Reviewerly/exHarmony huggingface dataset:
Analysing Online Review by Bank Employees: A Predictive Analytics Approach

Publicly available data on the web is a rich and important resource for generating valuable insights about our world. In this paper, we present the analysis of bank employee online review data obtained from an online platform that collects anonymous employee reviews about the companies they work(ed) with. The feature of anonymity here is important, it helps (especially existing) employees to review their employers freely, without fear of repercussions. Employers can also capitalise on this platform to better understand their employees’ opinions on the company’s performance. However, there are several common issues associated with the data found on such platforms. These include: (i) relatively small number of reviews associated with each employer, (ii) multicollinearity among predictors, and (iii) missing values in the inputs. We propose a solution framework to address these issues. Firstly, we show that a transfer learning approach can help to augment the data size by combining data from different employers. Secondly, we address the missing value issues, which include the use of a rather uncommon method known as missing value replacements using a proxy variable. Finally, we apply a decision tree approach to build reasonably reliable model despite the presence of multicollinearity in the predictors. Our results show that all these solutions put together help to generate a more robust and comprehensible model. In addition, the results also show that augmentation of data is key to address some of the fundamental issues typically encountered in this type of online review data. Finally, we believe that the presented solution can be adapted or extended to other analytics projects that involve the analyses of online review data.
Sports Analytics Using Probabilistic Model Checking and Deep Learning

Sports analytics encompasses the use of data science, AI, psychology, and IoT devices to improve sports performance, strategy, and decision-making. It involves collecting, processing, and interpreting data from various sources such as video recordings and scouting reports. The data is used to evaluate player and team performance, prevent injuries, and help coaches make informed decisions in game and training. We adopt Probabilistic Model Checking (PMC), a method commonly used in reliability analysis for complex safety systems, and explain how this method can be applied to sports strategy analytics to increase the chance of winning by taking into account the reliability of a player’s specific sub-skill sets. This paper describes how we have integrated PMC, machine learning, and computer vision to develop a new and complex system for sports strategy analytics. Finally, we discuss the vision of a new series of international sports analytics conferences (https://formal-analysis.com/isace/2023/).
Report of the Core Role of the Professional Librarian in Technical Services Interest Group, Core Virtual Interest Group Week, March 2023

Click to increase image sizeClick to decrease image size Disclosure statementNo potential conflict of interest was reported by the author(s).
Improvement speech culture in school

A thorough study of the native language learner umdidrdeba mind, umushadeba certain view of things, events, and actions necessary for developing human life habits, is being developed as an educated person. Speech Development orderly manner requires knowledge of the psychophysiological nature. Native language teachers not only to resolve these issues, but also to teachers of all subjects, as the speech not only contributes to the development of the mother tongue, but in general, the knowledge of different skills. Mastered in elementary science issues will depend on how the student will have learned how perfectly the mother tongue, the language you are thinking.
Lightweight Human Motion Recognition Method with Multiscale Temporal Features

Abstract To address the problems of the large size of human motion recognition models based on deep learning and insufficient mining of data features and thus low recognition accuracy, a lightweight human motion recognition algorithm based on multi-scale temporal features is proposed, the algorithm automatically extracted features through a multiscale feature fusion model. After, the integrated features are modeled by an improved time convolution network (TCN). In the TCN network structure, In the TCN network structure, the depthwise separable convolution is used instead of the normal convolutional layer to reduce the computational complexity, and the Leaky ReLU activation function is used instead of the ReLU function to improve the training efficiency. The experiments are based on the WISDM public dataset. finally achieve fast real-time recognition of actions, and structural and parametric optimization is performed through experiments to effectively improve the accuracy of results, and the final accuracy rate reaches 99.06%. In comparison with other methods, this method can reduce the model volume while maintaining a high accuracy rate.
Investigating the Effectiveness of Learning Data Structure and Algorithm (DSA) Programming using Augmented Reality

The COVID-19 pandemic has paved the way for rapid technological advancements, bringing about significant changes in teaching and learning methods. Augmented Reality (AR), an emerging technology, has emerged as an effective approach to improve the user experience and comprehension by providing a realistic view of concepts data structure objects. Educators have been actively working to transition from traditional or conventional learning styles to the process of learning using augmented reality application in order to improve the effectiveness of the level education system. This transition aims to improve individual students' learning, motivation, and facilitate more effective teamwork and group cooperation. One crucial course for computer science students is Data Structure and Algorithm (DSA), which serves as a prerequisite for several other courses. DSA poses challenges as it provides crucial concepts for programmers in efficiently handling data. A solid foundation in data structure and algorithm influences programmers able to efficiently write and develop cost for the effective solutions. However, many computer science students, especially those lacking a strong programming background, struggle to grasp the concepts of algorithm in DSA. This difficulty in understanding DSA concepts often leads to apprehension among students, making them hesitant to pursue a career as programmers after graduation. This research aims to address these challenges and has identified several objectives to achieve its goal. The objectives include analyzing and designing application features that attract students to learn DSA in an interactive manner, developing an AR application that facilitates user-application interaction, and investigating the effectiveness of the AR application in terms of usability, acceptability during the DSA learning process.
Lectometry step by step

Abstract The approach to lexical variation research demonstrated in this book rests crucially on the concept of an ‘onomasiological profile’, the set of synonymous names for a concept, differentiated by their relative frequency. This chapter introduces a workflow for the identification of onomasiological profiles that capitalizes on what was shown in Chapter 6: when we analyse near-synonyms in the same vector space, we can identify the areas where their sense clusters overlap, that is, where they are interchangeable. The distributional workflow for determining onomasiological profiles consists of a number of steps, involving both type-based and token-based vector representations: selecting an initial set of near-synonyms, setting the parameters that demarcate the range of models to be considered, pruning the models by removing out-of-concept clusters, and potentially also selecting models.
SCARE of Secret Ciphers under Rough Leakage Model

Most previousside-channel analysis-based reverse engineering (SCARE) methods were based on collision attack. However, the collision-based methods generally need noise-free traces and can hardly be verified by realistic implementations. This paper proposes a novel SCARE, which is named as OSHDG-SCARE, targeting on secret S-box. We define a graph as OSHDG (one-step HD graph) to recover the parameters of any secret S-box. Due to the redundancy of OSHDG, the multiple paths can filter the correct results and improve the success rate of the reverse method even when the samples deviate from leakage model. We classify the deviation of side channel signal as two types, which are the gap between real leakage and leakage model, and measurement noise. Experiments are performed on real power traces satisfying the inaccurate HW (Hamming Weight) leakage model from a software implementation of AES-like cipher. OSHDG-SCARE recovers the secret S-box when the deviation rate of traces is 1.45%.
Project Overview

With the advancement of modern information technology and its widespread application, software has evolved into the soul of modern computer systems. It is at the heart of national informatization construction and has become a powerful driver of the development of productive forces in contemporary society and the advancement of human civilization. Moreover, software plays an important role in the national economy, social development, and national defense construction.
Major Research Achievements

Software trustworthinessSoftware trustworthiness is an essential topic in software science. The evolution of trustworthy softwareTrustworthy software research is closely related to the evolution of software paradigm, development method, operation support, and metric evaluation.
Energy-Efficient VM Allocation and Migration Approach Using Swarm Intelligence Algorithm in Cloud Computing Environment

The cloud computing environment is a demanding platform to perform large-scale business operations for different clients. Several studies have been carried out in order to minimize the power consumption in data centres. Intelligent VM allocation and migration is an effective approach to minimize unwanted CPU utilization without violating the SLA. The modified best fit decreasing (MBFD) has been employed to allocate the VM to the best available host in an efficient manner. In this paper, a novel swarm intelligence-based Grasshopper optimization technique has been proposed in order to improve the VM selection. The proposed framework provides a significant solution in the cloud data centre for minimizing overall energy consumption. The obtained results using the proposed model are highly efficient in terms of power consumption and SLA violations.
A Construction of Three Party Post Quantum Secure Authenticated Key Exchange for Mobile Users

A three-party authenticated key exchange protocol enables two entities to agree on a session key with the help of a dedicated server through an insecure channel. Lattice based cryptography plays a very important role in authentication and key exchanges that protects against the threat of quantum attacks. However, it is not easy to design quantum resistant password based three-party protocol due to the high demand for security requirements and the limited resources nature of mobile devices. In this article, we have proposed a new post quantum three party key exchange based on a variant of lattice assumption, the ring learning errors. The protocol ensures security against impersonation attack, stolen smartcard attack, password guessing attack, and other existing attacks. In authentication phase, the protocol have used lattice based cryptography that plays a very important role in authentication and key exchanges that protects against the threat of quantum attacks. The proposed protocol ensures both securities against quantum attacks and efficiency due to simple algebraic operations that are polynomial addition and multiplications.
CheckPhish: Leveraging A Machine Learning Approach for Detecting Phishing Websites

Phishing is a cybercrime in which an attacker creates a fake website that appears to be a legitimate one to lure individuals into providing sensitive information such as credentials like usernames and passwords, bank account details, credit card details, and other personal details. With the increasing use of the internet for various purposes, phishing attacks have become a major concern for individuals, businesses, and organizations. Although there are several ways for the detection of phishing websites, phishers have found techniques to get around these methods. Machine learning is one of the finest methods for detecting these harmful activities. This is done so that machine learning algorithms can identify the characteristics that most phishing attacks have in common. This study provides machine learning-based methods for identifying phishing websites. The proposed approach uses a combination of features extracted from the website’s URL to train a machine-learning model that can classify a website as either safe or unsafe. To do that, we trained and tested eight machine-learning algorithms before selecting the best one by comparing accuracy, precision, recall, and F1 score. We have achieved an accuracy of 86.2% with the XGBoost classifier, which demonstrates how well our method works in identifying phishing sites.
Analysis of Clustering Effects In Cloud Workload Forecasting

Cloud workload forecasting is becoming progressively more crucial as cloud computing gains greater omnipresence. Due to the increased user service requests the availability of finite resources in cloud computing consistently poses a significant challenge in resource management. Our aim is to analyze the need for clustering in workload forecasting in cloud computing to provide better resource management. We use various clustering methods and find the method that outperforms the others. We compare the performance metrics of various forecasting models with clustering vs the forecasting models without clustering to analyze the effects of clustering. From our results obtained we conclude that performing clustering on cloud workload dataset improves the prediction efficiency of the forecasting models
A Validation System for Academic Records based on Blockchain

Academic certificate forgeries and the lack of resources to tackle them is a problem that implies big costs to society. It harms the figure of the certificates and impairs the trust of academic institutions. The solution proposed in this paper is aimed at recording and verifying the academic merits through a decentralized application or Dapp. Such an application is supported by a smart contract deployed in the Ethereum blockchain with a simple frontend or interface. The application makes use of a decentralized storing system based on IPFS in order to consume the data that are not managed by the blockchain. To assess the performance of the developed system, the latency of its most common operation (read operations) is measured as the number of requests per second increase. The obtained results show that the system is really fast, being some nodes able to respond to more than 1,000 requests in less than one millisecond
Data- and management-driven metaverse research

The metaverse has become a very important phenomenon in society because of the emergence of new technologies. The widespread adoption of the metaverse has generated significant discussions about the challenges and opportunities it presents. We invited three panelists to present their personal viewpoints on the metaverse in the 2022 AIS-SIG-ISAP Workshop on Information Systems in Asia-Pacific (ISAP). The discussion indicated that metaverse research is being conducted. Furthermore, it highlighted new research directions and offered research topics related to the advantages or disadvantages of the metaverse. The proposed research topics will offer new insights to academics and practitioners.
Less Is More: Understanding Network Bias in Proof-of-Work Blockchains

Blockchains are becoming increasingly important in today’s Internet, enabling large-scale decentralized applications with strong security and transparency properties. In a blockchain system, participants maintain and update the server-side state of an application by appending data as blocks onto an immutable, distributed ledger through a consensus protocol within a peer-to-peer network. There has been a significant increase in profit in mining blocks. For instance, Bitcoin miners currently receive over USD 200,000 per mined block. An essential determinant of these rewards is the time it takes to disseminate newly mined blocks across the network. This paper addresses the challenge of optimizing mining rewards by exploring topology design in a wide-area blockchain network utilizing a Proof-of-Work consensus protocol. We show that under low block times, the geographical location of a miner critically impacts the number of successful blocks mined by the miner. We also show that a miner may improve its success rate by increasing its connectivity to the network. However, contrary to the general wisdom that a faster network is always better for a miner, we show that increasing network connectivity (e.g., by adding more neighbors) is beneficial to a miner only up to a point after which the miner’s rewards degrade. This is because when a miner improves its connectivity, it inadvertently also aids other miners in increasing their connectivity. We also present a network-level collusion attack in which a miner can increase its block success rate by becoming part of a tightly connected cluster. Here too, we observe that the mining gains obtained increase with cluster size only up to a point, and decrease thereafter. Our findings highlight that the network topology is a key variable affecting miner performance in PoW blockchains that must not be overlooked. We demonstrate our observations via detailed simulations modeled using real-world measurement data.
Human-centered design of a smartphone-based self-test for HIV viral load monitoring

Abstract Background/Objective: HIV viral load self-testing could enable people living with HIV (PLHIV) to monitor their viral suppression status more easily, potentially facilitating medication adherence and safe behavior decision-making. Smartphone-based viral load testing innovations have the potential to reach resource-limited and vulnerable communities to address inequities in access to HIV care. However, successful development and translation of these tests requires meaningful investigation of end-user contexts and incorporation of those context-specific needs early in the design process. The objective of this study is to engage PLHIV and HIV healthcare providers in human-centered design research to inform key design and implementation considerations for a smartphone-based HIV viral load self-testing device prototype in development. Methods: Semi-structured in-depth interviews were conducted with PLHIV ( n = 10) and HIV providers ( n = 4) in Indiana, a state with suboptimal viral suppression rates and marked disparities in access to HIV care. Interview guides were developed based on contextual investigation and human-centered design frameworks and included a demonstration of the device prototype with feedback-gathering questions. Results: Thematic analysis of interview transcripts revealed important benefits, concerns, and user requirements for smartphone-based HIV VL self-testing within the context of PLHIV lived experience, knowledge, and barriers to care in Indiana. Conclusion: End-user needs and preferences were identified as key design specifications and implementation considerations to facilitate the acceptability and inform ongoing development and ultimately real-world translation of the HIV VL monitoring device prototype.
SLR-YOLO: An improved YOLOv8 network for real-time sign language recognition

This study proposes a method to help people with different degrees of hearing impairment to better integrate into society and perform more convenient human-to-human and human-to-robot sign language interaction through computer vision. Traditional sign language recognition methods make it challenging to get good results on scenes with backgrounds close to skin color, background clutter, and partial occlusion. In order to realize faster real-time display, by comparing standard single-target recognition algorithms, we choose the best effect YOLOv8 model, and based on this, we propose a lighter and more accurate SLR-YOLO network model that improves YOLOv8. Firstly, the SPPF module is replaced with RFB module in the backbone network to enhance the feature extraction capability of the network; secondly, in the neck, BiFPN is used to enhance the feature fusion of the network, and the Ghost module is added to make the network lighter; lastly, in order to introduce partial masking during the training process and to improve the data generalization capability, Mixup, Random Erasing and Cutout three data enhancement methods are compared, and finally the Cutout method is selected. The accuracy of the improved SLR-YOLO model on the validation sets of the American Sign Language Letters Dataset and Bengali Sign Language Alphabet Dataset is 90.6% and 98.5%, respectively. Compared with the performance of the original YOLOv8, the accuracy of both is improved by 1.3 percentage points, the amount of parameters is reduced by 11.31%, and FLOPs are reduced by 11.58% .
A security analysis of SOHO embedded systems

Due to many unusual constraints imposed on their design, embedded systems carry a large set of challenges related to feature implementation and security. Many systems extant today prove the extreme difficulty of designing a secure software architecture. Nevertheless, IoT systems are crucial in many environments ranging from large-scale, tightly secured commercial deployments to relatively unprotected home networks.
CONTROVERSIES OF ANTON MAKARENKO'S PROFESSIONAL SELF-ACTUALIZATION

This article is devoted to the general characteristics of the super-complex and rather multipolar personal professiogenesis of A. Makarenko. Particular attention is paid to the analysis of professional crisis aspects of the process: the complexity of motivational mechanisms of professional self-actualization of young A. Makarenko, the crisis of his professional self-determination, unrealized educational aspirations, opposition of talent and skill, professional readiness crisis, unfavorable social conditions and lack of professional freedom. It reveals the peculiarities of A. Makarenko's formal and informal educational activity, the complexity of the formation of motivational mechanisms of his personal and professional self-actualization, analyzes the manifestations of destructive factors of his professional activity. It concludes that the leading circumstance of A. Makarenko's professional self-actualization were many years of activity and self-improvement in the field, which did not meet the basic direction of his professional preferences. However, it was A. Makarenko's professional activity as a teacher-educator that determined the content of his further literary work, and the innovative essence, intensity and drama of this activity ensured his great success as a professional writer. A certain paradox of A. Makarenko's professional genesis is that the pedagogical profession, to which he devoted his life only due to objective circumstances, nevertheless became the sphere and condition of his ultimate professional self-actualization – both as an educator-practitioner and as a pedagogical thinker, as an educational manager, and as a writer. Giving these facts, we consider it necessary to emphasize that each of these professional roles of A. Makarenko in its development was not only an important motivating factor for all his other essences as a professional, but certainly generated new forms for expressing their perfection.