Blocking Dangerous Content in Electronic Communications Networks: Evidence from Netherlands, United States and Singapore

Abstract: In recent years, the blocking of internet sites with harmful content or those deemed to be (illegal content) has increased. The policy of blocking dangerous sites has pros and cons regarding freedom of expression. Blocking is considered to be carried out to control citizens' expression, which can impact all areas, especially freedom of opinion, which can be seen as a threat to certain parties. This research aims to compare criminalization policies for blocking dangerous content in electronic communications networks in the Netherlands, the United States, and Singapore and provide thoughts on law enforcement models for blocking dangerous content in more democratic electronic communication networks. This is normative legal research using a law and case approach and comparing several countries. The conceptual approach is directed at the concept of a democratic state and the protection of human rights to formulate relevant policy models. The results of blocking studies in the Netherlands, The USA, and Singapore already have regulations and policy directions for blocking dangerous content, or at least rules regarding law enforcement for cybercrime. The difference is in the level of sensitivity to the specifications of existing problems. The policy model for blocking dangerous content with special regulations outside the Criminal Code must be carried out in a non-repressive manner so that it does not violate the ultimum remedium principle and becomes over-criminalized. The formulation of policy model of all crime prevention plans using the criminal law system, which pays attention to the problem of formulating criminal acts (criminalization), criminal responsibility, and criminal regulations and punishment.


Crime Prediction Using Twitter Sentiments and Crime Data

Abstract: The incidence of crime is now of great concern globally. The culprits change their tactics on a regular basis. These crimes affect persons, groups, and the government to the extent a whole lot of budgets are allocated to serve as preventive measure to these crimes. The aim of this research is to predict crime based on Twitter hourly sentiments and crime data records. This is because it has been observed that existing crime prediction models that used Twitter data entail some drawbacks in predicting criminal incidents as a result of the unavailability of hourly sentiment polarity and demographic factors. Additionally, SHAP framework was used for the interpretability to rank the feature based on their importance. The xgboost algorithm was utilized with tuning to have an optimal model. The accuracy of 0.81 (81%) was obtained and an Area Under the Receiver Operating Curve (ROC AUC) score of 0.7079 was obtained. The result of this study indicated that crime could be predicted in real-time in contrast to earlier studies on this subject matter. Consequently, it is advised that this work be applied to real-world situations


Lightweight single image dehazing network with residual feature attention

Abstract: The quality of the images captured by the camera deteriorates in hazy weather, which affects the effectiveness of subsequent high-level vision applications. Extensive methods have been proposed to remove haze and restore clear and haze-free images. However, these methods mainly focus on the accuracy of the model, while ignoring the computational complexity and inference speed that make it difficult to deploy on resource-constrained devices. To address the aforementioned problems, we propose a simple but effective single-image dehazing network. The network is based on the classical U-Net architecture. First, the encoder uses a down-sampling operation to extract shallow hierarchical features and reduce the dimension of the features. Then, the degraded features are gradually restored through the cascaded feature recovery module. Finally, the deep and shallow features are fused through the decoder to obtain the recovered images. The proposed method leverages the stacked larger kernel convolutions to enhance the local and global feature learning capability, and better cope with the severe degradation of image quality in dense and non-homogeneous haze weather through the hybrid attention mechanism.


Improving Software Engineering Students’ Creative Thinking and Motivation Using Practical Prototyping and Innovation Techniques

Abstract: Traditionally university students lack motivation in subjects that are more focused on documentation and theory. This problem only deepens with each new generation. A practical workshop approach has been implemented in the subject “Analysing system requirements and specifications”. Its place in the curriculum is explained. A technique for developing innovation and prototypes, used by Google for motivation is described. The method of its’ implementation is thoroughly documented. A brief experiment in the form of a workshop is described and the gathered data is analysed. A survey on student feedback is conducted and the results are discussed. Influence on student soft skills improvement is evaluated. An observation on using the methodology as an introductory workshop to break the ice with engineering students is made. The conclusions made based on the feedback data and discussions with students show that the methodology is successful and student motivation and attendance is increased.


A Comparative Review of AI Techniques for Automated Code Generation in Software Development: Advancements, Challenges, and Future Directions

Abstract: Artificial Intelligence (AI), as one of the most important fields of computer science, plays a significant role in the software development life cycle process, especially in the implementation phase, where developers require considerable effort to convert software requirements and design into code. Automated Code Generation (ACG) using AI can help in this phase. Automating the code generation process is becoming increasingly popular as a solution to address various software development challenges and increase productivity. In this work, we provide a comprehensive review and discussion of traditional and AI techniques used for ACG, their challenges, and limitations. By analysing a selection of related studies, we will identify all AI methods and algorithms used for ACG, extracting the evaluation metrics and criteria such as Accuracy, Efficiency, Scalability, Correctness, Generalization, and more. These criteria will be used to perform a comparative result for AI methods used for ACG, exploring their applications, strengths, weaknesses, performance, and future applications.


Multi-objective Meta-heuristic Technique for Energy Efficient Virtual Machine Placement in Cloud Data Centers

Abstract: Cloud computing has emerged as an efficient scalable solution for storing and processing a large amount of data. Cloud data centers provide the resources on demand to the consumers on pay-per-use model. However, the large amount of datacenters is required to support the growing demand of the cloud consumers. This should be handled in an optimized way to avoid the resource wastage so that more consumers can get the benefits of the data centers. Virtualization is the technology of creating virtual version of the computers called Virtual Machines (VM). A Virtual Machine Placement problem is a fundamental challenge in cloud computing where the goal is to determine the optimal allocation of Virtual Machines to Physical Machines (PM) within a data center. An efficient Virtual Machine Placement technique helps to properly place the VMs on the PMs which significantly optimize the number of servers, maintenance cost, CPU utilization and power consumption. We present a novel hybrid approach that combines Ant Colony Optimization (ACO) algorithm and Sine Cosine Algorithm (SCA) for an efficient VM placement. Since SCA is an emerging search algorithm using Sine and Cosine functions in Engineering field, it has been used to explore the solutions obtained by ACO algorithm ACO algorithm has been applied to exploit the solutions of the search space for efficient VM placement which helps in power management and also to minimize the resource wastage. The result has been verified with the performance against other algorithms to prove that our proposed algorithm outperforms the other algorithms


Test to Code Traceability

Abstract: This paper presents a novel approach to establishing test-to-code traceability, using requirements as a mediator between functional tests and source code. Our approach consists of two steps: first, we link functional tests to requirements, and then we use these links to establish connections between requirements and code. We implement our approach using a combination of traditional and advanced machine learning models, including cosine similarity and GPT-LLAMA 2. By leveraging these models, we are able to automate the traceability process and ensure that tests are accurately linked to the relevant code components. Our implementation demonstrates the feasibility and effectiveness of our approach in establishing and maintaining traceability between functional tests, requirements, and source code.


Enhancing Similarity and Distance Measurements in Fermatean Fuzzy Sets: Tanimoto-Inspired Measures and Decision-Making Applications

Abstract: Fermatean fuzzy sets (FFSs) serve as a nascent yet potent approach for coping with fuzziness, with their efficacy recently being demonstrated across a spectrum of practical contexts. Nevertheless, the scholarly literature remains limited in exploring the similarity and distance measures tailored for FFSs. The limited existing measures on FFSs sometimes yield counter-intuitive outcomes, which can obfuscate the accurate quantification of similarity and difference among FFSs. This paper introduces a suite of similarity and distance measures tailored for FFSs, drawing inspiration from the Tanimoto measure. We delve into the characteristics of these novel measures and offer some comparative studies with existing FFSs measures, highlighting their superior efficacy in the processing of fuzzy data from FFSs. Our proposed measures effectively rectify the counter-intuitive situations encountered with many existing measures and demonstrate a significant enhancement in differentiating between diverse FFSs. Moreover, we showcase the real-world applicability of our proposed measures through case studies in pattern recognition, medical diagnostics, and multi-attribute decision-making.


Occurrence of Polycyclic Aromatic Hydrocarbons and Polychlorinated Biphenyls in Fogwater at Urban, Suburban, and Rural Sites in Northeast France between 2015 and 2021

Abstract: Polycyclic aromatic hydrocarbons (PAHs) and polychlorinated biphenyls (PCBs) exist in the atmosphere in the vapor and particulate phases, as well as in solubilized form in fog/rain/cloud waters. In the current paper, fogwater samples are collected during 42 events between 2015 and 2021 at four different sites (Strasbourg, Geispolsheim, Erstein, Cronenbourg) in the Alsace region. Organics are extracted using liquid–liquid extraction (LLE) supported on a solid cartridge (XTR Chromabond), and then analyzed by gas chromatography–tandem mass spectrometry (GC-MS/MS). The total PAHs and PCBs concentrations in fog samples vary between 0.58 and 6.7 µg L−1 (average of 2.70 µg L−1), and 0.14 and 15.5 µg L−1 (average of 2.75 µg L−1). Low-molecular-weight (LMW) PAHs are predominant and highly detectable compared to high-molecular-weight (HMW) PAHs, while pentachloro-biphenyls are the dominant PCB congener. The PAHs and PCBs concentrations have increased over the sampling years at all sites, except for a slight decrease in PCBs level at Geispolsheim. A diagnostic ratio (DR) and principal component analysis (PCA) are applied to suggest potential contamination sources at Strasbourg metropolitan. Their results reveal that PAHs derive from a mixture of pyrogenic activities, while PCBs mainly come from industrial activities. The results also reveal, in some cases, inter-site variability for simultaneous and successive fog events.


Building Organizational Defense: A Comprehensive Approach to Implementing IT Controls for Sox Compliance

Abstract: The security and resilience of organizational IT systems are of paramount importance in today's fast-paced digital environment. With the proliferation of “cyber threats”, organizations must adopt proactive measures to protect their assets and maintain operational continuity. The purpose of this article is to provide insight into the nuances of the implementation of a robust framework for “IT controls”, so that organizations seeking to strengthen their defenses can better understand the importance of each component. The article begins by emphasizing the foundational role of governance and oversight in establishing clear policies, procedures, and accountability structures. Strong governance sets the stage for cultivating a culture of compliance and accountability throughout the organization. Next, the article delves into the critical aspect of “risk management”, highlighting the importance of identifying and mitigating IT-related risks to safeguard organizational security and resilience. Through comprehensive “risk assessments”, organizations can prioritize mitigation efforts and allocate resources effectively. In order to prevent unauthorized access to critical IT systems and resources, access controls are discussed, emphasizing the importance of robust authentication mechanisms and role-based access controls. Change management processes are explored as essential components in managing IT changes effectively, minimizing the risk of disruptions and vulnerabilities associated with system changes. “Data security” emerges as a key focus area, with an emphasis on protecting the confidentiality, integrity, and availability of organizational data assets through robust encryption, access controls, and data loss prevention technologies. Incident management processes are highlighted as crucial for responding swiftly and effectively to security incidents, minimizing their impact and restoring normal operations promptly. Compliance and audit processes are discussed in the context of ensuring adherence to regulatory requirements and industry standards, mitigating the risk of non-compliance penalties and reputational damage. The importance of security awareness training in cultivating a culture of security among employees is emphasized, along with the need for continuous monitoring and improvement to adapt to evolving “threats” effectively. Overall, the article underscores the importance of implementing a comprehensive framework for IT controls to enhance “organizational security”, “compliance” and resilience in today's digital landscape. Through collaborative efforts across all levels of the organization, organizations can effectively “mitigate risks”, safeguard their assets and achieve their business objectives amidst evolving “cyber threats”.


Research and Development of a Modern Deep Learning Model for Emotional Analysis Management of Text Data

Abstract: There are many ways people express their reactions in the media. Text data is one of them, for example, comments, reviews, blog posts, messages, etc. Analysis of emotions expressed there is in high demand nowadays for various purposes. This research provides a method of performing sentiment analysis of text information using machine learning. The authors trained a classifier based on the BERT encoder, which recognizes emotions in text messages in English written in chat style. To handle raw chat-style messages, authors developed an enhanced text standardization layer. The list of emotions identified includes admiration, amusement, anger, annoyance, approval, caring, confusion, curiosity, desire, disappointment, disapproval, disgust, embarrassment, excitement, fear, gratitude, grief, joy, love, nervousness, optimism, pride, realization, relief, remorse, sadness, and surprise. The model solves the problem of multiclass multilabel text classification, which means that more than one class can be predicted from one piece of text. The authors trained the model on the GoEmotions dataset, which consists of 54,263 text comments from Reddit. The model reached a macro-averaged F1-Score of 0.50704 in emotions prediction and 0.7349 in sentiments prediction on the testing dataset. The presented model increased the quality of emotions prediction by 10.2% and sentiments prediction by 6.5% in comparison to the baseline approach.


Pelatihan Literasi dan Numerasi Mahasantri Melalui Program Kepenulisan Karya Ilmiah

Abstract: The importance of producing scientific papers in an academic environment is a necessity. However, it is a challenge for students who have just entered the world of writing. Therefore, this writing training activity aims to increase students' writing motivation and literacy and numeracy skills in compiling a scientific paper. The method of implementing activities uses the forum group discussion (FGD) method and the simulation method. The results of the activity showed that the writing training activities for students at Ma'had Aly Asslafiyyah went well. Participants have been able to prepare a draft of a scientific paper. Apart from that, 95% of participants had a strong desire to learn to improve their literacy and numeracy skills, as seen from the very positive response from participants participating in the training until the end of the activity. Through the Scientific Writing training activities at Ma'had Aly Assalafiyyah, students' enthusiasm and enthusiasm for improving their literacy and numeracy skills can be seen increasing, with follow-up plans aimed at strengthening the quality of learning and preparing students to contribute to the scientific world.


An Online Inquiry and Information Management System in Bicol Garments

Abstract: Information management plays a very significant role in an organization. It helps the organization manage files and documents in an organized way. The main objective of this study is to propose a system that would help improve the process of handling and recording customer information as well as enhance communication between the customer and the business organization. In order to ensure the improvement of the process in Bicol Garments, the proponents have identified the following problems: manual listing and processing of customers' information and job orders; as well as communication problems. With this, business process management was used for modeling and reengineering the workflow process. The study utilizes business process management to show the existing process and the proposed new process for Bicol Garments. By analyzing the As-Is process, the proponents noticed several problems in the current process and proposed a To-Be BPMN in order to resolve the issue. The experimentation in the study also shows that the use of the proposed system will improve the current process at Bicol Garments and provide ease of access to the customer. The study has proposed several protocols to help improve the process. These protocols were simulated in several instances and have shown improvements. However, the result of this study cannot be completely generalized because it has not yet been implemented in Bicol Garments.


A Study on Electromyography Signal as a Controller

Abstract: Human computer interaction (HCI) is the study of interfaces between human and computer. When an input keyboard is pressed the output is displayed in the monitor is a simple example of human and computer interaction. World Wide Web is yet another example of HCI. HCI is everywhere and has become an important aspect in human life. HCI have many subfields and one among them is the study of biosignals. Signals that are generated from living body during muscle contraction, eye movement, brain signal are biosignals and these signals have potential for developing an interface for human computer interaction. There are many such bio electric signals which can be made to use for developing interface and that can be done by acquiring these signals which will form a linkage with the computer technique. These types of signals are brain signal called Electroencephalogram (EEG), heart signal Electrocardiogram (ECG), eye movement signal Electrooculogram (EOG) and muscle signalElectromyogram (EMG). The paper focuses on the study of muscle signal controller as HCI, EMG signals are captured during contraction of a skeletal muscle. The signal is then amplified and converted into usable signals that will be fed as an input to computer and can be used for controlling certain devices.


A Novel Method for Self-Driving Solar-Powered Drones

Abstract: This project presented the transformative potential of integrating solar panels into drones. This innovative approach addresses the long-standing issue of limited battery life, enabling drones to operate continuously, adapt to changing mission demands, and contribute to sustainability efforts in the field of unmanned aerial vehicles. This development represents a significant step forward in the evolution of drone technology, promising a more versatile and self-sustaining future for drones across various sectors. Factors like high-speed flight, aggressive maneuvers, heavy payloads, and adverse weather can dramatically reduce battery life. Surveillance drones, for instance, are confined to covering limited areas before returning for battery changes or recharging. A groundbreaking solution lies in the incorporation of solar panels directly into the drones, allowing them to self-charge when required. This innovation ensures uninterrupted drone operation, regardless of the prospects of energy demands, thus marking a significant step forward in drone technology. With this integration of solar power, drones are poised to become not only versatile but also autonomous, promising a transformative development in the world of unmanned aerial vehicles.


Real-time recognition of Indonesian sign language using recurrent neural network

Abstract: Hand gestures serve as a vital means of communication for deaf individuals. They often face communication challenges in their daily interactions due to the language barrier. This underscores the necessity of sign language interpreters. However, prevailing methods primarily rely on the Indonesian Sign Language System (SIBI), despite the widespread use of Indonesian Sign Language (BISINDO) for communication. Additionally, the effectiveness of these methods hinges greatly on the accuracy of feature extraction. To address this limitation, this study introduces a Recurrent Neural Network (RNN) approach for BISINDO interpretation. Data acquisition involved the use of a webcam to capture video data, subsequently transformed into frames andarrays. Collected from three respondents, the dataset comprises 3,240 videos and 97,200 array data points, encompassing letters and numbers. Among the tested parameters, training results indicate that utilizing the Adam optimizer with a learning rate of 0.0001 and 500 epochs yields the highest accuracy and minimal loss compared to other configurations. Subsequently, this modelunderwent real-time testing, conducted five times for 36 classes, achieving an accuracy of 81.67%. It is important to note that errors may arise due to similarities within hand signal language, particularly involving characters such as I, J, D, P, M, and N.


Monetary Reform and Central Bank Digital Currencies: The Impact on Retail Banking

Abstract: This research aims to clarify the impact of monetary reform on retail banking, with the introduction of private crypto-currencies (CCs) and retail central bank digital currencies (CBDCs). Given the exponential increase in private de-centralized CCs, involving stablecoins and non-backed digital assets, total crypto assets have now developed into a USD 1.04 trillion market capitalization. Governments and monetary authorities were initially reticent, but now a number of central banks are developing their own wholesale and retail CBDCs. By conducting library research, document and content analysis involving secondary data, this research confirms, involving central bank admission, that the current centralized monetary system allows commercial banks to lend the vast majority of deposit money into existence. However, the impact of private CCs and introduction of retail CBDCs will eliminate credit creation, thus rendering the commercial banking model obsolete. This will inevitably involve a structural change in the global financial system, with the separation of the public issuance of money and the private issuance of equity finance and investment. Ultimately, retail CBDCs will have to develop into public stablecoins that are pegged to intrinsic value to satisfy the store of value function of money and provide stability as the common denominator for all economic transactions.


Deep-Feature-Based Asymmetrical Background-Aware Correlation Filter for Object Tracking

Abstract: Correlation filter-based video object-tracking algorithms have gained widespread attention due to their efficiency and excellent tracking performance. However, traditional correlation filtering tracking algorithms possess several limitations. (1) They extract image features only by using rule sampling, which ignores the shape information of the target, resulting in insufficient discriminative power of the features. (2) They focus only on the difference between the background and the object, ignoring the effects of more challenging intra-class interference. (3) They do not further evaluate the reliability of the optimal candidate samples, resulting in easy failure in occlusion scenarios. This paper proposes an asymmetric background-aware correlation filter method for object tracking with deep features to solve the above limitations. First, an asymmetrical background-aware sampling method based on the shape information of the object is proposed. This sampling method significantly differs from the symmetrical sampling method in the traditional correlation filter framework. By exploring the shape information of the object, improving the otherness between the background and object samples is easy, thus suppressing the intra-class interferences. Second, deep neural networks are introduced in the correlation filter framework to extract the deep object features and a spatio-temporal regularization factor is adaptively assigned to suppress the boundary effects, intra-class distractors and aberrance between frames. Finally, a multi-modal object pool is constructed to evaluate the optimal candidate sample in each frame. This template pool fully exploits object diversity and solves the tracking drift and failure caused by invalid appearance changes in scenes such as occlusion and vigorous motion scenarios. To validate the effectiveness of the proposed method, it was compared with the state-of-the-art methods on public datasets. The experimental results show that the tracking performance of the proposed method is competitive.


Analysis of the Content of Carbon Neutrality Education Presented in Middle and High School Science and Social Studies Curriculum

Abstract: This study analyzed the content of carbon neutrality education in the science and social studies curricula of middle and high schools. Through the analysis, it was confirmed that the curriculum contains some content related to carbon neutrality. Specifically, the science curriculum predominantly addresses responses to climate change, while the social studies curriculum focuses on the impacts of climate change. However, there was a highlighted need for both the science and social studies curricula to comprehensively and evenly incorporate content on the causes and effects of climate change. Furthermore, the study confirmed a strong linkage between the carbon neutrality learning elements in middle school and those in high school. The significance of this research lies in its analysis of the current status and necessities of carbon neutrality education in the curriculum, proposing effective strategies for its implementation.


Influence of Learning Experience by Academic Year on College Students’ Major Satisfaction and Perceived Competencies: A Study Focused on I University

Abstract: This study analyzes the impact of college students’ characteristics (demographic background, learning amotivation) and learning experiences (experience, collaboration, and inquiry-based learning) on major satisfaction and perceived competency development, associated with academic year. Data from 839 students across 1st, 2nd, and 3rd academic years at I University were collected and analyzed using hierarchical multiple regression. The results show that collaboration and inquiry-based learning, as well as faculty-student interaction have significantly influence on major satisfaction and perceived competencies, regardless of academic years. In addition, experiential learning shows significance only in the perceived competencies of 2nd, and 3rd academic years. The most influential learning experience varied by year: inquiry-based learning for 1st year students in both satisfaction and competency development, while for 2nd and 3rd year students, collaborative learning influenced major satisfaction, and inquiry-based learning did competency development. The study provides insights for educational strategies for teaching and learning, tailored to each academic year.


A Case Study on the Establishment of SMEs FOM–MES Interworking System for Multidimensional Analysis of 4M Data in Manufacturing Sites

Abstract: Most small- and medium-sized manufacturing companies have poor utilization of MES systems and manufacturing data owing to financial conditions and labor shortages. For corporate growth by improving the productivity and securing competitiveness of small- and medium-sized manufacturing companies, improving the use of 4M-based manufacturing data, analyzing data, managing the change, establishing decision-making, and applying FOM solutions to companies are essential. However, applying the FOM solution is difficult owing to the difficulty of securing MES system data and FOM solution analysis data file set. Through this study, the effect applied to actual companies was verified by establishing an FOM–MES linkage system and simplifying the process of producing FOM analysis data.


An Overview on Intuitionistic Fuzzy Multi-Hypergroup

Abstract: Abstract: The major goal of this work is to introduce the Intuitionistic Fuzzy Multi-Hypergroup and to introduce non membership on multihyper group to provide new path way in the field of hypergroup.


Review on Customer Experience with Personalized Recommendation and Targeted Ads

Abstract: Abstract: The application for malls or e commerce platform that divide the customers into profitable and non-profitable customers plays a vital role in the marketing sector. The administrator within the shopping centers chooses to promote methods and client division points to form a relationship with the first profitable clients by arranging the foremost appropriate marketing procedure. Numerous methods are applied to separate the advertising, but outstandingly tremendous data is uncommonly effective in decreasing their adequacy. Many works used association rule learning is used to establish a relationship between variables. With the use of an appropriate algorithm in this, we find what fitems customers frequently buy together by generating the set of rules and can be used those rules for various market strategies. By using that rules, we also develop a recommender framework that will offer assistance the mall managers or e commerce managers to empowering the market strategies. This not only helps customers have a better choice but also gives advice to businesses selling products with reasonable prices. Customer segmentation is done based on their interest using association learning algorithms like ECLAT algorithm or SFIT algorithm.


Android Based Object Detection System for Visually Impaired

Abstract: Abstract: This review paper presents the design and development of a comprehensive mobile application aimed at enhancing the daily lives of visually impaired individuals. The proposed mobile app offers real-time assistance for various visual recognition tasks, including object detection, distance estimation, currency recognition, barcode detection, color recognition, and emotion analysis. The primary objective of this application is to provide visually impaired users with a powerful tool to navigate their surroundings, identify objects and currency, scan barcodes, discern colors, and even gauge the emotions of those they interact with. The app integrates pre-trained deep learning models for object detection and facial emotion analysis, computer vision techniques for distance estimation, and optical character recognition for currency recognition. User feedback and engagement are central to the ongoing improvement of the application, ensuring that it remains a valuable resource for the community it serves. Ethical considerations and privacy concerns are also addressed, with a focus on data security and user privacy. By presenting the development and functionalities of this app, this review paper not only contributes to the field of assistive technology but also underscores the importanceof harnessing cutting-edge technology to improve the quality of life for visually impaired individuals.


Survey on Fraud App Detection Using Sentimental Analysis and Machine Learning

Abstract: Abstract: Ranking fraud is the practice of acting dishonestly or deceitfully to artificially improve an App's position on a popularity list in the mobile app market. In actuality, app makers are increasingly using ranking fraud. These behaviors include posting phoney app reviews or exaggerating the sales of their apps. Although the importance of combating ranking fraud has long been recognized, there is little literature and research available in this area. To achieve this, we give a thorough examination of fraud app detection in this work utilizing sentiment analysis and spam filtering and propose a technique for identifying it in mobile apps. We specifically suggest mining the active times, or leading sessions, of mobile Apps to precisely locate the ranking scam in the first place.


The metaverse in the world of health: The present future. Challenges and opportunities

Abstract: The World Health Organization has defined "digital health" as the use of information and communication technologies to improve health.In recent years, there has been a strong acceleration in the adoption of these digital tools, which has had a major impact on traditional healthcare models.We are currently witnessing the emergence of a large immersive virtual environment called the "metaverse."Its emergence creates new and challenging opportunities in health care.This article explores some metaverse-related concepts, provides specific examples of its use in pediatrics, describes experiences in the hospital setting, and finally delves into the resulting challenges and opportunities.


MFI-Net: Multi-Feature Fusion Identification Networks for Artificial Intelligence Manipulation

Abstract: Tampered images can easily be used for illegal activities, such as spreading rumors, economic fraud, fabricating false news, and illegally obtaining experience benefits, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc . With the improvement and development of artificial intelligence (AI), image manipulation technology has also been further improved, more and more retouching software in daily life adopts AI technology. So far, there is no AI-based tampered dataset. To address this challenge, we propose a dataset-IPM15K. It utilizes the most advanced image processing technology and contains a total of 150,00 doctored vital images. This dataset also could serve as a catalyst for progressing many vision tasks, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g ., localization, segmentation, and alpha-matting, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc . Additionally, we propose an effective multi-feature fusion identification network (MFI-Net) to identify these challenging images. Our model consists of four modules: the detail extraction module (DEM), which utilizes different sizes of convolutions and perceptual fields to extract more valuable information of tampered locations; the multi-branch attention fusion module (MAFM), which fully exploits contextual information of different levels to capture subtle traces of tampering; the feature decoder component (FDC), which combines fused features to identify tampered regions; and the detail enhancement block (DEB), which continues to supplement the detailed information of the detected regions. Extensive experiments on three public datasets and the proposed dataset show that MFI-Net outperforms various state-of-the-art (SOTA) manipulation detection baselines.


Neurodynamics for Equality-Constrained Time-Variant Nonlinear Optimization Using Discretization

Abstract: Time-variant problems are widespread in science and engineering, and discrete-time recurrent neurodynamics (DTRN) method has been proved to be an effective way to deal with a variety of discrete time-variant problems. However, this DTRN method is usually based on the study of continuous time-variant problems and lacks a direct study of discrete time-variant problems. To solve the above problem, based on a pioneering direct discretization technique, we study and develop a new DTRN method to solve equality-constrained discrete time-variant nonlinear optimization (EC-DTVNO) problem. Specifically, firstly, to solve the EC-DTVNO problem, the recent method widely used by researchers is Lagrange multiplier method. By introducing Lagrange multiplier to construct Lagrange function, the objective function and equality constraint are integrated into a discrete time-variant nonlinear system. Then, the corresponding error function is defined, and the corresponding DTRN method for solving the EC-DTVNO problem can be obtained by direct discretization technique. Thereafter, this DTRN method is analyzed theoretically and its convergence is proved. In addition, numerical experiments and application experiments further confirm the effectiveness and superiority of DTRN method.


An advanced data fabric architecture leveraging homomorphic encryption and federated learning

Abstract: Data fabric is an automated and AI-driven data fusion approach to accomplish data management unification without moving data to a centralized location for solving complex data problems. In a Federated learning architecture, the global model is trained based on the learned parameters of several local models that eliminate the necessity of moving data to a centralized repository for machine learning. This paper introduces a secure approach for medical image analysis using federated learning and partially homomorphic encryption within a distributed data fabric architecture. With this method, multiple users or clients (hospitals/medical data centers) can collaborate in training a machine-learning model without exchanging raw data. The approach complies with laws and regulations such as HIPAA and GDPR, ensuring the privacy and security of the data. The study demonstrates the method’s effectiveness through a case study on pituitary tumor classification, achieving a significant accuracy of 83.31%. However, the primary focus of the study is using the data fabric architecture to securely store and analyze medical images while complying with HIPAA and GDPR regulations. The results highlight the potential of these techniques to be applied to other privacy-sensitive domains and contribute to the growing body of research on secure and privacy-preserving machine learning.


The role of ethical climates in employee information security policy violations

Abstract: In the context of information security, the organizational social environment plays an important role in deterring employee violations of the Information Security Policy (ISP). As extant research has not yet determined the role ethical climates may play, this study seeks to understand their influence on employee ISP violations. To this end, we classified ISPs into two major categories: those related to communal information technology (IT) resources in the organization, such as databases or software applications, and those related to connective IT resources, such as email or Internet access. Then, we explored how rules-, performance-, and friendship-oriented organizational ethical climates affect ISP violations. Based on a survey of 177 professionals employed in the United States, we found that rules-oriented and friendship-oriented ethical climates deter ISP violations, whereas a performance-oriented one does not. Moreover, rules- and friendship-oriented ethical climates lead to fewer communal ISP violations, while performance-oriented ethical climates lead to more connectivity ISP violations. These findings underscore the importance of considering the type of IT resources that an ISP aims to protect as well as the ethical climate of an organization in order to better understand the causes of ISP violations.


Keyword-enhanced recommender system based on inductive graph matrix completion

Abstract: Going beyond the user–item rating information, recent studies have utilized additional information to improve the performance of recommender systems. Graph neural network (GNN) based approaches are among the most common. However, existing models that utilize text data require a lot of computing resources and have a complex structure that makes them difficult to utilize in real-world applications. In this research, we propose a new method, keyword-enhanced graph matrix completion (KGMC), which utilizes keyword sharing relationships in user–item graphs. Our model has a simpler structure and requires less computing resources than existing models that utilize text data, but it has the advantage of cross-domain transferability while providing an intuitive understanding of the inference results. KGMC consists of three steps: (1) keyword extraction from the review text, (2) subgraph extraction and keyword-enhanced subgraph construction, and (3) GNN-based rating prediction. We have conducted extensive experiments over eight benchmark datasets to examine the relative superiority of the proposed KGMC method, compared to state-of-the-art baselines. Additional experiments and case studies have been also conducted to demonstrate the transferability as well as keyword-based explainability of KGMC. Our findings highlight the practical advantages of our model for recommender systems and support its effectiveness in inductive graph-based link prediction.


Embracing RAISA in restaurants: Exploring customer attitudes toward robot adoption

Abstract: Humanity has experienced four main technological revolutions; the last two being the digital revolution (Internet) and the emerging artificial intelligence (AI) revolution. With robots, artificial intelligence, and service automation (RAISA) increasingly applied in all industries, knowledge on people's attitudes toward RAISA is limited. Factors affecting RAISA adoption in restaurants, an industry known for its quick adoption of new technology, are largely unknown. This study explores customer attitudes toward robot adoption in restaurants through a quantitative survey of 293 valid respondents. The results, analyzed using partial least squares structural equation modeling (PLS-SEM), show that positive attitudes toward technology, interest in RAISA, perceived relevance, and enjoyment positively impact RAISA adoption. Furthermore, customers' overall attitude toward technology positively affects their intention to use RAISA. Therefore, restaurant owners can start implementing new technologies since consumers are ready to embrace RAISA in restaurants.


Multi-level graph contrastive learning

Abstract: Graph representation learning has attracted a surge of interest recently, which targets learning discriminant representation for each node in the graph. Most of these representation methods focus on supervised learning and heavily depend on label information. However, annotating graphs are expensive in the real-world, especially in specialized domains (i.e. biology), as it requires the annotators with the domain knowledge to label the graph. To approach this problem, self-supervised learning provides a feasible solution for graph representation learning. In this paper, we propose a Multi-Level Graph Contrastive Learning (MLGCL) framework for learning robust representation of graph data by contrasting space views of graphs. Specifically, we introduce a novel contrastive view — space view. The original graph is a first-order approximation structure in the topological space where nodes are linked by feature similarity, relationship, etc. While the k-nearest neighbor (kNN) graph with community structure generated by encoding features preserves high-order proximity in feature space, it not only provides a complementary graph to the original graph from the feature space view but also is suitable for GNNs encoder. Furthermore, we develop a multi-level contrastive mode to preserve the local similarity and semantic similarity of graph-structured data simultaneously. Extensive experiments indicate MLGCL achieves promising results compared with the existing state-of-the-art graph representation learning methods on seven node classification datasets and three graph classification datasets.


Triple competitive differential evolution for global numerical optimization

Abstract: As optimization problems become more and more complex in real-world scenarios, the effectiveness of many existing differential evolution (DE) methods is critically challenged. To circumvent this predicament, this paper proposes a triple competitive DE (TCDE) to tackle increasingly complicated optimization problems. Specifically, a triple competition mechanism is devised to first randomly arrange individuals into exclusive triples and then let individuals in each triple compete with each other. Subsequently, heterogeneous mutation schemes are designed to update the three individuals in each triple based on the competition results. With the cooperation between these two techniques, on the one hand, better individuals in the triples tend to exploit the areas where they lie to find high-accuracy solutions, while worse individuals in the triples prefer to explore the intricate problem space to locate promising regions. On the other hand, different individuals are mutated by following the guidance of different leading exemplars, which ensures that individuals traverse the sophisticated solution space in diverse directions along with slight intensification to optimal regions. Furthermore, a modified adaptive parameter adjustment scheme is designed for individuals in each triple by considering their differences so as to meet the expectation that better individuals focus on exploitation while worse individuals concentrate on exploration. Finally, experiments are performed to first compare TCDE with totally 10 advanced DE variants and then further compare it with totally 10 CEC winners and their latest variants on the CEC'2014 and the CEC'2017 problem suites with totally 4 different dimensionality settings. Experimental results prove that TCDE achieves highly competitive or even much better performance than the totally 20 compared methods. Particularly, TCDE is demonstrated to be especially capable of solving complicated optimization problems. Furthermore, the efficacy of TCDE in tackling real-world optimization problems is substantiated through experiments conducted on the CEC'2011 real-world optimization problem set.


Deep feature extraction with tri-channel textual feature map for text classification

Abstract: The complexity and diversity of texts make it difficult for shallow text classification models to capture deeper text features. Therefore, this paper takes advantage of the BiLSTM-CNN hybrid network based on the self-attention mechanism to extract text features, and constructs a new text feature representation similar to RGB-channel images, that is, tri-channel text feature maps. Drawing on the effectiveness of ResNet and SA-Net in deep networks, we designed a deep feature extraction network to capture deeper features in the text. We use the tri-channel text feature map as the input to the deep feature extraction network, and propose the Deeper Feature Text Classification (DFTC) end-to-end text classification model. Experiments prove that the DFTC model is competitive compared to the most advanced methods on five challenging text classification datasets.


Shape-margin knowledge augmented network for thyroid nodule segmentation and diagnosis

Abstract: Thyroid nodule segmentation is a crucial step in the diagnostic procedure of physicians and computer-aided diagnosis systems. However, prevailing studies often treat segmentation and diagnosis as independent tasks, overlooking the intrinsic relationship between these processes. The sequencial steps of these independent tasks in computer-aided diagnosis systems may lead to the accumulation of errors. Therefore, it is worth combining them as a whole by exploring the relationship between thyroid nodule segmentation and diagnosis. According to the diagnostic procedure of thyroid imaging reporting and data system (TI-RADS), the assessment of shape and margin characteristics is the prerequisite for radiologists to discriminate benign and malignant thyroid nodules. Inspired by TI-RADS, this study aims to integrate these tasks into a cohesive process, leveraging the insights from TI-RADS, thereby enhancing the accuracy and interpretability of thyroid nodule analysis. Specifically, this paper proposes a shape-margin knowledge augmented network (SkaNet) for simultaneous thyroid nodule segmentation and diagnosis. Due to the visual feature similarities between segmentation and diagnosis, SkaNet shares visual features in the feature extraction stage and then utilizes a dual-branch architecture to perform thyroid nodule segmentation and diagnosis tasks respectively. In the shared feature extraction, the combination of convolutional feature maps and self-attention maps allows to exploitation of both local information and global patterns in thyroid nodule images. To enhance effective discriminative features, an exponential mixture module is introduced, combining convolutional feature maps and self-attention maps through exponential weighting. Then, SkaNet is jointly optimized by a knowledge augmented multi-task loss function with a constraint penalty term. The constraint penalty term embeds shape and margin characteristics through numerical computations, establishing a vital relationship between thyroid nodule diagnosis results and segmentation masks. We evaluate the proposed approach on a public thyroid ultrasound dataset (DDTI) and a locally collected thyroid ultrasound dataset. The experimental results reveal the value of our contributions and demonstrate that our approach can yield significant improvements compared with state-of-the-art counterparts. SkaNet highlights the potential of combining thyroid nodule segmentation and diagnosis with knowledge augmented learning into a unified framework, which captures the key shape and margin characteristics for discriminating benign and malignant thyroid nodules. Our findings suggest promising insights for advancing computer-aided diagnosis joint with segmentation.


Virtual quantum resource distillation: General framework and applications

Abstract: We develop the general framework of virtual resource distillation, an alternative distillation strategy proposed in Phys. Rev. Lett. 132, 050203 (2024), which extends conventional quantum resource distillation by integrating the power of classical postprocessing. The framework presented here is applicable not only to quantum states, but also to dynamical quantum objects such as quantum channels and higher-order processes. We provide a general characterization and benchmarks for the performance of virtual resource distillation in the form of computable semidefinite programs as well as several operationally motivated quantities. We apply our general framework to various concrete settings of interest, including standard resource theories such as entanglement, coherence, and magic, as well as settings involving dynamical resources such as quantum memory, quantum communication, and non-Markovian dynamics. The framework of probabilistic distillation is also discussed.


Application of a reconfigurable all-optical activation unit based on optical injection into a bistable Fabry–Perot laser in multilayer perceptron neural networks

Abstract: In this Letter, we theoretically investigate the application of a bistable Fabry-Perot semiconductor laser under optical injection as an all-optical activation unit for multilayer perceptron optical neural networks. The proposed device is programmed to provide reconfigurable sigmoid-like activation functions with adjustable thresholds and saturation points and benchmarked on machine learning image recognition problems. Due to the reconfigurability of the activation unit, the accuracy can be increased by up to 2% simply by adjusting the control parameter of the activation unit to suit the specific problem. For a simple two-layer perceptron neural network, we achieve inference accuracies of up to 95% and 85%, for the MNIST and Fashion-MNIST datasets, respectively.


Comprehensive Sensitivity Analysis Framework for Transfer Learning Performance Assessment for Time Series Forecasting: Basic Concepts and Selected Case Studies

Abstract: Recently, transfer learning has gained popularity in the machine learning community. Transfer Learning (TL) has emerged as a promising paradigm that leverages knowledge learned from one or more related domains to improve prediction accuracy in a target domain with limited data. However, for time series forecasting (TSF) applications, transfer learning is relatively new. This paper addresses the need for empirical studies as identified in recent reviews advocating the need for practical guidelines for Transfer Learning approaches and method designs for time series forecasting. The main contribution of this paper is the suggestion of a comprehensive framework for Transfer Learning Sensitivity Analysis (SA) for time series forecasting. We achieve this by identifying various parameters seen from various angles of transfer learning applied to time series, aiming to uncover factors and insights that influence the performance of transfer learning in time series forecasting. Undoubtedly, symmetry appears to be a core aspect in the consideration of these factors and insights. A further contribution is the introduction of four TL performance metrics encompassed in our framework. These TL performance metrics provide insight into the extent of the transferability between the source and the target domains. Analyzing whether the benefits of transferred knowledge are equally or unequally accessible and applicable across different domains or tasks speaks to the requirement of symmetry or asymmetry in transfer learning. Moreover, these TL performance metrics inform on the possibility of the occurrence of negative transfers and also provide insight into the possible vulnerability of the network to catastrophic forgetting. Finally, we discuss a sensitivity analysis of an Ensemble TL technique use case (with Multilayer Perceptron models) as a proof of concept to validate the suggested framework. While the results from the experiments offer empirical insights into various parameters that impact the transfer learning gain, they also raise the question of network dimensioning requirements when designing, specifically, a neural network for transfer learning.


QUANTUM CRYPTOGRAPHY AND U.S. DIGITAL SECURITY: A COMPREHENSIVE REVIEW: INVESTIGATING THE POTENTIAL OF QUANTUM TECHNOLOGIES IN CREATING UNBREAKABLE ENCRYPTION AND THEIR FUTURE IN NATIONAL SECURITY

Abstract:
This study provides a comprehensive review of quantum cryptography and its implications for U.S. national security in the face of emerging quantum technologies. The primary objective is to investigate the potential of quantum cryptographic methods in creating unbreakable encryption and their future role in enhancing digital security. Employing a systematic literature review and content analysis, the study draws on recent peer-reviewed articles, institutional reports, and academic journals from 2013 to 2023. The methodology focuses on evaluating the evolution, current state, and challenges of quantum cryptography, along with its integration into existing security frameworks. Key findings reveal that Quantum Key Distribution (QKD) and post-quantum cryptography (PQC) offer promising solutions against the threats posed by quantum computing to classical encryption methods. However, the practical implementation of these technologies faces significant challenges, including technological limitations and the need for global standardization. The study underscores the urgency for U.S. national security policy to prioritize the development and integration of quantum-resistant cryptographic technologies and to foster international collaboration for standardization. Finally, the study highlights the transformative potential of quantum cryptography in digital security, emphasizing the need for continued research and collaboration to overcome implementation challenges. Future research directions include the development of efficient quantum cryptographic protocols and ethical considerations surrounding the deployment of quantum technologies. This study contributes to the discourse on securing national interests in the face of advancing quantum computing capabilities.
 Keywords: Quantum Cryptography, Digital Security, Post-Quantum Cryptography, Quantum Key Distribution.


Benefits, Challenges, and Methods of Artificial Intelligence (AI) Chatbots in Education: A Systematic Literature Review

Abstract: In many fields, AI chatbots continue to be popular with new tools and attract the attention of universities, K12 schools, educational organizations, and researchers. The aim of this research is to review the research on AI chatbots by restricting it to the category of education and to examine this research from a methodological point of view. Therefore, we performed a systematic literature review with a sample of 37 SSCI articles published in the educational context. Within the scope of the selected studies, the advantages and disadvantages of AI chatbots in education for students and educators, as well as the types of chatbots used, year, keywords, and method were analyzed. According to the research results, increased motivation to learn and language skill development are advantages for students, while cost-effectiveness and reduced workload are advantages for educators. Limited interaction, misleading answers for learners, originality, and plagiarism are the most common disadvantages for educators. The study also includes research results and recommendations related to the methodological review.


Development of a Digital Twin Prototype for Industrial Manufacturing Monitoring System Using IoT and Augmented Reality

Abstract: The world is currently abuzz with the rapid development of technology in the era of Industrial Revolution 4.0. Various technological advancements are facilitating progress and accelerating the development of industrial technology. This evolution has led to automation in production processes, transitioning toward digitalization. With the implementation of sensors that provide real-time data, production processes can now be monitored remotely. However, direct monitoring is still necessary at times to periodically check the condition of each operating machine. Therefore, there is a need for technology that can monitor production processes and reduce high maintenance costs. Currently, numerous new technologies are emerging to enhance the performance and efficiency of production processes in various industries. One such technology is the digital twin. A digital twin is a visual representation that offers insights into the continuous operations of a system. This research focuses on an industrial manufacturing monitoring system that integrates the Internet of Things (IoT) and augmented reality (AR) technologies. The system is composed of an application and a prototype machine in the form of a conveyor, which can simulate a digital twin of the prototype machine. It also transmits sensor data and error notifications to the application in real time. The designed system can serve as a prototype for implementing digital twin technology, combining IoT and AR. This makes it possible to apply the technology to machinery and production tools in various industrial sectors.


Fundamental Components and Principles of Supervised Machine Learning Workflows with Numerical and Categorical Data

Abstract: This paper offers a comprehensive examination of the process involved in developing and automating supervised end-to-end machine learning workflows for forecasting and classification purposes. It offers a complete overview of the components (i.e., feature engineering and model selection), principles (i.e., bias–variance decomposition, model complexity, overfitting, model sensitivity to feature assumptions and scaling, and output interpretability), models (i.e., neural networks and regression models), methods (i.e., cross-validation and data augmentation), metrics (i.e., Mean Squared Error and F1-score) and tools that rule most supervised learning applications with numerical and categorical data, as well as their integration, automation, and deployment. The end goal and contribution of this paper is the education and guidance of the non-AI expert academic community regarding complete and rigorous machine learning workflows and data science practices, from problem scoping to design and state-of-the-art automation tools, including basic principles and reasoning in the choice of methods. The paper delves into the critical stages of supervised machine learning workflow development, many of which are often omitted by researchers, and covers foundational concepts essential for understanding and optimizing a functional machine learning workflow, thereby offering a holistic view of task-specific application development for applied researchers who are non-AI experts. This paper may be of significant value to academic researchers developing and prototyping machine learning workflows for their own research or as customer-tailored solutions for government and industry partners.


Unsupervised clustering of catalog-driven features for characterizing temporal evolution of labquake stress

Abstract: Summary Earthquake forecasting poses significant challenges, especially due to the elusive nature of stress states in fault systems. To tackle this problem, we employ features derived from seismic catalogs obtained from acoustic emission (AE) signals recorded during triaxial stick-slip experiments on natural fractures in three Westerly granite samples. We extracted 47 physically explainable features from AE data that described spatio-temporal evolution of stress and damage in the vicinity of the fault surface. These features are then subjected to unsupervised clustering using the K-means method, revealing three distinct stages with a proper agreement with the temporal evolution of stress. The recovered stages correspond to the mechanical behavior of the rock, characterized as initial stable (elastic) deformation, followed by a transitional stage leading to an unstable deformation prior to failure. Notably, AE rate, clustering-localization features, fractal dimension, b-value, interevent time distribution, and correlation integral are identified as significant features for the unsupervised clustering. The systematically evolving stages can provide valuable insights for characterizing preparatory processes preceding earthquake events associated with geothermal activities and waste-water injections. In order to address the upscaling issue, we propose to use the most important features and, in case of normalization challenge, removing non-universal features, such as AE rate. Our findings hold promise for advancing earthquake prediction methodologies based on laboratory experiments and catalog-driven features.


How Should We Slice Up the Executive Function Pie? Striving Toward an Ontology of Cognitive Control Processes

Abstract: Abstract There is a vast literature on executive functioning , or the control of one's thoughts and actions in pursuit of a goal. This broad construct is conceptualized as comprising multiple executive functions (EFs) that support academic achievement and other important life outcomes. However, there is a lack of consensus regarding what, exactly, these core EFs are . This commentary briefly reviews a few dominant models of EFs in developmental and cognitive psychology and outlines issues to be addressed; it then illustrates how insights from neuroscience could help us eliminate redundant terminology and identify EFs that have been largely overlooked. Reaching a consensus regarding the core set of EFs will inform efforts to scaffold EF development and improve educational outcomes.


On Faster Convergence of Scaled Sign Gradient Descent

Abstract: Communication has been seen as a significant bottleneck in industrial applications over large-scale networks. To alleviate the communication burden, sign-based optimization algorithms have gained popularity recently in both industrial and academic communities, which is shown to be closely related to adaptive gradient methods, such as Adam. Along this line, this paper investigates faster convergence for a variant of sign-based gradient descent, called scaled SIGNGD, in three cases: 1) the objective function is strongly convex; 2) the objective function is nonconvex but satisfies the Polyak-Łojasiewicz (PL) inequality; 3) the gradient is stochastic, called scaled SIGNSGD in this case. For the first two cases, it can be shown that the scaled SIGNGD converges at a linear rate. For case 3), the algorithm is shown to converge linearly to a neighborhood of the optimal value when a constant learning rate is employed, and the algorithm converges at a rate of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$O(1/k+1/k^{2}+1/k^{3})$</tex-math></inline-formula> when using a diminishing learning rate, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> is the iteration number. The results are also extended to the distributed setting by majority vote in a parameter-server framework. Finally, numerical experiments are performed to corroborate the theoretical findings.


A Study on the Dong-Ju Yoon's Lyrical World and the Ideal of Childhood Innocence

Abstract: The world of Dong-Ju Yoon’s poetry has a source of extreme and pure childhood innocence, like the innocent gaze of an unattached child, which enjoys the phenomenon itself with an intuition that can get close to the essence of things and accepts the world beyond its horizon. The consciousness and orientation of childhood innocence revealed in Dong-Ju Yoon’s poetry are not merely the result of conforming to the purposefulness of the times. However, they are inherited and deepened in his later works, constituting a consistent poetic sensibility that underlies the world of Dong-Ju Yoon’s poetry. Since there are many identical symbols, identifying consciousness of childhood innocence is necessary to understand the totality of the poet. Dong-Ju Yoon’s poetry coexists with childhood memories, reproduces them in epic paintings by dressing them in childhood innocence, and constructs the appearance of an archetypal world. The world of childhood innocence formed through the young narrator is expressed as a dynamic channel of reflection that seeks solutions to problems and overcomes the depression and lethargy of the poet's time. By symbolically depicting the lyrical world of the child, he vividly depicts the contradictions of the times and expresses the fundamental human emotions in his poems, forming the poet's authenticity. It is a search to find an outlet for hope through the style of children's poetry with clear and bright innocence and dynamic vitality without caving to the hardships of reality suffering from the dictatorship of a vast and oppressed world.


A Study on the Effect of Director's Authentic Leadership on Early Childhood Teacher's Professionalism: Centered on Mediating Effect of Organizational Culture of Early Childhood Educational Institute

Abstract: The purpose of this study is to investigate the relationship between variables affecting the early childhood teacher's professionalism after setting the director's authentic leadership as an independent variable, the organizational culture of early childhood educational institute as a mediating variable, and the early childhood teacher's professionalism as dependent variable, considering that the director's authentic leadership was a motivational factor affecting the organizational culture of early childhood educational institute. The research questions established to achieve such research objective are as follows. First, what is the relationship among the director's authentic leadership, organizational culture of early childhood educational institute, and early childhood teacher's professionalism? Second, do the director's authentic leadership and organizational culture of early childhood educational institute affect the early childhood teacher's professionalism? Third, does the director's authentic leadership affect the early childhood teacher's professionalism through a medium of organizational culture of early childhood educational institute? The research object in this study was final 225 early childhood teachers except the unreliable respondents out of 250 teachers who worked in 'D' city. From the result of this research above, it may be summed up as follows. First, this study has shown that it had statistically significant positive relationship among the director's authentic leadership, organizational culture of early childhood educational institute, and early childhood teacher's professionalism. Second, the internalized moral viewpoint, relational transparency, and balanced processing of information among the subfields of director's authentic leadership had positively significant direct effect on the early childhood teacher's professionalism. In addition, the rational culture, relationship culture, hierarchy culture among the subfields of organizational culture of early childhood educational institute had positively significant direct effect on the early childhood teacher's professionalism. Third, the director's authentic leadership affected the early childhood teacher's professionalism, mediated by the organizational culture of early childhood educational institute. This researcher considers it as the director's authentic leadership and organizational culture of early childhood educational institute were the related variables that affected the early childhood teacher's professionalism.


The Analysis of Educational Needs for Secondary School Teacher’s Emotional Competencies: Focusing on General Education Teachers

Abstract: The purpose of this study was to examine the gap between the importance level and their current performance level of secondary school teachers' emotional competencies and to identify the priority of education to improve emotional competencies. Using the emotional competency scale for secondary school teacher, a survey was conducted involving 250 teachers. The collected data were examined using a t-test, Borich's need analysis and Hershkowitz criticality function to determine the priorities of the educational needs regarding emotional competencies. The results of the study are as follows. First, the performance levels of secondary school teachers were lower than the importance levels in all of competencies, with statistically significant differences in all areas. Second, the highest priority educational competencies include emotional regulation, emotional understanding and appraisal, communication, and positivity, primarily focusing on individual emotional competencies. The results of this study indicate that, in the development of emotional competency enhancement programs for secondary school teachers, it is important to prioritize the emotional competencies that enable teachers to regulate, manage, and accurately understand their own emotions.


Residual Q-Networks for Value Function Factorizing in Multiagent Reinforcement Learning

Abstract: Multiagent reinforcement learning (MARL) is useful in many problems that require the cooperation and coordination of multiple agents. Learning optimal policies using reinforcement learning in a multiagent setting can be very difficult as the number of agents increases. Recent solutions such as value decomposition networks (VDNs), QMIX, QTRAN, and QPLEX adhere to the centralized training and decentralized execution (CTDE) scheme and perform factorization of the joint action-value functions. However, these methods still suffer from increased environmental complexity, and at times fail to converge in a stable manner. We propose a novel concept of residual Q-networks (RQNs) for MARL, which learns to transform the individual Q -value trajectories in a way that preserves the individual-global-max (IGM) criteria, but is more robust in factorizing action-value functions. The RQN acts as an auxiliary network that accelerates convergence and will become obsolete as the agents reach the training objectives. The performance of the proposed method is compared against several state-of-the-art techniques such as QPLEX, QMIX, QTRAN, and VDN, in a range of multiagent cooperative tasks. The results illustrate that the proposed method, in general, converges faster, with increased stability, and shows robust performance in a wider family of environments. The improvements in results are more prominent in environments with severe punishments for noncooperative behaviors and especially in the absence of complete state information during training time.


Transforming Education

Abstract: Generative AI systems are increasingly present in our daily lives, helping us make crucial decisions. They use machine learning algorithms and tools, fed with millions of data collected from the web, producing entirely new information and generating variations. And this is not just limited to texts — it can produce images, audio, videos, even code, or new programming languages. There are several fields where generative AI can have a considerable impact in the coming years. In this context, the issues proposed in this chapter are: What is generative AI? What is prompt engineering? How to transform education using generative AI and prompt engineering in creating synthetic content? To respond to the research problem, the following objective will be achieved: Investigate how to transform education using generative AI and prompt engineering in the creation of synthetic content. It is concluded that generative AI tools can also help create more efficient exercises. Teachers and educators can use technology to create instructional materials and present summaries of concepts.


A multilayer perceptron neural network approach for optimizing solar irradiance forecasting in Central Africa with meteorological insights

Abstract: Promoting renewable energy sources, particularly in the solar industry, has the potential to address the energy shortfall in Central Africa. Nevertheless, a difficulty occurs due to the erratic characteristics of solar irradiance data, which is influenced by climatic fluctuations and challenging to regulate. The current investigation focuses on predicting solar irradiance on an inclined surface, taking into consideration the impact of climatic variables such as temperature, wind speed, humidity, and air pressure. The used methodology for this objective is Artificial Neural Network (ANN), and the inquiry is carried out in the metropolitan region of Douala. The data collection device used in this research is the meteorological station located at the IUT of Douala. This station was built as a component of the Douala sustainable city effort, in partnership with the CUD and the IRD. Data was collected at 30-min intervals for a duration of around 2 years, namely from January 17, 2019, to October 30, 2020. The aforementioned data has been saved in a database that underwent pre-processing in Excel and later employed MATLAB for the creation of the artificial neural network model. 80% of the available data was utilized for training the network, 15% was allotted for validation, and the remaining 5% was used for testing. Different combinations of input data were evaluated to ascertain their individual degrees of accuracy. The logistic Sigmoid function, with 50 hidden layer neurons, yielded a correlation coefficient of 98.883% between the observed and estimated sun irradiation. This function is suggested for evaluating the intensities of solar radiation at the place being researched and at other sites that have similar climatic conditions.


OPAL: Occlusion Pattern Aware Loss for Unsupervised Light Field Disparity Estimation

Abstract: Light field disparity estimation is an essential task in computer vision. Currently, supervised learning-based methods have achieved better performance than both unsupervised and optimization-based methods. However, the generalization capacity of supervised methods on real-world data, where no ground truth is available for training, remains limited. In this paper, we argue that unsupervised methods can achieve not only much stronger generalization capacity on real-world data but also more accurate disparity estimation results on synthetic datasets. To fulfill this goal, we present the Occlusion Pattern Aware Loss, named OPAL, which successfully extracts and encodes general occlusion patterns inherent in the light field for calculating the disparity loss. OPAL enables: i) accurate and robust disparity estimation by teaching the network how to handle occlusions effectively and ii) significantly reduced network parameters required for accurate and efficient estimation. We further propose an EPI transformer and a gradient-based refinement module for achieving more accurate and pixel-aligned disparity estimation results. Extensive experiments demonstrate our method not only significantly improves the accuracy compared with SOTA unsupervised methods, but also possesses stronger generalization capacity on real-world data compared with SOTA supervised methods. Last but not least, the network training and inference efficiency are much higher than existing learning-based methods. Our code will be made publicly available.


Robust human locomotion and localization activity recognition over multisensory

Abstract: Human activity recognition (HAR) plays a pivotal role in various domains, including healthcare, sports, robotics, and security. With the growing popularity of wearable devices, particularly Inertial Measurement Units (IMUs) and Ambient sensors, researchers and engineers have sought to take advantage of these advances to accurately and efficiently detect and classify human activities. This research paper presents an advanced methodology for human activity and localization recognition, utilizing smartphone IMU, Ambient, GPS, and Audio sensor data from two public benchmark datasets: the Opportunity dataset and the Extrasensory dataset. The Opportunity dataset was collected from 12 subjects participating in a range of daily activities, and it captures data from various body-worn and object-associated sensors. The Extrasensory dataset features data from 60 participants, including thousands of data samples from smartphone and smartwatch sensors, labeled with a wide array of human activities. Our study incorporates novel feature extraction techniques for signal, GPS, and audio sensor data. Specifically, for localization, GPS, audio, and IMU sensors are utilized, while IMU and Ambient sensors are employed for locomotion activity recognition. To achieve accurate activity classification, state-of-the-art deep learning techniques, such as convolutional neural networks (CNN) and long short-term memory (LSTM), have been explored. For indoor/outdoor activities, CNNs are applied, while LSTMs are utilized for locomotion activity recognition. The proposed system has been evaluated using the k-fold cross-validation method, achieving accuracy rates of 97% and 89% for locomotion activity over the Opportunity and Extrasensory datasets, respectively, and 96% for indoor/outdoor activity over the Extrasensory dataset. These results highlight the efficiency of our methodology in accurately detecting various human activities, showing its potential for real-world applications. Moreover, the research paper introduces a hybrid system that combines machine learning and deep learning features, enhancing activity recognition performance by leveraging the strengths of both approaches.


Strategic Analysis of the Parameter Servers and Participants in Federated Learning: An Evolutionary Game Perspective

Abstract: Federated learning (FL) is a new decentralized deep learning paradigm developed for collaborative model training and solving the problem of data privacy and has received extensive attention from both the academic and business worlds. However, FL still faces challenges in encouraging participants to contribute private data and computational resources. Although many studies have applied game theory models to improve the incentive mechanism design of FL, they assume that the players are absolutely rational and that the game models are static. In this study, a mathematical model based on evolutionary game theory (EGT) is established to analyze the interaction between parameter servers and participants, considering that the participants are not completely rational in the long-term dynamic decision-making process. The evolutionarily stable status of the FL system and the strategies of the parameter servers and participants were analyzed under eight different scenarios. Based on the model analysis and results of the numerical experiments, managerial insights for maintaining a sustainable FL system are summarized.


Quantum Vision Transformers

Abstract: In this work, quantum transformers are designed and analysed in detail by extending the state-of-the-art classical transformer neural network architectures known to be very performant in natural language processing and image analysis. Building upon the previous work, which uses parametrised quantum circuits for data loading and orthogonal neural layers, we introduce three types of quantum transformers for training and inference, including a quantum transformer based on compound matrices, which guarantees a theoretical advantage of the quantum attention mechanism compared to their classical counterpart both in terms of asymptotic run time and the number of model parameters. These quantum architectures can be built using shallow quantum circuits and produce qualitatively different classification models. The three proposed quantum attention layers vary on the spectrum between closely following the classical transformers and exhibiting more quantum characteristics. As building blocks of the quantum transformer, we propose a novel method for loading a matrix as quantum states as well as two new trainable quantum orthogonal layers adaptable to different levels of connectivity and quality of quantum computers. We performed extensive simulations of the quantum transformers on standard medical image datasets that showed competitively, and at times better performance compared to the classical benchmarks, including the best-in-class classical vision transformers. The quantum transformers we trained on these small-scale datasets require fewer parameters compared to standard classical benchmarks. Finally, we implemented our quantum transformers on superconducting quantum computers and obtained encouraging results for up to six qubit experiments.


LEAF: A Federated Learning-Aware Privacy-Preserving Framework for Healthcare Ecosystem

Abstract: Over the last decades, the healthcare industry has been revolutionized like anything, especially after the Covid-19 surge. Various artificial intelligence approaches have also been explored during this era for their applicability in healthcare. However, traditional AI techniques and algorithms are prone to overfitting with minimum robustness to unseen or untrained data. So, there is a requirement for new techniques which can counterfeit the issues mentioned earlier. Federated learning (FL) can help to make specific AI services for the network of hospitals with less overfitting and more robust modules. However, with the inclusion of FL, the problem related to user privacy is the biggest challenge, making using FL in the real world a grand challenge. Most solutions presented in the literature used blockchain technology to mitigate the issues mentioned earlier. However, it prevents third-party systems from penetrating the decision process, but the network devices can access shared data. Moreover, blockchain implementation requires new paradigms and infrastructure with an additional overhead cost. Motivated by these facts, the paper presents a limited access encryption algorithm incorporating FL (LEAF) framework, i.e., an encryption technique that solves privacy issues with the help of edge-enabled AI models. The proposed LEAF framework preserves user privacy and minimizes overhead costs. The authors have evaluated the performance of the LEAF framework using extensive simulations and achieved superior results. The achieved accuracy of the proposed LEAF framework is 3% more than that of the traditional centralized and FL-based systems advantages without compromising user privacy. In the best scenario, the proposed framework’s encryption process also compresses the data size by 4-5 times.


FastAL: Fast Evaluation Module for Efficient Dynamic Deep Active Learning Using Broad Learning System

Abstract: State-of-the-art Active Learning (AL) methods often encounter challenges associated with a hysteretic learning process and an expensive data sampling mechanism. The former implies that data selection in the ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i + 1)-th round is solely based on the learned model’s results in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i -th round. The latter involves using model inference to calculate data value (e.g., uncertainty estimation based on model inference), which can be cumbersome, particularly when working with large datasets or Deep Neural Networks (DNNs). To address these challenges, we propose FastAL, an efficient and dynamic deep AL framework. Our approach includes an efficient method for calculating data value from the frequency domain perspective, generating multiple candidates. Then, we introduce the Fast Evaluation Module, which directly calculates each candidate’s contribution to future model training and selects the best options. In addition, current AL methods, particularly those based on uncertainty, are susceptible to data bias, which implies that selected data may not represent the original unlabeled data adequately. To alleviate this issue, we propose the De-similar Module, which removes partially similar data. The above three modules are model-agnostic and thus can be seamlessly integrated into any Active Learning framework. We conducted rigorous experiments on various benchmark datasets to validate our approach’s effectiveness. Our results demonstrate that FastAL outperforms other state-of-the-art methods by a significant margin, including those based on uncertainty, diversity, and expected model change.


Introducing instance label correlation in multiple instance learning. Application to cancer detection on histopathological images

Abstract: In the last years, the weakly supervised paradigm of multiple instance learning (MIL) has become very popular in many different areas. A paradigmatic example is computational pathology, where the lack of patch-level labels for whole-slide images prevents the application of supervised models. Probabilistic MIL methods based on Gaussian Processes (GPs) have obtained promising results due to their excellent uncertainty estimation capabilities. However, these are general-purpose MIL methods that do not take into account one important fact: in (histopathological) images, the labels of neighboring patches are expected to be correlated. In this work, we extend a state-of-the-art GP-based MIL method, which is called VGPMIL-PR, to exploit such correlation. To do so, we develop a novel coupling term inspired by the statistical physics Ising model. We use variational inference to estimate all the model parameters. Interestingly, the VGPMIL-PR formulation is recovered when the weight that regulates the strength of the Ising term vanishes. The performance of the proposed method is assessed in two real-world problems of prostate cancer detection. We show that our model achieves better results than other state-of-the-art probabilistic MIL methods. We also provide different visualizations and analysis to gain insights into the influence of the novel Ising term. These insights are expected to facilitate the application of the proposed model to other research areas.


The development of orthography and phonology coupling in the ventral occipito-temporal cortex and its relation to reading.

Abstract: The left ventral occipito-temporal (lvOT) cortex is considered to house the brain's representation of orthography (i.e., the spelling patterns of words). Because letter-sound coupling is crucial in reading, we investigated the engagement of the lvOT cortex in processing phonology (i.e., the sound patterns of words) as a function of reading acquisition. We tested 47 Polish children both at the beginning of formal literacy instruction and 2 years later. During functional magnetic resonance imaging, children performed auditory phonological tasks from small to large grain size levels (i.e., single phoneme, rhyme). We showed that orthographically relevant lvOT areas activated during small-grain size phonological tasks were skill-dependent, perhaps due to the relatively transparent mappings between orthography and phonology in Polish. We also studied activation pattern similarity between processing visual and auditory word stimuli in the lvOT. We found that a higher similarity level was observed in the anterior lvOT compared to the posterior lvOT after 2 years of schooling. This is consistent with models proposing a posterior-to-anterior shift in word processing during reading acquisition. We argue that the development of orthography-phonology coupling at the brain level reflects writing system-specific effects and a more universal pathway of the left vOT development in reading acquisition. (PsycInfo Database Record (c) 2024 APA, all rights reserved).


A Preliminary Investigation into Search and Matching for Tumor Discrimination in World Health Organization Breast Taxonomy Using Deep Networks

Abstract: Breast cancer is one of the most common cancers affecting women worldwide. They include a group of malignant neoplasms with a vari- ety of biological, clinical, and histopathological characteristics. There are more than 35 different histological forms of breast lesions that can be classified and diagnosed histologically according to cell morphology, growth, and architecture patterns. Recently, deep learning, in the field of artificial intelligence, has drawn a lot of attention for the computerized representation of medical images. Searchable digital atlases can provide pathologists with patch matching tools allowing them to search among evidently diagnosed and treated archival cases, a technology that may be regarded as computational second opinion. In this study, we indexed and analyzed the WHO breast taxonomy (Classification of Tumours 5th Ed.) spanning 35 tumour types. We visualized all tumour types using deep fea- tures extracted from a state-of-the-art deep learning model, pre-trained on millions of diagnostic histopathology images from the TCGA reposi- tory. Furthermore, we test the concept of a digital “atlas” as a referencefor search and matching with rare test cases. The patch similarity search within the WHO breast taxonomy data reached over 88% accuracy when validating through “majority vote” and more than 91% accuracy when validating using top-n tumour types. These results show for the first time that complex relationships among common and rare breast lesions can be investigated using an indexed digital archive.


Graph representation learning method based on three-way partial order structure

Abstract: In the era of big data, handling massive datasets to extract valuable information has become increasingly critical. Knowledge representation emerges as a pivotal method to address this challenge. In the domain of knowledge representation, there exist two primary approaches: symbolic representation and vector representation. The integration of symbolic and vector representations to harness their respective strengths has become the cutting-edge approach to address challenges in the field of knowledge representation. This paper proposes a method that integrates a partial order formal structure analysis (POFSA) with graph representation learning. Specifically, we initially construct three-way partial order structure graphs, then create an attribute graph based on this structure, which can be processed by the graph representation learning methods. Finally, we utilize the graph representation learning to construct embeddings for three-way attribute partial order structure diagram (APOSD). We comprehensively assesse these embeddings across eight different datasets and present the results. The experiments indicate the feasibility of our proposed approach which is proven a novel approach of combining symbolic and vector representations for handling complex data and implicit knowledge.


Blockchains for SMEs: A Fit-Viability perspective moderated by organizational innovation diffusion for supply chain performance

Abstract: This paper empirically examines the suitability of blockchain for improving supply chain performance in small-medium enterprises through the theoretical lens of the fit-viability model against the impact of organisation innovation diffusion level Data gathered from 206 respondents are analysed using partial least square-structured equation modelling (PLS-SEM) and artificial neural network (ANN). The findings of this study show empirically that enhanced supply chain connectivity, information sharing, and supply chain visibility are fit for enhancing supply chain performance. Economics, management support, and technology infrastructure are important for blockchain viability. Further, an organisation's innovation diffusion level affects the fitness of blockchain for supply chain performance but not viability. The transformational effects of blockchain are mainly centred on its security and trust-enhancing features. However, its deployment continues to be marred by technical complexities and often, scenarios that seem opportune may be a poor fit. This study is among the few that consider the impact of blockchain on supply chain performance through a fit-viability lens against the effect of organisation innovation diffusion level.


SwinHR: Hemodynamic-powered hierarchical vision transformer for breast tumor segmentation

Abstract: Accurate and automated segmentation of breast tumors in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays a critical role in computer-aided diagnosis and treatment of breast cancer. However, this task is challenging, due to random variation in tumor sizes, shapes, appearances, and blurred boundaries of tumors caused by inherent heterogeneity of breast cancer. Moreover, the presence of ill-posed artifacts in DCE-MRI further complicate the process of tumor region annotation. To address the challenges above, we propose a scheme (named SwinHR) integrating prior DCE-MRI knowledge and temporal-spatial information of breast tumors. The prior DCE-MRI knowledge refers to hemodynamic information extracted from multiple DCE-MRI phases, which can provide pharmacokinetics information to describe metabolic changes of the tumor cells over the scanning time. The Swin Transformer with hierarchical re-parameterization large kernel architecture (H-RLK) can capture long-range dependencies within DCE-MRI while maintaining computational efficiency by a shifted window-based self-attention mechanism. The use of H-RLK can extract high-level features with a wider receptive field, which can make the model capture contextual information at different levels of abstraction. Extensive experiments are conducted in large-scale datasets to validate the effectiveness of our proposed SwinHR scheme, demonstrating its superiority over recent state-of-the-art segmentation methods. Also, a subgroup analysis split by MRI scanners, field strength, and tumor size is conducted to verify its generalization. The source code is released on (https://github.com/GDPHMediaLab/SwinHR).