Reviewerly/exHarmony huggingface dataset:
Lexicon-Enhancement of Embedding-based Approaches Towards the Detection of Abusive Language

Detecting abusive language is a significant research topic, which has received a lot of attention recently. Our work focuses on detecting personal attacks in online conversations. As previous research on this task has largely used deep learning based on embeddings, we explore the use of lexicons to enhance embedding-based methods in an effort to see how these methods apply in the particular task of detecting personal attacks. The methods implemented and experimented with in this paper are quite different from each other, not only in the type of lexicons they use (sentiment or semantic), but also in the way they use the knowledge from the lexicons, in order to construct or to change embeddings that are ultimately fed into the learning model. The sentiment lexicon approaches focus on integrating sentiment information (in the form of sentiment embeddings) into the learning model. The semantic lexicon approaches focus on transforming the original word embeddings so that they better represent relationships extracted from a semantic lexicon. Based on our experimental results, semantic lexicon methods are superior to the rest of the methods in this paper, with at least 4% macro-averaged F1 improvement over the baseline.
Quantum limit of genuine tripartite correlations by bipartite extremality

The characterization of the extremal points of the set of quantum correlations has attracted wide interest. In the simplest bipartite Bell scenario, a necessary and sufficient criterion for identifying extremal correlations has recently been conjectured, but extremality of tripartite correlations is not well known. In this study, we analyze tripartite extremal correlations in terms of the conjectured bipartite extremal criterion, and we demonstrate that the bipartite part of some extremal correlations satisfies the bipartite criterion, even though they violate Svetlichny’s inequality, and therefore are considered (stronger) genuine tripartite nonlocal correlations. This phenomenon arises from the fact that the conjectured extremal criterion is automatically satisfied when the violation of the Clauser–Horne–Shimony–Holt (CHSH) inequality exceeds a certain threshold, the value of which is given by the maximum CHSH violation at the edges of the probability space. This also suggests the possibility that the extremality of bipartite correlations can be certified by verifying whether the CHSH violation exceeds the threshold.
Insights into Performance Fitness and Error Metrics for Machine Learning.

Machine learning (ML) is the field of training machines to achieve high level of cognition and perform human-like analysis. Since ML is a data-driven approach, it seemingly fits into our daily lives and operations as well as complex and interdisciplinary fields. With the rise of commercial, open-source and user-catered ML tools, a key question often arises whenever ML is applied to explore a phenomenon or a scenario: what constitutes a good ML model? Keeping in mind that a proper answer to this question depends on a variety of factors, this work presumes that a good ML model is one that optimally performs and best describes the phenomenon on hand. From this perspective, identifying proper assessment metrics to evaluate performance of ML models is not only necessary but is also warranted. As such, this paper examines a number of the most commonly-used performance fitness and error metrics for regression and classification algorithms, with emphasis on engineering applications.
A Two-Levels Data Anonymization Approach

The amount of devices gathering and using personal data without the person’s approval is exponentially growing. The European General Data Protection Regulation (GDPR) came following the requests of individuals who felt at risk of personal privacy breaches. Consequently, privacy preservation through machine learning algorithms were designed based on cryptography, statistics, databases modeling and data mining. In this paper, we present two-levels data anonymization methods. The first level consists of anonymizing data using an unsupervised learning protocol, and the second level is anonymization by incorporating the discriminative information to test the effect of labels on the quality of the anonymized data. The results show that the proposed approaches give good results in terms of utility what preserves the trade-off between data privacy and its usefulness.
Least-squares regressions via randomized Hessians

We consider the least-squares regression problem with a finite number of points. We analyze a novel approach, based on randomizing the Hessian matrix, to approximately solve this problem. The new algorithm is a variant of the averaged stochastic gradient descent method (SGD) with constant step-size. However, its updating rule relies on the entire response vector, and its convergence properties do not depend on the residuals. Without strong convexity assumptions, it is proven that the algorithm achieves a convergence rate for function values of $O(1/k)$ after $k$ iterations, where the constant behind the $O$ notation does not depend explicitly on the smallest eigenvalue of the Hessian matrix. The algorithm has a preprocessing cost proportional to the input size, and the running time of each iteration is proportional to the dimension. In the strongly-convex case, a restart version of the algorithm yields a convergence rate of $O(k^{-l})$ in $O(ld(n+k))$ time for arbitrary $l\ge2$, where the constant behind the $O$ notation depends on $l$ and on the smallest eigenvalue of the Hessian matrix. Our theoretical results are illustrated with numerical experiments.
Single-qubit rotations in parameterized quantum circuits

With the advent of hybrid quantum classical algorithms using parameterized quantum circuits the question of how to optimize these algorithms and circuits emerges. In this paper we show that the number of single-qubit rotations in parameterized quantum circuits can be decreased without compromising the expressibility or entangling capability of the circuit. We also compare expressibility and entangling capability across different number of qubits in parameterized quantum circuits. We also consider a parameterized photonics quantum circuit, without any single-qubit rotations, which yields an expressibility and entangling capability comparable to the best regular parameterized quantum circuits.
Hybrid Data-Model Parallel Training for Sequence-to-Sequence Recurrent Neural Network Machine Translation

Reduction of training time is an important issue in many tasks like patent translation involving neural networks. Data parallelism and model parallelism are two common approaches for reducing training time using multiple graphics processing units (GPUs) on one machine. In this paper, we propose a hybrid data-model parallel approach for sequence-to-sequence (Seq2Seq) recurrent neural network (RNN) machine translation. We apply a model parallel approach to the RNN encoder-decoder part of the Seq2Seq model and a data parallel approach to the attention-softmax part of the model. We achieved a speed-up of 4.13 to 4.20 times when using 4 GPUs compared with the training speed when using 1 GPU without affecting machine translation accuracy as measured in terms of BLEU scores.
Selfie Mask: A Face-Capturing HMD with Polarizing Plates

Head-mounted displays (HMDs) that provide a virtual reality (VR) experience using a simple configuration have been become popular. However, HMDs still have a well-known problem in that they hide the user's face when worn making it difficult for their avatars in VR space to communicate with each other because the users cannot relay their facial expressions and gaze information to their avatars. In this paper, we propose a new HMD constructed using polarizing plates that can capture the user's face and expressions while wearing it. The proposed method enables the user's face to be captured by an internal camera while blocking the scenery from the user's view using orthogonal polarizing plates. The proposed method can be implemented at low cost and can directly capture a full-color image of the user's face unlike conventional methods using infrared cameras. We implemented a prototype and confirmed that the proposed HMD captures the user's face while showing VR images. This paper discusses the unique characteristics and possible applications of the proposed method.
Design Anthropology: Working on, with and for Digital Technologies

This chapter outlines how the subfield now known as design anthropology has developed hand-in-hand with the study of the digital and with digital ways of working—one of many parallel fields concerned with computing. It evaluates some of the contributions to emerge. The chapter is predicated on the idea that every anthropologist needs to know more about this area, not only because we are all potentially implicated in it, but because we need to think about how it may or may not be productive for us and for others. Then the chapter describes and illustrates the interdisciplinary dialogues which design anthropologists are involved in. Computer-supported cooperative (or collaborative) work describes the intensive contextual study of group work to help design systems which support it. User-centred design refers to a set of design methodologies which start with potential users or communities of users.
An Indian Language Social Media Collection for Hate and Offensive Speech.

In social media, people express themselves every day on issues that affect their lives. During the parliamentary elections, people’s interaction with the candidates in social media posts reflects a lot of social trends in a charged atmosphere. People’s likes and dislikes on leaders, political parties and their stands often become subject of hate and offensive posts. We collected social media posts in Hindi and English from Facebook and Twitter during the run-up to the parliamentary election 2019 of India (PEI data-2019). We created a dataset for sentiment analysis into three categories: hate speech, offensive and not hate, or not offensive. We report here the initial results of sentiment classification for the dataset using different classifiers.
Analysis of Users Behavior on Micro-blogging Site Using a Topic

Peoples are generally influenced what everyone else is saying. ABharadwaj, Deepanshu personTripathi, Aprna observes a thing according to their nature, whether they are positive or negative kind of person. When these persons tweet other persons influenced by their thoughts. Social media is nowadaysAgrawal, Aman a huge platform to spread a news. In this paper, we proposed a method to identify the user’s behavior on the micro-blogging site Twitter. Tweets are extractedKhan, Mohd. Aamir according to the topic followed by the users behavior is analyzed according to their previous tweets. This method can be used in many ways to stop spamming on social media.
A Smoothing Optimization Approach Applied to the Supervised MDS Method

This paper presents an efficient approach to the Supervised MDS method. This method handles the problems of data visualization, supervised classification and bipartite ranking. In order to overcome the non-differentiable nature of the Supervised MDS method, the mathematical formulation proposed in this work is based on the hyperbolic smoothing technique. The performance of the algorithm is evaluated by computational experiments. The results show that the proposed methodology presented, in most cases, better results than the results available in the literature. Furthermore, the methodology presents a good performance in relation to the methods Logistic regression, Naive Bayes and Support Vector Machine.
BRAPT: A New Metric for Translation Evaluation Based on Psycholinguistic Perspectives

There are some metrics to evaluate automatic text translations in the literature. However, the state-of-the-art of these metrics still has limitations. One of them is the dependence of an exact and ordered pairing of words for evaluating similarity among texts. Another, is the non-consideration of the semantics of the text in such comparison. Previous studies point out the need to analyze the semantics of words in the evaluation of translations. In this scenario, this paper presents a novel metric capable of evaluating the differences in automatic text translations that takes into account the semantics of the words presented in the texts. As a proof of concept, we selected ten journalistic texts written in English. These texts have been translated to Portuguese by specialists and by automatic text translation tools. Experimental results show the potential of the proposed metric in evaluating these translations, indicating it can perform better than the state-of-the-art metric.
Tracking reference statistics – evaluation of methods

The use of a paper-based tally sheet systems for gathering reference statistics has been unchanged for decades. However, with new technology and data gathering tools being developed, libraries now have an alternative for gathering statistical data more efficiently. This paper will discuss the strengths and weaknesses of both paper-based tally sheet forms and electronic tracking systems being used in libraries and will encompass a variety of commercial, open source, and homegrown products. Preference for either tool will be explored and determine reasons for a libraries reluctance to embrace new technology and keeping with old methods.
The Effects of a Memorization Perception Strategy on College Students’ English Learning Ability

The purpose of the study is to explore the effects of changing students’ perception of their English memorization learning habits. College students in Korea usually tend to be tired of learning English by the time they enter university because the English secondary education curriculum has de-motivated them. Their affective ‘Learned Helplessness’ is inclined to make them resistant to learning English, and they become students called young-po-ja in Korean (someone who gave up learning English). In this situation, methods that may otherwise be effective are of no use in maintaining the student’s interest and giving them motivation to continue learning English. If this is not addressed, the gap between the good students and the young-po-ja will become larger. For this study, two surveys, an essay question, and a questionnaire, were administered in both 38-student class and 129-student class. The results of the two surveys showed that, among other factors, memorization is the biggest problem in students’ learning. Based on these results, an experiment with 38 students was conducted to determine whether changing the negative preconception of memorization into a positive view would affect their attitude toward learning English. In the process memorization strategies and a method of teaching students how to memorize were also explored. Three multi-word units memorization tests(one midterm and two delayed tests) and student reports were used for the final analysis. The results showed that the memorization perception strategy can promote their learning habits and improve their retention.
A Hybrid Approach to Representing Chessboard using Bitboard and Compact Chessboard Representation

Abstract A chess engine is a computer program that can perform position analysis in chess and take which are the most optimal move. The main process in a chess engine can be divided into three: move generation, searching, and evaluation. Each of these processes are interrelated and require efficient board representation methods. Some methods of representation from previous research are efficient in some processes but less efficient in others. This study proposes an efficient representation method with a hybrid approach, which combines Compact Chessboard Representation (C.C.R) and bitboard representation. The C.C.R method is used in move generation and searching processes to reach conciseness, and bitboard representation method is used in the evaluation process to make it faster. The result of the movement validity test for the hybrid representation method shows the move generation is valid, and a hybrid-based chess engine also can do searching and evaluation faster than a pure C.C.R based chess engine.
Estimation of a point along overlapping cervical cell nuclei in Pap smear image using colour space conversion

The identification of normal and abnormal cells is considered as one of the most challenging task for computer assisted Pap smear analysis system especially when abnormal cells are hidden behind overlapped cells. The complexity of the problem depends on whether only cytoplasm of two cells are overlapped, or only nuclei are overlapped with disjoint cytoplasm, and in some case both cytoplasm and nuclei are overlapped. Sometimes, Pap smear sample contains mixture of cells with disjoint/overlapped cytoplasm and nuclei. Hence, there is need for algorithm which segments cells from the cluster of overlapped cells. In case of overlapped nuclei, identifying the point of overlapping accurately is one of the significant steps in segmenting overlapped cells. This paper discusses image processing-based method which segment disjoint nuclei and identifies the point of intersection called as concavity point in the cluster of cells where only nuclei are overlapped.
Proposal of VO<SUB>2max</SUB> Estimation Formula Using Activity of Daily Living Measurement of Elderly Men and Women

This study proposed a multiple regression equation for predicting VOSUB2max/SUB of elderly men and women using activity of daily living variables required to conduct daily activities. The subjects of this study were 58 elderly men (72.4±5.9 yrs) and 117 elderly women (73.4±4.5 yrs) aged 65-90 who belong to the senior welfare center. The maximal graded exercise test using a cycle ergometer and activity of daily living representing muscle strength, endurance, static and dynamic flexibility, mobility, and agility were measured. For statistical processing, multiple regression analysis was performed, and the statistical significance level was α = .05. As a result, the VOsub2max/sub estimation formula for the elderly was 0.419 (standing up and sitting down a chair) + 0.199 (leg endurance against wall) + 5.383, and R²=0.406. In addition, the VOSUB2max/SUB estimation formula for elderly women is - 0.737 (standing up from a supine position) - 0.144 (waking around two cones in a figure 8) - 0.135 (%body fat) + 0.042 (one leg balance with eyes open) + 29.395, R²=0.367 was calculated. The conclusion is that if the maximal graded exercise test is not available, it is considered that VOSUB2max/SUB of the elderly can be predicted properly by using the estimation formula calculated based on the activity of daily living variable.
Drastic increase of key rate in <scp>continuous‐variable</scp> quantum key distribution using multimode states and <scp>non‐Gaussian</scp> operations

Quantum EngineeringVolume 2, Issue 3 e47 RESEARCH HIGHLIGHT Drastic increase of key rate in continuous-variable quantum key distribution using multimode states and non-Gaussian operations Anqi Huang, Corresponding Author angelhuang.hn@gmail.com Institute for Quantum Information & State Key Laboratory of High Performance Computing, College of Computer, National University of Defense Technology, Changsha, China Correspondence Anqi Huang, Institute for Quantum Information & State Key Laboratory of High Performance, Computing, College of Computer, National University of Defense Technology, Changsha 410073, China. Email: angelhuang.hn@gmail.comSearch for more papers by this author Anqi Huang, Corresponding Author angelhuang.hn@gmail.com Institute for Quantum Information & State Key Laboratory of High Performance Computing, College of Computer, National University of Defense Technology, Changsha, China Correspondence Anqi Huang, Institute for Quantum Information & State Key Laboratory of High Performance, Computing, College of Computer, National University of Defense Technology, Changsha 410073, China. Email: angelhuang.hn@gmail.comSearch for more papers by this author First published: 03 June 2020 https://doi.org/10.1002/que2.47Citations: 1 Funding information: National Key Research and Development Program of China, 2019QY0702; National Natural Science Foundation of China, 61901483 Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat No abstract is available for this article.Citing Literature Volume2, Issue3September 2020e47 RelatedInformation
A New Approach to Cryptographic Hashing: Color Hidden Hash Algorithm

Nowadays, the increase in internet speed has increased the amount of data. With this increase, secure transmission of the data gained importance. Some data are important and sensitive. Security has been becoming a major concern in keeping sensitive data for companies. There are many techniques to resolve this concern and hashing data is one of them. Keeping Data as hashed in suitable environment prevents systems from undesired situations. Hash algorithm provides a way to encipher data so that it could not be read or predicted. The existing hash algorithms are standard and the output values for the same data inputs are the same. This means that the hash output for each user is not unique. In this study, an innovative algorithm was designed by converting data to binary then matching them RGBA (Red, Green, Black, Alpha) color value. First, this algorithm converts input data to byte array then it constructs four-bit groups from this array. after that, for each four-bit group related to RGBA value is assigned. These RGBA values are associated with hash code length. The output could be used as hash code. In this way, a customized hash algorithm will be obtained. Different output can be generated for the same input values. Through this study, security is unique and more private for every user.
Trust-Based Chaos Access Control Framework by Neural Network for Cloud Computing Environment

Trust between users and Cloud Service Providers (CSP) is an important attribute in the cloud computing environment. Cloud trust is directly proportional to data security and data privacy in such a way that it affects the quality of service (QoS). Lack of control over data content and transparency in processing of data by the CSP could diminish the trust factor. In this chapter, Trust-Based Chaos Access Control (TBCAC) framework for cloud environment is proposed and evaluated through MATLAB. It is a neural network (NN) based framework that monitors, analyses, and enforces the trust requirements for data publication. The trust manager proposed in this chapter works well in a real environment and it acquires trust-related data regarding every cloud user’s transaction on the cloud server. The cloud manager used in the proposed architecture is competent for dynamically analyzing the data requesting pattern from the user through NN-based classifiers. The proposed architecture is evaluated using MATLAB environment with Amazon cloud services dataset for the demonstration purpose. The results prove that TBCAC effectively prevents the unauthorized behavior and access in the cloud computing environment.