Reviewerly/exHarmony huggingface dataset:
Assessing Explainable Recommendations from Knowledge Graph-based in an International Streaming Platform

Explainable recommendations can increase users’ confidence in the results provided by recommendation systems by providing justifications of why a certain item is recommended. In this way, the use of the Knowledge Graph (KG) guarantees an optimal organization of the data enabling one to trace the relationships between entities (users, recommended items, item attributes and features, and so on). Current proposals use different approaches such as embedding, connection, and propagation to deal with common problems that persist when generating recommendations, such as cold start or data lake. However, the complexity of recommendation models seems to increase when there is a large amount of data. In this work, we propose an analysis of the applicability of different frameworks based on knowledge graphs to obtain explanatory recommendations using a large dataset from an international streaming platform, with the idea of knowing the advantages and limitations of each approach to validate if complex models should really be used to obtain the best results. Through the experimentation of RippleNet, KGCN, KGAT, ECFKG, and DSKE, we focus on dataset structure, category-based, and refinement type of each framework. To conclude, we provide details on some general points of the evaluation of all frameworks using our dataset.
A Global Future Issues in Impact on Artificial Intelligence on Intellectual Property Law

Artificial intelligence (AI) has evolved in the shape of innovation and creation of mind. It is supposed to develop the creation in the daily life in future. Newly created AI technologies has a new experience for making opportunity in arts, creativity, industrial field, and for newly invented invention. There are social economic and ethical implementation that are need to addressed and need policy to adjust accordingly. Likewise, WIPO has a public consultation measure to encourage technological invention. Intellectual property always has a relationship between the technology and their policies to adapt pace with technology and cultural changes. AI has a potential power to develop the IP, issues against the basic question of inventor, author, and owner for the infringement. We came to know that the technological development of AI has attention to the policy makers in order to ensure the development of the Intellectual property regimes, and to the greater extent to mitigate harmful social, economic and ethical implication.
Product Layout Determination System Using the Association Rules Method Using the Equivalence Class Transformation Algorithm

Competition in the business world, specifically in the sales industry, requires companies to analyze the purchases made by customers during transactions in order to find effective business strategies. In the competitive fashion industry, merchants devise marketing strategies to increase sales. One strategy that can attract consumer interest is by organizing and arranging product displays, placing them in perfect layouts that align with customers' buying habits, making it easier for them to find and purchase products. Layout arrangement significantly influences customer satisfaction and purchase intent. The algorithm used in this study is Equivalence Class Transformation (ECLAT). The data used consists of transactional data from Aufco Clothing, specifically fashion products. A total of 1041 transactions were analyzed, using variables such as order number and items sold. The data was processed using JavaScript, with a minimum support of 0.2 and a minimum confidence of 0.7, resulting in 16 rules. The rules ranged from a min. confidence of 70% to a maximum confidence of 100%, forming 6 rules with 9 combinations of items.
Exploring Pair-Aware Triangular Attention for Biomedical Relation Extraction

Biomedical relation extraction (BioRE) has become a research hotspot recently due to its crucial role in facilitating clinical diagnosis, treatment, and medical discovery. The advent of domain-specific language models, such as BioBERT and PubMedBERT customized for the biomedical domain, has revolutionized this task by fully learning contextualized entity representations and achieving remarkable performance. However, we argue that relying solely on entity-level modeling while neglecting pair-aware representations can lead to sub-optimal results, particularly in the complicated context of the biomedical literature. To address this issue, in this paper, we propose a novel Triangular Attention framework for Biomedical Relation Extraction (called TriA-BioRE) to comprehensively capture pair-aware representations in the biomedical domain. Specifically, we present a triangular attention module, including two triangular multiplications utilizing outgoing and incoming edges, and two triangular self-attention operations centered on the starting and ending nodes, respectively, together to enhance the pair-level modeling omnidirectionally for better BioRE performance. Extensive experiments on three biomedical datasets demonstrate that TriA-BioRE achieves substantially better results than its strong competitors in BioRE task. For reproducibility, our code and data are available at https://github.com/JasonCLEI/TriA-BioRE.
SPOT: Sequential Predictive Modeling of Clinical Trial Outcome with Meta-Learning

Clinical trials are essential to drug development but time-consuming, costly, and prone to failure. Accurate trial outcome prediction based on historical trial data promises better trial investment decisions and more trial success. Existing trial outcome prediction models were not designed to model the relations among similar trials, capture the progression of features and designs of similar trials, or address the skewness of trial data which causes inferior performance for less common trials.
Blockchain and Machine Learning Security for the Internet of Things: A Bibliometric Analysis

The Internet of Things has developed as a catalyst for innovation across numerous industries, from home automation to smart cities to 4IR primarily Industry 4.0. Through the internet, IoT devices interact and exchange information. Beyond its potential for development, IoT devices are divergent, ubiquitous, memory-and computational-power-constrained, making them vulnerable to a range of security and privacy risks. Blockchain, a decentralised and tamper-proof technology, has recently been introduced in the BIoT domain for securing data handling activities and inculcating trusted transactions. In anticipation of IoT System vulnerabilities, Machine Learning (ML) algorithms yield accurate findings for large and complicated databases. This study conducts a bibliometric study from the Web of Science (WoS) database from 2017 to 2022 in the area of ML-based BIoT security using Prisma guidelines. Rankings of the most productive and influential writers, articles, and sources are based on total publications and citations. Text data content analysis, Author keywords, index keywords are utilized to highlight research hotspots and future trends in the ML-Based BIoT security domain.
DEVELOPMENT OF A COMPETENT MODEL OF REQUIREMENTS FOR THE RESULTS OF TRAINING SPECIALISTS IN THE FIELD OF COMPUTER GRAPHICS AND GAME SPECIAL EFFECTS

Today, the industry of computer graphics and game special effects and its application in the development of computer games, virtual/augmented reality (Ar / Vr) occupies a leading position in all areas of life. The training of specialists for the computer graphics and animation industry is very important. However, today, in higher educational institutions there are no disciplines that consider the knowledge and skills of working with computer graphics and special effects for game development and Ar / Vr. The computer games industry is mainly self-taught, who study independently in certain areas of graphics from separate courses. There is no complete course in computer graphics for the computer games industry, Ar/Vr. We have decided to develop an electronic course on the discipline “Fundamentals of creating computer graphics and game special effects for the industry of computer games and virtual/augmented reality”. We have presented in detail the description of the development of a computer model of requirements for the results of training of specialists in the field of computer graphics and game special effects in the gaming industry. After analyzing the theory, a list of professions of people who are employed in the industry was described and their functionality was highlighted. Based on previously identified professions, using international standards, the requirements for the competencies of IT specialists in the field of computer games, computer graphics and animation have been formed. From the identified competencies, a competency-based model of requirements for learning outcomes in the subject areas of the computer graphics and animation industry in the computer games industry was synthesized.
OHD: An Online Category-aware Framework for Learning with Noisy Labels under Long-Tailed Distribution

Recently, many effective methods have emerged to address the robustness problem of Deep Neural Networks (DNNs) trained with noisy labels. However, existing work on learning with noisy labels (LNL) mainly focuses on balanced datasets, while real-world scenarios usually also exhibit a long-tailed distribution (LTD). In this paper, we propose an online category-aware approach to mitigate the impact of noisy labels and LTD on the robustness of DNNs. First, the category frequency of clean samples used to rebalance the feature space cannot be obtained directly in the presence of noisy samples. We design a novel category-aware Online Joint Distribution to dynamically estimate the category frequency of clean samples. Second, previous LNL methods were category-agnostic. These methods would easily be confused with noisy samples and tail categories’ samples under LTD. Based on this observation, we propose a Harmonizing Factor strategy to exploit more information from the category-aware online joint distribution. This strategy provides more accurate estimates of clean samples between noisy samples and samples with tail categories. Finally, we propose Dynamic Cost-sensitive Learning, which utilizes the loss and category frequency of the estimated clean samples to address both LNL and LTD. Compared to extensive state-of-the-art methods, our strategy consistently improves the generalization performance of DNNs on several synthetic datasets and two real-world datasets.
C-DNN V2: Complementary Deep-Neural-Network Processor With Full-Adder/OR-Based Reduction Tree and Reconfigurable Spatial Weight Reuse

In this article, we propose a Complementary Deep-Neural-Network (C-DNN) processor V2 by optimizing the performance improvement from combination of CNN and SNN. C-DNN V1 showcased the potential for achieving higher energy efficiency by combining CNN and SNN. However, it encountered 5 challenges that hindered the full realization of this potential: Inefficiency of the clock gating accumulator, imbalance in spike sparsity across different time-steps, redundant cache power stemming from temporal weight reuse, limited performance of the SNN core for dense spike trains, and nonoptimal operation resulting from tile-based workload division. To overcome these challenges and achieve enhanced energy efficiency through the CNN-SNN combination, C-DNN V2 is developed. It addresses these challenges by implementing a Full-Adder/OR-based reduction tree, which reduces power consumption in the SNN core under high spike sparsity conditions. Additionally, it efficiently manages spike sparsity imbalances between dense and sparse SNN cores by integrating them simultaneously. The proposed reconfigurable spatial weight reuse method decreases the number of redundant register files and their power consumption. The spike flipping and inhibition method facilitate efficient processing of input data with high spike sparsity in the SNN core. Furthermore, fine-grained workload division and a high sparsity-aware CNN core are introduced to ensure optimal processing of each data in the domain with the highest energy efficiency. In conclusion, we propose the C-DNN V2 as an optimal complementary DNN processor, delivering 76.9% accuracy for ImageNet classification with a state-of-the-art energy efficiency of 32.8 TOPS/W.
Smart Island Lemukutan Through WebGIS Based Benthic Habitat Data Collection

Benthic habitats provide a variety of functions, both ecological and economic, for life in coastal areas and small islands. Smart islands or smart islands are currently being implemented in a number of small island developing countries with one of the challenges being technology based. One technology that can be used in management, including monitoring and supervision of resources, is the Geographic Information System (GIS). The synergy of GIS data and the help of the latest multimedia software technology is necessary to help display an interactive and easily accessible, in this case, webGIS technology. This research was carried out on Lemukutan Island which consists of 6 research stations, each of which has a distribution of coral reef location points based on coordinates. Based on the results of the analysis of the percentage of benthic habitats in the study locations ranging from bad to good categories. Furthermore, the data from field observations and analysis results are displayed on the Lemukan Island smart island website and can be accessed at https://webgissmartisland.com. Regular monitoring of coral reefs is very important and needs to be done to help input data for Smart Island Lemukutan so that it becomes a catalyst in realizing a smart island ecosystem in West Kalimantan Province by providing benthic habitat maps via webgis services and actualizing technological development in coastal areas and small islands. smart island based through the smart destination concept by integrating benthic habitat information via WebGIS services.
An Improved Genetic Algorithm Based on Chi-Square Crossover for Text Categorization

Text classification has gained importance due to the quickly rising content volume. During the text categorization process, it is necessary to complete tasks including extracting relevant information from different viewpoints, reducing the high feature space, and improving efficiency. Many studies on feature selection have been conducted, but increasing efficiency by reducing features remains a challenge. To evaluate its effectiveness, the Amazon review dataset is used in this proposed work. A chi-square-based enhanced genetic algorithm (CSEGA) approach is used in this paper to achieve the purpose of the study. The execution of the task includes the preprocessing task, followed by the optimization process for the selection of the optimal features. The unique crossover and selection process have made this work superior to other algorithms. With 93.0% accuracy, 94.5% precision, 90.5% recall, and a 92.47% F-score, the proposed approach outperforms other state-of-the art algorithms with fewer features.
ArCyb: A Robust Machine-Learning Model for Arabic Cyberbullying Tweets in Saudi Arabia

The widespread use of computers and smartphones has led to an increase in social media usage, where users can express their opinions freely. However, this freedom of expression can be misused for spreading abusive and bullying content online. To ensure a safe online environment, cybersecurity experts are continuously researching effective and intelligent ways to respond to such activities. In this work, we present ArCyb, a robust machine-learning model for detecting cyberbullying in social media using a manually labeled Arabic dataset. The model achieved 89% prediction accuracy, surpassing the state-of-the-art cyberbullying models. The results of this work can be utilized by social media platforms, government agencies, and internet service providers to detect and prevent the spread of bullying posts in social networks.
Usability Testing of Memorable Word in Security Enhancing in e-Government and e-Financial Systems

Most applications increase their security by adding an extra layer to the login process using two-factor authentication (2FA). In Saudi Arabia, One-Time Password (OTP), which is 2FA, is the most common method used as users log in to their accounts. However, some issues have emerged with using OTP as 2FA; these issues from previous research were investigated in the study. Also, the study proposed a new method of account authentication, which is a Memorable Word (MW). MW is the second and short password in which the user enters a certain number of characters instead of the whole password. The study conducted usability testing to compare two 2FA methods, OTP and MW. The study included 60 participants logged into a simulated website using both authentication methods. Then, all participants have to complete the questionnaire. The collected data analyses showed a favourable opinion of the MW method.
Group Intelligence Recommendation System based on Knowledge Graph and Fusion Recommendation Model

The challenge of how to further improve the accuracy of the system's recommendations in a data-limited environment is crucial as the use of group intelligence recommendation systems in everyday life increases. Through the fusion of different types of auxiliary information, this study develops a multi-feature fusion model based on the conventional recommendation model by introducing knowledge graphs. It also considers the homogeneity of push results caused by graph convolutional network smoothing when using knowledge graphs, and designs a fusion label propagation algorithm and graph convolution. The multi-feature fusion model had a maximum hit rate of over 80% and a normalised discount gain of up to 43% running time much lower than the conventional graph convolution recommendation model in the representation dimension interval [2, 32], while the fusion label propagation algorithm and graph convolution network model maintained a hit rate and normalised discount gain higher than the conventional model by 2 to 1 under 10 consecutive epochs. With a hit rate and normalised discount gain 2 to 10 percentage points higher than the conventional model, the coverage rate increased to 49.8%. This study is useful for research on group intelligence recommendation systems and can serve as a technical guide for improving the ability of group intelligence systems to make recommendations quickly.
Statistical Language Model-based Analysis of English Corpora and Literature

Despite widespread use of statistical language models in language processing, their ability to process natural languages is not advanced and they struggle to effectively capture linguistic information. Furthermore, there is a lack of automatic processing models in the field of natural language processing. In order to address these issues, and Improve the processing ability of statistical language models for English language a statistical language model optimization algorithm has been proposed. This algorithm is based on an improved resorting algorithm and is specifically applied to process English literary texts. Experimental results indicate that the proposed algorithm outperforms the N-gram algorithm in a majority of texts, with a maximum accuracy improvement of 14.5%. Additionally, in terms of the grammar analysis model, there is a high level of consistency between the model's scoring and the expert manpower scoring, as reflected by a correlation coefficient of 0.7893. This high level of consistency between the grammar analysis model and expert analysis results holds significant importance for the advancement of natural language processing.
Compression Analysis of Hybrid Model Based on Scalable WDR Method and CNN for ROI-based Medical Image Transmission

The image compression techniques are the fast-growing methods and have developed on large scale. Among them, wavelet-based compression methods are most promising and efficient techniques widely used in the field of medical image processing and transmission. The compression techniques are treated as lossy or lossless models and these can be applied on the medical images considering different situations. The medical image parts are separated into two regions. The central part of the image is treated as core region called region of interest (ROI) and others are treated as non-ROI. ROI based coding techniques are considered as most important in the medical field for efficient transmission of clinical data. The proposed method focuses on these concepts. The ROI parts considered are either smooth or textured regions. These are extracted using a segmentation method called singular value decomposition (SVD) method. An efficient run length coding method called wavelet difference reduction method (WDR) with region growing approach is used to code the extracted ROI part after applying 5/3 based integer wavelet transform. The remaining parts called non-ROI part or background artifacts are coded using Convolution Neural etwork (CNN) method. The proposed method is also restructured as layered structure to achieve adaptive scalable property and named as scalable WDR-CNN (SWDR-CNN) method. The proposed SWDR-CNN method has been evaluated using rate distortion metrics such as Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The coding gains in terms of PSNR values of SWDR-CNN method has been analysed and compared to popular scalable algorithm like S-SPIHT. The SWDR-CNN method has achieved better coding gain from 0.2 dB to 6 dB in terms of PSNR values. Hence, it is proved that proposed model can be used to code the ROI of images and has applications in the field of medical image data coding and transmission.
PEDAGOGICAL TESTING IN THE UNIVERSITIES OF UKRAINE AND THE USA

Problem. The article analyzes the current state of pedagogical testing in university education in Ukraine and the USA. Increasing the effectiveness of the organization of test control and assessment of knowledge, skills, and abilities of higher education students is becoming urgent.The purpose of this research is to substantiate the theoretical foundations of the effectiveness of pedagogical testing in universities in Ukraine and the United States.Research methodology based on general principles of philosophy, basic modern provisions of pedagogical science, psychology and reflects the relationship of methodological approaches to the study of the process of pedagogical testing and its use in universities of Ukraine and the USA.Object of research: educational process in universities of Ukraine and the USA.Scientific novelty. It is substantiated that the effectiveness of pedagogical testing is influenced by the state of the university's informatization, as well as: provision of the educational process with electronic textbooks, manuals, methodical recommendations; competence of professors and teaching staff in the development and use of tests; willingness to study and implement advanced pedagogical experience; logical construction of the test control system in the pedagogical process of the university; readiness of applicants to work with tests.Conclusions. Pedagogical testing is a modern process of assessment and control of knowledge, abilities and skills of higher education students. The application of pedagogical testing within the educational process of a separate university both in Ukraine and in the USA improves the process of effective assessment and control. Test control allows you to obtain unbiased knowledge about the level of initial achievements of higher education applicants, and therefore, to establish the quality of training and the quality of education of a future specialist. In the USA, the effectiveness of pedagogical testing is influenced by the state of the university's informatization, in particular, multimedia and telecommunication resources. The theoretical justification of pedagogical testing is based on the principles of: systematicity of its conduct; the openness of its purpose; objectivity and comprehensibility of its results; effectiveness of consequences.