Reviewerly/exHarmony huggingface dataset:
Decision-Making Information System for Academic Careers in Congolese Universities: From Analysis to Design of a Data Warehouse

Universities collect and generate a considerable amount of data on students throughout their academic career. Currently in South Kivu, most universities have an information system in the form of a database made up of several disparate files. This makes it difficult to use this data efficiently and profitably. The aim of this study is to develop this transactional database-based information system into a data warehouse-oriented system. This tool will be able to collect, organize and archive data on the student’s career path, year after year, and transform it for analysis purposes. In the age of Big Data, a number of artificial intelligence techniques have been developed, making it possible to extract useful information from large databases. This extracted information is of paramount importance in decision-making. By way of example, the information extracted by these techniques can be used to predict which stream a student should choose when applying to university. In order to develop our contribution, we analyzed the IT information systems used in the various universities and applied the bottom-up method to design our data warehouse model. We used the relational model to design the data warehouse.
Composing with Analog Tape in a Post-Digital Age

This essay explores the practical and theoretical dimensions of composing with analog tape in a post-digital age. Its point of departure is the belief that, instead of dismissing them as outmoded and impractical, we ought to embrace analog devices as invaluable tools for exploring the liminal realm in which encounters between concrete reality and abstract form take place. By working on sound as continuously varying electrical voltage as opposed to binary units of discrete value, a variety of compositional possibilities disclose themselves, particularly in relation to techniques of permutational variation. By reflecting on such techniques as implemented with analog rather than digital tools, crucial aesthetic insights emerge. The question of analog timbre is likewise explored, specifically in terms of aesthetic properties that testify to the unique physical origins of any given sound. Phenomenology as conceived and practiced by Husserl serves as a framework for these investigations. Its distinctive tools and methods enable exploration of the metaphysical dimensions of perceptual facts uncovered during encounters with analog and digital audio devices.
Multi-Modal Human Action Segmentation Using Skeletal Video Ensembles

Beyond traditional surveillance applications, sensor-based human action recognition and segmentation responds to a growing demand in the health and safety sector. Recently, skeletal action recognition has largely been dominated by spatio-temporal graph convolutional neural networks (ST-GCN), while video-based action segmentation research successfully employs 3D convolutional neural networks (3D-CNNs), as well as vision transformers. In this paper, we argue that these two inputs are complementary, and we develop an approach that achieves superior performance with a multi-modal ensemble. A multi-task GCN is developed that can predict both frame-wise actions as well as action timestamps, allowing for the use of fine-tuned video classification models to classify action segments and achieve refined predictions. Symmetrically, a multi-task video approach is presented that uses a video action segmentation model to predict framewise labels and timestamps, augmented with a skeletal action classification model. Finally, an ensemble of segmentation methods for each modality (skeletal, RGB, depth, and infrared) is formulated. Experimental results yield 87% accuracy on the PKU-MMD v2 dataset, delivering state-of-the-art performance.
Dimensionality Reduction Algorithms in Machine Learning: A Theoretical and Experimental Comparison

The goal of Feature Extraction Algorithms (FEAs) is to combat the dimensionality curse, which renders machine learning algorithms ineffective. The most representative FEAs are investigated conceptually and experimentally in our work. First, we discuss the theoretical foundation of a variety of FEAs from various categories like supervised vs. unsupervised, linear vs. nonlinear and random-projection-based vs. manifold-based, show their algorithms and compare these methods conceptually. Second, we determine the finest sets of new features for various datasets, as well as in terms of statistical significance, evaluate the eminence of the different types of transformed feature spaces and power analysis, and also determine the FEA efficacy in terms of speed and classification accuracy.
Retrospective Review of Higher Education Quality: Some Parallels Between Past and Present

Studies of education quality are currently a matter of particular relevance and importance. It would seem that the quality of education should interest the scientific community in different periods (historical periods) of time, however, as practice shows, these studies have been carried out mainly in recent times. The article describes some arguments on the quality of education based on the available historical data on the development of education in Russia. Given that it is not possible to consider in detail all historical periods in different countries within the framework of one article, a summary of the key points influencing the development of education in Russia before the revolutionary events (during a certain period of the reign of the Imperial House of Romanov) was made in a small comparison with the modern perception of the quality of education. Some factors influencing the quality of education in the period from the late 19th to early 20th centuries have been identified. The conclusion is made on changing/unchanging factors affecting the quality of education in modern reality in comparison with past experience in the development of the education system.
The Legal Education Culture of Young People: A Projection of the Future

The article presents the basic patterns of development of future generations' education, which now belong to the age category of children and young people. The goals of the future educational paradigm, which is to overcome the deformations of the youth's legal consciousness, to raise its spiritual development, to acquire legal knowledge, to develop its legal consciousness and legal culture, are determined. It substantiates the main direction and at the same time the global goal of education of the future, to which there is a culture of peace, which is possible only in the legal continuum of international interaction of states and people that now belong to the age category of children and youth. Factors of influence on the education of legal culture of youth, among which the level of development of the economy, quality of life of the population, and its relation to social transformations are generalized. This article highlights the role of the activity of all authorities, mass media, and their influence on the consciousness, will, behavior, and beliefs of citizens. The authors of the article also take into account such factors as the experience of the population, its attitude to culture, traditions, habits, way of life, social relations.
Quantum Computers: The Need for a New Cryptographic Strategy

The emerging technology of quantum computing is a double-edged sword, prime for increased computing efficiency but also constituting a looming threat against many currently deployed cryptographic algorithms. In turn, the security guarantees of wider systems that rely on such security foundations are also at risk. Planning and preparation against a quantum threat is increasingly urgent for critical systems as well as systems of systems. Transitioning such systems for quantum resistance implies more than dropping in suitable algorithms, and spans decision points from the type of solution and security that is needed, to hardware and software design choices, data sensitivity lifespan, and system dependencies. This chapter provides an overview of why quantum computers pose a security threat, discusses currently proposed solutions to it, and takes a close look at timelines and system considerations for a quantum-resistance transition.
A Postquantum Linkable Ring Signature Scheme from Coding Theory

Linkable ring signatures (LRSs) are ring signatures with the extended property that a verifier can detect whether two messages were signed by the same ring member. LRSs play an important role in many application scenarios such as cryptocurrency and confidential transactions. The first code-based LRS scheme was put forward in 2018. However, this scheme was pointed out to be insecure. In this paper, we put forward a code-based LRS scheme by constructing a new Stern-like interactive protocol and prove that it meets the security requirements of LRSs. We also give the specific parameters and the performance on the platform of our scheme.
Cryptocurrencies: Illegal in Pakistan?

This paper considers the question of cryptocurrency legality in Pakistan, arguing that, on the one hand, there are sound reasons for banning crypto in the country; but on the other hand, there are specific caveats that would need to be addressed before such a ban would be instated. The four caveats mentioned in the paper include: rapid currency depreciation, erratic central bank behavior, inflationary conditions, and innovation potential. The findings of the paper conclude that providing a proper mainstream economy is a prerequisite for a ban on cryptocurrencies in an emerging market.
Information-Theoretic Generalization Analysis for Topology-aware Heterogeneous Federated Edge Learning over Noisy Channels

With the rapid growth of edge intelligence, the deployment of federated learning (FL) over wireless networks has garnered increasing attention, which is called Federated Edge Learning (FEEL). In FEEL, both mobile devices transmitting model parameters over noisy channels and collecting data in diverse environments pose challenges to the generalization of trained models. Moreover, devices can engage in decentralized FL via Device-to-Device communication while the communication topology of connected devices also impacts the generalization of models. Most recent theoretical studies overlook the incorporation of all these effects into FEEL when developing generalization analyses and ignore designing algorithms to enhance the generalization of models based on their analysis. In contrast, our work presents an information-theoretic generalization analysis for topology-aware FEEL with data heterogeneity and noisy channels. Additionally, we propose a novel regularization method called Federated Global Mutual Information Reduction (FedGMIR) to enhance the performance of models based on our analysis. Numerical results validate our theoretical findings and provide evidence for the effectiveness of the proposed method.
Predicting satisfaction with democracy in Brazil considering data form an opinion survey

Purpose – This article compared machine learning algorithms in the context of satisfaction with democracy in Brazil. The models were trained with data from the Latinobarómetro survey, a private non-profit institution. Theoretical foundation – The Support Vector Classifier (SVC), Random Forest (RF), and Artificial Neural Networks (ANN) classification techniques were described, followed by evaluation metrics, such as accuracy, precision, recall, f1-score and the area under a receiver operating characteristic (auc-roc). Methodology – The data set was cleaned and the questionnaire was reduced to variables related to the local democracy index (IDL). Then, attribute transformations were performed, hyperparameters were analyzed and subsets with different class balances were created to evaluate the performance of the classifiers in different scenarios. Also, the attributes that most contributed to satisfaction with democracy were analyzed. Results – The best classifier was RF for the class of those dissatisfied with democracy, however, the ANN and SVC techniques obtained better results in the class of satisfied individuals. Evaluating the most important attributes for satisfaction with democracy, it was identified that they are related to the country's economic situation and political and governmental issues. Research implications – The models created were mainly able to identify people dissatisfied with democracy. The most important variables in this context were economy performance, government, political positioning, and democracy. This indicates directions for future studies and enables the development of strategies to change the perception of dissatisfied individuals. Originality/Value – Exploring data on democracy from the Latinobarómetro using machine learning techniques in the context of Brazil. Keywords: Machine learning; Democracy; Classification; Satisfaction.
An Energy-Aware Resource Management Strategy Based On Spark and YARN in Heterogeneous Environments

Apache Spark is a popular framework for processing big data. Running Spark on Hadoop YARN allows it to schedule Spark workloads alongside other data-processing frameworks on Hadoop. When an application is deployed in a YARN cluster, its resources are given without considering energy efficiency. Furthermore, there is no way to enforce any user-specified deadline constraints. To address these issues, we propose a new deadline-aware resource management system and a scheduling algorithm to minimize the total energy consumption in Spark on YARN for heterogeneous clusters. First, a deadline-aware energy-efficient model for the considered problem is proposed. Then, using a locality-aware method, executors are assigned to applications. This algorithm sorts the nodes based on the performance per watt (PPW) metric, the number of application data blocks on nodes, and the rack locality. It also offers three ways to choose executors from different machines: greedy, random, and Pareto-based. Finally, the proposed heuristic task scheduler schedules tasks on executors to minimize total energy and tardiness. We evaluated the performance of the suggested algorithm regarding energy efficiency and satisfying the Service Level Agreement (SLA). The results showed that the method outperforms the popular algorithms regarding energy consumption and meeting deadlines.
Back to Online Trading Companies for Digital Coins (Cryptocurrency) &amp;amp; their Dirty Legal Strategies to Drag Unprotected Investors: An Accounting &amp;amp; Finance View

Almost eight months have passed since I was misled and stolen by two online trading companies for cryptocurrencies and other investments, one company is in the United Kingdom and another is in the United States of America. And subsequently, I managed to write and publish my accidental experiential study on June 5 2023 in the journal of International Journal of Cryptocurrency Research titled as: “The New Era of Online Trading Companies for Digital Coins (Cryptocurrency) Between Integrity and Abuse: Accounting and Finance Perspective
High Performance of an In-Material Reservoir Computing Device Achieved by Complex Dynamics in a Nanoparticle Random Network Memristor

An in-material reservoir computing (RC) device with an Ag–Ag2S core–shell nanoparticle (NP) network is proposed. Network-wide nonlinear sine-wave outputs of higher frequencies and varying phases were produced from the different Ag+ ion diffusion rates and filament formation caused by the heterogeneous NP size in the thiol layer. Such emergent dynamics of multiple information regimes enabled the reconstruction of Fourier waves, with a maximum accuracy of 99% achieved only for trained outputs with mixed spatiotemporal complexities. Additionally, the device showed stable retrieval of past information with a two-times-step delay and successfully computed a two-step time-series prediction task with 87% accuracy.
Exploring the Synergies: A Comprehensive Survey of Blockchain Integration with Artificial Intelligence, Machine Learning, and IoT for Diverse Applications

Blockchain technology is a decentralized system for transaction validation that is safe, verifiable, and relevant to a number of sectors, including finance, the Internet of Things, big data, cloud computing, and edge computing. Combining blockchain, AI, ML, and IoT has the obvious benefit of granting AI greater autonomy and legitimacy. This survey report presents a thorough and three-dimensional analysis of the integration of blockchain with Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT) describing potential benefits and challenges. It looks at the current problems and challenges in the field as well as prospective directions for future study into how to combine blockchain with AI, ML, and IoT.
TO THE PROBLEM OF THE INFLUENCE OF DIGITAL MEANS ON THE PSYCHE OF A STUDYING CHILD

In the era of digital transformation of society and education, the mental development of a child goes differently than before. The purpose of the article is to briefly analyse the state of study of the problem of the influence of digital media on the cognitive development of pre-school and primary school age students. The analysis of foreign and domestic studies revealed the insufficiency, fragmentation and inconsistency of available data on the peculiarities of the cognitive sphere of students using digital media. Noting the impossibility of cancelling the sociocultural evolution of the psyche in the new digital environment, the prospect of systemic research into the child's mental development in the conditions of mixed reality on the basis of the working concept of digital socialisation is emphasised. The analysis of studies of the possible positive and negative effects of school education under conditions of digitalisation has shown the increasing cognitive inefficiency of this largely spontaneous practice. Since the digit itself is not capable of transforming information into knowledge and action, the transition to a properly organised digital education system requires the substantiation of the theory of effective digital learning and the development of digital educational products that offer new ways of organising students' attention, memory, thinking and communication, adequate to their age and psychological characteristics.
Enhancing Anomaly Detection in Agriculture by Considering the Impact of Concept Drift: An Exploratory Study Using Digital Replica

The advent of precision agriculture is disrupting traditional farming practices, leading to a significant increase in the data flow. However, current anomaly detection methods overlook the impact of concept drift. The phenomenon of concept drift refers to the changes in the underlying distribution of the data over time, which can result in unexpected and misleading results. As such, it is essential to consider the impact of theconcept drift in the context of anomaly detection in precision agriculture. This study uses a digital replica of an agricultural system equipped with intelligent agents to examine the system dynamics and the temporal variability of end-to-end data flow in precision agriculture. It highlights the need for robust methods that can effectively address the challenges posed by concept drift and systems’ dynamism enabling accurate and reliable anomaly detection in the IoT farming system.
Methods for Application Development by Non-Programmers: A Systematic Literature Review

The facilitation of software development for individuals lacking programming expertise has gained popularity owing to the imperative of expeditiously and efficiently delivering software products. The advent and evolution of Rapid Application Development (RAD) methodologies has facilitated the empowerment of non-programmers, particularly those with extensive business process expertise, to develop software, thus giving rise to the term 'Citizen Developers'. Currently, there exist numerous approaches encompassing frameworks, platforms, and tools that facilitate citizen development, including low-code platforms, no-code platforms, code automations, and spreadsheet-driven development. Nevertheless, the industry and software development community have yet to widely adopt these methods. Through an examination of the benefits and drawbacks of Re-CRUD code automation, it is anticipated that this approach may offer potential as a software development solution for individuals without programming expertise. However, enhancements to its ease of use and accessibility are necessary. The outcomes and suggestions derived from this analysis would furnish a more robust foundation for the assimilation and advancements of the methodologies of citizen development among the sector and the wider software development populace.
Motion Segmentation of Pedestrian Trajectories Using Angular Gaussian Mixture Model

Motion segmentation and identification of structural segments from an ensemble of human trajectories continue to be a challenge. These processes entail distinguishing and categorizing distinct motion patterns manifested by pedestrians within a group, as well as recognizing conspicuous segments that exhibit their overall motion. Accurate motion segmentation is vital in many areas, particularly computer vision, automation, and human behavior investigation and monitoring. However, owing to the complexity and irregularity of human motion, this task demands sophisticated procedures. Addressing this challenge, the Angular Gaussian Mixture Model (AGMM) is proposed in this study for visual motion segmentation. The angular features of the pedestrian trajectories are incorporated into the GMM, allowing the overall proposed framework to accurately expose the similarity between the trajectories and fulfill motion segmentation. The experimental findings conducted on the CUHK benchmark demonstrate that the proposed framework outperforms various state-of-the-art methods. The findings signify the superior performance achieved by the proposed approach in effectively and accurately segmenting motion trajectories within different crowded scenarios. Statistical evaluation approaches, such as normalized mutual information index (NMI), purity, rand index (RI), F-measure, also known as the F1-score (F1), and accuracy (ACC), were utilized employing an immense number of real-world video clips. This study has effectively laid the foundation for an essential and initial stride towards achieving comprehensive, high-level crowd behavior analysis.
Mitosis Detection in Breast Cancer Using Deep Learning

Breast cancer is a common gynecological malignant tumor. At the same time, breast cancer is also a cancer that can be detected early and prevented early. Using efficient mitotic cell detection technology for breast cancer screening and diagnosis is significant. The application of computer-aided analysis technology to cell detection tasks in pathological medical images has gradually become a hot research direction. In this study, the object detection technique based on YOLO v5 was first applied to the field of mitotic cell detection in pathological breast images. Aiming at the three difficult problems cell detection faces in pathological breast images, this paper explores the influence of detection models on domain generalization from the perspective of image preprocessing. A series of experimental studies show that the color correction strategy based on CycleGAN adopted in this study applied to the YOLO v5 detection model can effectively improve the model's applicability on the external test set.