Application of Multimedia Simulation Teaching in Youth Physical Fitness Training

Abstract: The multimedia simulation teaching mode introduces students into virtual scenes for learning. Whether it is enhancing students' interest in learning or enhancing their physical fitness, it is a new teaching mode. This article discusses the establishment of a BP neural network model to study the prediction of students' physical fitness and conducts experiments on predicting and evaluating adolescent physical fitness. The results indicate that the BP neural network model better reflects the functional relationship between various indicators of student physical fitness and the total test score, providing a more reasonable mathematical model for evaluating adolescent physical fitness.


Classical-Quantum Dual Encoding for Laser Communications in Space

Abstract: Abstract In typical laser communications classical information is encoded by modulating the amplitude of the laser beam and measured via direct detection. We add a layer of security using quantum physics to this standard scheme, applicable to free-space channels. We consider a simultaneous classical-quantum communication scheme where the classical information is encoded in the usual way and the quantum information is encoded as fluctuations of a sub-Poissonian noise-floor. For secret key generation, we consider a continuous-variable quantum key distribution protocol (CVQKD) using a Gaussian ensemble of squeezed states and direct detection. Under the assumption of passive attacks secure key generation and classical communication can proceed simultaneously. Compared with standard CVQKD. which is secure against unrestricted attacks, our added layer of quantum security is simple to implement, robust and does not affect classical data rates. We perform detailed simulations of the performance of the protocol for a free-space atmospheric channel. We analyse security of the CVQKD protocol in the composable finite-size regime.


A federated learning approach to QoS forecasting in cellular vehicular communications: Approaches and empirical evidence

Abstract: QoS forecasting for cellular vehicular communications allows cooperative, connected and automated mobility applications to tailor their behavior to the expected communication conditions on the road. In a nutshell, vehicles may, for example, execute cooperative maneuvers if the communication quality of service is only above a certain quantitative level whereas if not they revert to the individual autonomous mode. In this paper, we propose and show empirical methods for estimating packet-based QoS metrics obtained from 5G network measurements with a direct application to vehicular applications. As many distributed vehicular applications possess strict QoS requirements, we focus here on bounding packet-based statistical QoS quantiles, specifically for latency and loss. Our approach is based on training regression neural networks in a federated learning fashion and show that it can obtain predictions on par with centralized training without the vehicles needing to transmit raw measurement data. In contrast to QoS prediction using physical layer information, We briefly discuss the embedding of such much simpler application-level service within the 5G architecture. We also validate our approach through recovering classical closed-form delay quantiles that are obtained from analytical models of simple queueing systems. We show that our approach goes beyond these simple models in that it provides quantile estimates for the complex scenario of cellular vehicle communications and under different application traffic patterns including empirical data traffic traces as well as 5G testbed measurements.


Group Behavior Recognition Algorithm for Basketball Video Based on Hidden Tag Mining Strategy under Gamification Teaching and Learning

Abstract: The rapid development of Artificial Intelligence has facilitated the development of multimedia teaching and learning in the field of education, as well as providing intelligent methods for gamified teaching methods in the field of basketball. Currently, research on recognizing basketball video behavior primarily focuses on group behavior recognition methods for key individuals. However, there is currently no group behavior recognition method that can effectively distinguish features that reflect different events for temporal interactive behaviors such as dunks and steals in videos, as well as situations where key players are obstructed. The paper suggests a group behavior identification algorithm for basketball game videos based on the hidden tag mining technique in order to perform intelligent analysis in the fields of supplemental training and tactical analysis of gamified instruction. And this algorithm is integrated with the fusion of individual features to create the basketball video's group behavior identification algorithm with hidden label mining approach. According to the study's findings, the distribution of motion pattern features is more agglomerated and regular when the merging threshold is 0.6 and the best hyperparameter is 3. After the study applied the crucial individual feature extraction module, the overall group behavior identification accuracy increased by 6.44%. The group behavior recognition algorithm for basketball videos that incorporates individual features and hidden label mining techniques outperforms the most recent patio-temporal domain-based group behavior recognition algorithm by 15.56% when compared to the accuracy of commonly used algorithms. In summary, the study proposes a basketball video group behavior recognition algorithm with better performance. This algorithm achieves group behavior recognition in basketball game videos, promotes auxiliary training for students in gamified teaching mode, and provides a practical and feasible approach for intelligent analysis of other sports events. It also provides technical support for coaches and referees.


Revisiting code smell severity prioritization using learning to rank techniques

Abstract: Code Smell Severity Prioritization (CSSP) is crucial in helping software developers minimize software maintenance costs and enhance software quality, particularly when faced with limited refactoring resources. Traditional code smell prioritization methods rely heavily on manual and semi-automatic approaches based on developer experience, often demanding considerable time and effort from experienced experts. Leveraging automated machine learning techniques can effectively overcome these limitations. However, most existing machine learning-based CSSP works have only considered limited pointwise Learning To Rank (LTR) algorithms and have used inappropriate metrics (e.g., Accuracy, Spearman, and MAE) to assess the performance of models. To address these limitations, we make a comprehensive comparison of 41 pointwise, 4 pairwise, and 4 listwise LTR algorithms for CSSP on four code smell severity datasets. Furthermore, we propose the adoption of Severity@20% and Cumulative Lift Chart (CLC) as the primary evaluation metrics to assess CSSP models more effectively. The results show that: (1) The ordinal Bagging (O-Bagging) algorithm demonstrates the highest performance for CSSP, achieving superior results in Severity@20% and CLC. (2) The ordinal classification method can help the top-performing base classification algorithms Bagging and XGBoost achieve better performance for CSSP tasks. (3) A higher (lower) Accuracy, higher (lower) Spearman, and lower (higher) MAE do not reliably indicate better (worse) performance for CSSP. This further underscores that Accuracy, Spearman, and MAE are unsuitable metrics for evaluating CSSP models’ effectiveness. To summarize, our study suggest that developers employ the O-Bagging algorithm for CSSP, with Severity@20% and CLC serving as the primary evaluation metrics.


Deep neural network-based robust hologram watermarking using guided attack module

Abstract: A digital hologram (DH) is an ultra-high-value video content that includes three-dimensional information in two-dimensional data. Therefore, for the distribution of this content, the intellectual property rights must be protected. This paper proposes robust hologram watermarking based on deep neural networks using guided attack modules and attack rate control techniques. The proposed method robustly embeds the watermark into the original hologram to cope with various attacks while keeping the invisibility that the watermarked hologram is similar to the original hologram. Previous work considered PSNR during training. However, hologram exists in the complex domain. Thus SNR for complex numbers should be considered to confirm the reconstruction results.. In addition, we propose an enhanced network with increased depth to improve performance compared with previous work. Experiments show that the proposed method outperforms the previous work regarding robustness and invisibility against various attacks, such as Crop Out, Salt & Pepper Noise, and Rotation. Therefore, this study is expected to be widely used in various fields, such as digital image authentication, copyright protection, and data security.


Defense against adversarial attacks based on color space transformation

Abstract: Deep Learning algorithms have achieved state-of-the-art performance in various important tasks. However, recent studies have found that an elaborate perturbation may cause a network to misclassify, which is known as an adversarial attack. Based on current research, it is suggested that adversarial examples cannot be eliminated completely. Consequently, it is always possible to determine an attack that is effective against a defense model. We render existing adversarial examples invalid by altering the classification boundaries. Meanwhile, for valid adversarial examples generated against the defense model, the adversarial perturbations are increased so that they can be distinguished by the human eye. This paper proposes a method for implementing the abovementioned concepts through color space transformation. Experiments on CIFAR-10, CIFAR-100, and Mini-ImageNet demonstrate the effectiveness and versatility of our defense method. To the best of our knowledge, this is the first defense model based on the amplification of adversarial perturbations.


FCNN: Simple neural networks for complex code tasks

Abstract: Program analysis using deep learning has become a focus of research, and representing code as model input is a major challenge. While abstract syntax trees (ASTs) have proven effective, using them directly as model input introduces issues of long-term dependency. Approaches based on AST paths objectively alleviate the vanishing gradient problem caused by large-scale syntax trees, but still face limitations. This paper introduces a novel approach to code analysis using fully connected neural networks (FCNN). We propose a new context structure that cleverly preserves the up-down relationships between path nodes. Simultaneously, it employs an encoding method based on node embeddings, mitigating model sparsity. Despite its simplicity, FCNN demonstrates versatility in handling various tasks associated with code analysis. Our work underscores the importance of improvements and innovations in code representation compared to using more advanced and complex deep learning models in the field of code analysis. Evaluation on two common code analysis tasks, namely code classification and code similarity detection, validates the effectiveness of the proposed approach. In code classification, FCNN achieves an F1 score of 0.45, surpassing all comparison baselines. In code similarity detection, FCNN attains an F1 score of 0.91, outperforming RtvNN and CDLH by 35.82% and 10.98%, respectively.


Enhancing multi-modal fusion in visual dialog via sample debiasing and feature interaction

Abstract: Visual dialog aims to accomplish multiple rounds of dialog by fusing information extracted from images, captions, and previous question-answer pairs. As a vision-language task, visual dialog encounters challenges related to language bias and vision bias. These biases create an imbalance in multi-modal fusion, resulting in shortcut learning and significantly compromising the model’s robustness. Moreover, existing multi-modal fusion methods in visual dialog exhibit a low data interaction frequency, leading to insufficient fusion. To overcome the balance and sufficiency issues in multi-modal fusion, we propose a novel Parallel Attention Fusion visual dialog model with Counterfactual Sample debiasing (CS-PAF). Specifically, CS-PAF consists of two core ingredients: (i) a counterfactual sample generation module for model debiasing; and (ii) a parallel attention fusion network that enhances sufficiency in multi-modal data interaction. Notably, in contrast to other debiasing methods, our counterfactual sample generation applies contrastive learning to circumvent the high cost of manual annotations and ensure seamless integration with other models. Extensive comparisons with state-of-the-art approaches, along with comprehensive ablation and transferability studies across multiple datasets, substantiate the superiority and effectiveness of our CS-PAF. Our implement code is available at https://github.com/chenyulu2000/cspaf.


Audio Steganalysis Using Fractal Dimension and Convolutional Neural Network (CNN) Model

Abstract: The rate at which secret messages are being transmitted through various digital signal media is alarming; these operations are done in an unsuspicious manner and users transmit these messages without knowledge of the embedded secret messages. Audio steganalysis deals with detecting the presence of secret messages in audio messages. Some of the existing steganalysis methods are laden with having prior knowledge of the steganography methods adopted in embedding the secret message in an audio signal, which reduces the detection efficiency. Consequently, this research developed a Higuchi-based audio steganalysis method that detects secret messages without having prior knowledge of the embedding techniques used. The algorithm reduces the fractal dimension of the audio signal to extract relevant features, while convolutional neural network was used as classifier. The research records high accuracy (96%) when compared with previous research. The accuracy of the developed system shows its effectiveness in detecting embedded messages without prior knowledge of the deployed steganography method.


Security, Privacy, and Technical Challenges in a Blockchain-Internet of Things-Based Environment

Abstract: Blockchain and internet of things (IoT) is a combination that has massive potential to bring about revolutionary changes in various useful applications/ industries by enabling secure and transparent data exchange, decentralized control, and improved efficiency. However, this convergence also requires significant security, privacy, and technical challenges that must be addressed for successful implementation and widespread adoption. This chapter explains the security, privacy, and technical challenges that arise in a blockchain-IoT-based environment. Firstly, it examines the security issues related to the distributed nature of blockchain networks and the vulnerabilities that can be exploited in IoT devices and communication channels. Further, the authors discuss the importance of cryptographic techniques, secure key management, and access control mechanisms to protect data integrity and prevent unauthorized access.


Transformative Effects of Blockchain and IoT in Shaping Industry 5.0 Environments

Abstract: In the era of Industry 5.0, characterized by the convergence of physical and digital worlds, the integration of blockchain technology and the internet of things (IoT) has emerged as a pivotal paradigm shift. This chapter explores the perspectives, issues, and challenges associated with the amalgamation of Blockchain and IoT to foster a transformative environment for industries. This chapter begins by providing an overview of Industry 5.0 and its key principles, emphasizing the need for robust and secure data handling in this context. It then explains the application of blockchain as a distributed ledger technology to enhance the IoT ecosystem by enabling trust, security, and transparency in data transactions. Various use cases and real-world applications of this fusion (of IoTs and blockchain) are discussed, ranging from supply chain management to smart cities and healthcare. Despite its promising potential, the integration of blockchain and IoT also presents several critical issues and challenges.


Navigating Cloud Security Risks, Threats, and Solutions for Seamless Business Logistics

Abstract: This chapter explores cloud security challenges faced by businesses adopting cloud technologies for logistics operations. It provides a roadmap to strengthen cloud security, safeguard digital assets, and foster solid business-CSP relationships. The chapter covers hazards, solutions (e.g., threat intelligence platforms, MFA, intrusion detection, encryption), shared responsibility with CSPs, and selecting reliable providers. Practical guidelines for a comprehensive cloud security strategy are offered, including risk assessments, access controls, incident response, and employee training. Understanding these aspects helps companies proactively secure their data and ensure seamless logistics in the cloud. By gaining a deep understanding of the risks, threats, and solutions associated with cloud security in business logistics, companies can proactively safeguard their critical data and ensure the seamless and secure operation of their logistical processes in the cloud.


Blockchain Technology in Cloud Security

Abstract: As cloud computing becomes ubiquitous, the need for robust security measures intensifies. Traditional centralized access control systems in cloud environments pose vulnerabilities such as single points of failure, transparency deficits, and privacy concerns. This chapter explores the integration of blockchain technology as a transformative approach to enhancing cloud security. Blockchain, renowned for its decentralized ledger, immutability, and cryptographic security, offers innovative solutions to these challenges. The chapter discusses the motivation behind adopting decentralized access control, the fundamentals of blockchain, and its role in identity management. Real-world use cases demonstrate the practical application of blockchain in cloud security. Challenges, including scalability and regulatory compliance, are addressed. This chapter provides a comprehensive introduction to decentralized access control in cloud security, emphasizing its potential to revolutionize the field and contribute to the development of secure and resilient cloud systems.


Enhanced Security in Blockchain-Based Cyber Physical Systems

Abstract: Consortium blockchain provides pooled trust for the proposed method. It uses a unique certificateless authentication method based on the multi-signature scheme to guarantee safety. Software, and physical components comprise the cyber-physical system. Each piece functions on various time-based and three-dimensional stages and constantly interacts with other members. The framework eliminates the centralized trust model, i.e., the dependence on a single PKI certificate authority (CA) for public keys, works over an associate consortium, and uses smart-edge computation. Different industry partners cooperatively confirm both the gadget's character and information. It prevents disasters by reducing the impact of potentially malevolent associates. It agrees to a novel contract without requiring manifold authorisations, eliminating the need for a waiting period after each block addition. It adjusts, putting away information at the conveyed hash table. The suggestion adds more reliability to the transaction by 4.17%. The suggestion conserves energy by 12.5%.


Cloud Security Risks, Threats, and Solutions for Business Logistics

Abstract: Cloud computing has revolutionized the landscape of modern business logistics by offering scalable and cost-effective solutions for data storage, processing, and application deployment. However, with this newfound convenience comes a plethora of security challenges that businesses must address to protect their valuable assets and sensitive information. This chapter aims to provide a comprehensive overview of the security risks and threats associated with cloud adoption in the logistics industry, along with effective solutions to mitigate them by presenting an in-depth analysis of the primary security risks that businesses may encounter when leveraging cloud-based logistics solutions. Additionally, it discusses the risks posed by shared infrastructure, third-party integrations, and data jurisdiction concerns. Moreover, the chapter highlights the growing role of emerging technologies in bolstering cloud security measures. These technologies offer sophisticated threat detection and proactive response capabilities, enabling logistics companies to stay ahead of evolving cyber threats.


Revisiting Fully Homomorphic Encryption Schemes for Privacy-Preserving Computing

Abstract: Homomorphic encryption (HME) is a sophisticated encryption technique that allows computations on encrypted data to be done without the requirement for decryption. This trait makes HME, appropriate for safe computation in scenarios involving sensitive data and also in cloud computing. The data is encrypted using a public key and the calculation is conducted on the encrypted data. The computed result is then decrypted with a private key to acquire the final output. It protects data while allowing complicated computations to be done on the encrypted data, resulting in a secure and efficient approach to analyse sensitive information. The ability of HME to do computations on encrypted data without decryption makes it a valuable tool for achieving privacy. This chapter is intended to give a clear idea about the various fully HME schemes present in the literature, as well as analysing and comparing the results of each of these schemes. The authors also provide applications and open-source tools of HME schemes, along with how HME can be used to establish and preserve privacy in various forms.


Safeguarding Privacy Through Federated Machine Learning Techniques

Abstract: The chapter thoroughly addresses ML privacy threats like risks, attacks, and leaks. It explores the methods of differential privacy, homomorphic encryption, and SMPC. Federated learning is detailed, covering concepts, benefits, techniques (averaging, aggregation). Advancements include transfer learning, differential privacy, edge device use. Real cases show privacy's value in healthcare, finance, IoT. The conclusion touches on trends, regulations, privacy-utility balance. The chapter aims to overview privacy-preserving and federated ML, stressing their role in data security with insights for researchers, practitioners, policymakers for privacy-conscious ML. Valuable insights are provided for researchers, practitioners, and policymakers aiming for a privacy-conscious future in machine learning.


Role of Blockchain Technology in Protecting a Cloud Architecture

Abstract: Cloud computing has revolutionized the way organizations manage and deliver services, offering unparalleled scalability and cost-efficiency. However, the increasing adoption of cloud-based solutions has also brought forth a myriad of security challenges, ranging from data breaches to unauthorized access and tampering. In recent years, blockchain technology has emerged as a promising solution to address some of these security concerns. This chapter presents an in-depth exploration of the application of blockchain technology in cloud security. It begins by providing an overview of both cloud computing and blockchain, emphasizing their individual strengths and weaknesses in safeguarding data and transactions. Subsequently, it delves into the various security challenges faced in cloud environments and how traditional security measures often fall short.


Blockchain Security in Edge Computing

Abstract: In the past decade, edge computing and blockchain technology have been used in many applications with rapid growth. The chapter explores the intersection of blockchain technology and edge computing and investigates the security issues and solutions in this emerging domain. Edge computing refers to “the decentralized processing and storage of data at the network edge, closer to the data sources and end-users. It offers reduced latency, improved data privacy, and enhanced real-time decision-making capabilities.” However, it also introduces new security challenges due to the distributed and resource-constrained nature of edge devices. The integration of blockchain technology with edge computing holds promise in addressing these security issues. Blockchain can provide a decentralized and tamper-resistant ledger for recording and verifying transactions, ensuring data integrity, and establishing trust among edge devices and stakeholders. This chapter explores several use cases where blockchain and edge computing can synergistically enhance security.


Introduction to Cloud Computing and Cloud Services

Abstract: Cloud computing and cloud services have revolutionized the way organizations access and utilize computing resources. This chapter provides an overview of cloud computing, its evolution, and the advantages it offers. It discusses the different types of cloud services and the importance of service-level agreements. The chapter also explores cloud deployment models and their considerations. It highlights the impact of cloud computing on various industries and the challenges of migration. Cloud computing and cloud services have revolutionized the IT landscape. They provide organizations with scalable, flexible, and cost-effective solutions that drive innovation and enable businesses to focus on their core competencies. By carefully evaluating their needs and requirements, considering factors such as security, compliance, and customization, organizations can select the most suitable cloud service type and deployment model. Embracing cloud technologies empowers businesses to unlock new opportunities, increase efficiency, and stay competitive in the rapidly evolving digital world.


A Systematic Review With Recommendations on Intelligent Systems in Cognitive Healthcare

Abstract: Today's doctors and patients rely on online platforms to express opinions on health matters. Public health importance, especially after the COVID-19 pandemic in both developing and developed countries are focusing on building sustainable, resilient health systems and implementing policies to address prevailing healthcare challenges. Adoption of latest trends and technologies leads to the processing of extensive bigdata by heavy statistical evaluations through machine learning and deep learning techniques to predict prominent semantic patterns. The objective of this chapter is to spotlight on cognitive analysis to extract opinions expressed by users. Authors emphasize narrowing down the new potentials in the field of sentiment analysis through cognition and understanding variations in semantic orientation. The chapter explores new methods and procedures that benefit practitioners and researchers with respect to sentiment polarity using cognitive analytics to address the improvements in healthcare industry.


Key Insights into Developing Qualitative Concept Elicitation Work for Outcome Measures with Children and Young People

Abstract: Abstract Qualitative concept elicitation can develop meaningful patient-reported outcome measures for children and young people; however, the methods used for concept elicitation are often underreported for this population. This paper provides in-depth insight into the methods used for concept elicitation with children and young people, with a focus on key stages of concept elicitation that are challenging or unique to doing this research with children. Drawing on our experiences of developing wellbeing measures for children and young people aged 6–15 years, we detail the processes followed in our qualitative concept elicitation work, covering issues related to sampling and recruitment, encouraging informed assent and freedom over children and young people’s involvement in concept elicitation, and the use of creative and participatory methods to develop measure items. We provide reflections on the approaches taken to navigate challenging aspects of concept elicitation with children and young people. Our reflections suggest that using existing links and online recruitment methods can help to navigate organisational gatekeepers, and using appropriate processes to develop study information and obtain informed assent can ensure that research is inclusive and that children have the freedom to decide whether to be involved. Our adaptation of a creative and participatory activity to generate concepts for measure items suggests that such approaches can be engaging and may help to give children greater control over their participation. In detailing our methods, we hope to have developed a useful resource for other researchers, while highlighting the value of transparent reporting in this area.


Connection between single-layer quantum approximate optimization algorithm interferometry and thermal distribution sampling

Abstract: The quantum approximate optimization algorithm (QAOA) was originally proposed to find approximate solutions to combinatorial optimization problems on quantum computers. However, the algorithm has also attracted interest for sampling purposes since it was theoretically demonstrated under reasonable complexity assumptions that one layer of the algorithm already engineers a probability distribution beyond what can be simulated by classical computers. In this regard, a recent study has also shown that, in universal Ising models, this global probability distribution resembles pure but thermal-like distributions at a temperature that depends on the internal correlations of the spin model. In this work, through an interferometric interpretation of the algorithm, we extend the theoretical derivation of the amplitudes of the eigenstates and the Boltzmann distributions generated by a single-layer QAOA. We also review the implications of this behavior from practical and fundamental perspectives.


Quantifying and characterizing clones of self-admitted technical debt in build systems

Abstract: Self-Admitted Technical Debt (SATD) annotates development decisions that intentionally exchange long-term software artifact quality for short-term goals. Recent work explores the existence of SATD clones (duplicate or near duplicate SATD comments) in source code. Cloning of SATD in build systems (e.g., CMake and Maven) may propagate suboptimal design choices, threatening qualities of the build system that stakeholders rely upon (e.g., maintainability, reliability, repeatability). Hence, we conduct a large-scale study on 50,608 SATD comments extracted from Autotools, CMake, Maven, and Ant build systems to investigate the prevalence of SATD clones and to characterize their incidences. We observe that: (i) prior work suggests that 41-65% of SATD comments in source code are clones, but in our studied build system context, the rates range from 62% to 95%, suggesting that SATD clones are a more prevalent phenomenon in build systems than in source code; (ii) statements surrounding SATD clones are highly similar, with 76% of occurrences having similarity scores greater than 0.8; (iii) a quarter of SATD clones are introduced by the author of the original SATD statements; and (iv) among the most commonly cloned SATD comments, external factors (e.g., platform and tool configuration) are the most frequent locations, limitations in tools and libraries are the most frequent causes, and developers often copy SATD comments that describe issues to be fixed later. Our work presents the first step toward systematically understanding SATD clones in build systems and opens up avenues for future work, such as distinguishing different SATD clone behavior, as well as designing an automated recommendation system for repaying SATD effectively based on resolved clones.


Improving the Automated Diagnosis of Breast Cancer with Mesh Reconstruction of Ultrasound Images Incorporating 3D Mesh Features and a Graph Attention Network

Abstract: This study proposes a novel approach for breast tumor classification from ultrasound images into benign and malignant by converting the region of interest (ROI) of a 2D ultrasound image into a 3D representation using the point-e system, allowing for in-depth analysis of underlying characteristics. Instead of relying solely on 2D imaging features, this method extracts 3D mesh features that describe tumor patterns more precisely. Ten informative and medically relevant mesh features are extracted and assessed with two feature selection techniques. Additionally, a feature pattern analysis has been conducted to determine the feature's significance. A feature table with dimensions of 445 × 12 is generated and a graph is constructed, considering the rows as nodes and the relationships among the nodes as edges. The Spearman correlation coefficient method is employed to identify edges between the strongly connected nodes (with a correlation score greater than or equal to 0.7), resulting in a graph containing 56,054 edges and 445 nodes. A graph attention network (GAT) is proposed for the classification task and the model is optimized with an ablation study, resulting in the highest accuracy of 99.34%. The performance of the proposed model is compared with ten machine learning (ML) models and one-dimensional convolutional neural network where the test accuracy of these models ranges from 73 to 91%. Our novel 3D mesh-based approach, coupled with the GAT, yields promising performance for breast tumor classification, outperforming traditional models, and has the potential to reduce time and effort of radiologists providing a reliable diagnostic system.


Automated Text Analysis of Organic Chemistry Students’ Written Hypotheses

Abstract: Generating a testable hypothesis is a necessary skill for engaging in science, requiring both general reasoning skills and specific content knowledge of the phenomenon being investigated. While many students have the reasoning skills necessary for developing testable hypotheses in a general science context, it can be challenging for students to apply these skills to content areas where the phenomena being studied are unobservable, such as problems in organic chemistry. Generating hypotheses for experiments is a skill that students are expected to apply in chemistry teaching laboratories. Students can benefit from receiving real-time feedback to support developing this ability. However, providing feedback on writing can be challenging for instructors, especially in large-enrollment courses. For this study, we explored and compared the performance of several machine learning algorithms to classify organic chemistry students' written hypotheses for a science-general scenario and an organic chemistry-specific scenario. These models were trained to analyze students' written hypotheses for the presence of five features: (1) a testable prediction in a given situation; (2) a predicted change in the independent variable that is related to the dependent variable; (3) scientific content addressing the research question provided in the prompt; (4) a correct and completely defined independent variable; and (5) a correct and completely defined dependent variable. This work has implications for guiding future research on the feasibility of providing instructors with information about students' hypothesis-generating abilities that can support the delivery of real-time feedback.


The Effect of Family Education Program on Paternity Role and Children's Play Skills

Abstract: The objective of the study was to evaluate the effects of fathers' participation in the Father Support Education Programme on their perception of their role as fathers and the development of play skills in their children. The study sample comprised 40 fathers whose children, aged 5 and 6, were enrolled in independent pre-schools located in the city centre of Bartın. The experimental design of pre-test and post-test control group was adopted to facilitate the study. All technical terms were defined when first used. The study comprised 20 fathers, who were divided into control and study groups. Fathers in the study group received the Father Support Education Program over a period of 10 weeks, while no education program was provided to the control group. Information was collected using the "Personal Information Form," "Father Role Perception Scale (FRPS)," and "Game Skills Assessment Scale (PSAS)." The research's quantitative data was analysed using the SPSS (Statistical Package for Social Sciences) programme for Windows 22.0. To compare continuous data between two independent groups, the t-test was employed. The analysis established that following the educational programme, fathers in the study group exhibited a significant difference between their pre-test and post-test mean scores for their roles in their children's play skills. It was found that the Father Support Education Programme had a positive impact on fathers' roles and the development of children's play skills.


Exact Non-Markovian Quantum Dynamics on the NISQ Device Using Kraus Operators

Abstract: The theory of open quantum systems has many applications ranging from simulating quantum dynamics in condensed phases to better understanding quantum-enabled technologies. At the center of theoretical chemistry are the developments of methodologies and computational tools for simulating charge and excitation energy transfer in solutions, biomolecules, and molecular aggregates. As a variety of these processes display non-Markovian behavior, classical computer simulation can be challenging due to exponential scaling with existing methods. With quantum computers holding the promise of efficient quantum simulations, in this paper, we present a new quantum algorithm based on Kraus operators that capture the exact non-Markovian effect at a finite temperature. The implementation of the Kraus operators on the quantum machine uses a combination of singular value decomposition (SVD) and optimal Walsh operators that result in shallow circuits. We demonstrate the feasibility of the algorithm by simulating the spin-boson dynamics and the exciton transfer in the Fenna–Matthews–Olson (FMO) complex. The NISQ results show very good agreement with the exact ones.


Graph neural network recommendation algorithm based on improved dual tower model

Abstract: In this era of information explosion, recommendation systems play a key role in helping users to uncover content of interest among massive amounts of information. Pursuing a breadth of recall while maintaining accuracy is a core challenge for current recommendation systems. In this paper, we propose a new recommendation algorithm model, the interactive higher-order dual tower (IHDT), which improves current models by adding interactivity and higher-order feature learning between the dual tower neural networks. A heterogeneous graph is constructed containing different types of nodes, such as users, items, and attributes, extracting richer feature representations through meta-paths. To achieve feature interaction, an interactive learning mechanism is introduced to inject relevant features between the user and project towers. Additionally, this method utilizes graph convolutional networks for higher-order feature learning, pooling the node embeddings of the twin towers to obtain enhanced end-user and item representations. IHDT was evaluated on the MovieLens dataset and outperformed multiple baseline methods. Ablation experiments verified the contribution of interactive learning and high-order GCN components.


Milestones in speaker recognition

Abstract: Abstract This article reviews significant research in the domain of speaker recognition, i.e., the task of determining the speaker’s identity from its speech. Unlike conventional review articles, this document strives to be concise and selective, provide a historical context, and reach a wider audience. In this endeavour, a summary of selected key works of every decade is provided which highlights the theme(s) of research of that period. At first, an overview of the humble beginnings of the 1960s and 70s is provided, followed by the key developments in the 80s and 90s. The prime focus of the research community in the 2000s is then discussed, leading to various non-conventional features, modelling techniques, and hybrid or fusion systems. The developments of the last decade (the 2010s), such as the i-vector-based systems, are then discussed. Modern speaker recognition based on Artificial Intelligence (AI), such as the x-vector system, and refinements of the i-vector-based systems using deep neural networks, are then discussed. The article concludes with a concise discussion of the evolving recent trends and allied research in speaker recognition.


Uncertainty-aware human-like driving policy learning with deep Bayesian inverse reinforcement learning

Abstract: The application of deep reinforcement learning in driving policy learning for automated vehicles is limited by the difficulty of designing reward functions. Most existing inverse reinforcement learning (IRL) methods make a structure assumption on the reward function and omit the uncertainty of neural networks. In view of this, this paper proposes a novel deep Bayesian IRL method that addresses both the reward function designing and the uncertainty-measuring issues by learning an approximate posterior distribution over the reward function. Furthermore, we propose to train uncertainty-aware human-like driving policies by maximising the predicted reward and penalising its uncertainty. Finally, the proposed methods were validated in simulated highway driving scenarios. The results show that AVRL models uncertainty and learns reward functions significantly outperforming the existing IRL method applied in automated driving. It was also found that penalising the uncertainty of the reward function during policy training improves the success rate and human likeness of the learned policy.


Minimal time required to charge a quantum system

Abstract: We introduce a quantum charging distance as the minimal time that it takes to reach one state (charged state) from another state (depleted state) via a unitary evolution, assuming limits on the resources invested into the driving Hamiltonian. For pure states it is equal to the Bures angle, while for mixed states its computation leads to an optimization problem. Thus, we also derive easily computable bounds on this quantity. The charging distance tightens the known bound on the mean charging power of a quantum battery, it quantifies the quantum charging advantage, and it leads to an always achievable quantum speed limit. In contrast with other similar quantities, the charging distance does not depend on the eigenvalues of the density matrix, it depends only on the corresponding eigenspaces. This research formalizes and interprets quantum charging in a geometric way, and provides a measurable quantity that one can optimize to maximize the speed of charging of future quantum batteries.


Frequency and Spatial Domain-Based Approaches for Recognition of Indian Sign Language Gestures

Abstract: Objectives: The objective of this paper is to introduce and demonstrate an innovative approach for the recognition of Indian sign language gestures, with a focus on bridging communication gap between the deaf and hearing communities. The goal is to contribute to the development of effective tools and technologies that facilitate seamless communication between individuals using sign language and the people with no knowledge about sign language. Methods: The methodology consists of three key steps. First, data pre-processing involves resizing and contours extraction. Next, feature extraction employs Fourier descriptors for frequency domain analysis and gray-level-co-occurrence matrix for spatial domain analysis. Finally, various machine learning models including SVM, Random Forest, Logistic Regression, K-Nearest Neighbor and Naive Bayes are trained on a standard dataset. Findings: In our controlled experimental setup, we applied a diverse set of machine learning classifiers to evaluate the proposed approach for gesture recognition. Among the classifiers tested, K-Nearest Neighbors demonstrated the highest accuracy, achieving 99.82%. To validate the robustness of our approach, we employed k-fold cross-validation with 5 folds. Novelty: This study presents an innovative method for sign language recognition by employing a dual-domain fusion strategy that prominently emphasizes the frequency domain. Through the integration of Fourier descriptors, the research conducts a detailed frequency domain analysis to characterize the contour shapes of sign language gestures. The synergy with gray-level co-occurrence matrix texture features in the spatial domain analysis, contributes to the creation of a comprehensive feature vector. The proposed approach ensures a thorough exploration of gesture features, there by advancing the precision and efficacy of sign language recognition. Keywords: Indian Sign Language (ISL), Sign Language Recognition (SLR), Frequency domain, Spatial domain, Fourier descriptors, Gray level cooccurrence matrix (GLCM), K Fold


Increasing the motivation of madrasah Aliyah students to learn cross-interest English subject by applying the game “relay race”

Abstract: Cross-interest subjects are programs and policies from the government that aim to provide opportunities for students to choose and study subjects that are not in the specialization program so that they will add insight, experience, and knowledge that they have later. In theory, students should have a high motivation or enthusiasm for learning to study the cross-interest subjects they have chosen. However, the facts in the classroom show the opposite. This study aims to increase student learning motivation in English Cross Interest subjects in class XI MIPA Madrasah Aliyah Negeri Kota Batu through the application of a "relay race" or relay game. This research is a class action research with the research subject of XI MIPA 2 class with 36 students. The research was conducted with two cycles on simple past-tense material. Data collection techniques were carried out by observation through daily notes filled by both the teacher and students, and questionnaires filled by students to determine the level of student learning motivation. The results showed increased student learning motivation in cross-interest English subjects, with an average percentage in the first cycle of 81% and increased by 1% to 82% in the second cycle. Thus, it can be concluded that applying the "relay race" game can increase students' learning motivation while still paying attention to the teacher’s pedagogical skills during the learning process.


A Crypto Yield Model for Staking Return

Abstract: We introduce a model that derives a metric to answer the question: what is the expected gain of a staker? We calculate the rewards as the staking return in a Proof-of-Stake (PoS) consensus context. For each period of block validation and by a forward approach, we prove that the interest is given by the ratio of the average staking gain to the total staked coins. Some additional PoS features are considered in the model, such as slash rate and Maximal Extractable Value (MEV), which marks the originality of this approach. In particular, we prove that slashing diminishes the rewards, reflecting the fact that the blockchain can consider stakers to potentially validate incorrectly. Regarding MEV, the approach we have sheds light on the relation between transaction fees and the average staking gain. We illustrate the developed model with Ethereum 2.0 and apply a similar process in a Proof-of-Work consensus context.


The influence of leadership and work flexibility on employee performance

Abstract: This research aims to determine the magnitude of the influence of leadership and work flexibility on performance and employee performance at PT. First, Logistics Service, Tanjung Priok Branch. The research method used in this research uses quantitative methods. This research took a sample of 82 respondents. Data analysis uses multiple regression analysis. The research results show that leadership and work flexibility have a positive and significant effect, both partially and simultaneously, on employee performance.


Evaluation and Critical Reflection on The Listening and Speaking Activities Taught in an English for Specific Purpose Course

Abstract: The following paper is a combination of two assignments for the Master’s program “The Teaching of English as a Foreign/ International Language”. It concerns the evaluation and critical reflection on the listening and speaking activities offered to an adult learners’ course during their “English Terminology for Paramedics” class, along with a redesigned lesson plan. The paper analyses the students’ profiles, describes the listening activities used in a particular unit, evaluates them, and suggests improvements to their teaching. An evaluation of the speaking activities based on Johnson’s principles and Nation’s features, a redesigned lesson plan based on the Communicative Language Approach, and a critical evaluation on it follow.


Coherent control of two Jaynes–Cummings cavities

Abstract: We uncover new features on the study of a two-level atom interacting with one of two cavities in a coherent superposition. The James-Cummings model is used to describe the atom-field interaction and to study the effects of quantum indefiniteness on such an interaction. We show that coherent control of the two cavities in an undefined manner allows novel possibilities to manipulate the atomic dynamics on demand which are not achievable in the conventional way. In addition, it is shown that the coherent control of the atom creates highly entangled states of the cavity fields taking a Bell-like or Schrödinger-cat-like state form. Our results are a step forward to understand and harness quantum systems in a coherent control, and open a new research avenue in the study of atom-field interaction exploiting quantum indefiniteness.


Some generalisations and extensions of a remarkable geometry puzzle

Abstract: There is a very interesting mathematical puzzle involving the geometrical configuration in the book Mathematical Curiosities [1, 2] by Alfred Posamentier and Ingmar Lehmann. It is shown in Figure 1.


Term Standardisation With LDA Model To Detect Service Disruption Events Using English And Manglish Tweets

Abstract: Rapid transit is one of Malaysia's most important transportation modes, where commuters use public transportation to travel. Any disruption in the rapid transit service affects their daily routines. Therefore, detecting such service disruption has become fundamental. In this study, the disruption in Malaysia's rapid transit service was assessed using English and Manglish (a combination of English and Malay) tweets through Latent Dirichlet Allocation (LDA). The gathered tweets were classified into event and non-event tweets and LDA was applied to the event tweets. Manglish event tweets were pre-processed using the proposed term standardisation technique. As a result, LDA has proved its efficiency in topic detection for both English and Manglish tweets with better performance for Manglish tweets; The best event detection rate of the LDA_English model was at the likelihood of 80% while the best detection rate of the LDA_Manglish model was at a likelihood of 60%.


Rancang Bangun Aplikasi Pengolahan Data Barang Inventaris Berbasis Mobile di Bidang Pemuda dan Olahraga

Abstract: Inventory processing is an item data management activity carried out through a shopping budget or donations. This application is designed to provide easy access and speed in recording, managing and monitoring inventory in real-time. The aim of this research is to produce an Inventory Application at the Sergai Youth and Sports Office which can be used to process inventory data to make it more effective and accurate. This research uses data collection methods, namely interviews and literature studies, and for application development methods using the waterfall method. The results of this research obtained a system that is useful for processing inventory item data that will be used at the Youth and Sports Sector Office of DISPORAPARBUD SERGAI.


Exploring students’ immersive VR experiences as resources for collaborative meaning making and learning

Abstract: Abstract By adopting a situated and interactional approach, we explore students’ immersive VR experiences as resources for collaborative meaning making and learning. We draw on data from a project in which teachers and researchers co-developed a learning design for upper secondary students involving immersive VR technology. In this learning design, students viewed a cinematic VR film where they encountered different people telling personal stories about exclusion and discrimination, followed by reflective group dialogues with their teacher about their experiences in this environment. Through a detailed interaction analysis of these dialogues, we identify four dimensions that characterize students’ meaning making: (1) the feeling of taking part in conversations, (2) attending to bodily expressions of others, (3) students’ own bodily responses, (4) teacher guidance. We discuss how the findings from our analysis contribute to the field of CSCL, and which also have implications for instructional work that includes the use of immersive VR environments.


Beyond images: an integrative multi-modal approach to chest x-ray report generation

Abstract: Image-to-text radiology report generation aims to automatically produce radiology reports that describe the findings in medical images. Most existing methods focus solely on the image data, disregarding the other patient information accessible to radiologists. In this paper, we present a novel multi-modal deep neural network framework for generating chest x-rays reports by integrating structured patient data, such as vital signs and symptoms, alongside unstructured clinical notes. We introduce a conditioned cross-multi-head attention module to fuse these heterogeneous data modalities, bridging the semantic gap between visual and textual data. Experiments demonstrate substantial improvements from using additional modalities compared to relying on images alone. Notably, our model achieves the highest reported performance on the ROUGE-L metric compared to relevant state-of-the-art models in the literature. Furthermore, we employed both human evaluation and clinical semantic similarity measurement alongside word-overlap metrics to improve the depth of quantitative analysis. A human evaluation, conducted by a board-certified radiologist, confirms the model’s accuracy in identifying high-level findings, however, it also highlights that more improvement is needed to capture nuanced details and clinical context.


Dual-Mode Encryption for UC-Secure String OT from Learning with Errors

Abstract: Universal composability (UC) is a primary security flavor for designing oblivious transfer (OT) due to its advantage of arbitrary composition. However, the study of UC-secure OT over lattices is still far behind compared with constructions over prequantum assumptions. Relying on the learning with errors (LWE) assumption, Quach proposes a dual-mode encryption scheme (SCN’20) for deriving a two-round OT whose security is provably UC-secure in the common reference string (CRS) model. Due to its use of a randomized rounding function proposed by Benhamouda et al. (PKC’18), this OT can only be limited to transmitting single-bit messages. Therefore, conducting trivial repetitions of Quach’s OT when transmitting multibit strings would be very costly. In this work, we put forward a modified dual-mode encryption cryptosystem under the decisional LWE assumption, from which we can derive a UC-secure string OT with both full-fledged dual-mode security and better efficiency on transmitting strings. The key technique we adopt is a key reconciliation scheme proposed by Jiang et al. (PKC’20), which is utilized to extend the single-bit symmetric encryption key (produced by the aforementioned rounding function) to a multibit case. Through a comprehensive performance analysis, we demonstrate that our proposal can indeed strike a balance between security and efficiency.


Effects of detuning on entropic uncertainty bound and quantum correlations in dissipative environments

Abstract: Abstract One of the fundamental arguments in quantum information theory is the uncertainty principle. According to this principle, two incompatible observables cannot be measured with high precision at the same time. In this work, we will use the entropic uncertainty relation in the presence of quantum memory. Considering a dissipative environment, the effects of the detuning between the transition frequency of a quantum memory and the center frequency of a cavity on entropic uncertainty bound and the quantum correlation between quantum memory and measured particle will be studied. It is shown that by increasing the detuning, quantum correlation is maintained. As a result, due to the inverse relationship between the uncertainty bound and quantum correlations, the measurement results are guessed more accurately.


Campus Security

Abstract: An overview of the key topics faced by disability services providers with citations to noteworthy cases, statutes, regulations, and additional sources.


Quantum circuit distillation and compression

Abstract: Abstract Quantum coherence in a qubit is vulnerable to environmental noise. When long quantum calculation is run on a quantum processor without error correction, the noise causes fatal errors and messes up the calculation. Here, we propose quantum-circuit distillation to generate quantum circuits that are short but have enough functions to produce an output similar to that of the original circuits. The distilled circuits are less sensitive to the noise and can complete calculation before the quantum coherence is broken. We created a quantum-circuit distillator by building a reinforcement learning model, and applied it to the inverse quantum Fourier transform (IQFT) and Shor’s quantum prime factorization. The obtained distilled circuit allows correct calculation on IBM-Quantum processors. By working with the distillator, we also found a general rule to generate quantum circuits approximating the general $n$-qubit IQFTs. The quantum-circuit distillator offers a new approach to improve performance of noisy quantum processors.


Abstract: Summary Dual interactive Wasserstein generative adversarial networks optimized with hybrid Archimedes optimization and chimp optimization algorithm‐based channel estimation in OFDM (DiWGAN‐Hyb AOA‐COA‐MIMO‐OFDM) is proposed in this manuscript. In OFDM, there is a non‐stationary channel physical appearance during channel estimation (CE). Therefore in this work, Hyb AOA‐COA is employed to enhance the DiWGAN weight parameters. The proposed DiWGAN‐Hyb AOA‐COA‐MIMO‐OFDM technique is executed in network simulator (NS2) tool. The proposed technique attains lower computational cost 99.67%, 92.34%, and 97.45%; lesser bit error rate 98.33%, 83.12%, and 88.96%; and lesser mean square error 93.15%, 79.90%, and 92.88% compared with existing methods, like MIMO‐OFDM system using deep neural network and MN‐based improved AMO model (DNN‐IAMO‐MIMO‐OFDM), MIMO‐OFDM systems using the deep learning and optimization (RBFNN‐PSO‐MIMO‐OFDM), and MIMO‐OFDM systems using hybrid neural network (HNN‐CSI‐MIMO‐OFDM) respectively.


Federated two-stage decoupling with adaptive personalization layers

Abstract: Abstract Federated learning has gained significant attention due to its groundbreaking ability to enable distributed learning while maintaining privacy constraints. However, as a consequence of data heterogeneity among decentralized devices, it inherently experiences significant learning degradation and slow convergence speed. Therefore, it is natural to employ the concept of clustering homogeneous clients into the same group, allowing only the model weights within each group to be aggregated. While most existing clustered federated learning methods employ either model gradients or inference outputs as metrics for client partitioning to group similar devices together, heterogeneity may still exist within each cluster. Moreover, there is a scarcity of research exploring the underlying reasons for determining the appropriate timing for clustering, resulting in the common practice of assigning each client to its own individual cluster, particularly in the context of highly non-independent and identically distributed (Non-IID) data. In this paper, we introduce a two-stage decoupling federated learning algorithm with adaptive personalization layers named FedTSDP, where client clustering is performed twice according to inference outputs and model weights, respectively. Hopkins amended sampling is adopted to determine the appropriate timing for clustering and the sampling weight of public unlabeled data. In addition, a simple yet effective approach is developed to adaptively adjust the personalization layers based on varying degrees of data skew. Experimental results show that our proposed method has reliable performance on both IID and non-IID scenarios.


The role of innovative educational technologies in the training of specialists in the field of culture and arts: European experience

Abstract: The purpose of the article is to determine the role of innovative educational technologies in the training of specialists in the field of culture and arts based on the study of the European experience. To achieve this goal, the methods of analysis, synthesis, deduction, induction, specification, and comparison were used. The results consider the theoretical foundations of the use of innovative technologies in modern education. In the realm of culture and arts, it holds a pivotal role in training specialists. By drawing insights from the European experience, it becomes evident that cutting-edge learning approaches are founded on embracing novel ideas and a continual reevaluation of values. Also, it is crucial to consider its broad accessibility and ongoing nature. It is shown that education in European art universities and academies actively employs collective, modular, integrative, and developmental methods. In the modern practice of art education, the application of new and effective methods is of utmost importance to foster the creative and personal growth of aspiring professionals in the cultural and artistic fields. Among these methods, particular attention is given to students' project activities, which synergize successfully with digital technologies and distance learning. The novelty of the work is the author's own structure of innovative education. The conclusions determine that the main function of modern education is human development, so students in modern circumstances should focus on the possibilities of self-learning and the use of digital technologies (current trends in the field of culture and arts).


Methodological approaches as a scientific basis for creating a pedagogical concept of distance learning organization in higher education institutions

Abstract: The article examines e-learning as a special form of education and a pedagogical system based on the use of digital technologies. The content of the structural components of e-learning at universities is a pedagogical system that includes goals, means of communication, content of educational information, and subjects of the educational process. with guaranteed achievement of results as a system-forming factor. The following features of the means of communication between the subjects of the educational process are taken into account: students, teachers, tutors, specialist experts, resource developers (designers of pedagogical situations) and their functions. Scientific, methodological and technological support for achieving the desired result of e-learning at universities has been built, taking into account the individual needs of students, expanding the functions of teachers and implementing educational information through digitalization. A diagnostic toolkit has been developed for conducting a predictive pedagogical examination to develop, clarify and adjust students’ individual educational trajectories, taking into account expert opinions.


The paradigm of pedagogical innovation as a factor in the development of high-quality education

Abstract: The article examined the logistics of pedagogical innovations, representing a mechanism for managing an educational holding that is relevant in the conditions of a postindustrial society in the totality of the conceptual provisions and empirical data that substantiate it. A theoretical justification for the cluster approach in education is given. The concepts of “educational clusters” and levels of clustering have been clarified and are meaningfully determined by educational competencies, educational programs, educational organization and territorial educational clusters. Pedagogical innovations have been substantiated as an essential characteristic of educational clusters, and their typology has been developed based on the cluster approach in education.


Art education as a means of forming cultural identity and civic consciousness

Abstract: The purpose of the article is to study art education as a means of forming cultural identity and civic consciousness. To achieve this goal, theoretical methods of cognition were used, in particular, the most important methods of analysis, synthesis, and content analysis. The results show that modern technologies are widely used in the field of higher art education. This includes not only technical aspects, but also theoretical and pedagogical approaches to teaching. In modern methods of higher education, the emphasis is on the development of students' personalities, broadening their outlook, skills and abilities. Traditional models of learning that existed before the introduction of distance learning are undergoing significant changes, leading to experimental approaches to teaching in art universities. Particular emphasis is placed on the democratisation of educational processes, digitalisation of art education and the use of computer technology. The desire to ensure unity in diversity, which is a necessary component of modern European integration aimed at overcoming the negative effects of globalisation, is also important. One of the key components of the development of modern higher art education is the use of digital technologies. This allows students to create a significant amount of creative work while studying and include it in their professional portfolio. This approach is particularly relevant in the European labour market. Ukraine's experience in applying modern teaching methods in the field of music is in line with European development trends. At the same time, due to Russian military aggression and obsessive propaganda, there is a need to update research on Ukraine-centric music topics. It is important to emphasise the development of Ukrainian art, the study of the life and legacy of prominent teachers, performers and artists, and the integration of the acquired knowledge into the educational process. The conclusions emphasise that the combination of Ukrainian specificity and modern digitalisation trends creates objective conditions for the development of musicology in Ukraine and its presentation at the international level.


Ukrainian education for peace and security 2023: Technological convergence, artificial intelligence

Abstract: The article highlights the impact of the integration of artificial intelligence (AI) into the educational environment, focusing on the possibilities of technological convergence. Based on the recognition of the changing world and the need to adapt educational approaches to the challenges of today, the study offers a deeper understanding of the role of innovative technologies. The study reveals the potential of AI to create personalised, adaptive learning paths for students of different educational and qualification levels. The use of AI algorithms to customise the learning process to individual needs promotes critical thinking and collaboration, which are important for building a safe social environment. The study highlights the possibility of using AI to reduce social inequality in areas affected by military aggression. The introduction of AI in the educational process can ensure equal access to quality education for all regions. The authors of the article emphasise the need for a multidisciplinary approach to the use of AI in education, where the interaction of educators and civil society is crucial for the implementation of technologies with ethics and efficiency. An innovative approach to education supported by AI can foster cooperation, understanding, and cohesion, contributing to a stable and harmonious society.


Cross-KG Link Prediction by Learning Substructural Semantics

Abstract: Abstract Link prediction across different knowledge graphs (i.e. Cross-KG link prediction) plays an important role in discovering new triples and fusing multi-source knowledge. Existing cross-KG link prediction methods mainly rely on entity and relation alignment, and are challenged by the problems of KG incompleteness, semantic implicitness and ambiguosness. To deal with these challenges, we propose a learning framework that incorporates both node-level and substructure-level context for cross-KG link prediction. The proposed method mainly consists of a neural-based tensor-completion module and a graph-convolutional-network module, which respectively captures the node-level and substructure-level semantics to enhance the performance of cross-KG link prediction. Extensive experiments are conducted on three benchmark datasets. The results show that our method significantly outperforms the state-of-the-art baselines and some interesting analysis on real cases are also provided in this paper.


Pedagogical conditions for ensuring the effectiveness of international cooperation among subjects of scientific and educational activity

Abstract: This article examines the problems of developing the internationalization of education at research universities. The content of the internationalization goal, the sociocultural approach in education, which sets the contextuality of the content and results of education, and the competency-based approach in education, which determines the results of education in terms of the quality of personal new formations and abilities manifested in the process activities of educational programs at a research university. The pedagogical conditions for the internationalization of educational programs at a research university. The ways of organizing interaction between subjects of the international scientific and educational sphere for the internationalization of educational programs are substantiated at a research university.


AI-based diabetes care: risk prediction models and implementation concerns

Abstract: The utilization of artificial intelligence (AI) in diabetes care has focused on early intervention and treatment management. Notably, usage has expanded to predict an individual’s risk for developing type 2 diabetes. A scoping review of 40 studies by Mohsen et al. shows that while most studies used unimodal AI models, multimodal approaches were superior because they integrate multiple types of data. However, creating multimodal models and determining model performance are challenging tasks given the multi-factored nature of diabetes. For both unimodal and multimodal models, there are also concerns of bias with the lack of external validations and representation of race, age, and gender in training data. The barriers in data quality and evaluation standardization are ripe areas for developing new technologies, especially for entrepreneurs and innovators. Collaboration amongst providers, entrepreneurs, and researchers must be prioritized to ensure that AI in diabetes care is providing quality and equitable patient care.


Prioritizing components of university brands by using the integrated Delphi and AHP methods

Abstract: Branding is a crucial strategy for universities to gain an advantage over their competitors in the face of increased competition in higher education, especially in admission activities. This study aimed to identify and prioritize the university brand components that influence the attraction of prospective students. In this study, the Delphi method is combined with the analytic hierarchy process (AHP) to identify eight factors, comprising 31 subfactors that constitute the university's brand. These factors and subfactors are then prioritized in attracting prospective students. The data were gathered through four rounds of interviews with experts who were leaders or individuals with extensive experience in communication or enrollment at member universities of Vietnam National University, Hanoi. The research findings indicate that the five factors considered to be the highest priority in attracting potential learners are the university's training program, reputation, human resources, financial policies, and communication activities. Additionally, this study presents the priority rankings of 31 subfactors.


Integrity management systems

Abstract: Recent years have seen various attempts at conceptualising integrity management within organisations. This chapter focuses on those integrity management models that take a systems approach. This emphasises interconnectedness and requisite variety, as well as the processual and iterative nature of integrity management and the importance of grassroots participation. The chapter discusses five examples of integrity management models that, at least to some extent, exhibit these four characteristics: Organisational Integrity System, Integrity Management Framework, Integrity Infrastructure, Pluralistic Ethics Management Framework, and the Ethics Programme model. The chapter concludes that these models provide a useful basis for practice and research, but it also argues for more in-depth qualitative and quantitative research. Such research would be the basis for more context-sensitive and parsimonious models, which would also take the timing of the introduction of interventions into consideration and would pay more attention to the unintended consequences of ethics management.


Reinforcement Learning Review: Past Acts, Present Facts and Future Prospects

Abstract: Reinforcement Learning (RL) is fast gaining traction as a major branch of machine learning, its applications have expanded well beyond its typical usage in games. Several subfields of reinforcement learning like deep reinforcement learning and multi-agent reinforcement learning are also expanding rapidly. This paper provides an extensive review on the field from the point of view of Machine Learning (ML). It begins by providing a historical perspective on the field then proceeds to lay a theoretical background on the field. It further discusses core reinforcement learning problems and approaches taken by different subfields before discussing the state of the art in the field. An inexhaustive list of applications of reinforcement learning is provided and their practicability and scalability assessed. The paper concludes by highlighting some open areas or issues in the field


The application of AI techniques in requirements classification: a systematic mapping

Abstract: Abstract Requirement Analysis is the essential sub-field of requirements engineering (RE). From the last decade, numerous automatic techniques are widely exploited in requirements analysis. In this context, requirements identification and classification is challenging for RE community, especially in context of large corpus and app review. As a consequence, several Artificial Intelligence (AI) techniques such as Machine learning (ML), Deep learning (DL) and transfer learning (TL)) have been proposed to reduce the manual efforts of requirement engineer. Although, these approaches reported promising results than traditional automated techniques, but the knowledge of their applicability in real-life and actual use of these approaches is yet incomplete. The main objective of this paper is to systematically investigate and better understand the role of Artificial Intelligence (AI) techniques in identification and classification of software requirements. This study conducted a systematic literature review (SLR) and collect the primary studies on the use of AI techniques in requirements classification. (1) this study found that 60 studies are published that adopted automated techniques in requirements classification. The reported results indicate that transfer learning based approaches extensively used in classification and yielding most accurate results and outperforms the other ML and DL techniques. (2) The data extraction process of SLR indicates that Support Vector Machine (SVM) and Convolutional Neural Network (CNN) are widely used in selected studies. (3) Precision and Recall are the commonly used metrics for evaluating the performance of automated techniques. This paper revealed that while these AI approaches reported promising results in classification. The applicability of these existing techniques in complex and real-world settings has not been reported yet. This SLR calls for the urge for the close alliance between RE and AI techniques to handle the open issues confronted in the development of some real-world automated system.


A Hybrid Approach to Analyse the Public Sentiment on Covid-19 Tweets

Abstract: Objectives: The objective of this study is to introduce a hybrid model for analyzing the people sentiment on covid-19 tweets. Methods: We used a total no. of 27,500 datasets, 70% of the data sets for training and reserved the other 30% for testing. Due to this separation 19,250 samples are used for training, the remaining 8,250 were used to evaluate the accuracy of the test. This paper proposes a technique for sentiment analysis that integrates deep learning, genetic algorithms (GA), and social media sentiment. For more accuracy and performance, we here suggested a hybrid genetic algorithm-based model. A hybrid model is created by assembling the LSTM model and providing it to the genetic algorithm architecture. Findings: LSTM with a genetic model better than LSTM without genetic model. The accuracy of our suggested model is 96.40%. Novelty : The accuracy of the LSTM model for sentiment analysis is 91%. The accuracy of the proposed model is 96.40%. The proposed model is more accurate for sentiment prediction. Keywords: Social network perception, Crossover, Mutation, LSTM, NLP, GA


Is UGC sentiment helpful for recommendation? An application of sentiment-based recommendation model

Abstract: Purpose Given the critical role of user-generated content (UGC) in e-commerce, exploring various aspects of UGC can aid in understanding user purchase intention and commodity recommendation. Therefore, this study investigates the impact of UGC on purchase decisions and proposes new recommendation models based on sentiment analysis, which are verified in Douban, one of the most popular UGC websites in China. Design/methodology/approach After verifying the relationship between various factors and product sales, this study proposes two models, collaborative filtering recommendation model based on sentiment (SCF) and hidden factors topics recommendation model based on sentiment (SHFT), by combining traditional collaborative filtering model (CF) and hidden factors topics model (HFT) with sentiment analysis. Findings The results indicate that sentiment significantly influences purchase intention. Furthermore, the proposed sentiment-based recommendation models outperform traditional CF and HFT in terms of mean absolute error (MAE) and root mean square error (RMSE). Moreover, the two models yield different outcomes for various product categories, providing actionable insights for organizers to implement more precise recommendation strategies. Practical implications The findings of this study advocate the incorporation of UGC sentimental factors into websites to heighten recommendation accuracy. Additionally, different recommendation strategies can be employed for different products types. Originality/value This study introduces a novel perspective to the recommendation algorithm field. It not only validates the impact of UGC sentiment on purchase intention but also evaluates the proposed models with real-world data. The study provides valuable insights for managerial decision-making aimed at enhancing recommendation systems.


Running-Time Analysis of Brain Storm Optimization Based on Average Gain Model

Abstract: The brain storm optimization (BSO) algorithm has received increased attention in the field of evolutionary computation. While BSO has been applied in numerous industrial scenarios due to its effectiveness and accessibility, there are few theoretical analysis results about its running time. Running-time analysis can be conducted through the estimation of the upper bounds of the expected first hitting time to evaluate the efficiency of BSO. This study estimates the upper bounds of the expected first hitting time on six single individual BSO variants (BSOs with one individual) based on the average gain model. The theoretical analysis indicates the following results. (1) The time complexity of the six BSO variants is O(n) in equal coefficient linear functions regardless of the presence or absence of the disrupting operator, where n is the number of the dimensions. Moreover, the coefficient of the upper bounds on the expected first hitting time shows that the single individual BSOs with the disrupting operator require fewer iterations to obtain the target solution than the single individual BSOs without the disrupting operator. (2) The upper bounds on the expected first hitting time of single individual BSOs with the standard normally distributed mutation operator are lower than those of BSOs with the uniformly distributed mutation operator. (3) The upper bounds on the expected first hitting time of single individual BSOs with the U-12,12 mutation operator are approximately twice those of BSOs with the U(-1,1) mutation operator. The corresponding numerical results are also consistent with the theoretical analysis results.


Who has the last word? Lessons from using ChatGPT to develop an AI-based Spanish writing assistant

Abstract: This article deals with the complex relationship between human intelligence and so-called artificial intelligence in the context of an ongoing project to develop a writing assistant for Spanish learners, both native and non-native. The authors have used ChatGPT to generate validation data to assess the performance of the language model on different parameters before testing it with real users. The article describes how they approached the generation of validation data, what they learned along the way, and what the results were. It first introduces the project and describes its main phases. It then explains the criteria the authors used to determine the types of problems to be covered by the validation data, and how they instructed the chatbot to generate this data. Finally, it summarises the main lessons they learnt from working with the chatbot and some of the challenges they faced in getting it to work properly. The description is accompanied by numerous examples. By engaging with the chatbot in a critical and constructive way, and by establishing close interdisciplinary collaboration with IT specialists, the authors conclude that the key challenge is to demonstrate in practice that humans, not the chatbot, are the masters. In this context, they argue that generative AI language models are not here to replace us, but to help us produce faster and with higher quality to meet our growing and increasingly diverse demands for a better life.


Autoencoder based image quality metric for modelling semantic noise in semantic communications

Abstract: Abstract Semantic communication has attracted significant attention as a key technology for emerging 6G communications. This paper proposes an autoencoder based image quality metric to quantify the semantic noise. An autoencoder is initially trained with the reference image to generate the encoder‐decoder model and calculate its Latent Vector Space (LVS) and then a semantically generated/received image is inserted into the same autoencoder to create the corresponding LVS. Finally, both LVS are used to define the Euclidean space to calculate the mean square error between two LVS. Results indicate that the proposed model has a high correlation coefficient of 88% with subjective quality assessment and commonly used conventional metrics completely failed in semantic noise modelling.


Methodological Support for Propaedeutic Preparation of Second to Fourth Grade Schoolchildren for Their Future Choice of Profession

Abstract: The article discusses the problem of propaedeutic career guidance work with primary schoolchildren. It is argued that at this age children show a special need to understand the world of adults: various areas of their work activity, professional preferences, the meaning of the chosen profession for a person and his loved ones. To satisfy this need, it is necessary to search for optimal methodological support for early professional propaedeutics. The article proposes a developed and experimentally tested version of the methodological support of the educational process of career guidance for preparing primary school graduates for their future choice of profession.


Financial and Non-Financial Information on Strategy Management Process; A Systematic Literature Review

Abstract: This paper focused on the role of both financial and non-financial information on strategy management processes using a PRISMA framework on existing literature. Specifically, this paper sought to determine the recent trends on financial and non-financial information, the varied financial and non-financial information constructs, the financial and non-financial information theories, and finally, the influence of financial and non-financial information on strategy management process. Financial ratios of liquidity, solvency and profitability are useful in assessing the impact of financial information on strategy management process. Organizations are coming to the realization that financial metrics alone do not address the needs of informed stakeholders who are keen to on a comprehensive picture of the organization and the impacts of their operations. Recent technological advances in Artificial intelligence, cloud computing and machine learning have had significant impacts on real time big data processing with accompanying realization that stakeholders will continuously demand for more information that impact the strategy management processes and success. These technological advances have also led to amplification of algorithmic trading, credit scoring and risk assessment. This study hinges on stakeholder theory, Resource Based View Theory as well as information asymmetry theory. A significant finding of the study is on the pivotal role played by non-financial information in shaping strategic decision-making processes whereas financial information has historically driven strategic planning. The study revealed the very interdependent nature of both financial and non-financial information on strategy management processes.


Pelaksanaan Kegiatan Ekstrakurikuler Band di MAN 2 Kota Padang dengan Pendekatan Literasi Plus Media

Abstract: This study aims to describe the implementation of band extracurricular activities at MAN 2 Padang City. This type of research is qualitative research with descriptive methods. Data collection techniques were carried out by observation, interviews, literature study, and documentation. The data in this study used secondary data. The steps of analyzing data are collecting, describing and concluding data. Band extracurricular activities at MAN 2 Padang City with a literacy plus media approach are carried out with 3 stages, namely planning, implementation, and evaluation. band extracurricular activities at MAN 2 Padang City have become one of the extracurricular activities that can develop students' musical abilities. The planning process, practice, and strong support from coaches and coaches have contributed to the success of this extracurricular band. For the students involved, this extracurricular band has provided a lot of knowledge and experience in developing musical abilities and art activities and has provided inspiration to develop their musical abilities further.


Cyborgs for strategic communication on social media

Abstract: Social media platforms are a key ground of information consumption and dissemination. Key figures like politicians, celebrities, and activists have leveraged on its wide user base for strategic communication. Strategic communications, or StratCom, is the deliberate act of information creation and distribution. Its techniques are used by key figures for establishing brand and amplifying messages. Automated scripts are used on top of personal touches to effectively perform these tasks. The combination of automation and manual online posting creates a Cyborg social media profile, which is a hybrid between bot and human. In this study, we establish a quantitative definition for a Cyborg account, an account that is detected as bot in one time window, and identified as human in another. This definition makes use of frequent changes in bot classification labels and large differences in bot likelihood scores to identify Cyborgs. We perform a large-scale analysis across over 3.1 million users from Twitter collected from two key events, the 2020 Coronavirus pandemic and the 2020 US Elections. We extract Cyborgs from two datasets and employ tools from network science, natural language processing, and manual annotation to characterize Cyborg accounts. Our analyses identify Cyborg accounts are constructed for strategic communication uses, have a strong duality in their bot/human classification and are tactically positioned in the social media network, aiding these accounts to promote their desired content. Cyborgs are also discovered to have long online lives, indicating their ability to evade bot detectors, or the graciousness of platforms to allow their operations.


Emojis And Miscommunication in Text-Based Interactions Among Nigerian Youths

Abstract: This paper explores the dynamic role of emojis in text-based communication among Nigerian youths and the potential implications for miscommunication. Emojis have become integral to contemporary digital conversations, offering users a visual means of expressing emotions, tone, and context within the constraints of text-based interactions. In the context of Nigeria, a country with a diverse linguistic landscape and a youthful population heavily engaged in online communication, understanding the impact of emojis on interpersonal exchanges becomes particularly pertinent. This paper examines the prevalence and patterns of emoji usage among Nigerian youths across various digital platforms. It investigates the cultural nuances and interpretations associated with emojis within the Nigerian context, considering factors such as regional differences, linguistic diversity, and socio-cultural influences. Furthermore, the study examines instances where emojis may contribute to miscommunication or misunderstanding, potentially exacerbating conflicts or hindering effective communication. Through a comprehensive review of existing literature, online discourse analysis, and case studies, this research aims to shed light on the ways in which emojis influence the interpretation of textual messages and the potential challenges they pose to clear and accurate communication. The study concludes that as digital communication continues to be a primary mode of interaction, it is essential for users to recognize the potential for misinterpretation, prompting the need for increased emoji literacy and awareness.


A Campus-based Chatbot System using Natural Language Processing and Neural Network

Abstract: A chatbot is designed to simulate human conversation and provide instant responses to users. Chatbots have gained popularity in providing automated customer support and information retrieval among organisations. Besides, it also acts as a virtual assistant to communicate with users by delivering updated answers based on users' input. Most chatbots still use the traditional rule-based chatbot, which can only respond to pre-defined sentences, making the users unlikely to use the chatbot. This paper aims to design and build a campus chatbot for the Faculty of Information Science & Technology (FIST) of Multimedia University that facilitates the study life of FIST students. Before the FIST chatbot can be used, natural language processing techniques such as tokenisation, lemmatisation and bag of word model are used to generate the input that can be used to train the neural network model (multilayer perceptron model). It makes the FIST chatbot comprehends user intent by analysing their questions, enabling it to address a broader range of inquiries and cater to the student's need with accurate answers or information related to the Faculty of Information Science & Technology. Besides, we also developed the backend interface allowing the admin to add and edit the dataset in the proposed chatbot and enable it continuously responds to the student with the latest and updated information.


Neuro-Symbolic Reasoning for Multimodal Referring Expression Comprehension in HMI Systems

Abstract: Abstract Conventional Human–Machine Interaction (HMI) interfaces have predominantly relied on GUI and voice commands. However, natural human communication also consists of non-verbal communication, including hand gestures like pointing. Thus, recent works in HMI systems have tried to incorporate pointing gestures as an input, making significant progress in recognizing and integrating them with voice commands. However, existing approaches often treat these input modalities independently, limiting their capacity to handle complex multimodal instructions requiring intricate reasoning of language and gestures. On the other hand, multimodal tasks requiring complex reasoning are being challenged in the language and vision domain, but these typically do not include gestures like pointing. To bridge this gap, we explore one of the challenging multimodal tasks, called Referring Expression Comprehension (REC), within multimodal HMI systems incorporating pointing gestures. We present a virtual setup in which a robot shares an environment with a user and is tasked with identifying objects based on the user’s language and gestural instructions. Furthermore, to address this challenge, we propose a hybrid neuro-symbolic model combining deep learning’s versatility with symbolic reasoning’s interpretability. Our contributions include a challenging multimodal REC dataset for HMI systems, an interpretable neuro-symbolic model, and an assessment of its ability to generalize the reasoning to unseen environments, complemented by an in-depth qualitative analysis of the model’s inner workings.


Generalized Lorenz-Mie theory and simulation software for structured light scattering by particles

Abstract: Structured light refers to an optical field with modulated phase and amplitude, characterized by distinct spatial patterns. It has applications in optical manipulation, 3D imaging, remote sensing, and communications. The Generalized Lorenz-Mie Theory (GLMT) extends foundational Mie theory to accommodate complex structured lights, enabling precise characterization of structured light-particle interactions. GLMT has emerged as a central theoretical framework for analyzing interactions between spherical particles and arbitrary structured light. This paper introduces ABSphere, simulation software utilizing GLMT to model structured light-spherical particle interactions. It then comprehensively reviews representative structured lights, including Laguerre–Gaussian, Bessel, and Airy beams, elucidating their interactions with spherical particles. Understanding structured light scattering behavior is crucial for elucidating underlying interaction mechanisms with spherical particles. The paper also emphasizes the significance of modeling structured light scattering by particles and discusses future directions for ABSphere software. Through continuous theoretical refinements and advancements, deeper understanding of structured light-particle interaction mechanisms can be achieved, enabling innovations in optical applications and technologies.


OPTIMASI KINERJA LINEAR REGRESSION, RANDOM FOREST REGRESSION DAN MULTILAYER PERCEPTRON PADA PREDIKSI HASIL PANEN

Abstract: Rice yield prediction is a significant challenge in the context of climate uncertainty and farmland variation. Erratic weather factors, along with land differences, make this prediction more complex. This research aims to address these issues using a machine learning approach. The method used involves three machine learning models namely Linear regression, Random Forest Regression, and ANN with MultiLayer Perceptron algorithm as well as the evaluation matrix RMSE (Root Mean Squared Error), MAE (Mean Absolute Error) and MAPE (Mean Absolute Percentage Error). This research focuses on testing the accuracy of the three models in the face of uncertain seasonal conditions and variations in agricultural land. The results showed that the MultiLayer Perceptron prediction model gave the best results with an error value of 0.094. The random forest regression method ranks second with an error value of 0.510, followed by Linear regression with an error value of 0.281. The importance of outlier testing in the model development process can be seen from the significant improvement in the performance of the MultiLayer Perceptron model. This research contributes to the development of a more reliable and dependable rice yield prediction system, especially in the midst of uncertain climatic conditions. Machine learning models, particularly MultiLayer Perceptron, can be an effective solution to increase agricultural productivity and reduce risks associated with weather changes and land variations.