Reviewerly/exHarmony huggingface dataset:
POMDPStressTesting.jl: Adaptive Stress Testing for Black-Box Systems

POMDPStressTesting.jl is a package that uses reinforcement learning and stochastic optimization to find likely failures in black-box systems through a technique called adaptive stress testing (Lee et al., 2019).Adaptive stress testing (AST) has been used to find failures in safety-critical systems such as aircraft collision avoidance systems (Lee et al., 2015), flight management systems (Moss et al., 2020), and autonomous vehicles (Koren et al., 2018).The POMDPStressTesting.jl package is written in Julia (Bezanson et al., 2017) and is part of the wider POMDPs.jlecosystem (Egorov et al., 2017), which provides access to simulation tools, policies, visualizations, and-most importantly-solvers.We provide different solver variants including online planning algorithms such as Monte Carlo tree search (Coulom, 2006) and deep reinforcement learning algorithms such as trust region policy optimization (TRPO) (Schulman et al., 2015) and proximal policy optimization (PPO) (Schulman et al., 2017).Stochastic optimization solvers such as the cross-entropy method (Rubinstein, 1999) are also available and random search is provided as a baseline.Additional solvers can easily be added by adhering to the POMDPs.jlinterface.
Collaborative filtering recommendation algorithm based on user behavior and drug semantics

Abstract When the traditional collaborative filtering algori- thm is applied to drug recommendation, the recommendation effect is not good due to the sparsity of data. In view of the above problems, this paper proposes a collaborative filtering recommendation algorithm based on user behavior and drug semantics (UBDS-CF). Firstly, we construct the purchasing behavior matrix of users and drugs, and use the weighted cosine similarity to calculate the basic similarity between drugs; then construct the category label matrix of drugs, calculate the category similarity, and extract the feature vector of drug function text using the word vector model; category and main function together constitute the drug semantic information; finally, the above two similarities are integrated in a linear weighted manner to obtain the final similarity of drugs. Experimental results show that the average absolute error and root mean square error of the algorithm are significantly reduced, which proves the effectiveness of the proposed algorithm.
IIITK@DravidianLangTech-EACL2021: Offensive Language Identification and Meme Classification in Tamil, Malayalam and Kannada

This paper describes the IIITK team’s submissions to the offensive language identification, and troll memes classification shared tasks for Dravidian languages at DravidianLangTech 2021 workshop@EACL 2021. Our best configuration for Tamil troll meme classification achieved 0.55 weighted average F1 score, and for offensive language identification, our system achieved weighted F1 scores of 0.75 for Tamil, 0.95 for Malayalam, and 0.71 for Kannada. Our rank on Tamil troll meme classification is 2, and offensive language identification in Tamil, Malayalam and Kannada are 3, 3 and 4 respectively.
A Circumstantial Methodological Analysis of Recent Studies on NLP-driven Test Automation Approaches

From manual testing to test automation, test generation is advancing. With the emergence of new challenges—and legacy challenges already persisting—there is a great need of turning test creation activity into a way that is more responsive and effortless. Natural language processing, with its applicability in different domains, is swiftly adopted by researchers in software testing discipline to perform automation of such activities. Attempts like this will bring in prominent paradigm shifts in the conventional and mundane non-automated frameworks of test cases creation (software development activity) from requirement specifications. To explore, as how natural language processing could be employed to assist software testing, this paper presents a detailed article with methodological investigation of some recent research studies. The detailed knowledge will help the practitioners to get insights of how natural language processing (NLP) is being carried out in testing domain and what specific role does each term associated with it will play.
Research on User Experience Based on Competition Websites

Abstract The design of the website interface has an impact on the user experience in all aspects. A set of relatively perfect interactive interface is often in line with the needs, cognition and behavior of the target user to form a good experience. In order for users to have a good experience in their use, subjective evaluation and usability evaluation methods are usually used to test and tune after the interface is designed. This analysis method has its limitations. For example, it cannot reveal the thinking process and psychological activities of the user and the interface in the interaction process for the designer, and provide the tester with the time and space information of the user’s search and processing process. This paper adopts more cutting-edge research methods. First, we analyze the usability of three websites of the same type and different structure, such as evaluating the effectiveness by task completion rate and error rate, and evaluating efficiency by operating time and operating path. Secondly, conduct eye movement experiments on users, further assist and re-verify the usability evaluation method, and analyze its dynamic interaction characteristics, and conduct a comprehensive user experience research on competition websites from qualitative and quantitative perspectives.
An Automatic Abstractive Text Summarization Model based on Hybrid Attention Mechanism

Abstract Attentional sequence-to-sequence models based on RNN have achieved promising performances in the automatic abstractive summarization technology. However, there are still some shortcomings, including the inaccuracies and lack of key information. In this paper, an abstractive text summarization model based on the hybrid attention mechanism is presented. The model aims at adopting sentence-level attention mechanism to guide the word-level attention distribution. Moreover, this model modulates the weight of sentence-level attention value to alleviate the adverse effect of high variance on word-level attention distribution for short documents. The experimental results on the LCSTS dataset show that the presented model can effectively improve the ROUGE scores and can better summarize the source document while retaining the important information. An example in this paper shows that the model can generate summaries that are similar to human-written ones.
Spatial-Resolution Independent Object Detection Framework for Aerial Imagery

Earth surveillance through aerial images allows more accurate identification and characterization of objects present on the surface from space and airborne platforms. The progression of deep learning and computer vision methods and the availability of heterogeneous multispectral remote sensing data make the field more fertile for research. With the evolution of optical sensors, aerial images are becoming more precise and larger, which leads to a new kind of problem for object detection algorithms. This paper proposes the “Sliding Region-based Convolutional Neural Network (SRCNN),” which is an extension of the Faster Region-based Convolutional Neural Network (RCNN) object detection framework to make it independent of the image’s spatial resolution and size. The sliding box strategy is used in the proposed model to segment the image while detecting. The proposed framework outperforms the state-of-the-art Faster RCNN model while processing images with significantly different spatial resolution values. The SRCNN is also capable of detecting objects in images of any size.
Question Classification for Efficient QA System

Natural Language Processing (NLP), a subfield of Artificial Intelligence (AI), supports the machine to understand and manipulate the human languages in different sectors. Subsequently, the Question and answering scheme using Machine learning is a challengeable task. For an efficient QA system, understanding the category of a question plays a pivot role in extracting suitable answer. Computers can answer questions requiring single, verifiable answers but fail to answer subjective question demanding deeper understanding of question. Subjective questions can take different forms entailing deeper, multidimensional understanding of context. Identifying the intent of the question helps to extract expected answer from a given passage. Pretrained language models (LMs) have demonstrated excellent results on many language tasks. The paper proposes model of deep learning architecture in hierarchical pattern to learn the semantic of question and extracting appropriate answer. The proposed method converts the given context to fine grained embedding to capture semantic and positional representation, identifies user intent and employs a encoder model to concentrate on answer span. The proposed methods show a remarkable improvement over existing system&#x0D;
Quantum of Space of the Universe—Correction of Previous Mistakes

A large number of scientific works, from ancient times to the present, have been dedicated to the search for "bricks" that make up the foundations of the material world.Justification of quantum of space parameters of the Universe is a complicated scientific problem, as its reliable information is unknown.Therefore, errors may appear in it, which must be corrected in a timely manner.In the latest works from this sphere, the quanta of the space of the Universe are replaced by hexahedral prisms instead of balls, which solves the problem of their dense packing.However, the mistake was the deformation of these prisms.The purpose of this work is to eliminate this deficiency.Its scientific novelty is the substantiation of the specified of refined parameters of the quantum of the space of the Universe on the basis of strict scientific provisions and the physical laws of nature.The solution to this problem is an urgent and important scientific and applied task, since it develops knowledge about the quantum foundations of the material world and the Universe as a whole.Research methods which used in this work: The performed work is based on the methods of deduction and induction in the research of the material world based on the application of the well-known reliable laws of physics and the general principles of the development of the theory of knowledge.Other research methods are still unknown, since the work performed is associated with new scientific discoveries, the search for which is difficult to formalize by known technique methods.Results and their discussion: The work is based on the hypothesis that was put forward that at the quantum-mechanical level of the material world, a longitudinal quantum shift by the wavelength λ G and a transverse quantum shift by λ G of the quantum of the Universe space is carried out in the time interval T G , which can be found on the basis of the Heisenberg uncertainty principle.The parameters obtained made it possible to clarify the length and shape of quanta of the space of the Universe, as well as the conditions for its rotation.It was also taken into account that the hexagonal prism of the circular quantum of the space of the Universe is composed of 6 trihedral prisms of elementary quanta of space.So
Dialogue Act-based Breakdown Detection in Negotiation Dialogues

Thanks to the success of goal-oriented negotiation dialogue systems, studies of negotiation dialogue have gained momentum in terms of both human-human negotiation support and dialogue systems.However, the field suffers from a paucity of available negotiation corpora, which hinders further development and makes it difficult to test new methodologies in novel negotiation settings.Here, we share a human-human negotiation dialogue dataset in a job interview scenario that features increased complexities in terms of the number of possible solutions and a utility function.We test the proposed corpus using a breakdown detection task for human-human negotiation support.We also introduce a dialogue act-based breakdown detection method, focusing on dialogue flow that is applicable to various corpora.Our results show that our proposed method features comparable detection performance to text-based approaches in existing corpora and better results in the proposed dataset.
Optimizing Recruitment Process Within Businesses: Predicting Interview Attendance Using C4.5 Algorithm

An essential component of the job recruitment process involves conducting interviews, during which decision is made on the appropriateness of a candidate for a particular job. An important challenge faced by interviewers during the recruitment process relates to determining whether or not selected candidates will attend the job interview. Candidates failing to attend interviews because of various reasons lead to time wasting and as such, effective prediction is needed by human resource managers about whether or not a candidate will attend an interview. In other words, if the human resource department knows exactly whether or not the job seeker is attending the interview, a more efficient interview invitation strategy could be worked upon. However, even though different machine learning techniques are available for prediction, limited work has been undertaken to address this recruitment process related issue. Taking cognizance of this problem, this paper investigates, analyzes, and predicts candidate attendance during interviews in order to optimize recruitment process within businesses. For this, C4.5 algorithm was applied on an open dataset related to job interview attendance. Following generation of a classification tree, results showed some insightful patterns on when candidates do not attend job interviews.
Data-Efficient Pretraining via Contrastive Self-Supervision

For natural language processing `text-to-text' tasks, the prevailing approaches heavily rely on pretraining large self-supervised models on increasingly larger `task-external' data. Transfer learning from high-resource pretraining works well, but research has focused on settings with very large data and compute requirements, while the potential of efficient low-resource learning, without large `task-external' pretraining, remains under-explored. In this work, we evaluate against three core challenges for resource efficient learning. Namely, we analyze: (1) pretraining data ($X$) efficiency; (2) zero to few-shot label ($Y$) efficiency; and (3) long-tail generalization, since long-tail preservation has been linked to algorithmic fairness and because data in the tail is limited by definition. To address these challenges, we propose a data and compute efficient self-supervised, contrastive text encoder, pretrained on 60MB of `task-internal' text data, and compare it to RoBERTa, which was pretrained on 160GB of `task-external' text. We find our method outperforms RoBERTa, while pretraining and fine-tuning in a 1/5th of RoBERTa's fine-tuning time.
CIRO: The Effects of Visually Diminished Real Objects on Human Perception in Handheld Augmented Reality

Augmented reality (AR) scenes often inadvertently contain real world objects that are not relevant to the main AR content, such as arbitrary passersby on the street. We refer to these real-world objects as content-irrelevant real objects (CIROs). CIROs may distract users from focusing on the AR content and bring about perceptual issues (e.g., depth distortion or physicality conflict). In a prior work, we carried out a comparative experiment investigating the effects on user perception of the AR content by the degree of the visual diminishment of such a CIRO. Our findings revealed that the diminished representation had positive impacts on human perception, such as reducing the distraction and increasing the presence of the AR objects in the real environment. However, in that work, the ground truth test was staged with perfect and artifact-free diminishment. In this work, we applied an actual real-time object diminishment algorithm on the handheld AR platform, which cannot be completely artifact-free in practice, and evaluated its performance both objectively and subjectively. We found that the imperfect diminishment and visual artifacts can negatively affect the subjective user experience.
iPads in the classroom: teachers’ perspectives post-implementation

This small-scale qualitative case study aimed to investigate English language teachers’ perceptions of the impact of iPads in classrooms in ideal, actual and realistic terms. The study took place at the end of a five-year initiative that saw iPads introduced to English Language classrooms in a UAE Federal Institution. The investigation was carried out from a constructivist epistemology perspective using observations and interviews, analysed through the lens of the SAMR Model (Puentedura, 2010). While curriculum constraints limited the impact in this initiative, the device has the potential to engage students in collaborative tasks and assist in classroom management. A potential two-dimensional SAMR model is proposed, as are opportunities for further research. ﺗﮭ د ف د را ﺳﺔ اﻟ ﺣﺎﻟ ﺔ اﻟﻧ وﻋﯾ ﺔ اﻟ ﻣ ﺻ ﻐرة ھذه إﻟ ﻰ اﻟﺑ ﺣ ث ﻓ ﻲ ﺗ ﺻ ورا ت ﻣﻌﻠ ﻣ ﻲ اﻟﻠ ﻐﺔ ا ﻹﻧ ﺟﻠﯾ زﯾ ﺔ ﺣول ﺗﺄﺛﯾ ر ا ﻷﺟﮭزة اﻟﻠ وﺣﯾ ﺔ ﻓ ﻲ اﻟﻔ ﺻ ول اﻟد ر ا ﺳ ﯾ ﺔ ﺑ ﺻ و ر ة ﻣﺛﺎﻟﯾ ﺔ و و اﻗ ﻌﯾ ﺔ و ﺣ ﻘﯾﻘﯾ ﺔ. و ﻗد ﺑﻧﯾ ت ھ ذه اﻟد ر ا ﺳ ﺔ ﻋ ﻠ ﻰ ﻣﻧ ظ و ر ﻧ ظ ر ﯾ ﺔ اﻟﻣﻌر ﻓﺔ اﻟ ﺑﻧﺎﺋﯾ ﺔ ﺑﺎ ﺳ ﺗ ﺧ دا م اﻟﻣ ﻼ ﺣ ظ ﺎ ت و اﻟﻣﻘﺎﺑ ﻼ ت ، وا ﻟﺗ ﻲ ﺗم ﺗ ﺣﻠﯾﻠ ﮭﺎ ﻣ ن ﺧﻼل ﻋد ﺳﺔ ﻧ ﻣوذ ج SAMR .(Puentedura, 2010) ( ﻓﺑﺎﻟ رﻏم ﻣ ن أ ن ﻗﯾ ود اﻟ ﻣﻧﺎ ھﺞ ﺣد ت ﻣ ن ﺗﺄﺛﯾ ر ا ﻷﺟﮭزة اﻟﻠ و ﺣ ﯾ ﺔ إ ﻻ أ ن ﻟ دﯾ ﮭﺎ اﻟ ﻘد ر ة ﻋ ﻠ ﻰ إ ﺷ ر ا ك اﻟط ﻼ ب ﻓ ﻲ اﻟﻣﮭﺎم اﻟ ﺗ ﻌﺎ و ﻧﯾ ﺔ و اﻟﻣ ﺳ ﺎ ﻋ دة ﻓ ﻲ إ دا ر ة اﻟ ﻔ ﺻ و ل اﻟد ر ا ﺳ ﯾ ﺔ. ﺗﻘﺗ ر ح ھ ذه اﻟد ر ا ﺳ ﺔ ﻧ ﻣو ذ ج SAMR ذو أﺑ ﻌﺎد ﺛﻧﺎﺋﯾﺔ، ﺑﺎ ﻹ ﺿ ﺎﻓﺔ إﻟ ﻰ ﻓ ر ص ﻹﺟرا ء ﻣزﯾد ﻣ ن اﻟﺑ ﺣو ث.
LioNets: a neural-specific local interpretation technique exploiting penultimate layer information

Artificial Intelligence (AI) is having an enormous impact on the rise of technology in every sector. Indeed, AI-powered systems are monitoring and deciding on sensitive economic and societal issues. The future is moving towards automation, and we must not prevent it. Many people, though, have opposing views because of the fear of uncontrollable AI systems. This concern could be reasonable if it originated from considerations associated with social issues, like gender-biased or obscure decision-making systems. Explainable AI (XAI) is a tremendous step towards reliable systems, enhancing the trust of people in AI. Interpretable machine learning (IML), a subfield of XAI, is also an urgent topic of research. This paper presents a small but significant contribution to the IML community. We focus on a local-based, neural-specific interpretation process applied to textual and time series data. Therefore, the proposed technique, which we call “LioNets”, introduces novel approaches to present feature importance-based interpretations. We propose an innovative way to produce counterfactual words in textual datasets. Through a set of quantitative and qualitative experiments, we present competitiveness of LioNets compared to other techniques and suggest its usefulness.
An Overview on Resources for Development of Hindi Speech Synthesis System

Most of the information in digital world is accessible to few who can read or understand a particular language. The speech corpus acquisition is an essential part of all spoken technology systems. The quality and the volume of speech data in corpus directly affect the accuracy of the system. However, there are a lot of scopes to develop speech technology system using Hindi language which is spoken primarily in India. To achieve such an ambitious goal, the collection of standard database is a prerequisite. This paper summarizes the Hindi corpus and lexical resources being developed by various organizations across the country. In this paper, a survey of efforts in database developments for Hindi language has been performed. It discusses some core linguistic resources of Hindi language, available through various resources developed for usage in text-to-speech synthesis and speech recognition technology.
“Are you kidding me?”: Detecting Unpalatable Questions on Reddit

Abusive language in online discourse negatively affects a large number of social media users.Many computational methods have been proposed to address this issue of online abuse.The existing work, however, tends to focus on detecting the more explicit forms of abuse leaving the subtler forms of abuse largely untouched.Our work addresses this gap by making three core contributions.First, inspired by the theory of impoliteness, we propose a novel task of detecting a subtler form of abuse, namely unpalatable questions.Second, we publish a context-aware dataset for the task using data from a diverse set of Reddit communities.Third, we implement a wide array of learning models and also investigate the benefits of incorporating conversational context into computational models.Our results show that modeling subtle abuse is feasible but difficult due to the language involved being highly nuanced and context-sensitive.We hope that future research in the field will address such subtle forms of abuse since their harm currently passes unnoticed through existing detection systems. Preceding CommentMain Comment (Reply)They were safe in turkey.Turkey isn't even safe for (Kurdish) Turks.Do you even watch the news?NH dems or Bernie bros?You don't really understand that term "Berniebro" do you?Just parroting it.Fuck the refs.Fuck the stars.Fuck Texas and every shit-kick fuck that calls it home.Would you like some salt with that?SJW's happened.Why the fuck did you put an apostrophe there?So no article, right?Thought not.Oh, your brain stopped functioning at that?Well then, I'll repeat myself.The abstract is enough, the article, you can find it yourself.I'm not going to waste my time.
Bitcoin and the South Sea Company: A comparative analysis

This paper examines historical Bitcoin price data together with the price data of a well-known and generally accepted historical asset price bubble (the 1720 South Sea Bubble) with the aim of identifying possible similarities. In order to find empirical evidence of speculative bubble tendencies, the article analyses distribution moments and autoregressive models of time series of both assets. Results show that historical daily prices of both assets—taking into account one year before and one year after the maximum price level—clearly show the two phases of bubble expansion and subsequent crash. Furthermore, various similarities between the South Sea Bubble and Bitcoin can be found in descriptive statistics, such as mean of return, standard deviation, and skewness. Statistical tests also show several explosive moments in the time series of the South Sea Company and Bitcoin returns, which implies that both assets exhibit more than one financial bubble.
Cloud Computing and Its Role in the Information Technology

The concept of Cloud Computing has been distinguished as one of the major computing models in recent years. Cloud computing has become a great innovation that has important consequences not just for services on the internet but also for the entire Information technology (IT) market. Its emergence aims to optimize on-demand technology, hardware and information provisioning as a service, reaching the economy of scale in the distribution and operation of IT strategies. A great deal of cloud computing research has been concerned over some obstacles and challenges that rely upon behind the lure of cloud computing. Security has been always raised as one of the most critical issues of cloud computing where resolving such an issue would result in constant growth in the use and popularity of the cloud. Security requirements represent a major issue that has to be met in order of easing some of these obstacles. This article presents the role of cloud computing in the IT sectors.