Reviewerly/exHarmony huggingface dataset:
Development of a Speech to Indian Sign Language Translator

Communication is essential in our daily lives. It serves as the foundation for all human interactions. It is how people communicate with one another and receive information from them. Anyone cannot share their feelings or understand their thoughts unless they communicate effectively. A person with a Speaking or Hearing problem cannot communicate effectively with others and, as a result, cannot compete with other people. According to the World Health Organization, 63 million people in India are deaf, either completely or partially. To communicate with others, a Deaf or Dumb person usually uses Sign Language. Normal people who do not know Sign language, on the other hand, cannot communicate effectively with them. To bridge this communication gap, we propose a system is capable of recognizing spoken words and display the corresponding ISL gestures to provide a convenient and real-time communication between a disabled community and normal community human beings using Deep Learning Algorithms.
An Out-of-the-Box Application for Reproducible Graph Collaborative Filtering extending the Elliot Framework

Graph convolutional networks (GCNs) are taking over collaborative filtering-based recommendation. Their message-passing schema effectively distills the collaborative signal throughout the user-item graph by propagating informative content from neighbor to ego nodes. In this demonstration, we show how to run complete experimental pipelines with six state-of-the-art graph recommendation models in Elliot (i.e., our framework for recommender system evaluation). We seek to highlight four main features, namely: (i) we support reproducibility in PyTorch Geometric (i.e., the library we use to implement the baselines); (ii) reproduced graph models span across various GCN families; (iii) we prepare a Docker image to provide a self-consistent ecosystem for the running of experiments. Codes, datasets, and a video tutorial to install and launch the application are accessible at: https://github.com/sisinflab/Graph-Demo.
Image-based Face Verification for Student Identity Management — the TRUSTID Case Study

Managing attendance and confirming student identity in online lessons is a contemporary problem of Higher Education Institutions (HEIs) that comes with its challenges, especially in classes with large numbers of students. The development and deployment of fully automatic and continuous image-based face verification techniques may provide a natural solution for this problem and assist instructors in handling the complexity of managing student identity in a remote classroom.
A Better Logical Semantics for Prolog

This paper describes a semantics for the language of pure positive Prolog programs that improves on the widely accepted Least Herbrand Model (LHM) semantics. The LHM semantics gives meaning only to complete Prolog programs, and not to components of programs. Yet programmers clearly understand the meaning of a predicate even if not all predicates used in its definition are defined in the program. For example, programmers understand the meaning of the two rules defining transitive closure without having to know the specific graph that it applies to. A semantics for the Prolog language should also provide such a meaning. We motivate the meaning of Prolog rules as inductive definitions by starting with Clark’s completion, analyzing its limitations, and then describing how it can be modified to yield a logical theory of inductive definitions.
Logic-Based Explainable and Incremental Machine Learning

Mainstream machine learning methods lack interpretability, explainability, incrementality, and data-economy. We propose using logic programming to rectify these problems. We discuss the FOLD family of rule-based machine learning algorithms that learn models from relational datasets as a set of default rules. These models are competitive with state-of-the-art machine learning systems in terms of accuracy and execution efficiency. We also motivate how logic programming can be useful for theory revision and explanation based learning.
A semantic account of partial reduplication in Turkish

This study analyzes the factors governing the scope of partial reduplication in Turkish, with particular emphasis on why certain forms are unattested. We propose that whether a given adjective participates in partial reduplication can be determined based on its semantic class: scalar adjectives can, non-scalars cannot. We further demonstrate that, among scalar adjectives, partial reduplication is also sensitive to maximal/non-maximal contrast; the former yields “completely” semantics while the latter yields “very” semantics. The model we propose captures both the class of adjectives that can undergo partial reduplication as well as the resulting interpretation when they do so. As such, our proposal contrasts with earlier ones in providing principled reasons for the class of partially reduplicated adjectives.
The Effect of Workplace Ostracism on Knowledge Sharing Influence and Task Performance: The Mediating Role of Organization

The focus of this research study is to investigate how workplace ostracism influenced employee task performance, as mediated by knowledge sharing. Workplace ostracism may make it hard to engage with one another and limit opportunities for social interaction which may have an impact on how well coworkers share knowledge and how well they carry out the necessary result. The study is purely applied. The study's technique also aids in providing the actual picture of workplace ostracism and how it impacts employee task performance with the mediation of knowledge sharing. Employees selected from IT sector organizations in Pakistan's Karachi are the study's target respondents. The population is to be around 400 participants. This research's main focus is to broaden the literature by understanding the influence of knowledge sharing mediated the relationships between workplace ostracism and task performance.
Information Security Analysis of Online Education Management System using Information Technology Infrastructure Library Version 3

The rapid development of information affects many aspects of human life. So that the field of information security becomes one aspect that must be considered. This study aims to measure the information security awareness and to improve daily operational activities of managing IT services effectively and efficiently. Salemba Adventist Academy has used the Wium Online Education Management System (WIOEM) online system, but in its implementation the security aspects of the system are not yet known. The Information Technology Infrastructure Library (ITIL) v3 framework which is globally recognized for managing information technology is broken down into five parts: Service Strategy, Service Design, Service Transition, Service Operation, Continual Service Improvement. This study focuses on Service Operations with 4 attributes, namely: Security, Privacy, Risk, and Trust. The data collection method used by the researcher was through observation in the form of a questionnaire in taking the number of samples to several students by taking population samples using the Lemeshow method. After the data were collected, the results of the ITIL indicator questionnaire are calculated based on the data security level. The results show that the Security indicator is Level 1, the Privacy indicator is level 3, the Risk indicator is level 3, and the Trust indicator is level 4 on the Data Security Level scale. This shows that the WIOEM system can be used properly according to user expectations and meets several levels of data security according to ITIL v3 framework.&#x0D; &#x0D;
Sequential and Graphical Cross-Domain Recommendations with a Multi-View Hierarchical Transfer Gate

Cross-domain recommender systems could potentially improve the recommendation performance by means of transferring abundant knowledge from the auxiliary domain to the target domain. They could help address some key challenges in recommender systems, such as data sparsity and cold start. However, most existing cross-domain recommendation approaches represent the user preferences based on a single kind of user’s feature or behavior and fail to explore the hidden interaction effects of different kinds of features or behaviors. In this article, we propose the S equential and G raphical Cross -Domain Recommendations with a Multi-View Hierarchical Transfer Gate (SGCross) to transfer user representations from multiple perspectives. The SGCross model constructs a user profile by learning the personal preference from a personal view, the dynamic preference from a temporal view, as well as the collaborative preference from a collaborative view. Specifically, a Multi-view Hierarchical Gate (MHG) is designed to transfer the informative representations of user knowledge on different views from the auxiliary domain separately, aiming to enhance the user representations. Furthermore, a two-stage attentive fusion module is designed to integrate transferred information at two levels: the domain level and the view level. Extensive experiments on the Amazon dataset and the Douban dataset have demonstrated that SGCross effectively improves the accuracy of cross-domain recommendations and outperforms the state-of-the-art baseline models.
XRSpotlight: Example-based Programming of XR Interactions using a Rule-based Approach

Research on enabling novice AR/VR developers has emphasized the need to lower the technical barriers to entry. This is often achieved by providing new authoring tools that provide simpler means to implement XR interactions through abstraction. However, novices are then bound by the ceiling of each tool and may not form the correct mental model of how interactions are implemented. We present XRSpotlight, a system that supports novices by curating a list of the XR interactions defined in a Unity scene and presenting them as rules in natural language. Our approach is based on a model abstraction that unifies existing XR toolkit implementations. Using our model, XRSpotlight can find incomplete specifications of interactions, suggest similar interactions, and copy-paste interactions from examples using different toolkits. We assess the validity of our model with professional VR developers and demonstrate that XRSpotlight helps novices understand how XR interactions are implemented in examples and apply this knowledge in their projects.
Combination fairness with scores in outlier detection ensembles

We revisit score combinations for outlier detection ensembles (ODEs). Different detectors' scores vary in ranges and scales. Normalization transforms these into a common range. We assert that ODE score combinations may remain biased despite normalization. Determining a suitable normalization for an outlier model is challenging, which captures original score statistics apt for different parameters across different datasets. Thus, treating higher normalized scores of one detector as superior outlying probabilities to other detectors may be inappropriate. With differences between top rankers' scores, detectors with higher normalized scores dominate during combination. This limits the effective utilization of diversity, undesired for ensembles. We propose a framework FairComb for providing unbiased combination treatment to outputs of all detectors. It stands for combination fairness in ODEs. We define combination parity, which quantifies differences between detectors' participation in ensemble results. Higher combination parity indicates higher combination unfairness. This fairness work differs from others, which aim for fair treatment of protected data groups. The proposed FairComb checks for significant score variations across an ensemble. A reduction approach is used to mitigate these variations. We use a baseline approach WtSR to prove that addressing this issue promotes fairer combinations. Experiments on benchmark datasets demonstrate the effectiveness of FairComb.
NFT Coin Price Prediction (non-fungible token)using K-Nearest Neighbors Method

NFT or non-fungible tokens are online certificates of ownership that can be traded, based on data units stored in digital ledgers (ledgers) belonging to blockchain technology.Non-fungible means that it cannot be exchanged and is unique.NFT has been around since 2014.But now, it is increasingly being considered as a fairly practical method for trading digital artwork or art.To buy NFT assets, it requires special coins in the form of NFT coins, which consist of various types, such as mana coins, sand, axs and other NFT coins.The NFT coins are used to process NFT purchase transactions.The movement of NFT coins over time is relatively erratic and uncertain.This NFT coin price prediction will be very useful for investors to know how the investment flow of each price works, because the price of each NFT coin will change from time to time.Through the literature study stage, interviews and viewing daily NFT coin price data where the attributes used are date, open, high, low, close volume, and marketcap.Based on the experimental KNN (K-Nearest Neighbors) method on a data set using the parameter values of K 3, 5, and 7, it can be said that the KNN model that has the best accuracy is KNN with a value of K=7 with an accuracy of 65% .The greater the value, the greater the Mean Square Error and Mean Absolute Error values.The research conducted predicts the Close value of the NFT coin price in a period of 1 day.
Uncovering Cyberbullying Themes from Unconsented Facebook Pitik Post Through Text Mining Techniques

Pitik, referred as street photography; is a colloquial phrase for taking random photographs, arbitrarily from people without permission and/or consent. Empirically, numerous comments circulating in Facebook Pitik posts containing words related to humiliation, embarrassment, shaming, stalking, and bullying. There are yet no studies conducted to confirm or prove the existence and extent of cyberbullying themes in Facebook Pitik trends. Cyberbullying is not new to social media environments and as technologies and trends change over time, the medium of cyberbullying also changes. We show in this study the proofs of the existence and the extents of cyber bullying themes in Facebook Pitik posts. In this study, we utilized methods of natural language processing – specifically text mining and emotion polarity computation, also known as sentiment analysis. Using Facepager software, 68,000 documents/comments are collected from select Facebook pages of photographers involved in the trend of Pitik posting. Results showed that the collected documents contain 26.29 % pertaining to harassment; 35.48% to flaming; and 19.45% to denigration. The existence of negative emotions is also seen from collected documents including anger, uncertainty, constraining, fear, sadness, and disgust. Findings may help policy makers to enhance the Facebook community standards making its app safer and free from issues relating to cyberbullying, especially in unconsented Pitik posts.
Learning in horizontal scaling MongoDB data sharding on windows

MongoDB is a multi-storage NoSQL database. With the fast growth of technology, the number of large databases has exponentially increased, and relational databases cannot fulfill the need for managing such large amounts of data. To address this issue, intensive research was made on the NoSQL (Not Only SQL) data management model, specifically focusing on MongoDB, and a novel approach to manage large-scale remote sensing data is proposed. Social networking sites generate massive amounts of data, and Structured Query Language (SQL) and NoSQL are predominantly used for data storage. NoSQL proves to be a superior choice for web applications due to its ability to handle large data volumes, unlike Relational Database Management Systems (RDBMS). While various data partitioning techniques such as hash and round-robin exist, they are not efficient for small transactions involving only a few tuples. The results demonstrate that the proposed method of different segmentation overcomes the limitations of conventional approaches, facilitating horizontal expansion of the database and making it more suitable for managing extensive stored data. This research provides an essential technical support for effectively managing large amounts of stored data.
Introduction to Axispot(TM), Real-time Spatio-temporal Data-management System, and Its High-speed Spatio-temporal Data-search Technology

We introduce the Axispot TM real-time spatio-temporal data-management system, which is a key component in responding to the demands for next-generation services such as communication between connected vehicles and augmented reality.We also describe a high-speed spatio-temporal data-search technology as a key function of Axispot, which can not only accumulate a large amount of data sent all at once from moving things (MTs), such as people or automobiles, but also search for MTs in a particular area and at a certain time from a large amount of data stored in a database in real time.
The Adoption of Contact-Tracing Applications and the Integration of a Health Pass

Contact tracing applications (CTAs) have been presented as important tools in the fight against the Covid-19 pandemic. In France, the government developed the ‘StopCovid' CTA which later became ‘TousAntiCovid.' This research aims at understanding the determinants of the use of this CTA and of the intention to integrate the health pass. To do that, this study focuses on the perceived value of its use based on the privacy calculus theory. A quantitative study was conducted using a sample of 779 French people. The results show that the use of the CTA and the intention to integrate the health pass are influenced, as hypothesized, by perceived value, distrust towards the government, and personal innovativeness. Perceived value is positively influenced by social and individual benefits, as well as social influence, and, to a lesser extent, negatively influenced by social risks but not by individual risks.
Long and short-range relevance context network for semantic segmentation

Abstract The semantic information can ensure better pixel classification, and the spatial information of the low-level feature map can ensure the detailed location of the pixels. However, this part of spatial information is often ignored in capturing semantic information, it is a huge loss for the spatial location of the image semantic category itself. To better alleviate this problem, we propose a Long and Short-Range Relevance Context Network. Specifically, we first construct a Long-Range Relevance Context Module to capture the global semantic context of the high-level feature and the ignored local spatial context information. At the same time, we build a Short-Range Relevance Context Module to capture the piecewise spatial context information in each stage of the low-level features in the form of jump connections. The whole network adopts a coding and decoding structure to better improve the segmentation results. Finally, we conduct a large number of experiments on three semantic segmentation datasets (PASCAL VOC2012, Cityscapes and ADE20K datasets) to verify the effectiveness of the network.
Curbing cyberbullying on Facebook: An analysis of mitigation strategies in universities in Kenya

Cyberbullying is the wilful and repeated use of electronic devices and platforms to harm others. It is becoming increasingly practised amongst university students in digital spaces. This paper examines the mitigation strategies Kenyan university students and administrators adopt to help curb cyberbullying. The study underpinning this paper applied mixed methods research approach employing both qualitative and quantitative studies. The total population of undergraduate students was 610,563, while that of the dean of students was 49. The study used stratified and information-oriented sampling techniques. The study sample comprised 4,770 undergraduate students and 24 deans of students from 16 public and eight private chartered universities in Kenya. Quantitative data was collected from the students using questionnaires. Qualitative data was also collected from the students using focus group discussions and deans of students using key informant interviews. The study's findings indicate that students responded to cyberbullying by enhancing their online security, blocking perpetrators of cyberbullying, flagging and reporting offenders, seeking counselling, and taking legal action against them. The effectiveness of these strategies varied depending on the type of cyberbullying suffered. The authors conclude that cyberbullying is a prevalent socio-technological ill affecting undergraduate students in universities in Kenya. A myriad of strategies to cope with the vice has been adopted. However, this paper recommends concerted efforts of the students, deans of students, universities, government, and society in devising and implementing comprehensive strategies for curbing cyberbullying in universities in Kenya.
Federated Opposite Learning Based Arithmetic Optimization Algorithm for Image Segmentation Using Multilevel Thresholding

Multilevel thresholding is a widely used method in image segmentation. However, the traditional methods are costly to obtain the optimal thresholds through exhaustive search. The Nature-inspired algorithm is a gradient-free optimizer that overcomes these shortcomings and generates the best thresholds with high quality and efficiency. For this purpose, this paper suggests an improved arithmetic optimization algorithm with federated opposite learning for multilevel thresholding image segmentation, namely FOL-AOA. In this method, the federated opposite learning strategy is incorporated to avoid the particles being trapped into local optimal and increase the population diversity. The cross-entropy is employed as the objective function minimized by FOL-AOA. To assess the performance of the proposed method, we considered the use of a variety of benchmark images under different threshold levels and compared them against five predecessor approaches. The obtained results manifest that FOL-AOA outperforms the comparison methods in terms of the fitness values as well as two image quality indicators such as PSNR and SSIM.
Identifying DoH Tunnel Traffic Using Core Feathers and Machine Learning Method

DNS protocol is a plaintext domain name resolution protocol, which has the risk of privacy disclosure. DNS over HTTPS (DOH) protocol is designed to encrypt DNS traffic, which solves the privacy problem. However, many network attackers use the DOH tunnel for malicious transmission. From the passive traffic, there is no obvious difference between normal DOH traffic and DOH tunnel traffic, which brings great challenges to identify them. At present, researches mainly focus on the plaintext DNS covert tunnel, but less on the encrypted DOH tunnel. In this paper, we propose DOH covert tunnel detection method based on core features and machine learning method using two steps. Firstly, we detect DOH traffic according to the threshold of features. On this basis, we use core features and machine learning methods to detect tunnel traffic in all DOH traffic. Finally, we use self collected and public datasets to verify our method. The results show that the method achieves up to 99 % precision and recall that is superior to state of the art method.