Reviewerly/exHarmony huggingface dataset:
THE CHALLENGES OF REGULATING HATE SPEECH ON SOCIAL MEDIA IN LIGHT OF THE THEORY OF FREEDOM OF EXPRESSION

Although international human rights instruments declare that everyone has the right to freedom of thought and expression, many countries today have laws that censor or limit certain forms of expression on social media, including speech that fuels hatred and violence. Some free speech advocates prefer an open market of ideas where no expression is restricted thinking that the best answer to harmful speech is discussions that allow different ideas to challenge any speech freely. Still some others, on the other hand, argue that restrictions on hate speech on social media are vital for protecting minority communities from the harms caused by this speech. Hate speech raises a very complex question that tests the limits of free speech. Thus, based on the theories that underpin freedom of expression, this piece of work strives to analyze the regulatory dilemma on hate speech on social media by exploring the arguments and challenges that frame the regulatory process.
BASGD: Buffered Asynchronous SGD for Byzantine Learning

Distributed learning has become a hot research topic due to its wide application in cluster-based large-scale learning, federated learning, edge computing and so on. Most traditional distributed learning methods typically assume no failure or attack on workers. However, many unexpected cases, such as communication failure and even malicious attack, may happen in real applications. Hence, Byzantine learning (BL), which refers to distributed learning with failure or attack, has recently attracted much attention. Most existing BL methods are synchronous, which are impractical in some applications due to heterogeneous or offline workers. In these cases, asynchronous BL (ABL) is usually preferred. In this paper, we propose a novel method, called buffered asynchronous stochastic gradient descent (BASGD), for ABL. To the best of our knowledge, BASGD is the first ABL method that can resist malicious attack without storing any instances on server. Compared with those methods which need to store instances on server, BASGD takes less risk of privacy leakage. BASGD is proved to be convergent, and be able to resist failure or attack. Empirical results show that BASGD significantly outperforms vanilla ASGD and other ABL baselines when there exists failure or attack on workers.
Granting Inmates Information Technology-Based Rights

The world has entered industrial revolution 4.0, which has an effect on granting inmates rights that are based on information technology (IT) and encourages active participation of communities in supporting the implementation of an appropriate correctional system.However, this use of IT has not been optimized to ensure that, together with their families, inmates are able to obtain relevant information and that there is certainty in obtaining their rights.Therefore, this study aimed to investigate the use of IT in granting inmates their rights.Thus, the study employed an evaluative juridical approach with mixed methods (quantitative and qualitative).Data were collected through interviews, observations, and surveys using a SERVQUAL (service quality) model with the following five dimensions: tangibility, reliability, accessibility, communication, and credibility.The findings show that service quality is influenced by the credibility and integrity of the correctional staff.Therefore, the readiness of facilities and infrastructure, such as to add some features in the correctional database system, encourage self-service, and to ensure credibility in collecting inmates' data, can help curb the shortcomings associated with employees' credibility and integrity in the process of granting inmates their IT-based rights.
Commentary

In recent years, co-design and participatory methods have gained prominence in academic research and in the media and policy discourses. One can find media articles suggesting how these methods enhance creativity and create better products, and policy recommendations and governmental reports on how to use them to facilitate more inclusive communities. This chapter captures this trend, reflecting on the growing involvement of older people throughout the research process and focusing on understanding lived experiences and perceptions of later life within Age Studies and Science and Technology Studies. Participatory design (PD) is increasingly seen as the ‘appropriate’ approach to create, develop, implement and test technologies with and for older people. Following the ethos of participatory research, this methodology aims to empower users by supporting their involvement in the creation and evaluation of sociotechnical systems designed for them. PD for technology design has been employed in a multitude of ways as it became popularised in human–computer interaction, medicine and the social sciences.
Implementation of Artificial Neural Network for Image Recognition Using Chinese Traffic Sign Image Dataset

In the last few decades, the increasing potential of Information technology revolutionized data and information management, in particular, the data acquisition, data processing, and predictions. The effort has been truly interdisciplinary, where, image processing techniques, and artificial intelligence (AI) based model implementation have played their roles. The latest technology innovations have enabled the researchers to execute computational experiments which would have never been possible if these would have been tried using the conventional methods. Traffic signage detection is considered one of the most researched subjects in area of computer vision and image processing. So far, there has been limited research conducted on this subject using the Chinese Traffic Sign research database (TSRD) and also the results obtained are not so encouraging. This paper provides innovative approach of using HSV (Hue, Saturation, Value) base image transformation approach on TSRD dataset and implementing artificial neural network for image detection purposes. This research is conducted on a pilot of 10 image categories taken from TSRD and the results have found to be highly encouraging. The results observed on small dataset have encouraged to extend research covering the entire image dataset.
Free Feeder/Bus Ticketing Systems Modelling for Students in Surakarta City Based on Android Platform

Abstract High population growth causes an increase in community mobility to meet their travel needs. This has an impact on the use of private vehicles that are not controlled, which is causing transportation problems. The solution to the transportation problem is to use public transportation as a mode of travel. The government is obliged to provide adequate public transportation so that people switch from using private vehicles to using public transport vehicles. In order to meet the needs of public transportation, therefore, the Surakarta City Government provides a free BST bus/feeder fleet specifically for student. In connection with this, a ticketing BST bus/feeder system is needed to monitor and supervise public transportation users. The aim of study is to develop a free ticket system for student to use public transportation in the form of BST bus/feeder using Android smartphone technology. By using this system, students are expected to depart and return from school using public transportation provided by the city government for free without the need to be escorted and picked up by parents or guardians. Study results is an android application called Ride for Free, which is user friendly and helps students to their daily riding activities to school.
The effect of organization learning culture, physical work place environment, employee trust, employee satisfaction on employee performance of Thailand pharmaceutical industry

The aim of the current study is to investigate the impact of physical work place environment (PWPE), organizational learning culture (OLC), employee satisfaction (ES) and employee trust (ET) on the employee performance (EP) of the pharmaceutical industry of Thailand. For this purpose, data was collected from 251 employees of the pharmaceutical industry in Thailand which yield a 41.2% response rate. The Structural Equation Modeling (SEM) results have shown that OLC has positive and significant association with the EP. In the same vein, PWPE also shown the positive and significant association with the EP. Moreover, ET also has a positive and significant association with the EP. In addition, ES also has a positive and significant association with the EP of the pharmaceutical industry of the Thailand. Based on the findings,the current study contributed a body of literature which could become a new area of research in future. The research limitations and future directions of the study are also discussed at the end of the study.
Top-$k$ eXtreme Contextual Bandits with Arm Hierarchy

Motivated by modern applications, such as online advertisement and recommender systems, we study the top-$k$ extreme contextual bandits problem, where the total number of arms can be enormous, and the learner is allowed to select $k$ arms and observe all or some of the rewards for the chosen arms. We first propose an algorithm for the non-extreme realizable setting, utilizing the Inverse Gap Weighting strategy for selecting multiple arms. We show that our algorithm has a regret guarantee of $O(k\sqrt{(A-k+1)T \log (|\mathcal{F}|T)})$, where $A$ is the total number of arms and $\mathcal{F}$ is the class containing the regression function, while only requiring $\tilde{O}(A)$ computation per time step. In the extreme setting, where the total number of arms can be in the millions, we propose a practically-motivated arm hierarchy model that induces a certain structure in mean rewards to ensure statistical and computational efficiency. The hierarchical structure allows for an exponential reduction in the number of relevant arms for each context, thus resulting in a regret guarantee of $O(k\sqrt{(\log A-k+1)T \log (|\mathcal{F}|T)})$. Finally, we implement our algorithm using a hierarchical linear function class and show superior performance with respect to well-known benchmarks on simulated bandit feedback experiments using extreme multi-label classification datasets. On a dataset with three million arms, our reduction scheme has an average inference time of only 7.9 milliseconds, which is a 100x improvement.
Binary Image Steganography based on Permutation

Abstract We propose a steganographic scheme based on permutations, which improves the capacity of embedding information in a series of p host binary images. Given a host binary image block of size m x n bits and any embedding technique T, where T can hide Q(m, n) bits of data in the image; given p images, T can hide p x Q(m, n) bits of data in these images. Our scheme improves the capacity of embedding information in p images such that, instead of p x Q(m, n) bits, it can hide p x log 2 (p) + p x Q(m, n)bits. The results which have been obtained by experiments, show that our model performs a better hiding process in terms of hiding capacity.
Evaluation of the effectiveness of telephone consultations and the level of satisfaction of patients attending the Unidad Docente Asistencial de Mastología [Mastology Care Teaching Unit] using this method during the COVID health emergency.

e13610 Background: In March 2020, a health emergency was decreed due to COVID-19 and this produced changes in the organization of health services. One of the objectives of the reorganization that took place was a reduction in face-to-face consultations (FTFC), and the promotion of telephone consultations (TC). Objective: Evaluate the effectiveness of TC and the level of satisfaction of patients attending the Mastology Unit using this method during the COVID health emergency. Methods: A prospective, cross-sectional study. A survey was used to assess the effectiveness of TC and the level of satisfaction with the method. Results: : Forty-two patients were surveyed, of which 69% were receiving adjuvant treatment and 14.2% palliative treatment. With regard to the effectiveness of TC, 76.1% of those surveyed had their appointment resolved by telephone; 78.6% of patients preferred to do the consultation by telephone; 97.6% believed that sufficient time was spent and that the timing of the appointment was appropriate. The responses showed a high degree of satisfaction with the care received. The medical care met the expectations of all patients, and 83.3% believed that once normalcy was restored, TC would be an option for their situation Conclusions: The TC method was evaluated with a high degree of satisfaction and allowed care to be maintained during the emergency. Although it is early to assess the healthcare impact of TC and the method's capacity to resolve issues, preliminary results show that it is a useful and valuable tool in clinical practice during periods of healthcare emergency.
Simulating of X-states and the two-qubit XYZ Heisenberg system on IBM quantum computer

Abstract Two qubit density matrices which are of X-shape, are a natural generalization of Bell Diagonal States (BDSs) recently simulated on the IBM quantum device. We generalize the previous results and propose a quantum circuit for simulation of a general two qubit X-state, implement it on the same quantum device, and study its entanglement for several values of the extended parameter space. We also show that their X-shape is approximately robust against noisy quantum gates. To further physically motivate this study, we invoke the two-spin Heisenberg XYZ system and show that for a wide class of initial states, it leads to dynamical density matrices which are X-states. Due to the symmetries of this Hamiltonian, we show that by only two qubits, one can simulate the dynamics of this system on the IBM quantum computer.
Seasonality in Infection Predictions Using Interpretable Models for High Dimensional Imbalanced Datasets

Seasonality plays a significant role in the prevalence of infectious diseases. We evaluate the performance of different approaches used to deal with seasonality in clinical prediction models, including a new proposal based on sliding windows. Class imbalance, high dimensionality and interpretable models are also considered since they are common traits of clinical datasets.We tested these approaches with four datasets: two created synthetically and two extracted from the MIMIC-III database. Our results corroborate that clinical prediction models for infections can be improved by considering the effect of seasonality. However, the techniques employed to obtain the best results are highly dependent on the dataset.
Open Hybrid Model: A New Ensemble Model for Software Development Cost Estimation

Given various features of a software project, it may face different administrative challenges requiring right decisions by software project managers. A major challenge is to estimate software development cost for which different methods have been proposed by many researchers. According to the literature, the capability of a proposed model or method is demonstrated in a specific set of software projects. Hence, the aim of this study is to present a model to take advantage of the capabilities of various software development cost estimation models and methods simultaneously. For this purpose, a new model called "open hybrid model" was proposed based on the firefly algorithm. The proposed model includes an extensible bank of estimation methods. The model also includes an extensible bank of rules to describe the relation between existing methods. Considering project conditions, the proposed model tries to find the best rule for combining estimation methods in the methods bank. Three datasets of real projects were used to evaluate the precision of the proposed model, and the results were compared with those of other 11 methods. The results were compared based on performance parmeters widely used to show the accuracy and stability of estimation models. According to the results, the open hybrid model was able to select the most appropriate methods present in the methods bank.
The Image Local Autoregressive Transformer

Recently, AutoRegressive (AR) models for the whole image generation empowered by transformers have achieved comparable or even better performance to Generative Adversarial Networks (GANs). Unfortunately, directly applying such AR models to edit/change local image regions, may suffer from the problems of missing global information, slow inference speed, and information leakage of local guidance. To address these limitations, we propose a novel model -- image Local Autoregressive Transformer (iLAT), to better facilitate the locally guided image synthesis. Our iLAT learns the novel local discrete representations, by the newly proposed local autoregressive (LA) transformer of the attention mask and convolution mechanism. Thus iLAT can efficiently synthesize the local image regions by key guidance information. Our iLAT is evaluated on various locally guided image syntheses, such as pose-guided person image synthesis and face editing. Both the quantitative and qualitative results show the efficacy of our model.
Icatforlearning Website for Teaching Translation and Interpreting Online Class

COVID-19 pandemic spreads worldwide, and educational sectors have received significant impacts because of it. Schools and Universities try to find a new method for the continuation of the teaching and learning process. As a result, online learning during the COVID-19 pandemic is rising. To support this learning, technology and new media are needed. One of the online media used by lecturers in teaching translation and interpreting is the icatforlearning website. It was designed to fulfill the students’ and lecturers’ needs in the teaching and learning process. This research aimed to portray the use of the icatforlearning website in translation and interpreting classes and find the obstacles students face using that website. This study used qualitative research. The subjects are the students of translation and interpreting class in the English Department. There are four classes for translation and interpreting class. The data were collected through observation and interviews. The result shows that icatforlearning can support the online teaching and learning process, although the students faced a few obstacles.
«Television style»: the role of serials in shaping the lifestyle and behavior of the viewer

The study examines the impact of television broadcasts on human thinking, behavior and principles. Emphasis is placed on an overview of the impact of TV series on people’s lives on the example of the series «Queen’s Gambit», the root causes of change are considered, the typology of thinking is touched upon and visual statistics are presented.
C-Rex: A Comprehensive System for Recommending In-Text Citations with Explanations

Finding suitable citations for scientific publications can be challenging and time-consuming. To this end, context-aware citation recommendation approaches that recommend publications as candidates for in-text citations have been developed. In this paper, we present C-Rex, a web-based demonstration system available at http://c-rex.org for context-aware citation recommendation based on the Neural Citation Network [5] and millions of publications from the Microsoft Academic Graph. Our system is one of the first online context-aware citation recommendation systems and the first to incorporate not only a deep learning recommendation approach, but also explanation components to help users better understand why papers were recommended. In our offline evaluation, our model performs similarly to the one presented in the original paper and can serve as a basic framework for further implementations. In our online evaluation, we found that the explanations of recommendations increased users’ satisfaction.
On the Generalization Power of Overfitted Two-Layer Neural Tangent Kernel Models

In this paper, we study the generalization performance of min $\ell_2$-norm overfitting solutions for the neural tangent kernel (NTK) model of a two-layer neural network with ReLU activation that has no bias term. We show that, depending on the ground-truth function, the test error of overfitted NTK models exhibits characteristics that are different from the "double-descent" of other overparameterized linear models with simple Fourier or Gaussian features. Specifically, for a class of learnable functions, we provide a new upper bound of the generalization error that approaches a small limiting value, even when the number of neurons $p$ approaches infinity. This limiting value further decreases with the number of training samples $n$. For functions outside of this class, we provide a lower bound on the generalization error that does not diminish to zero even when $n$ and $p$ are both large.
Learning strategies mediated by technology: students’ perception

Nowadays, the incorporation of technology in the educational field has transformed the scenario of the teaching-learning process, generating a change in the role of its actors, where it determines the vertices of the perfect triangulation between teachers, students, and content (Coll, 2011). Therefore, it is crucial to explore the virtual universe and implement technology-mediated learning strategies that promote the appropriation of knowledge and assess students' perceptions to contribute to new virtual teaching proposals. This research aims to explore the students' perceptions of the Regional Enrique Cabrera Barroso High School, BUAP, regarding the use of strategies applied in the Schoology platform to promote Biology learning. For this, a descriptive quantitative study was carried out in a focus group using a Likert-type scale questionnaire, obtaining the following results: the students demonstrated a high level of satisfaction with the virtual course evaluating virtual strategies as an ad hoc complement to achieve the learning objectives. They also stated that the assimilation and appropriation of knowledge depend on those technology-mediated strategies, contributing to a highly positive perception.
Perbandingan Algoritma Decision Tree C4.5 Dan Naive Bayes pada Analisa Tekstur Gray Level Co-Occurrence Matrix Menggunakan Citra Wajah

Texture analysis is commonly used as a process for image classification and interpretation.An image classification process based on texture analysis generally requires feature extraction stages, which consist of three types of methods namely statistical methods, spactral methods and structural methods.The GLCM (Gray-Level Co-Occurrence Matrix) method is included in the statistical method in which statistical calculations use gray level distribution by measuring the contrast level, granularity (size), and roughness of an area from the neighboring relationships between pixels in the image.The purpose of this study is to find a good value of accuracy by comparing the C4.5 decision tree and naive bayes on the Gray Level Co-occurrence Matrix (GLCM) texture analysis using face images.The tools used for feature extraction use MATLAB and for the classification of the algorithm use Rapid Miner tools.From the results of the study for the best value in the comparison of algorithms with Gray-Level Co-Occurrence Matrix (GLCM) texture analysis using face images.For the accuracy of the two classes with C4.5 classification algorithm is 58.64%, while for the Naive Bayes classification algorithm accuracy value is 75.45% while for the accuracy of the 3 classes with C4.5 classification