be for people already staying in shelters or involved in county programs, not for entry or exit. People who move in show symptoms of COVID-19 and should be isolated from the rest of the shelter population pending test results, Jolin said.
Since the individual hotel rooms all have exterior doors and many have floor access, Jolin said they are perfect for recovering patients.
At the press conference, county officials said a dozen people had already been moved in.
"Here, we have a full staff on site 24 hours a day. There is also a nurse on site who does the routine checks," Jolin said.
These staff are made up of county employees with medical training, nonprofit staff and others who have volunteered, and the county is looking for more people to help.
"We need trained medical staff and we need compassionate people with experience in social services to sign up for volunteer teams or full-time paid temporary work," Kafoury said.
If you are interested, you can register for these positions by visiting this link.
The Jupiter NEXT hotel, which is just across the street, is always open for regular business, Pearson said. In addition to his regular guests, Pearson said Jupiter NEXT is working with healthcare providers across Portland to house frontline medical workers who cannot return home due to the risks of exposure.
Copyright 2020 KPTV-KPDX Broadcasting Corporation. All rights reserved.


text:
EAC Homepage
EAC teams are part of:
ACCAC - Arizona Community College Athletic Association
NJCAA - National Junior College Athletic Association
EAC Football is part of:
WSFL - Western States Football League
Eastern Defeats Central
Thatcher, AZ --
The Eastern Arizona College basketball team returned to the win column on Wednesday night when they defeated Central Arizona 72-57 at home in Thatcher.
The game was tight for much of the first half before EAC went on a 13-0 run over the last three minutes to take a 34-25 lead into the break. Central cut the margin to as few as five at the start of the second half but Eastern then slowly built the lead to twenty and then held on for the victory.
Four Gila Monsters scored in double figures led by Jordan Jamestown's 16 points. Will Bynum added 14, Marcus Palmer 13, and Dontae Bryant had a season best 12 points off the bench. Gabor Boros added eight points and a game high nine assists and Bynum led the rebounding with six. After a rough shooting outing in their last game EAC torched the nets at a 66% clip from the field, 57% from 3 and 82% from the foul line.
EAC (17-5, 12-3) will go on the road for their next two games starting this Saturday when they travel to Scottsdale. Game time is set for 7:30 MST and all of the action can be heard live via the internet at www.swsn.net. For all of the latest information on Gila Monster basketball log onto www.eachoops.com.
615 N. Stadium Ave. - Thatcher, AZ 85552
1-800-678-3808 x. 8414
Disclaimer - © 2011


A recent issue of our newsletter, Sounding Board, focused on the topic of mergers and strategic alliances in the cultural nonprofit sector, and generated more than the usual amount of feedback from clients and industry leaders. While some readers thanked us for tackling a controversial and pressing issue head on, others took us to task on a variety of issues. We welcome these diverse viewpoints and devote this edition of On Our Minds to sharing them with you, along with our responses, in hopes of continuing the dialogue.
do not survive, is not their strength, but rather their adaptability.
We stand corrected that "adaptability" rather than "strength" more accurately captures Darwinian theory and agree that it is as relevant in institutional settings (and a factor in their survival) as it is for a species. We believe that organizations need to be able to adapt to a changing world – perhaps today more than ever – and that institutional collaboration provides a key strategic opportunity to do so.
• What are the institutions' goals and objectives for the collaboration?
• Where do the organizations want to remain independent?
There is no question that it is difficult to obtain internal consensus about organizational collaboration, and that the barriers to doing so often derail consummation of the agreements. This is sometimes because the decision makers – whether consciously or not – substitute their individual interests for those of the organizations (and/or the communities) they represent. A facilitated process, in which a small collaboration subcommittee of respected trustees is empowered by each Board to develop a set of recommendations regarding the form and terms of a possible merger or strategic alliance, can mitigate internal resistance. The subcommittee should separate recommendations into "first order" policy issues that have to be resolved before a decision to proceed can be made, and "second order" issues that can, and should, be saved for later in the process. The "second order" issues (e.g., board and professional leadership issues) are often the ones that derail the process. While it is human nature to want these resolved early, it is better to decide first whether, and in what form, the collaboration makes sense before addressing some of the more sensitive issues. It is still the purview of the full complement of trustees to give final approval, but the smaller group can create a confidential forum in which to explore possible solutions, keep the process focused on institutional rather than individual interests, and determine when and how to advocate for a positive decision from other trustees.
There is always a risk that organizational leaders who promise to share decision making authority or give priority to the needs of a collaboration partner, will not live up to their commitments. While there are no guarantees that executives will "walk the walk," this is not a reason to eschew exploring an organizational collaboration. There are many ways to incorporate checks and balances into collaboration agreements, including dispute resolution provisions and remedies for breaches of the letter or intent of the agreements. In the absence of pressure from market forces that force organizational efficiencies in the commercial world, it is clear that funders often play a key role. The nonprofit sector generally needs internal or external funding sources to provide a financial incentive (either in the form of a "carrot" or a "stick" or both) to consider organizational collaborations they would otherwise resist. Funders can also discourage organizations from believing that all that is needed is a recapitalization strategy; that just one more capital campaign will solve the financial crisis. To succeed in these times and to maximize allocation of resources to mission-related activities, nonprofit leaders must learn how to share organizational control and collaborate. If not, the Darwinian process of natural selection will unnecessarily dissipate valuable programmatic resources.
This entry was posted in Uncategorized and tagged adaptability, collaboration, merger. Bookmark the permalink.


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\section{Introduction}

Mobile app users write about their experience on App Stores which can reveal important information about the app for software engineers.
App reviews are studied extensively \cite{2020survey} and shown to be a helpful resource for requirement engineering \cite{luliang}, release planning  \cite{ciurumelea2017analyzing}, software maintenance  \cite{al2020classification}, change file localization \cite{zhou2020TSE}, and testing \cite{grano2018testing}.
Filtering informative reviews \cite{arminer}, identifying bugs \cite{maalej}, classifying usability and quality concerns \cite{luliang}, and finding security and privacy issues \cite{besmer2020privacy} are among the topics that have been investigated.
The list of topics of interest are numerous and there are several studies that use supervised machine learning to extract useful software engineering information from app reviews \cite{2020survey}.
Supervised techniques require to be trained on a labeled dataset.
Although app review analysis is shown to be useful for software developers in practice, the manual effort required to label datasets seems to be a barrier in many studies \cite{2020survey}.
Recent works try to alleviate the problem by incorporating semi-supervised learning \cite{deocadez2017semi-supervised} or active learning \cite{dhinakaran2018active-learning}.
However, when the classes of interest change, e.g. privacy issues \cite{besmer2020privacy},
a new labeled dataset is required and previous curated datasets cannot be used.

Recently, Pre-Trained Models (PTM) are used in many applications from Natural Language Processing (NLP) to Computer Vision.
In NLP, PTMs are large language models trained using a deep neural network in an unsupervised manner, which are used for many downstream tasks \cite{how-far-ptm}.
For example, BERT \cite{bert} is a PTM which is used for question answering and sentiment classification.
Because the PTMs are trained on large corpora, they learn contextual information which can be fine tuned in the downstream tasks;
meaning that PTMs eliminate the need to train models for downstream tasks from scratch \cite{wada2020medicalBERT}. In addition, PTMs reduce the amount of effort to build models for each task separately, and they reduce the amount of required labeled dataset \cite{roberta}.
As a result, PTMs are used for many of the transfer learning tasks and in settings where the model have not seen an example during training (referred to as zero shot learning) \cite{zero}.
PTMs are used extensively and led to many advances in natural language processing, however, their applicability for software engineering is barely known.
Only a few studies exist that explore the use of PTMs for sentiment analysis in software engineering \cite{how-far-ptm, biswas2020achieving}.

\textcolor{black}{However, domain specific PTMs that are trained on domain specific data, show significant improvements over general purpose PTMs such as BERT (i.e. PTMs that are trained on general purpose data) for NLP tasks in fields such as science or law \cite{sci-bert, legal-bert}.
These domain specific PTMs leverage unsupervised training on a large domain specific corpus to compensate the lack of high-quality, large-scale labeled data in the specified domains.
Similarly, the classification of app reviews aims to extract useful information for software engineers/app developers, which are domain specific information.
In addition, app reviews are short text and many reviews are noisy and unrelated, which can introduce more complications.
However, to what extent the PTMs can be useful in the context of app review classification, and whether domain specific PTMs trained on app reviews can improve the results, is unknown.}
\textcolor{black}{Moreover, deep learning approaches using Convolutional Neural Networks (CNN) to implement transfer learning from non-contextual word embedding is shown to have comparable results with traditional machine learning approaches for classification of requirements related app reviews \cite{stanik}. In contrast, another study using CNN shows improvements over previous approaches for app review classification \cite{aslam2020convolutional}. Though these studies do not use PTMs, they use a deep learning approach which sheds doubt on whether PTMs can be useful.}

In this study, we aim to explore the benefits of PTMs compared to the existing approaches for app review analysis, specifically app issue-classification tasks.
\textcolor{black}{
We define the app issue-classification as the task of extracting useful information from users' feedback which can be requirements related, release planning related, and software maintenance related. The extracted information helps identifying different aspects, including feature requests, aspect evaluations (e.g., feature strength, feature shortcoming, application performance), problem reports, usability, portability, reliability, privacy and security, energy related, appraisals, inquiries about the application, etc. from app reviews. }
Our goal is to investigate accuracy and time efficiency of PTMs for classification of different datasets with various labels and multiple tasks (i.e. issue classification and sentiment analysis of app reviews).
Therefore, experiments will be conducted in different settings.
These experiments will provide baselines on the applicability of PTMs for app review analysis including cost of using them (in term of required time for predictions), and their capability to reduce the manual effort required for labeling large datasets.
We expect that PTMs can achieve at least the same performance as the current approaches, as well as being beneficial to be used for multiple classification tasks.
The contributions of this study are:
\vspace{-1mm}
\begin{itemize}
	\item This is the first study that explores the applicability of PTMs for automatic app issue classification tasks compared to the existing tools.
	\item We will conduct an extensive comparison between four PTMs and four existing tools/approaches on six different app review datasets with different sizes and labels.
	\item We are the first to explore the performance of general versus domain-specific pre-trained PTMs for app review classification.
	\item This is the first empirical study to examine the accuracy and efficiency of PTMs in four different settings: binary vs. multi-class classification, zero shot setting, multi-task setting, and setting in which training data is from one resource (e.g. App Store) and the model is tested on data from another platform (e.g. Twitter).
\end{itemize}

\section{Research Questions}
\label{sec:rqs}

\textit{\textbf{RQ1:} How accurate and efficient are the PTMs in the classification of app reviews compared to the existing tools? }

In this research question, we explore how the current PTMs perform compared to the existing tools, including their required time for training and prediction. The existing tools are based on curated rules, feature engineered machine learning algorithms, and deep learning models.
The results can give us insights about the applicability of using PTMs in practice.

\textit{\textbf{RQ2:} How does the performance of the PTMs change when they are pre-trained on app-review dataset, instead of a generic dataset (e.g. Wiki-documents, book corpus)?}

The current PTMs are trained using general text scraped from web. In some studies, domain specific models are trained e.g. LEGAL-BERT \cite{legal-bert} and medical BERT \cite{wada2020medicalBERT} and have shown to improve the performance compared to the non-domain-specific models. In RQ2, we intend to explore the performance of a domain-specific pre-trained model when trained on app reviews. The results will help the researchers focus on building domain specific models (e.g. building app review models in languages other than English) versus using the general PTMs.

\textit{\textbf{RQ3:} How do the PTMs perform in the following settings? \\
    (a) Binary vs multi-class setting, \\
    (b) Zero-shot classification, \\
    (c) Multi-task setting (i.e. different app-review analysis tasks),  \\
    (d) Classification of user-reviews collected from different resources (i.e., Twitter,  App Store).}

The answers to this research question will help us understand the applicability of PTMs and their performance in different settings (a), when labeled dataset is not available (b), the task changes (c), or different resources are used (d). In part (d) we explore the transferability of the models as the distributions of data on various platforms is different \cite{ruder2017transfer}.
We expect that PTMs achieve reasonable results for these settings.

\section{Research Materials}
\label{sec:materials}
In this section, we briefly discuss about the considered datasets, prior approaches, and the pre-trained models.
\subsection{Datasets}
We conduct our study on six publicly available datasets.

\subsubsection{\textbf{Dataset 1 ($D_1$)}}
This dataset is procured by Gu and Kim \cite{gukim} and contains reviews for 17 popular Android apps from Google Play  in 16 most popular categories, such as games and social. The authors have manually labeled 2,000 reviews for each app (34,000 in total) in five classes according to their predefined rules: Aspect Evaluation (5,937), Praise (8,112), Feature Request (2,323), Bug Report (2,338), and Others (15,290).

\subsubsection{\textbf{Dataset 2 ($D_2$)}}
This dataset contains 6,406 app reviews from Google Play and 10,364 tweets (sampled from 5 million tweets written in English) which are labeled manually into three classes: Problem Report, Inquiry, and Irrelevant \cite{stanik}.
They included Problem Report (1,437), Inquiry (1,100), and Irrelevant (3,869) records from Google Play. The number of records in each of these categories from Twitter data is 2,933, 1,405 and 6,026 respectively.

\subsubsection{\textbf{Dataset 3 ($D_3$)}}
This dataset is provided by Lu and Liang. \cite{luliang}. Researchers selected two popular Apps, one from Apple App Store (iBooks in the books category) and one from Google Play (WhatsApp in the communication category).
They sampled the raw reviews collected from each platform and manually classified 2,000 review sentences for each app (4,000 in total).
The reviews are classified into six categories:
Usability (432), Reliability (587), Portability (119), Performance (121), Feature Request (558), and Others (2,183).

\subsubsection{\textbf{Dataset 4 ($D_4$)}}
This dataset was procured by Maalej and Nabil \cite{maalej} and contains 2,000 manually labeled reviews from random apps selected from top apps in different categories (1,000 from Apple App Store and 1,000 from Google Play), where half of the apps are paid and half of them are free apps.
The authors ensured that there are 200 reviews for each of the 1, 2, 3, 4, and 5 stars in each of the 1000-reviews datasets.
In addition, they provide 2,400 more reviews which are manually labeled.
These reviews are for selected 3 random Android apps and 3 iOS apps from the top 100 apps (400 reviews for each app).
This dataset contains 4,400 labeled reviews in total with four categories: Bug Report (378), Feature Request (299), User Experience (737), and Rating (2721).

\subsubsection{\textbf{Dataset 5 ($D_5$)}}
This dataset is published by Guo et al. \cite{guo2020caspar} and contains 1,500 app reviews, which are manually labeled as User Action (428), App Problem (399), and Neither (673).
The reviews are selected from over 5.8 million records about 151 apps from Apple App Store.

\subsubsection{\textbf{Dataset 6 ($D_6$)}}
This dataset was procured by Guzman et al. \cite{guzman}; it contains reviews of 3 apps from Apple App Store and 4 apps from Google Play. The apps are popular and from diverse categories.
They sampled 260 reviews per app and five annotators labeled the records, summing to 1,820 reviews that are manually labeled.
This dataset includes seven categories, namely: Bug Report (990), Feature Strength (644), Feature Shortcoming (1281), User request (404), Praise (1703), Complaint (277), and Usage Scenario (593)\footnote{Note that adding numbers in all categories will exceed the total number, because some reviews belong to multiple groups. We will follow the steps in \cite{guzman} to calculate the evaluation metrics for this dataset.}.

\textit{\textbf{Reasons behind choosing these Datasets ($D_1$ to $D_6$):}}
We considered these datasets for their diverse characteristics from three perspectives, other than having different categories.
\textit{First}, the considered datasets were constructed from various repositories:
Apple App Store, Google Play, and Twitter.
\textit{Second}, the size of the datasets vary and they have different number of records for each label.
For example, \textit{Dataset 2} is around four times larger than \textit{Dataset 5}. Also, \textit{Dataset 2} contains approximately 2,000 reviews per label, where \textit{Dataset 1} has around 400 reviews per label. The size differences can provide insights about the ability of PTMs for different sizes (total training set and the available data for each label).
\textit{Third}, except \textit{Dataset 1} and \textit{Dataset 5} which are balanced dataset, the other four datasets are imbalanced.
These differences ensure that we explore the capability of PTMs for different sizes, platforms, and in more realistic settings.

\subsubsection{\textbf{Merged Dataset ($D_7$)}
    \textcolor{black}{This dataset will be compiled by merging all the 6 datasets, $D_1$--$D_6$. This \textit{Merged Dataset} has 55,933 app reviews with 16 labels. As some of the labels used in the datasets are the same or have similar definitions, we have grouped some of them.

    For grouping the labels, we have consulted the definitions of the classes from the studies published the datasets $D_1$--$D_6$.

    This dataset would be closer to practical applications, where a lot of irrelevant data exists and multiple classes of informative reviews are of interest to be extracted from this highly imbalanced dataset.

    The labels in the Merged Dataset, the grouped labels from $D_1$--$D_6$, and the number of app reviews per group are provided in Table \ref{table:annotation}}.

    \begin{table}[htbp]
    \caption{Description for Merged Dataset (D7)}

    \begin{center}
    \renewcommand{\arraystretch}{1.5}
    \resizebox{1.0\linewidth}{!}{%
    \begin{tabular}{m{2cm} | m{4.5cm} | m{1.5cm} }
        \hline
        {\textbf{Labels in Merged Dataset}} & {\textbf{Grouped Labels [Label Name: Dataset Initial (\# of Reviews)]}} & {\textbf{\# Total App Reviews}}\\
        \hline
        {Performance} & {Performance: D3 (121)} & 121\\
        \hline
        {Portability} & {Portability: D3 (119)} & 119\\
        \hline
        {Usability} & {Usability: D3 (432)} & 432\\
        \hline
        {Reliability} & {Reliability: D3 (587)} & 587\\
        \hline
        {Usage Scenario} & {Usage Scenario: D6 (593)} & 593\\
        \hline
        {Feature Strength} & {Feature Strength: D6 (644)} & 644\\
        \hline
        {User Experience} & {User Experience: D4 (737)} & 737\\
        \hline
        {Feature Shortcoming} & {Feature Shortcoming: D6 (1,281)} & 1,281\\
        \hline
        {Inquiry} & {Inquiry: D2 (1,100), User Action: D5 (428)} & 1,528\\
        \hline
        {Problem} & {Problem Report: D2 (1,437), App Problem: D5 (399), Complaint: D6 (277)} & 2,113\\
        \hline
        {Rating} & {Rating: D4 (2,721)} & 2,721\\
        \hline
        {Bug Report} & {Bug Report: D1 (2,338), Bug Report: D4 (378), Bug Report: D6 (990)} & 3,706\\
        \hline
        {Feature Request} & {Feature Request: D1  (2,323),  Feature Request: D3 (558), Feature Request: D4 (299), User request: D6 (404)} & 3,584\\
        \hline
        {Aspect Evaluation} & {Aspect  Evaluation: D1 (5,937)} & 5,937\\
        \hline
        {Praise} & {Praise: D1 (8,112), Praise: D6 (1703)} & 9,815\\
        \hline
        {Irrelevant} & {Others: D1 (15,290), Irrelevant: D2 (3,869), Others: D3 (2,183), Neither: D5 (673)} & 22,015\\
        \hline
        {\textbf{Total}} & {} & {\textbf{55,933}} \\
        \hline
    \end{tabular}
    }
    \end{center}
    \label{table:annotation}
    \end{table}

\subsection{Prior Approaches}

In this section, we briefly discuss the four widely-used approaches for app review analysis and the reasons of choosing them in our experiments.
\textcolor{black}{We have conducted literature review and reviewed analysis of app review surveys to select these prior approaches.}

\subsubsection{\textbf{AR-Miner}}
Chen et al. proposed App Review Miner (AR-Miner) \cite{arminer} which extracts valuable information from user reviews.
Provided a collection of user reviews, AR-Miner first applies a pre-trained classifier that separates non-informative reviews. Then, AR-Miner applies Latent Dirichlet Allocation (LDA) over the informative reviews to chunk them into different groups for prioritizing them by an efficient ranking model proposed by the authors.
AR-Miner has been widely used to mine app-issues from user-reviews \cite{panichella2015can, ARM-citer2, ARM-citer3, ARM-citer4, ARM-citer5, ARM-citer6}. Therefore, it is considered as a prior approach in our experiments.

\textbf{\textit{Reason behind choosing AR-Miner:}} \textcolor{black}{AR-Miner is the first framework to accommodate application developers in mining informative topics from a large volume of app reviews. This was the first effective attempt to leverage an unsupervised Topic Modeling technique to extract edifying issues from app reviews. Developers embraced AR-Miner for its ability to filter out non-informative reviews and display the key user feedbacks in an intuitive, concise manner.
The effectiveness of AR-Miner was validated by conducting a comprehensive set of experiments on user reviews of four Android apps. We have included their dataset in our study. Researchers compared the AR-Miner results against real app developers' decisions in different studies \cite{panichella2015can, ARM-citer2, ARM-citer3, ARM-citer4, ARM-citer5, ARM-citer6}. They analyzed the advantages of AR-Miner over manual inspection and other techniques used in a traditional channel \cite{finkelstein, johann, moran, sarro2015}. Based on the empirical results, AR-Miner has proven to be effective and efficient in extracting informative reviews.}

\subsubsection{\textbf{SUR-Miner}}
Gu et al. \cite{gukim} proposed Software User Review Miner (SUR-Miner), which is a framework that summarizes users' sentiments, opinions, and emotions toward different aspects of the application.
SUR-Miner
parses aspect-opinion pairs from reviews by considering their structures. For this purpose, it uses pre-defined sentence templates/patterns\footnote{https://guxd.github.io/srminer/appendix.html}. Then, for each review, it combines the sentiment of the sentences with its aspect-opinion pairs. These are used in the final step to summarize the software aspects.
SUR-Miner generates reliable summaries and achieved high F1-score for aspect-opinion identification, sentiment analysis, and app review classification compared to the previous works \cite{SRM-citer1, SRM-citer2}. It is adopted in many studies \cite{SUR-citer3, palomba2017recommending, SUR-citer5, SUR-citer6, SUR-citer7, features} and therefore we choose it as a prior approach.

\textbf{\textit{Reason behind choosing SUR-Miner:}} \textcolor{black}{SUR-Miner is the first framework that fully took advantage of user reviews' monotonic structure and semantics; it also defined sentence patterns to extract aspect opinion pairs from app-review. Researchers carefully selected five distinct text features, specifically tailored for app reviews, and leveraged these features to train a supervised machine learning classifier. Based on the empirical evaluation, we choose this Machine Learning approach as the terminal classes categorized by the SUR-Miner model were shown to be significantly more accurate and precise than other methods, demonstrating its effectiveness \cite{SRM-citer1, SRM-citer2}.}

\subsubsection{\textbf{Ensemble Methods}}
Guzman et al. systematically evaluated different sets of ensemble methods and identified one for classifying user reviews \cite{guzman}.
They selected machine learning techniques that were used for text classification, and compared the performance of each algorithm individually, for app review classification.
They also studied the performance of these models when their results are combined using ensemble methods.
Ensemble methods are well known machine learning techniques as they can enhance the prediction performance of single classifiers, maintaining their strengths and reduce their vulnerabilities.
In this work, Logistic Regression, Naive Bayes, Support Vector Machines, and Neural Network Classifier were grouped to vote for the final prediction.
This ensemble method outperformed the individual algorithms with statistical significance.
It was tested on a large dataset of seven diverse apps, where it shows that in all cases, this ensemble method either outperformed or matched its best baseline. Also, this method influenced further research in the field of app review analysis \cite{EM-citer1, EM-citer2, ciurumelea2017analyzing, EM-citer4, EM-citer5, EM-citer6} and therefore is used in our study.

\textbf{\textit{Reason behind choosing Ensemble Methods:}} \textcolor{black}{Different studies have attempted to identify the best Machine Learning approach for app issue classification without spending too much time perfecting custom-engineered features for different issues \cite{maalejea, augmenting, sml, luliang, changelog, features}. Guzman et al. \cite{guzman} first leveraged different machine learning algorithms and combined their predictions to circumvent the problem of various machine learning models being used for classification of issues from app reviews. Their study showed that recall improved significantly after combining the predictions of Logistic Regression and Neural Networks. Software developers and software evolution experts widely adopted this approach to analyze user reviews and prioritize their tasks \cite{feedback, alsubaihin, categorizing, findingFeatures}.}

\subsubsection{\textbf{Deep Learning Model leveraging Non-contextual Word-embedding}}
Stanik et al. used a Deep Convolutional Neural Network (CNN) \cite{stanik} for classifying app reviews. Their model contains an embedding layer and its weights are initialized with a word embedding model, e.g. word2vec or FastText.
This CNN architecture outperforms the shallow Neural Networks classifying app reviews in the determined groups, with a small margin.
This method uses non-contextual word embedding within a deep neural network and performed a transfer of knowledge representation. This approach is one of the first studies leveraging transfer learning and deep learning and is used in other studies for issue classification task \cite{mcnn-citer1, mcnn-citer2, aslam2020convolutional}, and therefore we choose it as a prior approach in our study.

\textbf{\textit{Reason behind choosing this Deep Learning Approach:}} \textcolor{black}{This study is a recent work and the purpose of \cite{stanik} was to understand and evaluate the extent to which deep learning models could be used to categorize user feedback into different predefined categories. Based on this study, researchers determined that the domain experts' knowledge incorporation with traditional machine learning model could achieve comparable results to those of the deep learning approach due to the substantial performance improvements provided by using simple yet powerful features in the traditional machine learning techniques. Nevertheless, the developers and researchers widely adopted this deep learning approach as the improvement of pre-trained word embeddings bring considerable performance gain to the classifier in other domains \cite{yang2018, reimers2019, ren2016}. }

\textcolor{black}{It is worth noting that the number of studies for app review classification is numerous. We choose these four prior approaches as they are well cited and widely used in many other studies. More importantly, they use different machine learning and deep learning approaches which can be representative of multiple techniques used in other studies. The best of these four approaches in terms of the performance metrics defined in Section \ref{sec:evaluation-metrics} will be chosen as the baseline.}

We have already considered two more tools that we eliminated from our study: AR-Doc and CLAP.
AR-doc uses a parser based on a NLP Classifier, which uses a set of 500 sentence-structures manually evaluated for four types of classes: Info. Giving, Info. Seeking, Feature Request, and Problem Discovery. To use AR-Doc, we will need to build different sets of sentence structures for each dataset and their respective labels, which is infeasible.
In addition, we could not find any publicly available implementation of CLAP. We contacted the authors of CLAP but did not receive a response; and therefore unable to use CLAP.

\subsection{Pre-Trained Models (PTM)}
In this section, we briefly discuss our choice of the four PTMs. All these models have Transformer deep learning architecture which is based on attention mechanism \cite{xlnet}.
As Transformer is currently the main architecture for PTMs \cite{roberta}, we choose the following Transformer PTMs: BERT, XLNet, RoBERTa, and ALBERT.
The same PTMs are used for sentiment classification in software engineering \cite{how-far-ptm}.

\subsubsection{\textbf{BERT}}
Devlin et al. \cite{bert} designed BERT to learn contextual word representations from unlabeled texts. Contextual word embeddings designate a word's representation based on its context by capturing applications of words across different contexts.
BERT employed a bidirectional encoder to learn the words' contextual representations by optimizing for Masked Language Model (MLM) and Next Sentence Prediction (NSP) tasks.
For MLM, 15\% of each sequence's words are replaced with a token ([MASK]) beforehand for BERT to attempt at predicting the masked words, based on the context provided by the non-masked words.
For NSP, the model takes sentence-pairs as input for learning to predict pair-match is correct or wrong. During training, 50\% of the inputs are true consequent pairs, while the other 50\% are randomized non-consequent sentence-pairs.
Devlin et al. trained two versions: small-sized BERT\textsubscript{BASE} and big-sized BERT\textsubscript{LARGE}. BERT\textsubscript{BASE} is a smaller model with 12 layers and 110 million parameters. BERT\textsubscript{LARGE} on the other hand has 24 layers and 340 million parameters.
BERT\textsubscript{LARGE} is more computationally expensive and consumes more memory compared to BERT\textsubscript{BASE}. Thus, in this work, we will use BERT\textsubscript{BASE}.

\textbf{\textit{Reason behind choosing BERT:}} BERT advanced the state-of-the-art for 11 NLP tasks. It achieved an absolute improvement of 7.6\% over the previous best score on General Language Understanding Evaluation (GLUE) benchmark\footnote{https://gluebenchmark.com/}; it also achieved 93.2\% accuracy on SQuAD 1.1\footnote{Stanford Question Answering Dataset: https://rajpurkar.github.io/SQuAD-explorer/explore/1.1/dev/}, outperforming human performance.

\subsubsection{\textbf{XLNet}}
XLNet \cite{xlnet} uses Auto-Regressive (AR) language modeling and Auto-Encoding (AE).

The model is "generalized" because it captures the bi-directional context using a mechanism called Permutation Language Modeling (PLM).
XLNet integrates auto-regressive models and bi-directional context modeling, yet overcoming the disadvantages of BERT.
PLM is the idea of capturing bidirectional context on all permutations of terms present in an input sequence.
XLNet discards the one-directional linear modeling to maximize the log-likelihood over all permutations of the sequence-terms. Each position is expected to learn utilizing contextual information from the entire sequence, thereby capturing the bidirectional context. No [MASK] is needed, and input data need not be corrupted.

BERT neglects the dependency between masked positions.
Consider the sentence "New Delhi is a city." Then, input to BERT to be "[MASK] [MASK] is a city," and the objective of BERT would be predicting "New" given "is a city" and predicting "Delhi" given "is a city".
In this objective, there is no dependency between learning "New" and "Delhi." So, BERT can result in a prediction like "New Gotham is a city."
If we assume that the current permutation is [is, a, city, New, Delhi], BERT would predict the tokens 4 and 5 independent of each other. Whereas, XLNet, predicts in the order of the sequence. i.e., first predicts token 4 and then predicts token 5: it computes
the likelihood of "New" given "is a city" plus the likelihood of "Delhi" given "New, is a city".

\textbf{\textit{Reason behind choosing XLNet:}} XLnet outperformed BERT on many NLP tasks; for 8 different tasks XLNet beat BERT by a substantial margin. This model achieved best results for 18 NLP tasks, including sentiment classification natural language inference.

\subsubsection{\textbf{RoBERTa (Robustly optimized BERT approach)}}
RoBERTa outperformed all the state-of-the-art benchmarks upon release \cite{roberta}. Liu et al. modified BERT's pretraining steps that yield substantially better performance on all the classification tasks.
RoBERTa increased the amount of mini-batch sizes, data, and training time to train the model.
RoBERTa is also trained on dataset that includes longer sequences than before. The masking pattern in RoBERTa was also modified to be generated spontaneously.

\textbf{\textit{Reason behind choosing RoBERTa:}} RoBERTa outperforms BERT on nine different NLP tasks on the GLUE benchmark; it also equals or exceeds XLNet's model in four out of nine individual tasks.

\subsubsection{\textbf{ALBERT (A Lite BERT)}}
ALBERT \cite{albert} applies three parameter reduction techniques: Factorized embedding parameterization, Cross-layer parameter sharing, and Inter-sentence coherence loss.
In the first one, researchers separated the hidden layers' size from the input embeddings' size (previously of same sizes). They projected one-hot vectors to embedding and the hidden space with lower dimensions; it increased the hidden layer-size without significantly increasing the vocabulary embeddings' parameter size.
Second, all the parameters across all layers are shared. The big-scale ALBERT model has substantially fewer parameters than BERT\textsubscript{LARGE}.
Finally, the NSP task is swapped with Sentence-Order Prediction (SOP) loss which help ALBERT perform better.

\textbf{\textit{Reason behind choosing ALBERT:}} ALBERT uses parameter reduction techniques which resulted in having 18 times less parameters than BERT. Its' training time is 1.7 faster and has negligible inferior performance than the original BERT\textsubscript{LARGE} model.
The much larger ALBERT architecture, which contains fewer parameters than BERT\textsubscript{LARGE}, achieved higher results, F1 score of 92.2\% and 89.4\% on the SQuAD 2.0 and the GLUE benchmarks, respectively; it also achieved 89.4\% accuracy on the ReAding Comprehension from Examinations benchmark (RACE benchmark).

\section{Execution Plan}
\label{sec:execution}

\subsection{Experimental Setup} \label{experimental-setup}
\textcolor{black}{
Following \cite{stanik}, we will use k-fold cross validation on each dataset $D_i$ separately, where $D_i$ represents one of the seven datasets in our study. We use k-fold cross validation as a more rigorous approach than splitting the dataset into train and test set once \cite{how-far-ptm}. Also, this method is used previously for app review classification in software engineering domain \cite{micromacro}.
In the k-fold cross validation, we split a dataset $D$ into $k$ equal size disjoint parts/folds $D\textsuperscript{(1)}, ..., D\textsuperscript{(k)}$. We then build $k$ classifiers $c\textsuperscript{(i)}$, each time using $k-1$ splits as training and one part $D\textsuperscript{(i)}$ as test set. This way, each split of the data can be used as test set once.
As a result, we will have $k$ different test set performances.
As the datasets in the study are imbalanced, we will use an alternate option of cross validation called stratified k-fold cross validation \cite{apple, imbalancedLearning}.
In this method, the only difference is that the distribution of the examples from each class in the original dataset is preserved in each fold $D\textsuperscript{(i)}$.
The stratified k-fold cross validation is commonly used in machine learning practices as it reduces the experimental variance. Therefore, when comparing different methods, it is easier to identify the best method \cite{apple}.}
\textcolor{black}{
We consider $k=5$ as this value has been shown empirically to have test error rate estimates that do not suffer from high bias or high variance \cite{james2013introduction}. The value of $k=5$ is also used in previous app review classification studies \cite{stanik}.
For the evaluation, we use metrics explained in section \ref{sec:evaluation-metrics}.
It is worth mentioning that we apply the stratified 5-fold cross validation on each of the datasets $D_1$--$D_7$ separately. For example, we split $D_1$ into 5 folds $D_1\textsuperscript{(1)}, ..., D_1\textsuperscript{(5)}$ and build 5 classifiers for $D_1$. We then compute the evaluation metrics for $D_1$. We continue this process  for each of the datasets $D_2$--$D_7$ and report the performance on each of them separately.}

Note that the number of labels in the datasets $D_1-D_6$ ranges between [3,7] and
all the considered prior approaches were designed for multi-class classification.
Therefore, we can retrain \textcolor{black}{and evaluate them using the stratified 5-fold cross validation.}
Among the prior approaches, only AR-Miner requires some adaptation. AR-Miner has three phases: classification of non-informative reviews, topic modeling of informative reviews, and ranking of the groups according to their relevance to developers' application. As all datasets $D_1$--$D_7$ are labeled,
we will directly utilize the second step to group all the reviews (from both training and test set) in a dataset into the corresponding number of labels available. Following the literature \cite{arminer}, we will count the training set reviews in a resulting group and use the label with the majority count to annotate the group.
This annotation indicates the class that the reviews in the group belong to. Then, we will examine the group's test set reviews to compute the evaluation metrics.

For evaluating the PTMs, for each dataset, we will use the training set to fine tune the PTMs and then evaluate them on the test set. We will obtain all the four PTMs that are pre-trained on general domain corpora from Hugging Face library\footnote{https://huggingface.co/}.
To build a classifier, a feed-forward dense layer and Soft-max activation function will be added on top of each model.
We will follow \cite{how-far-ptm} and \cite{bert} to set the hyper-parameter values. In our work, we will set the batch size to 16 and Adam learning rate to $2e-5$. The models will be trained for 4 epochs and \textit{AdamW} optimizer will be used for all models.

\textcolor{black}{We plan to execute all the experiments on a Linux machine with Intel 2.21 GHz CPU and 16G memory. For training PTMs from scratch, we plan to use 2 $\times$ NVIDIA Tesla V100 32GB to enhance the parallelization performance.}

\subsection{Evaluation Metrics} \label{sec:evaluation-metrics}
\label{sec:analysis}

In this section, we briefly describe the evaluation metrics. For all the classifications, we will use three metrics: Precision (P), Recall (R), F1-score (F1), and their micro and macro average values.
To answer the time efficiency of the models, we will examine the training time for the prior approaches and the fine tuning time for PTMs. We will also report the prediction times and changes in time for all models.

\textbf{\textit{Precision (P):}} Precision can be calculated by dividing the number of records that their labels are correctly predicted by total number of predicted observations in that class: $
P = \frac{TP}{TP+FP}.
$
Here, TP refers to the number of records that their label is correctly predicted, and FP refers to the the number of records falsely predicted to belong to this class.
In multi-class classification, for each group \textit{A},
all the observations that belong to other labels and are falsely predicted as group \textit{A} are added to compute the FP.

\textbf{\textit{Recall (R):}} For each group \textit{A}, Recall can be calculated by dividing the number of accurately predicted observations in \textit{A} by the number of all observations available in the corresponding class: $
R = \frac{TP}{TP+FN}
$
Here, FN is the number of observations in class \textit{A} which are falsely predicted as other labels.

\textbf{\textit{F1-Score (F1):}} F1 Score is the weighted average of Precision and Recall:

\begin{equation}
\label{eq:f1}
\resizebox{0.25\hsize}{!}{$
    \mbox{F1} = \frac{2 \cdot (P \cdot R)}{P + R}
    $}
\end{equation}

As we have multi-class classification and the datasets are imbalanced, we use micro-average and macro-average metrics as follows. Here, the micro-average calculates the contribution of all records in all classes (therefore the contribution of the class with the predominant number of records is taken into account), whereas the macro-average is the average of the values for each class (therefore, each class contributes equally in the final value).
The micro- and macro-averaged precision (P) are computed as:

\begin{equation}
\resizebox{0.5\hsize}{!}{$
        P_{micro} = \frac{\sum_{j=1}^m TP_j}{\sum_{j=1}^m TP_j + \sum_{j=1}^m FP_j}
    $}
\end{equation}
\begin{equation}
\resizebox{0.33\hsize}{!}{$
        P_{macro} = \frac{\sum_{j=1}^m P_j}{m}
   $}
\end{equation}

\vspace{-1mm}
$TP_j$ and $FP_j$ are the number of true positive and false positive predictions for the $j$-th class, respectively. $P_j$ is the precision for class $j$ and $m$ is the number of classes.
Similarly, the micro- and macro-average of Recall (R) and F1-score will be calculated and denoted as R\textsubscript{micro}, R\textsubscript{macro}, F1\textsubscript{micro}, and F1\textsubscript{macro}.

\textcolor{black}{
As we are using stratified 5-fold cross validation, for each dataset $D$, we will report averages of these metrics obtained from each of the k classifiers:
\begin{equation}
\resizebox{0.5\hsize}{!}{$
    F1\textsuperscript{avg}\textsubscript{micro} = 1/k \sum_{n=1}^{k} F1\textsubscript{micro}\textsuperscript{(i)}
   $}
\end{equation}
\begin{equation}
\resizebox{0.5\hsize}{!}{$
    F1\textsuperscript{avg}\textsubscript{macro} = 1/k \sum_{n=1}^{k} F1\textsubscript{macro}\textsuperscript{(i)}
   $}
\end{equation}}

\textcolor{black}{The $F1\textsubscript{micro}\textsuperscript{(i)}$ and $F1\textsubscript{macro}\textsuperscript{(i)}$ refer to the test performance of classifier $c\textsuperscript{(i)}$ on held out test set $D\textsuperscript{(i)}$.
Similarly, the P\textsuperscript{avg}\textsubscript{micro}, P\textsuperscript{avg}\textsubscript{macro}, R\textsuperscript{avg}\textsubscript{micro}, and  R\textsuperscript{avg}\textsubscript{macro} will be computed.}

Following previous works \cite{how-far-ptm}, \textit{if a model has higher values for both F1\textsuperscript{avg}\textsubscript{micro} and F1\textsuperscript{avg}\textsubscript{macro}, we consider it to be better than other models.}

\textbf{\textit{Time:}}
To compare the time efficiency, we will report the training time for other approaches and fine-tuning time for PTMs, and the prediction time of all the approaches.
Prediction time is the time the model requires to process the test data and predict labels.
The time will be measured in seconds and will be reported for each dataset.
\textit{A model is considered to be more time efficient than another if the prediction time is less;} as training a model or fine-tuning a PTM is a one-time process and need not be repeated.

\textbf{\textit{Time Change in Percentage:}}
\textcolor{black}{We will employ all four prior approaches on all datasets $D_1$--$D_7$ and choose their best as our \textit{baseline} approach. In addition to the Time mentioned above, we will also report the increase and decrease in percentage for the studied PTMs with respect to the considered baseline for the measured Time. The reason of reporting this change in percentage is that time-duration depends on hardware configuration, and percentage change provides a better understanding about the change in time for the readers.}

\subsection{Study Design}
The following steps will be taken to answer each research question.

\subsubsection{RQ1 - Accuracy and Efficiency of PTMs}
All prior approaches and PTMs will be trained and tested on all of the datasets separately, \textcolor{black}{as explained in section \ref{experimental-setup}. }
We only consider the reviews (not tweets) from \textit{Dataset 2} for RQ1. For each dataset and approach, all the evaluation metrics described in section \ref{sec:evaluation-metrics} will be reported.
We will highlight the best model for each dataset with highest F1\textsuperscript{avg}\textsubscript{micro} and F1\textsuperscript{avg}\textsubscript{macro}.
The lowest training/fine-tuning and prediction times will also be highlighted for each dataset, along with the time change in percentages.
We denote the best performing PTM among others as \texttt{PTM-X} to pursue experiments in other research questions.
If no PTM achieves the best results in terms of both micro and macro F1-score, the PTM that achieves the highest F1\textsuperscript{avg}\textsubscript{micro}-score (denoted as \texttt{PTM-XM1}) and the PTM with the highest F1\textsuperscript{avg}\textsubscript{macro}-score (denoted as \texttt{PTM-XM2}) will be used in RQ2.

\subsubsection{RQ2 - Domain-Specific PTMs vs. General PTMs}
In RQ2, we are interested to evaluate the performance of PTMs when they are pre-trained on domain-specific corpus rather than non-domain-specific corpora.
In RQ2, we will use \texttt{PTM-X} (from RQ1) and all the six datasets.
We can either pre-train \texttt{PTM-X} using just the domain-specific app reviews or combine them with the general domain documents to pre-train \texttt{PTM-X} from scratch.
In the first approach, \texttt{PTM-X} with the same architecture will be trained from scratch on app review sentences.
But pre-training \texttt{PTM-X} on a small number of domain-specific documents has its disadvantages; the model may become less adaptable and overfit on our dataset.
Therefore, we will use another approach \cite{wada2020medicalBERT}, where we will pre-train the \texttt{PTM-X} simultaneously with both general and domain-specific documents.
Following the literature \cite{wada2020medicalBERT}, we will double the frequency of pre-training domain-specific documents during the optimization on the Masked Language Modeling (MLM) task.
We will pre-train three different \texttt{PTM-X}s, where we will respectively integrate 2.8 million, 5.6 million, and 10 million app review sentences that we have collected from
Google Play, with the general domain documents from Wiki-texts. Our collected dataset has already 5.6 million reviews with over 10 million sentences for more than 400 apps from different categories. The dataset includes app\_name, app\_category, review, rating, reply\_text, and date.
This will give us three versions of a domain-specific pre-trained model which we refer to as \textit{Customized PTM-X} (\texttt{CPTM-X}).
We denote the three sizes of the \texttt{CPTM-X} as \texttt{CPTM-X\textsubscript{base}}, \texttt{CPTM-X\textsubscript{medium}}, and \texttt{CPTM-X\textsubscript{large}}, respective to the number of the app review sentences integrated for pre-training.
Finally, we will fine-tune all the \texttt{CPTM-X}s on the six datasets according to the steps and evaluation metrics explained in RQ1. The best \texttt{CPTM-X} will be used in RQ3.

\subsubsection{RQ3 - Extent of PTMs' Capacity}
We are considering four different settings to determine the PTMs' capacity regarding app review analysis.

(i) \textit{Binary vs multi-class setting:}
\textcolor{black}{Previous studies have shown that the pre-trained Transformer based models deviate from their original performance when the classification task involves multiple classes instead of binary classes \cite{adhikari2019docbert, chang2019x}.
\textcolor{black}{Studies on app review classification also show that binary classification yields better results \cite{Jha2019MiningNR, maalejea}}.
We are, therefore, interested in evaluating the performance of the PTMs in both binary and multi-class settings, especially, investigating the performance of \texttt{CPTM-X}.}
We will investigate the accuracy and time efficiency of PTMs for binary classification compared to the original multi-class settings.
We will convert all the six datasets into binary-class datasets by randomly choosing a class (i.e. label) for each dataset and switching all the other labels in that dataset into "Others".
Then, we will use these modified datasets to regenerate the PTM results for the binary classification task on each dataset.
The four PTMs and \texttt{CPTM-X} will be used to record the evaluation metrics. All other steps are similar to RQ1.

(ii) \textit{Zero-shot classification setting:}
The zero shot learning (ZSL) that we will investigate in our study is the recent approach in which we evaluate the models on fully-unseen-labels.
In this setting, we assume that the system is never trained on any labeled data for the task we are interested in, which is different from some settings in which labels are partially seen by the data. The setting we choose is more realistic and can provide insights about potential benefits of PTMs in practice, where the developers might want to use the same model to derive new unseen aspects of the dataset.
Using PTMs for this kind of zero shot classification requires a different training.

For ZSL, we will explore the method proposed by Yin et al. \cite{zero} in which classification is considered as a natural language inference (NLI) task.
This approach determines the compatibility of two distinct sequences by embeddeing both sequences and labels into the same space.
In NLI, a pair of sentences are considered: "premise" and "hypothesis"; and the task is to predict whether the "hypothesis" is an entailment of "premise" or not (contradiction).
We follow the steps in \cite{zero} to prepare the datasets and models and set up our study for ZSL. We will use the reviews as "premise" and candidate labels as ''hypothesis". So, the NLI model can predict whether the hypothesis is an entailment of the premise. This prediction will be compared to the actual labels for the reviews to calculate the evaluation metrics.
We will fine tune \texttt{PTM-X} on MultiNLI dataset\footnote{https://cims.nyu.edu/~sbowman/multinli/} and test it on the six datasets to determine how well PTMs can classify issues from app-reviews without being trained on them. In addition, we will use \texttt{RoBERTa-large-nli}\footnote{https://huggingface.co/joeddav/xlm-roberta-large-xnli}, which is a readily available fine tuned model for zero-shot classification.

(iii) \textit{Multi-task setting:}
\textcolor{black}{Research has confirmed that when a new classification task (e.g. sentiment classification) is introduced, a new model should be trained and the models for another classification task (e.g. app issue classifiers) cannot be applied for this new task.
In addition, the same model or classification technique can have different performance on various analysis tasks. For example, Hemmatian and Sohrabi found Naive Bayes (a probabilistic classifier) to be performing better than Decision Tree (a non-probabilistic classifier) for sentiment classification tasks \cite{hemmatian2019survey}; on the other hand, Maalej et al. reported that using Bag-of-Word technique and Decision Tree outperformed Naive Bayes for app issue classification task \cite{maalejea}.
Therefore, we are interested in evaluating the performance of PTMs in both app issue classification and sentiment analysis task settings.
In sentiment analysis, the task is to classify the sentiment of a given review into positive, neutral, or negative polarities \cite{how-far-ptm}, which is different than the app issue classification.}
In this setting, we aim to evaluate the \texttt{PTM-X} and \texttt{CPTM-X} on the new task of sentiment classification for app reviews.
\textcolor{black}{We will use another dataset\footnote{https://www.kaggle.com/lava18/google-play-store-apps} that consists of 37,185 app reviews with three sentiment polarity labels: \textit{positive} (13,758), \textit{neutral} (9,993), and \textit{negative} (13,434)}.
The experimental settings and evaluation metrics we will use are similar to RQ1. The results will determine how these models perform for a different classification task in the same domain (app reviews).

(iv) \textit{Multiple resources:}
The distribution of the data from multiple resources varies and models built for App Stores are not suitable to classify user feedbacks from another resource such as Twitter \cite{SUR-citer7}. In this setting, we will study the accuracy and time efficiency of the PTMs for classification of data collected from multiple resources.
For this experiment, \textit{Dataset 2} will be used as it contains App Store reviews and Twitter data with same labels.
First, we will fine-tune the \texttt{PTM-X} and \texttt{CPTM-X} on App Store reviews of \textit{Dataset 2} and report the evaluation metrics (training steps are similar to RQ1) when tested on Twitter data.
In addition, we will fine-tune the \texttt{PTM-X} and \texttt{CPTM-X} on Twitter part of \textit{Dataset 2} and report the results when tested on App Store reviews.
This way, we will gain insights about the PTMs' capacity to classify app reviews collected from different resources for which the PTMs were not fine-tuned on.

\section{Threats to validity}
\label{sec:threats}

\subsection{Internal Validity}
    \textcolor{black}{
    A possible internal validity can be related to the obtained results. To mitigate this threat, we use stratified k-fold cross-validation to avoid the bias that might be introduced to the results by the test set.
    \textcolor{black}{In addition, to mitigate threats to the validity of the results, hyper-parameter values will be kept the same for all PTMs and Deep learning models in all the steps of experiments. We will also keep the values of alpha and beta for LDA as reported in AR-Miner.} We run all the experiments on a single machine, and we report the machine-configuration to enforce the reproducibility of the results. Furthermore, we consider the same metrics to compare the PTMs with prior approaches. Along with the generated raw duration, we will also provide the change of time in percentage as compared to the baseline. Our adopted stricter micro and macro-metrics diminish the ambiguity while providing comparisons among the considered approaches' associated results. }

\subsection{Construct Validity}
    \textcolor{black}{The selection of the prior approaches and PTMs can pose a validity threat to our study. We identified the four most common approaches as priors by examining the highly practiced methods, tools, and techniques employed by researchers and application developers. The considered approaches include different Machine Learning algorithms, Ensemble methods, and Deep Learning Approaches for app issue classification tasks and are selected after conducting literature review.
    The PTMs were adopted by following previous study \cite{how-far-ptm} that conducted an empirical study on PTMs performance for sentiment analysis in Software Engineering. Additionally, we pre-trained transformer-based models by incorporating app-review-related datasets with the previously used generic dataset to better understand the potential of domain specific PTMs (\texttt{CPTM-X}) for app issue classification.
    Another threat to the study can be related to dataset $D_7$. This dataset is a merge of other datasets and has labels borrowed from them. Although we consulted the definitions of the labels from their publishers, there might be a chance that the samples in one group of a dataset is closely related to the samples from another label of another dataset, thus, affecting the results. We will investigate the results of $D_7$ closely to mitigate potential threats.}

\subsection{External Validity}
    \textcolor{black}{
    In this study, we empirically study the ability of the PTMs in issue classification of app reviews. The app review classification mainly focuses on extracting useful information in the context of software engineering; which can be used by app developers for requirements engineering, release planning, and other tasks as mentioned in the paper. Our study is therefore limited to app review classification for software engineers and we do not study PTMs for other purposes such as finding issues from business perspectives \cite{tang2019systematicCommerce}, product reviews \cite{zhao2017productReview}, and applications such as intention mining \cite{huang2018intentionMining}. We also do not study summarization of relevant issues from app reviews.
    However, we conduct our study on six different datasets. In addition, by merging them, we run experiments on $D_7$ which has multiple classes covering different aspects of apps useful for app developers.
    In the future, we plan to curate datasets labelled with security and energy consumption (related to batter issues) issues and conduct the same experiments on these datasets to extend the external validity of the results.
    Another threat in the generalizability of the results lies in different tasks we considered. We only use sentiment classification for the alternate task in the multitask setting. Although PTMs \textit{might} be useful for other tasks, we do not study them in this work.
    To ensure the generalizability of our study in the specified scope, we have incorporated datasets that include app reviews from diverse app categories and from two platforms i.e., Google Play and Apple App Store. The datasets have various sizes and different labels. In addition, we experiment on issue classification from another platform, i.e. Twitter, which has been shown to require different models than App Stores due to the differences of the platforms, noise data, and user feedback.}

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


Since the 1980's, organizations have chosen ITIL best practices to improve the quality of services offered to their customers. But in the meantime, the IT landscape has changed. Businesses demand faster and better responses. This led to a new philosophy: DevOps.
DevOps finds its roots as a counter response to the rigid beast ITIL had become in organisations that deal with IT Service Management. While a lot of businesses have strict, long and bureaucratic ITIL processes in place, DevOps promotes an increase in speed. They do that by eliminating silos, enabling the collaboration between developers and operators and automating a lot of tasks. So, on first sight, we are dealing with two contradicting philosophies. However, it's not because the business demands the faster and better execution of IT service requests (DevOps) that they don't need a process to manage it (ITIL). The challenge is to strengthen the organization's service delivery by integrating ITIL and DevOps.
There are already many inspiring examples of ITIL and DevOps integration. But at the same time – unfortunately enough – there's still a lot of tension between ITIL and DevOps engineers. They have different work attitudes which are not always easy to integrate. ITIL engineers work according to response times documented in service level agreements. For example, if they receive a customer request that is labelled priority 4 (low priority), they have one week to solve it. However, a developer having a problem with an installation can become frustrated when he logs a ticket that labels this as a priority 4. He doesn't want to be stuck for a week, so he restarts the installation himself without logging a ticket. The best solution to this problem is to automate the installation, keeping everybody satisfied: the ITIL and DevOps engineers, as well as the customer.
Adapting ITIL to work well with the DevOps philosophy requires a change in the way of working, both on a cultural and technological level and, most importantly, on a manager level. Teams, both engineers and developers, have to be educated. What problems do they experience? How do they perceive this from the ITIL and DevOps perspective? What can they do about it? What do the teams need from each other to move forward? In the end, the common goal is to provide an IT service management solution that works well for the customer.


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Why Won't the Baton Rouge Home Appraiser Talk to Me?
Is A Baton Rouge View Adjustment Supportable For Fannie Mae Appraisal Forms?
Why Did RE Agent Send Appraiser 5 and 6 Year Old Homes As Comps For 20 Year Old Subject?


text:
Q: Xamarin/Tizen: Executing Navigation.PopAsync() crashes the app I have a navigation page that sets up three pages. The first page loads, the user has to pick an option from a listview and then it loads the second page with PushAsync(). At this point the user can now navigate between the three pages by turning the clock face. If I call PopToRootAsync() on the second page it works fine. If the rotary clock face is turned clockwise it loads a third page via PushAsync().
The problem is if I call PopAsync() on that third page OR I change the PopToRootAsync() on the second page to a PopAsync(), the app crashes.  I have no way to determine what the error is either as I just get segmentation fault and nothing is written to the Tizen log that is seemingly indicative of why it crashed.
Is there some reason that a PopAsync() would cause this? I know I saw some other articles this could occur if the MainPage is not loaded into a NavigationPage but I'm doing that. I've been looking through stuff and writing debug logs for days but have nothing to show for it.  Any help would be more than appreciated. Thank you!
App.cs
public App()
{
     MainPage = new NavigationPage(new ServerSelectionPage());
}

ServerSelection.cs
private void ServerSelection_OnItemTapped(object sender, ItemTappedEventArgs args)
{
    App.SERVER = (Server)args.Item;
    Navigation.PushAsync(new ArrowsPage());
}

PageBase.cs
public async void Rotate(RotaryEventArgs args)
{
    Page _currentPage = Page.REMOTE_BUTTONS;

    if (this.GetType() == typeof(ButtonsPage))
        _currentPage = Page.REMOTE_BUTTONS;
    else if (this.GetType() == typeof(ArrowsPage))
        _currentPage = Page.REMOTE_ARROWS;
    else
        _currentPage = Page.SERVER_SELECTION;

    // When rotating (previous rotation is ongoing, do nothing)
    if (_rotating)
    {
        return;
    }

    _rotating = true;

    if (!(App.SERVER is null))
    {
        if (_currentPage == Page.SERVER_SELECTION)
        {
            if (args.IsClockwise)
                await Navigation.PushAsync(new ArrowsPage());
        }
        else if (_currentPage == Page.REMOTE_DIRECTIONAL)
        {
            if (args.IsClockwise)
                await Navigation.PushAsync(new ButtonsPage());
            else
                await Navigation.PopToRootAsync();
        }
        else
        {
            try
            {
                if (!args.IsClockwise)
                    await Navigation.PopAsync(); // This will crash the app
            }
            catch(Exception ex)
            {
                Log.Debug(ex.Message);
            }
        }
    }

    _rotating = false;
}

A: After reading the comment by @vin about checking if the Navigation object is null I suddenly had the thought that it may not be the Navigation page but the RotaryFocusObject. So I changed
 if (!args.IsClockwise)
    await Navigation.PopAsync();

to
 if (!args.IsClockwise)
 {
   RotaryFocusObject = null;
   await Task.Delay(300);
   await Navigation.PopAsync();
 }

and it no longer crashes. The delay is necessary as if you call PopAsync right after setting the object to null it can still sometimes crash.
So apparently if you pop the current page it causes an error as the focus of the rotary dial is still set to the current navigation page. Why this error does not seemingly occur if you use the Navigation.PopToRootAsync() makes no sense to me.

A: Navigation.PopToRootAsync() and Navigation.PopToRootAsync() are causing destroy of Page renderer, and the Rotate handler is belong to Page Renderer,
If Page renderer was deleted before completion of Rotate Native Callback that is belong to Page renderer, it will be crashed.
await Task.Delay() let to returning native callback.

redpajama_set_name: RedPajamaStackExchange


This entry was posted in News on January 13, 2016 by rhodeislandasla.
Providence, RI, January 5, 2016 — The Rhode Island Chapter of the American Society of Landscape Architects (ASLA) announced the 2015-2016 Design Awards during the RIASLA Holiday & Awards Party in Newport, RI on December 10th. Four Merit Awards, Three Honor Awards, and Five Special Recognition Awards were presented during the evening's ceremonies.
Kate Field & Associates received the first Merit Award for Bridge House in Small Scale Built Design. BETA Group received two merit awards, one for the Washington Pedestrian Bridge in Large Scale Built Design and one for Veterans Memorial Park in Planning and Analysis. The fourth Merit Award was presented to Searle Design Group for Swan Point Cemetery Office Complex Renovations & Additions in Large Scale Built Work.
L + A Landscape Architecture received a Special Recognition Award in Communications for Gardens of Suzhou, as well as Honor awards for City Walk in Planning and Analysis and Kiruna New Town Hall as an On The Boards project. An Honor Award was also given to Veri | Waterman Associates for Pvt. James H Quinn Square in Large Scale Built Design.
In addition to presenting the design awards, four Special Recognition awards were given out to chapter members for their dedication to the RIASLA.
The 2015-2016 awards were juried by the Southern California Chapter of the American Society of Landscape Architects.
This entry was posted in News on January 5, 2016 by rhodeislandasla.


Docs Talk is an industry event organized within the framework of the "Lemesos International Documentary Festival" (1 - 8 August 2016), and is addressed to professional directors / producers and to anyone who is interested in learning more about the various aspects of documentary production today.
DocsTalk will take place at Evagoras Lanitis Center, behind the medieval castle in Lemesos. Major Sponsor of Festival is the Ministry of Education and Culture of Cyprus.
DocsTalk is co-organized with Creative Europe Desk Cyprus.
The event is supported by the Director's Guild of Cyprus.
Documentary Campus Masterschool participants. Join us and learn from Joelle's in-depth feedback and insider tips on how to get the right message across your trailer.
Born in Belgium, Joelle Alexis is one of the top film editors based in Tel Aviv. She has been working on full length documentaries as well as on fiction films for over 20 years. Her works are selected and traveling around the globe at festivals like Cannes, Berlin, San Sebastian, IDFA, Nyon, Sundance, Toronto and many others.
Veteran sales agent Jan Rofekamp will talk about the markets, the festivals and all the other issues and opportunities that exist in the contemporary documentary world.
Jan Rofekamp sits on the EDN Board and is part of the international advisory board of IDFA. He was recently appointed Head of the Documentary Campus Masterschool starting his position in 2016. Recently he joined the advisory board of the Amsterdam based Documentary Impact Academy. Over the last few years he increased his activities as a workshop teacher and lecturer to the benefit of younger generation filmmakers.
The presentation will be focusing on the Creative Europe Programme 2014-2020, and the various possibilities for support within the cultural, creative and audiovisual sectors.
For the first time in media history, creative professionals reaching young generations today have to tell stories to an audience more advanced in technology - the digital natives. For this generation all platforms count. So how do we tell stories for them? And how do we reach them? Through examples of real life stories done for kids and youth audiences, EDN Film and Media Consultant Ove Rishoj Jensen is offering a closer look at what works, when it comes to telling stories to younger audiences.
''Yes, there are women, indeed, who shoot long documentaries – and masterly as well. We have found one of them'', thus read the Jury Statement of an Award Marianna's Economou film has claimed at DOK Leipzig in 2015.
During this talk, Economou will talk in depth about the creative process of making a complex film that brings current affairs to another level by telling its story with a tender approach to its characters.


Blades for windmills arrive on a cargo ship to the Sprague Energy terminal in Searsport in this 2016 file photo. The Maine Ocean School, a public magnet high school, expects a fall launch in Searsport for the 2018-19 academic year.
A Maine high school aimed at pumping out future ship captains, oceanographers, marine biologists and other seaworthy professionals is close to picking its first crop of students.
The Maine Ocean School expects a fall launch in Searsport for the 2018-19 academic year.
"There's a lot of things in the air right now," said Eric Jergenson, an assistant professor at Maine Maritime Academy who serves as chairman of Maine Ocean School's board.
Among the big items to check off will be hiring the school's director. The board has been fielding applications, but needs to complete human relations training before it can review the candidates, a process that should start in the next couple of weeks, Jergenson said. They'll also need to find an administrative assistant.
Once those positions are filled, the search for teachers and other staff will start.
Representatives of the school have been setting up booths at boat shows and other public events in a push to drum up interest. They also hosted summer day programs, taking groups of prospective students out on schooner trips, toured the Mack Point port facility in Searsport, and visited Penobscot Marine Museum. They expect to have similar excursions this summer.
"We are first and foremost interested in knowing the students are interested in the mission and ocean focus of the school," Jergenson said in explaining what qualities they looked for in students.
The school will include courses in required content areas — such as math, English, arts and world languages — but those programs will sometimes feature maritime twists. Students can specialize in programs focused on engineering, transportation, marine sciences or management.
The board expects to cap enrollment at 30 in the school's first year, adding more slots and expanding course offerings in future years. Jergenson said organizers hope the school grows to have more than 100 students, but that could be several years down the road.
It's not yet clear where the school will be located, but organizers have pitched the idea of holding classes in an underused wing of Searsport District Middle and High School, but the arrangement is still being ironed out, according to Jergenson. Offices likely will be set up elsewhere in town.
"The setup of the school will be campuslike, spread across Searsport," Jergenson said.
Organizers say the Penobscot Bay town is an ideal location because of its proximity to a seaport, marine museum, and several colleges and universities with strong maritime programs.
For now, the school will rely on local families willing to host students who can't commute. Eventually, it plans to open student dormitories.
As a public magnet school, Maine Ocean School will be funded through a state reimbursement for a portion of the cost of each student the school educates. A nonprofit foundation will be responsible for raising the rest of the money needed to support the school.
Limestone's Maine School of Science and Mathematics is the state's only other public magnet.


text:
kosefoorum » Rock » The Byrds And Steppenwolf, Dave Grusin - Candy (The Original Motion Picture Soundtrack)
The Byrds And Steppenwolf, Dave Grusin - Candy (The Original Motion Picture Soundtrack) mp3 download
Performer: The Byrds
Album: Candy (The Original Motion Picture Soundtrack)
Style: Psychedelic Rock, Pop Rock, Soundtrack
Other Formats: VOC ASF AUD MP1 DTS AU WAV
The Byrds Psychedelic Rock Pop Rock Soundtrack 1968
This release has been blocked from sale in the marketplace.
As the motion picture Candy boasts the rare McGuinn/Grusin theme, Myra Breckinridge had John Phillips doing the honors, but it is this movie which is the precursor to all that came after, slyly merging stylish rock with the psychedelic excess of Tower Records film albums - Riot on Sunset Strip, Psych-Out, and Wild in the Streets. Where those discs had hip underground bands like Strawberry Alarm Clock, Chocolate Watchband, and the Seeds pervading the celluloid, jazz musician Dave Grusin ups the ante. Welcome to our Vinyl Shop Rare & Collectable The Byrds And Steppenwolf, Dave Grusin – Candy (The Original Motion Picture Soundtrack). Jazz composer David Grusin, who would years later release an album on Epic also entitled Candy, co-writes an orchestrated Child of the Universe with Roger McGuinn.
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text:
Alla Turca - Piano Sonatas (Inspiration)
Christian Zacharias
9 SONGS • 1 STUNDE UND 1 MINUTE • MAR 03 2016
Piano Sonata No. 5 in G Major, K. 283: I. Allegro
Piano Sonata No. 5 in G Major, K. 283: II. Andante
Piano Sonata No. 5 in G Major, K. 283: III. Presto
Piano Sonata No. 8 in A Minor, K. 310: I. Allegro maestoso
Piano Sonata No. 8 in A Minor, K. 310: II. Andante cantabile con espressione
Piano Sonata No. 8 in A Minor, K. 310: III. Presto
Piano Sonata No. 11 in A Major, Op. 6 No. 2, K. 331 "Alla Turca": I. Tema con variazioni
Piano Sonata No. 11 in A Major, Op. 6 No. 2, K. 331 "Alla Turca": II. Menuetto
Piano Sonata No. 11 in A Major, Op. 6 No. 2, K. 331 "Alla Turca": III. Allegretto. Turkish March
℗ 1987 Warner Music Group Germany Holding GmbH / A Warner Music Group Company © 2016 Warner Music Group Germany Holding GmbH / A Warner Music Group Company


Meet Amber of Modest June! Today she'll be telling us about some of her favourite films! As a girl whose favourite films are The Godfather and Fight Club I think she can show me a thing or two about classic movies! I hope you enjoy her post!
Hello friends! With summer comes the oportunity to catch up on reading and movies (hopefully) and while my plate's a bit full with summer courses, I've made it my goal to watch at least one film each week. While my friend Kate has a wonderful classic film blog that I've been working my way through, I thought it might be fun to compose a list of some of my favorite films in the hopes of learning yours and exchanging ideas! Without further ado..
1. Any Wednesday-- This film on adultery is really something, and while I'm not usually a fan of these sort of stories, once can't help but get rapped up in the craziness of the film. It actually reminds me of Three's Company just for the action of it, the confusion, and the physical humor. Plus, 'drunk Jane' in the film is pretty hilarious.
2. Sundays in New York-- another Jane Fonda film (sensing a pattern?). Jane Fonda is amazing in this. Plus, this older film has so much sexual innuendo in it, something that truly surprised me considering the period it's from. I couldn't believe that they were getting away with it considering the censorship of the time.
3. Two for the Road-- This isn't my favorite Audrey Hepburn film, but it'd definitely in my top. THIS film is the original (500) Days of Summer, chronicling the moments of a relationship out of order. It's unlike many of Audrey's other films, but the gritty story is one that will keep you hooked. Fair warning-- there's a very annoying littler girl in this film. Ah!
4. River of No Return-- I'm not usually a fan of westerns, but this story has Marilyn Monroe as a saloon singer, features adventure, music, and life/death battles. As an added bonus, Marilyn uses her real speaking voice, something that's pretty refreshing.
5. Funny Face-- Fred Astaire dancing with Audrey Hepburn? Yes please. I'd love to hear your favorite films as well!
I'm just now delving into classic films,but my favorites so far are Two for The Seesaw, Singin' in the Rain & The Wizard of Oz.


Cute game designed for children to learn and use numbers in a fun way. Your little one will know how to read, identify and count numbers. Furthermore, it will inspire child's interest and curiosity in math. Cool animations and effects, tap anywhere for surprises and knowledge!
- Watch panda Miumiu's fantastic music video!


text:
Вениамин Михайлович Майзель ( 20 июня 1900, Минск — 28 февраля 1943, Уфа ) — советский учёный в области механики и машиностроения. Член-корреспондент АН УССР (1939). Доктор инженерно-технических наук (1929), профессор (1930). Автор научные трудов, посвященных гидродинамике, турбомашиностроению, термоупругости, проблеме напряженного состояния упругих систем под влиянием температуры . Разработал метод экспериментального определения напряженного состояния нагретого тела (метод Майзеля).

Биография
Родился в Минске в семье слуцких мещан - Михеля Ицковича и Голды Ицковны Майзелей. Окончил в 1918 г. Ковенскую графа Платова гимназию (средний балл - 5,0). Поступил вольнослушателем в Харьковский Женский политехнический институт Южно-русского общества технологов. Окончил Харьковский Технологический институт (1921), Институты народного образования (1922) и народного хозяйства (1923) (все — в Харькове). В период с 1922 по 1925 работал инж.-конструктором в КБ гидромашиностроения. В 1925–27 зам. нач. научно-технологического управления Высшего совета народного хозяйства УССР. В период с 1928 по 1941 преподавал в вузах Харькова (с 1934 профессор Харьковского университета). Одновременно работал в Институте строительной механики АН УССР (в 1930–41 возглавлял кафедру динамики и прочности машин), в 1936–41 – зав. кафедрой теории механики Харьковского университета. В 1936 зав. секцией механики НИИ математики и механики, возглавлял группу динамики машин (1941–43).

Умер в 1943 году. Прах захоронен в колумбарии на Донском кладбище.

Научные труды
 «К исследованию движения жидкости внутри вращающихся лопастей центробежных насосов с радикальным профилем лопаток при любом значении внутреннего диаметра лопасти». Х., 1928;
 «К вопросу о работе вязких жидкостей в центробежных насосах». Х., 1931;
 «Справочное руководство по машиностроению». Х., 1937–38;
 «Обобщение теоремы Бетти–Максвелла на случай термического напряженного состояния и некоторые его приложения» // Докл. АН СССР. 1941. Т. 30; Температурная задача теории упругости. К., 1951
 «Температурная задача теории упругости». Киев, 1951

Примечания

Ссылки

Персоналии по алфавиту
Учёные СССР
Родившиеся в Минске
Выпускники Харьковского политехнического института
Выпускники Харьковского университета
Преподаватели Харьковского университета
Члены-корреспонденты НАН Украины
Механики СССР
Похороненные на Донском кладбище