Reviewerly/exHarmony huggingface dataset:
THE DYNAMIC INTERPLAY OF PHONOLOGY AND SEMANTICS IN MEDIA AND COMMUNICATION: AN INTERDISCIPLINARY EXPLORATION

Phonology, semantics, and pragmatics are the three aspects of language fundamental to know how language works and how people process and use it. By examining the interaction between the memory hypothesis and linguistics in speech and semantic organization, this study seeks to better understand how individuals use language to express themselves. This study delves into finding out the association between phonology and semantics by developing a multi-faceted correspondence organization and applying ideas from computer network science and chart hypothesis. A correspondence framework of the International Phonetic Alphabet and semantic network examination in R was used to investigate the information once obtained through a web-based information gathering drive. As per the discoveries, the dynamic and ensnared association between phonology and semantics affects the correspondence that happens in the media. The aftereffects of this study offer new points of view on the idea of language and the job it plays in human communication. Understanding how semantics and phonics interact in different situations is essential for modern communication, this study is an effort to analyze the relation between words' meanings and sounds. By studying words usage in different mediums, we want to learn how these important elements affect platform interactions. Thus, the present study explains how semantics and phonology shape media and how this link can improve communication. The review suggests a multi-faceted framework that divides the linguistic framework into various layers with different hub relationships. This advanced four-layer contact network is designed to identify and analyze language confusion with precision and accuracy. It incorporates free associations, word associations, and three semantic layers to measure articulation, semantics, punctuation, and jargon. Using a comprehensive inside-and-out technique, this evaluation examines phonology and semantics through multi-faceted network analysis, allowing for effective assessment of language understanding and improvement.&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/soc/0075/a.php" alt="Hit counter" /&gt;&lt;/p&gt;
IBM unveils a 1000-qubit computer

IBM has announced two new quantum computers, one of which is the second largest ever made
Semantic Coherence Guided Multi-View Similarity For Image Classification With Varied Supervision

Pair-wise similarity has been widely used for image classification by propagating the class information from labeled images to unlabeled images and predicting the classes of unlabeled images accordingly. Although widely used, pair-wise similarity based classification methods have two drawbacks. On one hand, visual-semantic similarity consistency does not always hold due to semantic gap. On the other hand, the reliability of pair-wise similarity is also influenced by the number and underlying distribution of labeled images. A well designed classification model cannot be easily generalized to few-shot classification tasks. To solve these two problems, in this paper, we propose a semantic coherence guided multi-view similarity (SCG-MV) for image classification method with varied supervision levels. We measure the semantic coherence between image-pairs for classifier learning. This is achieved by first conducting line search along the line segment between each image pair, and then measure the semantics of each interval. The similarity of one image-pair can then be obtained by aggregating the semantic coherence of all the intervals. In this way, we can model image similarities more finely and discriminatively. To make use of multi-view information, we also use the semantic coherence guided image similarities to learn multi-view classifiers. The proposed semantic coherence guided method can be combined with other similarity based methods for image classification with various levels of supervision. We conduct both few-shot and fully-supervised classification experiments on four datasets with the results well prove the discriminative power and generalization ability of the proposed method.
A novel supplier managed inventory order assignment platform enabled by Blockchain Technology

Supplier Managed Inventory (SMI) can be considered as an enabler for supply chain coordinating in which the supplier takes over the customer’s inventory to optimize the supply chain. However, successful implementation of SMI is centered on high level of trust, accurate data transfer, and efficient interaction between parties. This requires the sharing of information through supply chain stakeholders which face resistance and challenges due to the fear that this information is revealed to its competitors and transparency of data. This paper has investigated the application of Blockchain technology and its potential for successful SMI implementation. The paper has proposed a Blockchain framework for the coordination of suppliers and customers. The framework includes a mathematical model for multiple supplier customer order fulfillment which is embedded in blockchain framework. The paper has demonstrated case studies to evaluate the performance of the proposed model with literature discussing the details of its blockchain framework.
Cerebellum-Inspired Model Predictive Control for Redundant Manipulators with Unknown Structure Information

When the structure information of a redundant manipulator is unknown, motion control methods that do not rely on its model are attractive. Due to the numerous advantages of model predictive control (MPC), such as the direct handling of constraints, this paper proposes a model-free MPC algorithm for redundant manipulators with unknown structure information. In this paper, a cerebellum-inspired model based on the echo state network (ESN) is employed to replace the kinematic model of the redundant manipulator, and an MPC algorithm based on the cerebellum model and neural dynamics (ND) approach is developed. Unlike existing studies, this work considers both performance optimization and system constraints of the redundant manipulator, and can achieve high-precision prediction and tracking by designing an online training algorithm for the cerebellum model. Furthermore, this paper proposes an ND-based correction algorithm to modify the prediction model and an ND solver to solve the MPC scheme. Theoretical analyses confirm the convergence of both the ND-based correction algorithm and ND solver. Simulation and experimental results consistently demonstrate that the proposed cerebellum-inspired MPC (CIMPC) algorithm is effective and outperforms comparison algorithms in terms of tracking performance.
Dependency Prediction of Long-Time Resource Uses in HPC Environment

High-Performance computing provides a new infrastructure for scientific calculation and its simulation. However, unbalanced load distribution among the processors causes a decreased performance in such computations, and creates a massive requirement of computing resource allocation, that requires an increased simulation. Therefore long-range resource utilization prediction becomes essential to achieve optimal performance in an HPC environment. This paper introduces a novel ensemble technique, which includes two algorithms, the Feature-based capability prediction algorithm(FBCA), and the Accuracy and Relative Runtime Error Prediction Algorithm (ARRE). A three-level architectural framework (the simulation environment, resource prediction, and resource queue) has also been proposed and tested on Phold and SoS. The proposed framework can deal with the requirements of computing and simulations. The FBCA algorithm reduces the redundancy between available features, and the ARRE algorithm ensures our ensemble technique’s effectiveness. We have compared the performance of the proposed schemes with other existing methods such as the Regressive Approach, Linear Regression and Random Forest, and found that our proposed algorithm achieves better accuracy from 8% to 18%.
A Fully Outsourced Attribute-based Signcryption Scheme Supporting Privacy-preserving Policy Update in Mobile Cloud Computing

Existing fine-grained and secure access control systems deployed in mobile cloud computing (MCC) typically focus on offloading encryption and/or decryption workloads to the delegated proxy in the cloud server. However, the privacy and authenticity management of outsourced data with flexible and efficient access policy update in MCC is generally overlooked by existing works. In fact, the signcryption feature is generally required in data access control system where the trust between data owner and multiple mobile users is crucial while the policy update management deals with the dynamic handling of user privilege control lifecycle. In this paper, we propose a privacy-preserving access control scheme supporting signcryption and efficient policy update with policy hiding in MCC setting. Essentially, a fully outsourced attribute-based signcryption (ABSC) and policy update method are devised to allows the data owner to offload ABS operation and policy update cost to be executed by the delegated proxy. Finally, we present our experiments to demonstrate that our proposed scheme is computationally more efficient compared to related works.
WCBnet: Weighted Convolutional Block Modelling of Signed-Value Error Levels for Image-Wise Copy-Move and Splicing Detection

Image manipulation which can easily generate hard-to-perceive fake information by image editing tools has become a threat of spreading visual mis/disinformation. With the speed and growth of such visual information presence in social media with respect to the current geopolitical affairs, tools for highly accurate verification of the authenticity of images are vital for AI-based fact checking. This work presents an efficient convolutional neural network (CNN) based approach for image manipulation detection. Our method, called WCBnet, starts with extracting learned features from the signed-value error levels (SEL) of compressed images on hierarchical convolution blocks. This is followed by adaptively concatenating, weighting and fusing these multi-level features by considering self-attention over all blocks according to different error levels corresponding to different manipulation types. We evaluate the performance of the proposed approach with respect to common manipulation datasets and compare with the state-of-the-art. WCBnet trained using around 2500 images of CASIA 2.0 dataset, resulted in the best F1-score for CASIA 1.0, Defacto, Coverage and Columbia datasets after fine-tuning by a small portion of those datasets. On average WCBnet improves the F1 score with respect to the second-best performing methods by 27.5%, 34.3%, 16.2% and 6.1% for these four datasets, respectively.
Gallery-Query Protocol for Evaluating Face Image Quality Metrics

As more automated face recognition systems are integrated into society, face image quality estimators (QE) become important. These QEs help quantify whether an input face image contains reliable information necessary for face recognition. However, to assess the effectiveness of face QEs, it is essential to have reliable evaluation protocols. Current face QE evaluation protocols require long computation times and do not have explicit real-world implications. In this paper, we propose a novel face QE evaluation protocol named “Gallery-Query (GQ) Protocol”. The GQ protocol is significantly faster in evaluating face QEs when compared to previous approaches. Furthermore, it has a very explicit real-world use case in constructing an optimal gallery set for face recognition tasks. In addition to this, we used these evaluation protocols to investigate the generalizability of face Quality Estimators (QEs) across various face recognition models, which also has implications for real-world use cases.
Watson: Web Application Interface Data Collector for Feedback Reporting

Resolution of bug reports is an essential part of software maintenance activity. But insufficient information is one of the main problems that developers complain about. Most bugs are reported manually, and generally, the reports do not contain sufficient information for the developers to reproduce the bug and find a solution. Our proposed solution, named Watson, is a framework developed in Typescript with a small UI in Vue.js. The objective is to reduce the user’s effort and time to report, standardizing bug reporting by collecting and attaching information about user interaction, network requests, and screen video. The main difference from other tools is that Watson is a framework that can be installed via NPM and integrated into most JavaScript or TypeScript web projects. Watson was evaluated empirically with 12 participants, considering how they perceived Watson’s usefulness, we conduct an experiment with two scenarios. In the first, the users used an application without Watson and once they found a bug, they reported it manually on GitLab. In the second, they used the same application, and when they found another bug, they reported it through Watson UI. The participants perceived by using Watson they spent less effort and time to report a bug, and as testers and developers, they judged the video, network logs, and user interaction as useful for solving bugs. The fact that Watson can be installed and integrated into another project was a relevant point that the participants liked.
Phenomenon of Fake Agile in Software Development

Nowadays, agile methodology is in the limelight, and it has attracted a lot of attention because it has gained significant popularity in adapting to changing needs and increasing project success rates in software development. However, with its widespread adoption, concerns have emerged regarding the misinterpretation and misapplication of Agile principles, resulting in what we term "Fake Agile." This paper investigates the phenomenon of Fake Agile, its causes, and its implications for software development projects. Key indicators and characteristics of Fake Agile practices have been identified through drawing on a comprehensive literature review. This study identifies the factors that contribute to the emergence of Fake Agile, including organizational pressures, lack of proper training, and incomplete understanding of Agile principles. Furthermore, we examine the potential consequences of Fake Agile on project outcomes, team dynamics, and customer satisfaction. In order to overcome the challenges posed by Fake Agile, this study proposes a set of guidelines for distinguishing genuine Agile practices from Fake Agile implementations. The purpose of these guidelines is to help practitioners and organizations effectively adopt agile principles and avoid the pitfalls of fake agile. The findings of this research contribute to increasing the body of knowledge about Agile methodology by elucidating the risks associated with misinterpretation and misuse. By understanding the causes and consequences of Fake Agile, software development professionals and organizations can make informed decisions to adopt and implement Agile practices more effectively, thereby improving project outcomes and maximizing the benefits of Agile methods.
Salient Object Detection on 360° Omnidirectional Image with Bi-Branch Hybrid Projection Network

With the advent of panoramic cameras, modeling saliency in 360° omnidirectional images becomes very urgent and challenging. However, severe distortions limit the prediction accuracy of 360° saliency model. In this paper, we devise a bi-branch hybrid projection network (HPNet), which exploits characteristics of equirectangular projection (ERP) and cubic map projection (CMP) formats to predict salient objects in 360° omnidirectional images. Specifically, an ERP image and a CMP image are first fed into a bi-branch network to aggregate the comprehensive features of the omnidirectional image. Subsequently, to explore the coherence among ERP and CMP images, we design a hybrid projection feature fusion module to efficiently combine CMP and ERP features extracted from different layers. Ultimately, a progressive prediction module is developed to refine the features and locate salient objects incrementally, and then produce the final saliency map for the 360° omnidirectional image. Experimental results illustrate that our model is superior to the existing advanced methods in two publicly available datasets.
MSB-Based Reversible Data-Hiding in Encrypted 3D Object Using a Hamiltonian Path

Nowadays, 3D objects are frequently stored and shared online, where they become vulnerable to attacks. Therefore, applying security measures such as encryption is crucial. Once the 3D object is encrypted and stored online, a third party who is not authorized to access the content of the 3D object may need to embed hidden data in the 3D object. In this paper, we propose a new MSB-based reversible data-hiding in the encrypted domain method for 3D objects, which uses a Hamiltonian path to establish a unique order for the vertices of a 3D object. For the reconstruction step, we select the smallest distance between vertices to accurately predict the MSB value of the mantissa for each coordinate. Our proposed method is format compliant, avoids any size expansion, and does not require auxiliary file, while guaranteeing the reversibility of the marked encrypted 3D object, even when tested on a large database of 3D objects.
THE IMPACT OF MUSICAL ART ON THE EMOTIONAL WORLD OF A PERSON AND SOCIETY

The article "The Influence of Musical Art on the Emotional World of a Person and Society" is a comprehensive study of the role that music plays in shaping the emotional state and socio-cultural environment. The author has carefully analysed the impact of music on the emotional spectrum of an individual, exploring various styles of music and their impact on the psyche and emotional state of the listener.&#x0D; The article highlights the significance of music as a key element of culture that can not only transform emotions on a personal level, but also influence collective emotional experiences in society. The author examines the interaction of music with the human mental state, identifying how different genres and compositions can evoke different emotional reactions and influence cognitive processes.&#x0D; Particular attention is paid to the role of music in shaping socio-cultural norms and stereotypes. The article explores how music affects the perception of various social situations, as well as how it promotes social cooperation and mutual understanding among individuals. The role of musical art as an element of cultural dialogue and a means of expressing social problems is defined.&#x0D; At the same time, the article analyses current scientific approaches to the study of the impact of music on mental health. The author considers the possibilities of using music therapy in clinical practice to treat various mental disorders and strengthen the emotional state of patients.&#x0D; The general conclusion of the article indicates that understanding the impact of music can have important consequences for the development of society. The author emphasises the importance of integrating music into educational and therapeutic practices in order to create a more balanced and emotionally rich society.
THE PROBLEM OF PERSONNEL MANAGEMENT IN PEDAGOGICAL THEORY

The article deals with the problem of personnel management in pedagogical theory. Three groups of scientific and pedagogical researches have been identified: the first – scientific works of famous economists and managers; the second is the work of educators based on the development of personnel management by economists and managers, attempts to transfer Western approaches to the local soil; the third is the works of educators based directly on the study of school management problems. It is revealed that the first group considers management problems throughout the development of human history, explains the origin of management terms, examines and compares management models that exist today. It is emphasized that the adaptive management of the school team, the psychology of motivation and the needs of people in the organization, the formation of positive group motivation, the scenarios of training sessions with teachers are the main issues considered by the authors of the second group. It is noted that the management of education in market conditions, training and retraining of personnel, creation of opportunities for mastering new educational technologies, motivation of pedagogical activities, innovative climate in the educational institution – these issues are the content of the works of the authors of the third group.&#x0D; The article emphasizes that until recently the very concept of «personnel management in education» or «personnel management» was absent in management practice (in some dictionaries there is no definition of the term «management»), therefore works on management as such and educational management came to us from the works of foreign scientists. Personnel management as an independent scientific direction was formed within the humanistic management paradigm and the concept of human resources management. The need for the emergence of personnel management is due to the fact that in the context of personnel management, an employee is considered as the owner of human, intellectual and social capital, possessing professional and qualification characteristics that ensure his personal efficiency and competitiveness.
PEDAGOGICAL DESIGN OF THE CONTENT OF TECHNOLOGY EDUCATION AS A SCIENTIFIC PROBLEM

The article researches the key aspects of pedagogical design as a methodological basis for forming the content of technological education. The significance of this research lies in the study of the possibilities of creating new educational materials and pedagogical methods for the successful implementation of technological literacy in the educational process. The article focuses on the analysis of the principles of pedagogical design, taking into account the specifics of technological education. Important aspects of the development of pedagogical strategies and methods aimed at effective development of technological literacy among students of higher education are highlighted. The relevance of the research in the context of modern challenges of technological education, such as globalization, market reforms and rapid changes in technological processes, is emphasized.&#x0D; It was determined that pedagogical design in the field of technological education is an important tool for adapting the educational process to current requirements. It promotes the implementation of innovative teaching methods, in particular using modern technologies, which improves the efficiency and individualization of education, in particular: adaptation of the principles of pedagogical design to the educational sector contributes to the optimization of the educational process and provides a systematic approach to the development of educational programs; requires constant improvement and adaptation to social and technological changes. The application of the evaluation and feedback system in the context of pedagogical design is an important component that contributes to the continuous improvement of learning and increasing the efficiency of the educational process. Thanks to them, teachers and students of higher education can analyze their achievements, identify strengths and weaknesses, integrate technical knowledge with pedagogical methods, and develop critical thinking. These tools make it possible to improve educational processes and achieve better learning results.