A continuous digital elevation representation model for DEM super-resolution

Abstract: The surface of the Earth is continuous, and obtaining precise elevation data at arbitrary query positions is essential for many applications and analyses. However, existing digital elevation models suffer from a precision gap caused by discretization. Therefore, we develop a new continuous representation model (CDEM) that allows height values to be obtained at any arbitrary query position. Inspired by recent research on the implicit neural representation model, we train an encoder–decoder network to learn CDEM from discrete elevation data for DEM super-resolution tasks. The encoder targets generating latent codes from discrete elevation data, while the decoder composes these latent codes with query positions to predict corresponding elevation values. Such a learning pipeline is well-suited for DEM super-resolution tasks. To improve model accuracy, we also propose predicting the bias of elevation values between the query position and its closest known position. Real-world terrain surfaces exhibit inherent roughness with numerous small variations in localized regions, resulting in high-frequency targets that are difficult for neural networks to fit. In order to facilitate the network's ability to model data with high-frequency variations, we introduce positional encoding to map query positions into a higher-dimensional space. Compared to the Bicubic interpolation method and state-of-the-art TfaSR model, our method is demonstrated to obtain more accurate elevation values and preserve more details of terrain structure on the TFASR30, Pyrenees, and Tyrol datasets. Specifically, our EBCF-CDEM model demonstrates performance improvements over the TfaSR model, with reductions in the RMSE of elevation, slope, and aspect by 7.03%, 4.81%, and 3.07% respectively at ×4 super-resolution scale on the TFASR30 dataset, by 9.92%, 7.06%, and 6.15% at ×8 super-resolution scale on the Pyrenees dataset. Extensive experiments further validate the generalizability of our EBCF-CDEM, compared to the TfaSR model, our results in terms of RMSE of elevation, slope, and aspect are reduced by 14.78%, 12.12%, and 8.99% respectively at ×8 super-resolution scale on the Tyrol dataset, and 26.92%, 7.14% and 5.36% on the TIFASR30to10 dataset. We release our source code (including the datasets) at https://github.com/AlcibiadesTophetScipio/EBCF-CDEM to reproduce our results and encourage future research.


Supervised Stratified Subsampling for Predictive Analytics

Abstract: Predictive analytics involves the use of statistical models to make predictions; however, the power of these techniques is hindered by ever-increasing quantities of data. The richness and sheer volume of big data can have a profound effect on computation time and/or numerical stability. In the current study, we develop a novel approach to subsampling with the aim of overcoming this issue when dealing with regression problems in a supervised learning framework. The proposed method integrates stratified sampling and is model-independent. We assess the theoretical underpinnings of the proposed subsampling scheme, and demonstrate its efficacy in yielding reliable predictions with desirable robustness when applied to different statistical models. Supplementary materials for this article are available online.


Clustering dimensionless learning for multiple-physical-regime systems

Abstract: The conventional physical analysis has relied on the researchers' intelligence and physical insights to establish mathematical models and analyze the dependence of physical systems on dominant parameters in different physical regimes. In this work, a novel data-driven method is proposed to identify different physical regimes without a prior knowledge of governing equations and discover the dominant dimensionless parameters. The proposed method consists of two parts: the first is a data division via cluster analysis, which is utilized to identify different physical regimes via grouping data points into clusters with the weights taken from the key features in active subspace method; the second is a data-driven analysis of dominant dimensionless parameters via the active subspace, which is utilized to discover dominant dimensionless parameters by use of clustering and its resultant information (e.g. eigenpairs of clusters). We use three example problems to demonstrate this method: pipe flows, the spread of oil slicks on a calm sea, and the eddy viscosity in turbulent channel flows. The results obtained show that the present method can identify distinct physical regimes, and discover dominant dimensionless parameters, while the data-driven dimensional analysis without clustering cannot be directly used to the physical systems of multiple physical regimes.


Accountable Malicious Entity Detection Using Re-Encryption Mechanism to Share Data

Abstract: Recently data sharing through the cloud is widely used and becoming a trend. But protecting data confidentiality and integrity is essential. Data confidentiality and integrity are maintained using cryptography. However, since it cannot ensure that the data hasn't been altered, there is still a trust issue. The proxy re-encryption (PRE) technique was presented as a solution to this. It includes encrypting the data twice, allowing one to determine whether or not it has been altered. The PRE system is also prone to attacks that forge re-encryption keys. Here the data is encrypted twice so that the data can be checked whether it is modified or not. But the PRE system is prone to the abuse of re-encryption keys. Therefore, accountable proxy re-encryption (APRE) is proposed. Here, if the data is altered by proxy, the system will detect whether proxy is malicious or the delegator is trying to frame proxy as malicious. Also, the authors extend the algorithm and implemented base64 encoding and decoding. This prevents many passive cyber attacks.


Enhancing Cloud Security

Abstract: Cloud computing has revolutionized the way organizations store, process, and manage data, offering flexibility and scalability. However, the rise in cyber threats poses significant challenges to maintaining robust cloud security. This chapter delves into the pivotal role that Artificial Intelligence (AI) and Machine Learning (ML) play in enhancing cloud security. By harnessing the capabilities of AI and ML, organizations can proactively detect, mitigate, and respond to evolving cyber threats, ultimately fortifying their cloud infrastructure.AI-driven techniques empower security systems to recognize patterns, anomalies, and potential threats within vast datasets. ML algorithms can learn from historical attack data, enabling the prediction of future threats and the development of more effective defense mechanisms. Moreover, AI-enhanced authentication and access control mechanisms bolster identity management, reducing the risk of unauthorized access and data breaches.


A Model for Trust Decision, Data Analysis, and Evaluation to Identify Quality Web Services

Abstract: Cloud computing has emerged as a powerful paradigm for delivering web services, and includes scalability, flexibility, and cost efficiency. Due to functional overlap and diversity, web services form a major challenge for selecting adequate services to develop user-provider trust. To address the issue, this study presented a machine learning based trusted model to assist users in selecting trustworthy web services. In the initial stage, using K-Means clustering method the services are selected based on three clusters such as high, medium, and low trust. Next, the trust score is generated by evaluating performance parameters to identify the best services. Experiments conducted with QWS datasets demonstrate that the proposed approach efficiently predicts adequate services with a minimum error rate and high accuracy gain. This technique achieves a 99.32%, 99.36% and 99.48% accuracy rates for the low, medium, and high trust prediction, respectively. The result shows that it is more effective than existing approaches and builds a strong trust relation between users and providers.


Role-Based access Control (RBAC) and Attribute-Based Access Control (ABAC)

Abstract: This chapter explores the profound impact of artificial intelligence (AI) and machine learning (ML) on the realm of cloud security. As organizations increasingly migrate their operations and data to cloud environments, ensuring robust security measures becomes paramount. The integration of AI and ML technologies introduces novel ways to enhance threat detection, prevention, and response in the cloud. This chapter delves into various aspects of this synergy, discussing the benefits, challenges, and future prospects of utilizing AI and ML for safeguarding cloud infrastructures. This chapter also presents the benefits, challenges, and future directions. This abstract underscores the transformative potential of AI and ML in fortifying cloud infrastructures and safeguarding sensitive information in the digital age.


Data Storage and Transmission Security in the Cloud

Abstract: Cloud computing has profoundly changed the face of data management for enterprises, providing increased scalability, ease of access, and cost savings. Nonetheless, this change has highlighted the crucial need for strengthened security measures to protect sensitive data from the ever-changing spectrum of cyber threats. Following the cloud's rise as a storehouse for large datasets, the quest for sophisticated security solutions has gained traction. This motivation has resulted in the incorporation of artificial intelligence (AI) into the cloud security architecture. As cloud storage becomes increasingly popular, organizations are becoming more concerned about data security. Sensitive data is transmitted, ranging from messages and images to financial and health information. As technology advances, there is a growing threat to customer data in the cloud, making greater cloud security more important than ever. This study aims to give a comprehensive insight into the role of AI in data storage and transmission security in the cloud.


Privacy and Surveillance in Digital Era

Abstract: With a majority of the human population on the internet, online activity has become the order of the day. And that is why the internet is where criminals of every hue and color join forces and innovate in innovative ways. Not all “lands” have laws to deal with cybersecurity and privacy. Criminals across the globe have exploited the gap between technology and laws. The Indian Constitution recognizes privacy as a fundamental right, albeit with certain restrictions. Governments, in the name of providing secure cyberspace, infringe upon the privacy rights of citizens. And surveillance in India is chiefly governed by the Indian Telegraph Act u/s 5, which permits the government and its agencies to surveil provided a qualifying prerequisite preexists. Various surveillance apparatuses have been established by the GoI. Allegedly, the surveillance regime poses a grave threat and a chilling effect on privacy and freedom of expression rights as enshrined in the Indian Constitution. This chapter discusses cybersecurity, privacy, and surveillance regimes in Indian cyberspace.


An Intelligent Data Retrieving Technique and Safety Measures for Sustainable Cloud Computing

Abstract: Data refers to raw facts and figures. After processing data, the information is created. Information like facts or statistics that can be recorded, stored, and analysed can be in various forms such as text, numbers, images, audio, or video. The amount of data generated every day in the data-driven world of today makes it nearly difficult to keep it all locally. Cloud services are being used by so many people and businesses to store these massive and hefty volumes of data on cloud servers. Additionally, as the need for cloud computing grows, so does the need for data recovery methods and services. The ability to recover data and information from the backup server when the primary server is down is the primary goal of recovery services and technologies. A secondary server to hold backups for the primary cloud server will be more expensive to add than the primary server itself, which is already quite expensive and time-consuming to build. The goal of this chapter is to provide realistic, cost-effective solutions to this urgent problem.


Algorithmic implementation and optimisation of path planning

Abstract: Path planning algorithms are widely used in the fields of robotics and robotic arms, unmanned devices, automatic navigation, etc., and are an important technical basis for promoting the development of automation as well as the popularisation of artificial intelligence technology. This paper will briefly introduce various path planning algorithms implemented by mathematical models or inspired by biological features or genetics from the aspects of geometric search algorithms, intelligent search algorithms, artificial intelligence algorithms, and hybrid algorithms, including the characteristics, advantages and disadvantages, and important improvements of typical path planning algorithms as well as hybrid algorithms which are made by imitating and improving each other of several algorithms. Meanwhile, the development trend of path planning algorithms is also summarised, and the outlook of its development is made to provide reference for related fields.


Joint optimization of utility and privacy in mobile edge computing

Abstract: In mainstream research on Mobile Edge Computing (MEC), considerations for user privacy are often overlooked. This paper introduces Differential Privacy techniques to MEC, which obfuscate specific parameter values when transmitting user data, preventing leakage of sensitive information. Simultaneously, privacy, latency, and energy are incorporated as components of the utility function. We model the task offloading process using a deep reinforcement learning model and propose the use of the Deep Deterministic Policy Gradient (DDPG) algorithm in conjunction with a Laplacian privacy budget for joint optimization of privacy and utility during offloading. After sufficient training, the DDPG model can effectively approximate the optimal solution. Compared to traditional work, the optimized framework gives ample consideration to user privacy needs and outputs the optimal task offloading ratio solution with relatively low time complexity. This approach effectively safeguards user privacy while also considering the efficiency of task offloading.


Research on the sketch recognition and automatic plotting of deep learning

Abstract: Deep learning is one of the most progressive technologies around the world. With its profits of high flexibility and less economic consumption on development, more and more people are attracted to the industry and strive to improve its environment. As two of the biggest branch incorporated with such a novel and robust technology, automatic plotting, and sketch recognition have developed simultaneously. In this paper, various experiments and designs are listed to display the application of the technology. The paper indicated the possibility of such a combination through these tests and experiments. Because of the introduction of deep learning, these fields, such as facial recognition, and medicine, reduced the challenges, solved the potential risk, and paved the path for smoother improvement. Beyond the fact that deep learning still already achieves so much work, the success of in these technologies also helps its growth such as remedy the fact of limited data. It indicates its widespread usage in different areas, suggesting a confirmed future of exploiting.


Sentiment analysis and implementation in film evaluation utilizing BERT

Abstract: This study explores the use of the dmsc_v2 dataset, which is a rich collection of over 2 million ratings and commentary data from over 700,000 users on 28 films, to train the BERT model for sentiment analysis. This expansive dataset, drawn from the popular Chinese movie-rating website, Douban, has been meticulously curated for this research. In the context of the methodology, it is comprehensive and involves multiple stages. Initially, data preprocessing is conducted to refine and format the dataset suitably for model training. Subsequently, the BERT model is trained using the prepared data. Following the training process, the model's performance is critically evaluated to validate its efficacy and accuracy. The resulting model is adept at performing sentiment classification on comments pertaining to films across various social media platforms such as Weibo, Xiaohongshu, and more. This is particularly beneficial as it enables a nuanced analysis of user opinions and trending topics, offering invaluable insights for businesses, movie producers, or marketers. The findings of this study demonstrate that the BERT sentiment analysis model, developed with the dmsc_v2 dataset, exhibits impressive performance and has expansive potential for application within the sphere of social media commentary analysis. The successful development and validation of this model underscore its potential to transform the way sentiment analysis is conducted, especially in the context of entertainment and social media discussions.


Employees’ information security awareness (ISA) in public organisations: insights from cross-cultural studies in Sweden, France, and Tunisia

Abstract: The dominance of national culture in organisational environments has been widely acknowledged. This study aims to explore the impact of national culture on information security awareness by examining cultural characteristics in the public sector. Drawing on Hofstede's model, a literature review was conducted to analyze existing knowledge and identify cultural traits in selected countries. Empirical data were collected through semi-structured interviews conducted in Sweden, France, and Tunisia. The data were analyzed using the Human Aspects of Information Security (HAIS) model, revealing distinct patterns and emphasising the role of culture in shaping behaviours. The findings underscore the importance of involving Swedish employees in the policy design process to effectively enhance information security awareness. For French employees, it was found that learning from past experiences and respecting hierarchical structures are crucial factors in raising awareness. Conversely, in Tunisia, information security initiatives should take into account the social and relational aspects to promote awareness effectively. These insights provide valuable guidance to managers in multinational organisations, enabling them to comprehend the underlying forces and dynamics in countries with similar characteristics.


How enterprise social media usage contributes to employee resilience: moderating role of individual adaptability

Abstract: Earlier research on the ways in which enterprise social media (ESM) usage contributes to employee resilience is rather limited. Using boundary spanning theory, this paper proposes a research model for investigating how enterprise social media usage contributes to employee resilience. Using Smart PLS 4.0 to analyze the survey data from 370 employees, this study found that, first, when enterprise social media is used for work and social purposes, it has a positive effect on employee boundary spanning, while the effect of social-related ESM usage on employee boundary spanning is larger than work-related ESM usage. Second, employee boundary spanning has the greatest effect on the behavioural dimension of employee-level resilience, followed by the cognitive dimension of employee-level resilience and the contextual dimension. Third, employees' boundary spanning acts as a partial mediator between enterprise social media and employees' different levels of resilience. Finally, it is interesting to find that individual adaptability can weaken the relationship between work-related ESM usage and employee boundary spanning, while individual adaptability can strengthen the relationship between social-related ESM usage and employee boundary spanning. The research findings suggest ways of using enterprise social media for specific purposes to increase employees' different levels of resilience in organisations.


Soothing the Self-Threat of Idea Theft

Abstract: Abstract The creative process has the potential to increase wellbeing and foster human flourishing (Dolan and Metcalfe, 2012; Forgeard and Eichner, 2014; O’Brien and Murray, 2015; Conner et al., 2018; Kaufman, 2018), yet has received little attention in the humanistic management literature. In this paper, we present three experiments showing that idea originators experience greater relationship conflict with counterparts who have committed perceived “idea theft”, i.e., proposed identical or related ideas. We test a model that identifies a mechanism—self-threat—that mediates the relationship between idea theft and relationship conflict and identifies an intervention to ameliorate that self-threat via self-affirmation. Study 1 demonstrates the idea originator’s attribution of malign attributions to, and negative moral emotions toward, the counterpart. Study 2 demonstrates that idea originators ostracize both those who propose identical ideas and those who propose merely related ideas. In Study 3, evidence for self-threat as the mechanism underlying the negative relational consequences is provided via both a direct test of mediation as well as via moderation by self-affirmation. Our research contributes to the nascent idea theft literature and suggests a way to reduce its negative consequences.


Secure Sharing of Electronic Medical Records Based on Blockchain

Abstract: As network technology advances and more people use devices, data storage has become a significant challenge due to the explosive growth of information and the threat of data leaks. In traditional medical institutions, most medical data is stored centrally through cloud computing technology in the institution’s data center. This centralized storage method has many security risks, and once the central server is attacked, it will lead to the loss of medical data, which will lead to the leakage of patients’ private data. At the same time, electronic medical records are the most critical data in the current medical field. In the traditional centralized healthcare service system (HSS), there are data leakage problems and tampering with electronic medical records due to human factors. At the same time, each hospital is built independently, resulting in the current centralized healthcare service system having a data silo problem, making it difficult to share medical data between institutions securely. With the increase in the number of users in the system, the electronic medical record data in the system also increases gradually, resulting in the increasing overhead of decryption calculation. Therefore, this paper proposes a blockchain-based access control scheme with multiparty authorization to ensure the security of electronic medical records. The scheme uses an SM encryption algorithm to encrypt the medical data in the system. It adds the patient’s signature to ensure the confidentiality and security of the data, and the encrypted electronic medical records (EMRs) are stored in the InterPlanetary File System (IPFS) to realize the distributed storage of EMR. In addition, role-based multiauthorization access control is implemented through smart contract-based to ensure the security of EMR. We have analyzed the security of this paper’s solution and compared its performance with the existing schemes based on other cryptographic algorithms. The experimental results show that the proposed solution significantly improves the secure sharing of EMR and provides system performance.


An image encryption algorithm based on multi-layered chaotic maps and its security analysis

Abstract: This study introduces a novel symmetric image encryption algorithm that employs a multi-layered architecture of chaotic maps, incorporating both confusion and diffusion components. The selected chaotic maps, namely Aizawa, Ricker, Sine-Circle, and Chirikov, have been well-established as effective sources of chaos in the existing literature. Within the proposed algorithm, these chaotic maps are strategically utilised in distinct layers to enhance the security of the encrypted image. The algorithm demonstrates applicability to real-world scenarios, distinguished by its simplicity, high-speed performance, and low computational complexity. The efficacy of the method is underlined by its dependence on both original images and secret keys, rendering it resilient against brute force and differential attacks. Rigorous experimentation, encompassing correlation, histogram analysis, NBCR, NPCR, UACI, PSNR, and NIST values, establishes the proposed encryption algorithm's robustness and security against diverse attack modalities, including differential and statistical approaches. These findings position the algorithm as a compelling solution for safeguarding sensitive information in various practical applications.


Fusion learning of preference and bias from ratings and reviews for item recommendation

Abstract: Recommendation methods improve rating prediction performance by learning selection bias phenomenon–users tend to rate items they like. These methods model selection bias by calculating the propensities of ratings, but inaccurate propensity could introduce more noise, fail to model selection bias, and reduce prediction performance. We argue that learning interaction features can effectively model selection bias and improve model performance, as interaction features explain the reason of the trend. Reviews can be used to model interaction features because they have a strong intrinsic correlation with user interests and item interactions. In this study, we propose a preference- and bias-oriented fusion learning model (PBFL) that models the interaction features based on reviews and user preferences to make rating predictions. Our proposal both embeds traditional user preferences in reviews, interactions, and ratings and considers word distribution bias and review quoting to model interaction features. Six real-world datasets are used to demonstrate effectiveness and performance. PBFL achieves an average improvement of 4.46% in root-mean-square error (RMSE) and 3.86% in mean absolute error (MAE) over the best baseline.


Cultural probes: a speculative and creative method for environmental research

Abstract: This article considers cultural probes as an experimental qualitative method in promoting collaborative and prefigurative participation in research. While the extant literature tends to frame cultural probes either as the discounted ethnographic tool or the means to gather design inspiration, such a framing also results in an irreconcilable methodological and conceptual dichotomy. Seeking to overcome this binary, this paper draws inspiration from Michel de Certeau’s concept of “poaching” to contemplate how cultural probes may elicit and provoke both inspirational and analytical data. Through an empirical study of the everyday waste practices of 10 young university students in Hong Kong, this study discusses the rationale of different cultural probes designs and participants’ experience in completing the probes. This paper argues that the cultural probes may offer a speculative and creative qualitative inquiry to cultivate one’s “attentiveness”, generate “politics of reconfiguration”, and make sense of “uncertainty” in knowledge production.


EIoU-distance loss: an automated team-wise player detection and tracking with jersey colour recognition in soccer

Abstract: The surge in demand for advanced operations in sports video analysis has underscored the crucial role of multiple object tracking. This study addresses the escalating need for efficient and accurate player and referee identification in sports video analysis. The challenge of identity switching among players, especially those with similar appearances, complicates multi-player tracking. Existing algorithms relying on manually labeled data face limitations, particularly with changes in jersey colors. This paper introduces an automated algorithm employing Intersection over Union (IoU) loss and Euclidean Distance (EUD), termed EIoU-Distance Loss, to track players and referees. The method prioritizes identity coherence, aiming to mitigate challenges associated with player and referee recognition. Comprising BackgroundSubtractionMOG2 for player and referee detection and IoU with EUD for connecting nodes across frames, the proposed approach enhances tracking performance, ensuring a clear distinction between different identities. This innovative method addresses critical issues in sports video analysis, offering a robust solution for tracking players and referees in dynamic game scenarios.


Review-based recommendation under preference uncertainty: An asymmetric deep learning framework

Abstract: Online reviews are one of the most trusted resources for inferring customer needs and understanding consumer decision-making behavior. This study attempts to integrate textual reviews and user-item ratings to improve recommendation performance. To achieve this goal, we propose a deep neural network model. Specifically, the proposed model applies a review-level aggregation strategy to learn user preferences while using an aspect-based document-level aggregation strategy to learn item representation. In this process, we introduce two attention modules at the review and aspect levels, respectively. The review-level attention is used to learn the user preferences that are most related to the target item. The aspect-level attention attempts to learn the item's aspect features that users are most concerned about. In addition, we design a latent stochastic attention mechanism based on the probabilistic generative mechanism, to model the user preference uncertainty. For evaluation, we conduct extensive experiments on several real-world datasets. Using several state-of-the-art methods as comparisons, we find that the proposed model can significantly improve rating predictive power in the context of the recommendation system. Based on ablation experiments, we find that the enhanced predictive power benefits from the preference uncertainty and the attention mechanism. From the qualitative analysis, we suggest that the proposed model can yield many interpretable results.


Toward an ontology for EA modeling and EA model quality

Abstract: Abstract Models have long since been used, in different shapes and forms, to understand, communicate about, and (re)shape, the world around us; including many different social, economic, biological, chemical, physical, and digital aspects. This is also the case in the context of enterprise architecture (EA), where we see a wide range of models in many different shapes and forms being used as well. Researchers in EA modeling usually introduce their own lexicon, and perspective of what a model actually is, while accepting (often implicitly) the accompanying ontological commitments. Similarly, practitioners of EA modeling implicitly also commit to (different) ontologies, resulting in models that have an uncertain ontological standing. This is because, for the subject domain of enterprise architecture models (as opposed to the content of such models), no single ontology has gained major traction. As a result, studies into aspects of enterprise architecture models, such as “model quality” and “return on modeling effort”, are fragmented, and cannot readily be compared or combined. This paper proposes a comprehensive applied ontology, specifically geared to enterprise architecture modeling. Ontologies represent structured knowledge about a particular subject domain. It allows for study into, and reasoning about, that subject domain. Our ontology is derived from a theory of modeling, while clarifying concepts such as “enterprise architecture model”, and introduces novel concepts such as “model audience” and “model objective”. Furthermore, the relevant interrelations between these different concepts are identified and defined. The resulting ontology for enterprise architecture models is represented in OntoUML, and shown to be consistent with the foundational ontology for modeling, Unified Foundational Ontology.


Wavelet-based adaptive implicit large-eddy simulation of turbulent channel flow

Abstract: The wavelet-based adaptive implicit large-eddy simulation approach to computational modeling of turbulent flows is introduced. The direct solution of the wavelet-filtered compressible Navier–Stokes equations is attained without employing any explicit modeling procedure for the unclosed terms. Instead, the dissipative properties of the numerical method using adaptive wavelet collocation are exploited for implicit subgrid-scale modeling. The performance of the newly proposed method was preliminarily assessed by carrying out adaptive simulations of fully developed turbulent supersonic channel flow, which represents a well-established benchmark for wall-bounded attached compressible turbulent flows. The comparison against higher-fidelity solutions proved both the feasibility and the effectiveness of the implicit modeling approach for wavelet-based large-eddy simulation. The consistency of the wavelet-based approach with turbulence physics guaranteeing the correct amount of implicit dissipation was demonstrated.


Identifying the Importance of UX Dimensions for Different Software Product Categories

Abstract: Abstract Billions of users around the world use mobile applications and computer software to achieve their professional and personal goals. This situation drives User Experience (UX) researchers and practitioners to assess the importance of UX dimensions across different products, to facilitate the design, development and evaluation of new products. To that end, this study surveyed a group of 200 end users and 8 UX experts from Canada to document the importance of 21 UX dimensions for 15 software product categories. The results confirmed that the importance of UX dimensions varies between product categories. Comparing the findings to those of similar studies conducted in Germany and Indonesia revealed that, while culture influences the rating of UX dimensions, the importance of UX dimensions is still determined by the product category. Comparisons between the importance ratings of UX dimensions between end users and experts and within end users were not significant in 77% and 97% of cases, respectively. Results showed that task-based product categories rely more on pragmatic dimensions (i.e. functionality and usability) while leisure-based products value hedonic dimensions (i.e. pleasure) as well. This study benefits researchers and practitioners by enabling them to select the most important UX dimensions for evaluating their products. CCS CONCEPTS: • Human-centered computing • Human-Computer Interaction (HCI) • HCI design and evaluation methods. Additional Keywords and Phrases: User experience, UX dimension, UX evaluation, culture.


Joint Alignment Networks For Few-Shot Website Fingerprinting Attack

Abstract: Abstract Website fingerprinting (WF) attacks based on deep neural networks pose a significant threat to the privacy of anonymous network users. However, training a deep WF model requires many labeled traces, which can be labor-intensive and time-consuming, and models trained on the originally collected traces cannot be directly used for the classification of newly collected traces due to the concept drift caused by the time gap in the data collection. Few-shot WF attacks are proposed for using the originally and few-shot newly collected labeled traces to facilitate anonymous trace classification. However, existing few-shot WF attacks ignore the fine-grained feature alignment to eliminate the concept drift in the model training, which fails to fully use the knowledge of labeled traces. We propose a novel few-shot WF attack called Joint Alignment Networks (JAN), which conducts fine-grained feature alignment at both semantic-level and feature-level. Specifically, JAN minimizes a distribution distance between originally and newly collected traces in the feature space for feature-level alignment, and utilizes two task-specific classifiers to detect unaligned traces and force these traces mapped within decision boundaries for semantic-level alignment. Extensive experiments on public datasets show that JAN outperforms the state-of-the-art few-shot WF methods, especially in the difficult 1-shot tasks.


Analyzing third-party data leaks on online pharmacy websites

Abstract: Abstract Purpose With digitalization, using essential digital services such as online services has become increasingly common. These services process sensitive health related data, such as customers’ prescription medicine orders, which makes ensuring stringent data privacy crucial. The current study examines third parties such as analytics services on Finnish pharmacy websites and investigates the nature and contents of data leaks on these websites. Methods We perform an extensive network traffic analysis to reveal data leaks among 163 Finnish online pharmacies. We also study a set of privacy policies of these online pharmacies, and provide a legal analysis regarding the interpretation of the concept of data concerning health in the context of online pharmacies. Results Our findings reveal serious data leaks among Finnish online pharmacies. We found 145 pharmacies had third-party services on their websites and only 18 did not. Out of all 163 online pharmacies, 57 (35.0 %) leaked a specific prescription medicine name connected with identifying personal data on the customer. We argue that the information concerning purchases on the prescription medicines should be interpreted as data concerning health to ensure efficient protection of customers’ right to data protection and privacy. Conclusions We hope that these concerning results will serve as a wake-up call for the developers and maintainers of online pharmacies and other web services processing sensitive data. Any third-party services incorporated into websites processing sensitive personal data should be closely inspected in terms of data leaks, or preferably not used at all.


Software development metrics: to VR or not to VR

Abstract: Abstract Context Current data visualization interfaces predominantly rely on 2-D screens. However, the emergence of virtual reality (VR) devices capable of immersive data visualization has sparked interest in exploring their suitability for visualizing software development data. Despite this, there is a lack of detailed investigation into the effectiveness of VR devices specifically for interacting with software development data visualizations. Objective Our objective is to investigate the following question: “How do VR devices compare to traditional screens in visualizing data about software development?” Specifically, we aim to assess the accuracy of conclusions derived from exploring visualizations for understanding the software development process, as well as the time required to reach these conclusions. Method In our controlled experiment, we recruited N=32 volunteers with diverse backgrounds. Participants interacted with similar data visualizations in both VR and traditional screen environments. For the traditional screen setup, we utilized a commercially available set of interactive dashboards based on Kibana, commonly used by Bitergia customers for data insights. In the VR environment, we designed a set of visualizations, tailored to provide an equivalent dataset within a virtual room. Participants answered questions related to software evolution processes, specifically code review and issue tracking, in both VR and traditional screen environments, for two projects. We conducted statistical analyses to compare the correctness of their answers and the time taken for each question. Results Our findings indicate that the correctness of answers in both environments is comparable. Regarding time spent, we observed similar durations, except for complex questions that required examining multiple interconnected visualizations. In such cases, participants in the VR environment were able to answer questions more quickly. Conclusion Based on our results, we conclude that VR immersion can be equally effective as traditional screen setups for understanding software development processes through visualization of relevant metrics in most scenarios. Moreover, VR may offer advantages in comprehending complex tasks that require navigating through multiple interconnected visualizations. However, further experimentation is necessary to validate and reinforce these conclusions.


<scp>O‐SCDR</scp>: Optimal cluster with attention based shared‐account cross‐domain sequential recommendation using deep reinforcement learning technique

Abstract: Abstract Sequential recommendation involves suggesting subsequent items in a series of user activities. When recommending relevant items to users within the same account, the challenge lies in discerning diverse user behaviours to provide tailored recommendations based on individual preferences and timing. Cross‐domain sequential recommendation (CDSR) focuses on accurately extracting cross‐domain user preferences from both within‐sequence and between‐sequence interactions among items. Current approaches typically concentrate on learning preferences within a single domain using intra‐sequence item interactions, followed by a transfer module for cross‐domain preferences. However, this sequential process and implicit method are constrained by the effectiveness of the transfer module and may overlook inter‐sequence item associations. In this study, we propose an optimal cluster with attention‐based shared‐account cross‐domain sequential recommendation (O‐SCSR) system using deep reinforcement learning techniques. Our approach commences by formulating a modified hummingbird optimization (MHO) algorithm for clustering, effectively identifying latent users who share the same account to enhance the understanding of user interactions within shared‐account scenarios. Additionally, we design a domain filter based on quantum classic deep reinforcement learning (QCDRL), intelligently selecting interactions contributing to O‐SCSR. By quantifying rewards from transferred domain knowledge, the QCDRL‐based filter retains only valuable interactions for the task of SCDR. Finally, we validate the efficacy of our proposed O‐SCDR method using real‐world datasets, namely HVIDEO and HAMAZON. Through simulation results comparing the O‐SCDR system with existing state‐of‐the‐art systems, we demonstrate its effectiveness and legitimacy.


Evaluation of the number of clusters in a data set using <i>p</i> -values from multiple tests of hypotheses

Abstract: This article proposes a novel, nonparametric, interpoint distance-based measure to investigate whether there exist any groups in a set of given data, and if so then, how many groups are prevailing in total. It is a cluster accuracy index useful for arbitrary-dimensional data set, in association with any clustering algorithm having the number of groups specified a priori. We perform univariate, nonparametric, multiple statistical tests of hypotheses, where as many dependent tests as the sample size are carried out using the interpoint distances. They possess p-values to be combined to reach a decision, which is taken in a step-wise process for a possible number of clusters. It reduces unnecessary computations compared with the other accuracy measures from the literature. Data study establishes the proposed index's efficiency and superiority.


Advances in automated support for requirements engineering: a systematic literature review

Abstract: Abstract Requirements Engineering (RE) has undergone several transitions over the years, from traditional methods to agile approaches emphasising increased automation. In many software development projects, requirements are expressed in natural language and embedded within large volumes of text documents. At the same time, RE activities aim to define software systems' functionalities and constraints. However, manually executing these tasks is time-consuming and prone to errors. Numerous research efforts have proposed tools and technologies for automating RE activities to address this challenge, which are documented in published works. This review aims to examine empirical evidence on automated RE and analyse its impact on the RE sub-domain and software development. To achieve our goal, we conducted a Systematic Literature Review (SLR) following established guidelines for conducting SLRs. We aimed to identify, aggregate, and analyse papers on automated RE published between 1996 and 2022. We outlined the output of the support tool, the RE phase covered, levels of automation, development approach, and evaluation approaches. We identified 85 papers that discussed automated RE from various perspectives and methodologies. The results of this review demonstrate the significance of automated RE for the software development community, which has the potential to shorten development cycles and reduce associated costs. The support tools primarily assist in generating UML models (44.7%) and other activities such as omission of steps, consistency checking, and requirement validation. The analysis phase of RE is the most widely automated phase, with 49.53% of automated tools developed for this purpose. Natural language processing technologies, particularly POS tagging and Parser, are widely employed in developing these support tools. Controlled experimental methods are the most frequently used (48.2%) for evaluating automated RE tools, while user studies are the least employed evaluation method (8.2%). This paper contributes to the existing body of knowledge by providing an updated overview of the research literature, enabling a better understanding of trends and state-of-the-art practices in automated RE for researchers and practitioners. It also paves the way for future research directions in automated requirements engineering.


Trajectory-based alignment for optical see-through HMD calibration

Abstract: Abstract In order to align the virtual and real content precisely through augmented reality devices, especially in optical see-through head-mounted displays (OST-HMD), it is necessary to calibrate the device before using it. However, most existing methods estimated the parameters via 3D-2D correspondences based on the 2D alignment, which is cumbersome, time-consuming, theoretically complex, and results in insufficient robustness. To alleviate this issue, in this paper, we propose an efficient and simple calibration method based on the principle of directly calculating the projection transformation between virtual space and the real world via 3D-3D alignment. The proposed method merely needs to record the motion trajectory of the cube-marker in the real and virtual world, and then calculate the transformation matrix between the virtual space and the real world by aligning the two trajectories in the observed view. There are two advantages associated with the proposed method. First, the operation is simple. Theoretically, the user only needs to perform four alignment operations for calibration without changing the rotation variation. Second, the trajectory can be easily distributed throughout the entire observation view, resulting in more robust calibration results. To validate the effectiveness of the proposed method, we conducted extensive experiments on our self-built optical see-through head-mounted display (OST-HMD) device. The experimental results show that the proposed method can achieve better calibration results than other calibration methods.


An extended trust and distrust network-based dual fuzzy recommendation model and its application based on user-generated content

Abstract: The social network-based recommendation algorithm can not only effectively solve the information overload problem, but also fully exploit the personalized preferences of users. However, current studies fail to consider the distrust feature and propagation characteristic of social networks. In addition, methods for quantifying user preferences and social relationships are deficient and inconsistent with reality. Therefore, an extended trust and distrust network-based dual fuzzy recommendation model (ETD-DFR) is proposed. First, the trust and distrust network is constructed, and then the network is extended based on the social balance theory; secondly, the weighted PageRank algorithm is utilized to calculate the trust and distrust between users; Further, a dual fuzzy framework is proposed to represent the fuzzy features of user preferences as well as trust and distrust; In addition, Louvain's algorithm is employed to partition the network into subgroups to quickly find trusted users for recommendation. An empirical analysis is conducted based on the dataset of consumer review websites, and the experimental results show that the performance of the proposed ETD-DFR model is excellent. Finally, comparative and sensitivity analyses are constructed to further verify the proposed ETD-DFR model is efficient and robust.


Personnel Training for Common Facility Management Issues in Thermal-Energy-Storage Chiller Plant using a Serious 3D Game

Abstract: Background This study introduces an innovative personnel training method for facility management and maintenance of Thermal-Energy-Storage (TES) chiller plants using a serious 3D game. Training games can improve the decision-making of personnel where they can learn to deal with management of TES chiller plants in the context of this study in an active learning approach. Intervention The research aims to investigate the effectiveness of an immersive learning experience with computerized simulation and synthetic task environments as a training game for facility managers. The serious 3D game adopts a first-person perspective, allowing players to assume the role of a facility manager and actively learn how to address maintenance issues commonly encountered in chiller plants. Methods The study implemented a first-person-based serious 3D game centered around a TES chiller plant. Participants, in the role of facility managers, engaged with the game and followed instructions to gain practical knowledge in managing maintenance issues within a controlled and simulated setting. Results The findings demonstrate increased engagement and interest among personnel when learning how to manage chiller plant issues within a serious 3D game environment. Notably, personnel experienced reduced pressure compared to real-life scenarios, as they navigated the challenges without the presence of a supervisor. Limitations The study’s limitations include a higher proportion of male participants in the qualitative content analysis, which may affect the generalizability of the findings. Additionally, the absence of a control group limits the ability to make direct comparisons with traditional training methods. Conclusion The results suggest that serious 3D games hold potential as an effective training tool for facility management and maintenance personnel. By engaging in active training within simulated and synthetic task environments, personnel can enhance their skills and decision-making capabilities. However, further research with a more diverse participant sample and a control group is warranted to fully evaluate the effectiveness of this innovative personnel training approach.


Self‐supervised multicontrast super‐resolution for diffusion‐weighted prostate MRI

Abstract: This study addresses the challenge of low resolution and signal-to-noise ratio (SNR) in diffusion-weighted images (DWI), which are pivotal for cancer detection. Traditional methods increase SNR at high b-values through multiple acquisitions, but this results in diminished image resolution due to motion-induced variations. Our research aims to enhance spatial resolution by exploiting the global structure within multicontrast DWI scans and millimetric motion between acquisitions.We introduce a novel approach employing a "Perturbation Network" to learn subvoxel-size motions between scans, trained jointly with an implicit neural representation (INR) network. INR encodes the DWI as a continuous volumetric function, treating voxel intensities of low-resolution acquisitions as discrete samples. By evaluating this function with a finer grid, our model predicts higher-resolution signal intensities for intermediate voxel locations. The Perturbation Network's motion-correction efficacy was validated through experiments on biological phantoms and in vivo prostate scans.Quantitative analyses revealed significantly higher structural similarity measures of super-resolution images to ground truth high-resolution images compared to high-order interpolation (p <$$ < $$ 0.005). In blind qualitative experiments, 96.1%$$ 96.1\% $$ of super-resolution images were assessed to have superior diagnostic quality compared to interpolated images.High-resolution details in DWI can be obtained without the need for high-resolution training data. One notable advantage of the proposed method is that it does not require a super-resolution training set. This is important in clinical practice because the proposed method can easily be adapted to images with different scanner settings or body parts, whereas the supervised methods do not offer such an option.


Text mining and sentiment analysis: A new lens to explore the emotion dynamics of mother‐child interactions

Abstract: Abstract Emotions are highly dynamic and social in nature. Traditional approaches to studying emotion expression face obstacles such as substantial time investments, susceptibility to human biases, and limited capacity to capture nuanced emotional patterns. To address these challenges, this research leveraged text mining and sentiment analysis to explore the dynamic patterns of emotion expression within the context of mother‐child interactions. We analyzed 8,841 conversation transcripts involving 1,462 mother‐child dyads, sourced from the Child Language Data Exchange System. Polarity scores were calculated and analyzed to uncover the temporal patterns of mother and child emotional sentiment. Our findings revealed that mothers tended to exhibit heightened levels of positive emotion at the beginning and conclusion of conversations, whereas children displayed a more linear positive trend. Using model‐based cluster analysis, we identified two distinct clusters of mothers characterized by varying degrees of emotion expression variation and two clusters of children showing different rates of elevation in positive emotion. At the dyadic level, the differences between mother and child polarity scores varied as a function of time, with an increase of difference from the beginning to the 20 th percentile point, a decrease until the 90 th percentile, and then an increase again towards the end of the conversation. This study demonstrates the utility of text mining and sentiment analysis in developmental studies, particularly in the context of parent‐child interactions. The findings hold informative implications for interventions that focus on fostering healthy parent‐child relationships.


Modular ocean trace elements sampling for the international GEOTRACES studies – Evidence from analysis of dissolved Fe and Pb

Abstract: A new modular system was reported in this paper for the purpose of sampling and processing of trace elements in the open ocean. The idea of designing is based on the concept of assembly and combination, which is so called "Modular Ocean Trace Elements Sampling" (MOTES). In application, the system is tested during a cruise in the Subtropical Pacific Ocean between 140° E to 142° E, and between 6° N and 20° N, with sampling water depth down to 7000 m. The results provide evidences for low procedural blanks and good reproducibility in repeated samplings through the entire water column for trace elements work. Data illustrate low, reproducible and representative concentrations of dissolved Fe (dFe) and Pb (dPb) compared to the results reported in the literature and show the consistency of oceanographic profiles in sub-basin-wide spatial dimensions.


Constructing tests for skill assessment with competence‐based test development

Abstract: Abstract Competence‐based test development is a recent and innovative method for the construction of tests that are as informative as possible about the competence state (the set of skills an individual has available) underlying the observed item responses. It finds application in different contexts, including the development of tests from scratch, and the improvement or shortening of existing tests. Given a fixed collection of competence states existing in a population of individuals and a fixed collection of competencies (each of which being the subset of skills that allow for solving an item), the competency deletion procedure results in tests that differ from each other in the competencies but are all equally informative about individuals' competence states. This work introduces a streamlined version of the competency deletion procedure that considers information necessary for test construction only, illustrates a straightforward way to incorporate test developer preferences about competencies into the test construction process, and evaluates the performance of the resulting tests in uncovering the competence states from the observed item responses.


Comparing programming languages for data analytics: Accuracy of estimation in <scp>Python</scp> and <scp>R</scp>

Abstract: Abstract Several open‐source programming languages, particularly R and Python, are utilized in industry and academia for statistical data analysis, data mining, and machine learning. While most commercial software programs and programming languages provide a single way to deliver a statistical procedure, open‐source programming languages have multiple libraries and packages offering many ways to complete the same analysis, often with varying results. Applying the same statistical method across these different libraries and packages can lead to entirely different solutions due to the differences in their implementations. Therefore, reliability and accuracy should be essential considerations when making library and package usage decisions while conducting statistical analysis using open source programming languages. Instead, most users take this for granted, assuming that their chosen libraries and packages produce accurate results for their statistical analysis. To this extent, this study assesses the estimation accuracy and reliability of Python and R's various libraries and packages by evaluating the univariate summary statistics, analysis of variance (ANOVA), and linear regression procedures using benchmarking data from the National Institutes of Standards and Technology (NIST). Further, experimental results are presented comparing machine learning methods for classification and regression. The libraries and packages assessed in this study include the stats package in R and Pandas, Statistics, NumPy, statsmodels, SciPy, statsmodels, scikit‐learn, and pingouin in Python. The results show that the stats package in R and statsmodels library in Python are reliable for univariate summary statistics. In contrast, Python's scikit‐learn library produces the most accurate results and is recommended for ANOVA. Among the libraries and packages assessed for linear regression, the results demonstrated that the stats package in R is more reliable, accurate, and flexible; thus, it is recommended for linear regression analysis. Further, we present results and recommendations for machine learning using R and Python. This article is categorized under: Algorithmic Development > Statistics Application Areas > Data Mining Software Tools


Deep‐Q‐Network hybridization with extended Kalman filter for accelerate learning in autonomous navigation with auxiliary security module

Abstract: Abstract This article proposes an algorithm for autonomous navigation of mobile robots that mixes reinforcement learning with extended Kalman filter (EKF) as a localization technique, namely EKF‐DQN, aiming to accelerate the maximization of the learning curve and improve the reward values obtained in the learning process. More specifically, Deep‐Q‐Networks (DQN) are used to control the trajectory of an autonomous robot in an environment with many obstacles. To improve navigation capability in this environment, we also propose a fusion of visual and nonvisual sensors. Due to the ability of EKF to predict states, this algorithm is used as a learning accelerator for the DQN network, predicting future states and inserting this information into the memory replay. Aiming to increase the safety of the navigation process, a visual safety system is also proposed to avoid collisions between the mobile robot and people circulating in the environment. The efficiency of the proposed control system is verified through computational simulations using the CoppeliaSIM simulator with code insertion in Python. The simulation results show that the EKF‐DQN algorithm accelerates the maximization of rewards obtained and provides a higher success rate in fulfilling the mission assigned to the robot when compared to other value‐based and policy‐based algorithms. A demo video of the navigation system can be seen at: https://bit.ly/3reEZrU .


ANALISIS YURIDIS KEWENANGAN PEMERIKSAAN KENDARAAN BERMOTOR DI JALAN OLEH PPNS DI BIDANG LALU LINTAS DAN ANGKUTAN JALAN

Abstract: Crime is a common problem in society and can be committed by anyone, both the perpetrator and the victim. Criminal policy used in tackling crime in Indonesia consists of two methods, namely preventive and repressive. In its implementation, it involves crime prevention investigators, police investigators, and civil servants (PPNS). However, the problem is the overlap between police investigators and PPNS because both have different authorities in conducting investigations. One of the well-known PPNS in Indonesia is the Road Traffic and Transport PPNS. In handling traffic and transport criminal offences, the investigation is carried out by two investigation agencies in accordance with Article 259 paragraph 1 of Law 22/2009 on traffic and transport. In the implementation of crime policy, it is important to coordinate activities between police and PPNS investigators to avoid overlapping authority. The role of the community in preventing crime by reporting suspicious activities to the authorities is also very important. In this way, it is hoped that the public can prevent crime by reporting suspicious activities to the authorities.


REVIEW OF EVOLVING CLOUD COMPUTING PARADIGMS: SECURITY, EFFICIENCY, AND INNOVATIONS

Abstract:
The paper provides a comprehensive analysis of the dynamic landscape of cloud computing, with a focus on security, efficiency, and innovative trends. It delves into the evolving paradigms that shape the current state of cloud computing, offering insights into the challenges and opportunities that arise as the technology continues to advance. Security is a central theme in the review, addressing the critical concerns associated with data privacy, integrity, and access control in cloud environments. The study explores the latest security measures and protocols implemented to safeguard sensitive information, emphasizing the importance of robust security frameworks in an era marked by cyber threats and vulnerabilities. Efficiency is another key aspect under scrutiny, with the review evaluating performance optimization strategies, resource allocation mechanisms, and scalability solutions. The authors examine how cloud providers are adapting to meet the growing demand for efficient and sustainable computing resources, highlighting best practices and emerging trends that contribute to enhanced performance. Furthermore, the review sheds light on the innovative trends shaping the future of cloud computing. It explores cutting-edge technologies, such as edge computing, serverless architectures, and containerization, and their impact on reshaping the cloud landscape. The paper concludes by summarizing the key takeaways and emphasizing the importance of understanding and adapting to the evolving paradigms in cloud computing to ensure a secure, efficient, and innovative digital future.
 Keywords: Cloud Computing, Information Technology, Computing, Security, Innovations, Review.


A constructivist and pragmatic training framework for blockchain education for IT practitioners

Abstract: Abstract Blockchain is a newly emerging technology in the past decade that has significantly impacted various aspects. “Scientific popularization” among IT practitioners on this technology and its use cases become a pressing need. However, constructing an effective blockchain teaching approach for this purpose is a challenging task. A training framework consisting of constructivist and pragmatic approaches is proposed, aiming to provide IT practitioners with an effective Teaching and Learning (T &L) process about blockchain on both theory and application aspects. The outcomes of this study are to 1) propose an effective teaching methodology, 2) assess the effectiveness of constructivist and pragmatic approaches and 3) extract the elements facilitating blockchain T &L. Mixed quantitative and qualitative research methods were adopted, including questionnaires and knowledge quizzes. 1267 participants were involved in the training that implemented the proposed framework. Their performance and responses indicated that the framework is effective and flexible. The findings from this empirical research can serve as a reference for educators in blockchain to implement a systemic approach that facilitates the T &L process and improves the field of blockchain and education in the future.


Federated learning with hybrid differential privacy for secure and reliable <scp>cross‐IoT</scp> platform knowledge sharing

Abstract: Abstract The federated learning has gained prominent attention as a collaborative machine learning method, allowing multiple users to jointly train a shared model without directly exchanging raw data. This research addresses the fundamental challenge of balancing data privacy and utility in distributed learning by introducing an innovative hybrid methodology fusing differential privacy with federated learning (HDP‐FL). Through meticulous experimentation on EMNIST and CIFAR‐10 data sets, this hybrid approach yields substantial advancements, showcasing a noteworthy 4.22% and up to 9.39% enhancement in model accuracy for EMNIST and CIFAR‐10, respectively, compared to conventional federated learning methods. Our adjustments to parameters highlighted how noise impacts privacy, showcasing the effectiveness of our hybrid DP approach in striking a balance between privacy and accuracy. Assessments across diverse FL techniques and client counts emphasized this trade‐off, particularly in non‐IID data settings, where our hybrid method effectively countered accuracy declines. Comparative analyses against standard machine learning and state‐of‐the‐art FL approaches consistently showcased the superiority of our proposed model, achieving impressive accuracies of 96.29% for EMNIST and 82.88% for CIFAR‐10. These insights offer a strategic approach to securely collaborate and share knowledge among IoT devices without compromising data privacy, ensuring efficient and reliable learning mechanisms across decentralized networks.


Open space radar specific emitter identification using MSAK‐CNN‐LSTM network

Abstract: Abstract To enhance the capability of identifying unknown emitters in open spaces, an open‐multiscale attention kernel (MSAK)‐convolutional neural network‐long short‐term memory (CNN‐LSTM) structure is proposed. To this end, first, a MSAK module and CNN‐LSTM structure are introduced, and then, the depth and complexity of the feature extraction network are improved to enhance its representation capability. To classify unknown emitters accurately, the MSAK‐CNN‐LSTM model is improved to obtain an open‐MSAK‐CNN‐LSTM model with open‐set recognition capability. Additionally, the two preprocessing procedures are summarised, and their strengths and weaknesses are compared. Experimental results show that the proposed open‐MSAK‐CNN‐LSTM model achieves satisfactory accuracy in identifying unknown emitters in open space. In addition, it has significant advantages in low signal‐to‐noise ratio (SNR) scenarios.


Advantages of transformer and its application for medical image segmentation: a survey

Abstract: Abstract Purpose Convolution operator-based neural networks have shown great success in medical image segmentation over the past decade. The U-shaped network with a codec structure is one of the most widely used models. Transformer, a technology used in natural language processing, can capture long-distance dependencies and has been applied in Vision Transformer to achieve state-of-the-art performance on image classification tasks. Recently, researchers have extended transformer to medical image segmentation tasks, resulting in good models. Methods This review comprises publications selected through a Web of Science search. We focused on papers published since 2018 that applied the transformer architecture to medical image segmentation. We conducted a systematic analysis of these studies and summarized the results. Results To better comprehend the benefits of convolutional neural networks and transformers, the construction of the codec and transformer modules is first explained. Second, the medical image segmentation model based on transformer is summarized. The typically used assessment markers for medical image segmentation tasks are then listed. Finally, a large number of medical segmentation datasets are described. Conclusion Even if there is a pure transformer model without any convolution operator, the sample size of medical picture segmentation still restricts the growth of the transformer, even though it can be relieved by a pretraining model. More often than not, researchers are still designing models using transformer and convolution operators.


Territory, COVID-19 and access to immunization: conceptual analysis on the challenges of the pandemic

Abstract: Between the pandemic situation triggered in 2020, vaccination emerges as an action of fundamental importance in combating Covid-19. In Brazil, the deconstruction of public health policies over the years, especially after Collor's election in 1989, has heightened the weaknesses in the application of the National Immunization Program which began in 2021, especially regarding the lack of control by the State, as well as increasing the difficulties in complying with previously defined vaccination criteria in complex and diverse territorial spaces like ours.


Reading Interest and Teacher’s Efforts in Increasing Reading Interest for Elementary Schools

Abstract: Interest in reading is one aspect that is the progress of the nation and state. Reading provides a cognitive experience that can stimulate the mind. The research was conducted at Mangkubumen Kidul Elementary School No.16 Laweyan District, Surakarta City. The purpose of the research conducted by researchers is to analyze the efforts made by teachers to increase students' reading interest. The research method used by researchers is qualitative research method with a case study approach. Data collection methods used are observation, interviews and documentation. The research subjects included school principals, teachers and students in class II and class V. The object of the study was the teacher's efforts to increase students' interest in reading at Mangkubumen Kidul Elementary School No. 16. The data validity technique used is data triangulation. Data analysis used is data reduction, data presentation and conclusion. The results and research findings include teachers having strategies by motivating students always read and creating an academic and literative environment. The obstacle experienced is the competition between reading books and current gadgets which greatly influences students to have a good character of interest in reading.


Temperature prediction in data center combining with deep neural network

Abstract: The stable temperature of the computer rooms in data centers (DCs) is crucial for the servers’ safety operation. The increase in server temperature can lead to poor operational performance and even malfunctions due to high temperature. Therefore, accurate temperature prediction becomes particularly important. In this paper, a data-driven server temperature prediction model is constructed by combining numerical methods with deep learning prediction algorithms. Firstly, the numerical model of the computer room with servers configured is built, where the heat elements such as the CPU are equivalent to the integrated heat source (IHS). The Computational Fluid Dynamic (CFD) method is utilized for the numerical calculation of the temperature and velocity fields in the DC computer room. Besides, the correctness of the numerical model is verified through experiments. The experimental results show that the temperature Mean Absolute Error predicted by the numerical model does not exceed 0.83, and the temperature calculation accuracy reaches 99.7%. Then, the more efficient Deep Neural Network (DNN) is applied to train the temperature data generated by the CFD numerical model. Besides, an Attention Mechanism has been added to facilitate the capture of important feature information during the training process. Meanwhile, searching for optimal hyperparameters of the DNN model by Bayesian Optimization, and the Attention-DNN-Bayesian model is established for temperature prediction. The mean absolute error of the DNN model is 3.51, while the ADB model is 0.65, which reduces by 81.48%.


Role of the School Library in Supporting the Teaching and Learning Process and Student Literacy at SDN Sawojajar 1

Abstract:
Background: A school library is a library established by each school where the library has an important role in the student teaching and learning process.
 Purpose: This research aims to determine what efforts the Library at SDN Sawojajar 1 made to support the teaching and learning process and student literacy.
 Design and methods: This research uses a descriptive qualitative approach. The data collection techniques used were observation, interviews, and documentation.
 Results: The results of the research show that the efforts made by the SDN Sawojajar 1 Library, which plays a role in supporting the teaching and learning process and literacy, are by providing various kinds of collections, implementing the Steel Corner program, implementing the literacy movement (GLS) program, and making library visits mandatory. The policy of providing a collection of textbooks, applicable books, and references helps meet students' needs in understanding the school curriculum. It stimulates interest in reading outside the context of formal learning. The Reading Corner Program provides opportunities for students to read books according to their interests. In contrast, the Literacy Movement (GLS) provides a holistic approach by integrating audio-visual media to improve student literacy. With the various efforts made by the library, it is hoped that it can become a dynamic and effective learning center in supporting the teaching and learning process and increasing student literacy at SDN Sawojajar 1.


Can an Ideal Library Guarantee High Elementary School Students' Interest in Reading?

Abstract: Reading is a very important activity because it is used to disseminate information for learning. This is due to access to information and knowledge related to reading. This research aims to explore reading interest and the factors that influence reading interest in private elementary schools in Surakarta. This research uses a qualitative approach with a case study method. The informant selection technique in this research used several data collection techniques, namely observation, interviews, and documentation. The results of this research indicate that interest in reading in Surakarta private elementary schools is relatively low. Due to students' lack of reading ability, reading must be ordered by the teacher. Students often play during recess. The influencing factors are internal and external factors. Internal factors are factors that come from oneself, while external factors are from outside, such as the family environment, peers, gadgets, and libraries that are far from the classroom. In conclusion, the low interest in reading among Surakarta Private Elementary School students is influenced by several factors.


Ox-Ethnio: Blockchain E-Voting System

Abstract: Abstract: In comparison to conventional paper-based systems, electronic voting, or e-voting, offers many benefits, including increased efficiency and fewer mistakes. It has developed into many different forms. However, gaining public approval of these systems remains a constant problem, especially when it comes to making them more resilient to possible weaknesses. With its potential to increase the overall resilience of electronic voting systems, blockchain technology is emerging as a market disruptor. With the use of blockchain technology, this project aims to create an efficient e-voting infrastructure by using its transparency and cryptographic underpinnings. The proposed technique satisfies the essential requirements for electronic voting systems and offers end-to-end variabilities essay delves into the planned e-voting mechanism and its actual implementation on the Multichain platform in great depth. Furthermore, Electronic voting systems, when applied in elections, need to uphold legality, accuracy, security, and convenience. Nevertheless, the potential challenges associated with digital voting methods can impede their widespread adoption. Blockchain technology has emerged as a promising solution to address these concerns, offering decentralized networks for electronic voting. Its main appeal lies in the ability to provide end-to-end verification, making it a preferred choice for developing electronic voting systems.


Boosting HPC data analysis performance with the ParSoDA-Py library

Abstract: Abstract Developing and executing large-scale data analysis applications in parallel and distributed environments can be a complex and time-consuming task. Developers often find themselves diverted from their application logic to handle technical details about the underlying runtime and related issues. To simplify this process, ParSoDA, a Java library, has been proposed to facilitate the development of parallel data mining applications executed on HPC systems. It simplifies the process by providing built-in scalability mechanisms relying on the Hadoop and Spark frameworks. This paper presents ParSoDA-Py, the Python version of the ParSoDA library, which allows for further support of commonly used runtimes and libraries for big data analysis. After a complete library redesign, ParSoDA can be now easily integrated with other Python-based distributed runtimes for HPC systems, such as COMPSs and Apache Spark, and with the large ecosystem of Python-based data processing libraries. The paper discusses the adaptation process, which takes into consideration the new technical requirements, and evaluates both usability and scalability through some case study applications.


Computational methods for metastasis detection in lymph nodes and characterization of the metastasis-free lymph node microarchitecture: A systematic-narrative hybrid review

Abstract: Histological examination of tumor draining lymph nodes (LNs) plays a vital role in cancer staging and prognostication. However, as soon as a LN is classed as metastasis-free, no further investigation will be performed and thus, potentially clinically relevant information detectable in tumor-free LNs is currently not captured.To systematically study and critically assess methods for the analysis of digitized histological LN images described in published research.A systematic search was conducted in several public databases up to December 2023 using relevant search terms. Studies using brightfield light microscopy images of hematoxylin and eosin or immunohistochemically stained LN tissue sections aiming to detect and/or segment LNs, their compartments or metastatic tumor using artificial intelligence (AI) were included. Dataset, AI methodology, cancer type, and study objective were compared between articles.A total of 7201 articles were collected and 73 articles remained for detailed analyses after article screening. Of the remaining articles, 86% aimed at LN metastasis identification, 8% aimed at LN compartment segmentation, and remaining focused on LN contouring. Furthermore, 78% of articles used patch classification and 22% used pixel segmentation models for analyses. Five out of six studies (83%) of metastasis-free LNs were performed on publicly unavailable datasets, making quantitative article comparison impossible.Multi-scale models mimicking multiple microscopy zooms show promise for computational LN analysis. Large-scale datasets are needed to establish the clinical relevance of analyzing metastasis-free LN in detail. Further research is needed to identify clinically interpretable metrics for LN compartment characterization.


The Effects of Mobile Learning and Group Discussion for Psychotropic Drug Education on Nursing Students’ Learning, Satisfaction, and Attitude: An Educational Intervention

Abstract: Background: Familiarity with and management of drug side effects is among nurses' main educational needs with respect to pharmacological care in psychiatry departments. Objectives: This study aimed to determine the effects of psychotropic drug education through mobile learning and group discussion on nursing students’ learning, satisfaction, and attitude. Methods: This educational intervention was done at EbneSina Psychiatric Hospital, affiliated with Shiraz University of Medical Sciences, in 2018. A total of 28 students were randomly divided into 2 groups, each containing 16 and 12 subjects. The first group underwent the educational intervention through mobile learning. The second group received the educational intervention through group discussion. The students’ learning and satisfaction were assessed and compared before and 2 weeks after the intervention. Attitudes were also assessed within the mobile learning group. Data were analyzed using paired and independent sample t-tests. Results: The results revealed a significant difference in satisfaction between the 2 groups after the intervention (38.69 ± 4.78 in the mobile learning group vs 33.13 ± 5.51 in the discussion group; P = 0.02). However, no significant difference was found after adjustment for baseline, age, and sex. The results also indicated that the students in the mobile learning group developed a significantly more positive attitude after the intervention compared to the baseline (P = 0.038). Conclusions: The study findings indicated that the utilization of smartphones for learning about psychotropic drug management in psychiatric departments might be effective in enhancing student's learning outcomes, satisfaction, and attitude. Thus, further studies with larger sample sizes are recommended to be conducted on other medical and nursing apprenticeships.


HCLR-Net: Hybrid Contrastive Learning Regularization with Locally Randomized Perturbation for Underwater Image Enhancement

Abstract: Abstract Underwater image enhancement presents a significant challenge due to the complex and diverse underwater environments that result in severe degradation phenomena such as light absorption, scattering, and color distortion. More importantly, obtaining paired training data for these scenarios is a challenging task, which further hinders the generalization performance of enhancement models. To address these issues, we propose a novel approach, the Hybrid Contrastive Learning Regularization (HCLR-Net). Our method is built upon a distinctive hybrid contrastive learning regularization strategy that incorporates a unique methodology for constructing negative samples. This approach enables the network to develop a more robust sample distribution. Notably, we utilize non-paired data for both positive and negative samples, with negative samples are innovatively reconstructed using local patch perturbations. This strategy overcomes the constraints of relying solely on paired data, boosting the model’s potential for generalization. The HCLR-Net also incorporates an Adaptive Hybrid Attention module and a Detail Repair Branch for effective feature extraction and texture detail restoration, respectively. Comprehensive experiments demonstrate the superiority of our method, which shows substantial improvements over several state-of-the-art methods in terms of quantitative metrics, significantly enhances the visual quality of underwater images, establishing its innovative and practical applicability. Our code is available at: https://github.com/zhoujingchun03/HCLR-Net .


Multi‐Hop anonymous payment channel network based on onion routing

Abstract: Abstract Payment channel networks (PCNs) are one of the most promising off‐chain solutions to the blockchain scalability problem. However, most current payment channel schemes suffer from overly ideal routing assumptions or fail to provide complete path privacy protection. To address the above problems, the multi‐hop anonymous and privacy‐preserving PCN scheme are analyzed and a multi‐hop anonymous PCN based on onion routing is proposed based on it. The scheme removes the routing assumptions of the original scheme, designs an onion routing‐based payment channel network, and modifies the way relay nodes construct elliptic curve discrete logarithmic problems to resist wormhole attacks. Finally, the security analysis shows that the scheme in this paper has atomicity, relational anonymity and resistance to wormhole attacks. The latest bilinear pairing anonymous multi‐hop payment (EAMHL+) scheme is used to compare communication and computing overhead. The results show that the scheme in this paper requires less communication overhead and is more efficient in terms of computational overhead when the average hop number of the payment channel network is 3 hops and the network diameter is 6 hops.


MVIndEmo: a dataset for micro video public-induced emotion prediction on social media

Abstract: Abstract Distinct from the realm of perceived emotion research, induced emotion pertains to the emotional responses engendered within content consumers. This facet has garnered considerable attention and finds extensive application in the analysis of public social media. However, the advent of micro videos presents unique challenges when attempting to discern the induced emotional patterns exhibited by content consumers, owing to their free-style representation and other factors. Consequently, we have put forth two novel tasks concerning the recognition of public-induced emotion on micro videos: emotion polarity and emotion classification. Additionally, we have introduced a accessible dataset specifically tailored for the analysis of public-induced emotion on micro videos. The data corpus has been meticulously collected from Tiktok, a burgeoning social media platform renowned for its trendsetting content. To construct the dataset, we have selected eight captivating topics that elicit vibrant social discussions. In devising our label generation strategy, we have employed an automated approach characterized by the fusion of multiple expert models. This strategy incorporates a confidence measure method that relies on three distinct models for effectively aggregating user comments. To accommodate adaptable benchmark configurations, we provide both binary classification labels and probability distribution labels. The dataset encompasses a vast collection of 7,153 labeled micro videos. We have undertaken an extensive statistical analysis of the dataset to provide a comprehensive overview composition. It is our earnest aspiration that this dataset will serve as a catalyst for pioneering research avenues in the analysis of emotional patterns and the understanding of multi-modal information.


Soft Factors and Interaction Vertices from Light‐Cone Actions

Abstract: Abstract Universal factors associated with the emission of a soft boson in gauge theories and gravity, formulated in the light‐cone gauge, are presented. The inverse‐soft method, for constructing higher‐point amplitudes from lower‐point ones, using these factors is reviewed. These ideas are then examined in (light‐cone) superspace and applied to both the super Yang–Mills and supergravity theories. One highlight is a compact result for the quartic interaction vertex in supergravity, a crucial ingredient for finiteness analyses.


Geochemistry of the North Gondwana Paleozoic Araba and Naqus formation siliciclastics, Sinai: implications for provenance, paleoweathering, and tectonic setting

Abstract: The Araba and Naqus formations in Ras Milan, Sinai, constitute part of the major continuous Paleozoic clastic belt stretching across northern Gondwana. They are sandstone-dominated successions, resting on Precambrian granitic basement, with lowermost thin basal polymictic conglomerate deposits (1–2 m), marking the unconformity. The Araba Formation section is composed mainly of varicoloured violet to reddish brown, fine to coarse-grained laminated and planner and overturned cross-bedded sandstones, while the overlying Naqus Formation is characterized by chaotically distributed poorly sorted quartz pebbles to cobbles within a moderately-sorted coarse- to medium-grained quartz arenite and by abundance and variety of large-scale tabular and trough cross bedding. Modal QFL classification of Araba and Naqus formation sandstones revealed that they are merely quartzose sandstones (quartz arenites). Geochemistry of the succession shows extremely wide compositional ranges for almost all major elements, but the Araba Formation is relatively enriched in SiO2, TiO2, Fe2O3*, Na2O, P2O5, and depleted in Al2O3, MgO, CaO and K2O relative to Naqus Formation. Only the Araba Formation samples are depleted in the transition elements Ni, Cr and Co and oppositely possess extreme enrichment in the HFSEs (i.e. Zr, Hf, Y, Nb, Ta, Th, U), whereas both formations show variable depletion in the LILE Rb, K and Ba, and excessive REE abundances relative to the PAAS and UCC, and LREE-enriched patterns with marked negative Eu anomalies. The provenance of the Naqus and Araba formations could be an earlier felsic magmatic source, which has undergone extensive sedimentary recycling. They possess high compositional maturity and were subjected to intensive to moderate weathering during humid climatic conditions. Passive margin tectonic setting is implied which denotes the derivation of Araba and Naqus formation siliciclastics from stable continental regions or plate interiors and deposition in intracratonic basins.


“TimeOntoImperfection”: An OWL 2 ontology to representing temporal data imperfection using imperfection theories

Abstract: Dealing with temporal data imperfection is a crucial issue in several application domains. In fact, failure to handle these imperfections can have significant consequences and lead to incorrect analysis and decision-making. This is particularly true when handling imperfect temporal data inputs in applications for Alzheimer’s patients as a real example. In this context, there is a need for a global ontology that provides a semantic representation of temporal data imperfection. In the literature, there is a big number of ontologies that represent data. Some represent only perfect temporal data. Some others represent imperfect data but not temporal ones. To the best of our knowledge, there is no ontology that represents temporal data imperfection. In this paper, we represent “TimeOntoImperfection”, a usable global ontology that represents four types of imperfection: imprecision, uncertainty, both uncertainty and imprecision and conflict. We describe the structure of “TimeOntoImperfection”, then we conduct a case a study in which we illustrate the usefulness of our ontology. Finally, we introduce the validation part in the context of CAPTAIN MEMO - an ontology based memory prothesis dedicated to alzheimer patients- and we discuss the encouraging results derived from the evaluation step.


Functional data learning using convolutional neural networks

Abstract: Abstract In this paper, we show how convolutional neural networks (CNNs) can be used in regression and classification learning problems for noisy and non-noisy functional data (FD). The main idea is to transform the FD into a 28 by 28 image. We use a specific but typical architecture of a CNN to perform all the regression exercises of parameter estimation and functional form classification. First, we use some functional case studies of FD with and without random noise to showcase the strength of the new method. In particular, we use it to estimate exponential growth and decay rates, the bandwidths of sine and cosine functions, and the magnitudes and widths of curve peaks. We also use it to classify the monotonicity and curvatures of FD, the algebraic versus exponential growth, and the number of peaks of FD. Second, we apply the same CNNs to Lyapunov exponent estimation in noisy and non-noisy chaotic data, in estimating rates of disease transmission from epidemic curves, and in detecting the similarity of drug dissolution profiles. Finally, we apply the method to real-life data to detect Parkinson’s disease patients in a classification problem. We performed ablation analysis and compared the new method with other commonly used neural networks for FD and showed that it outperforms them in all applications. Although simple, the method shows high accuracy and is promising for future use in engineering and medical applications.


A Survey on Online On-Demand Laundry Services

Abstract: The evolution of on-demand laundry services in the digital era as well as the technical and commercial details that affect these services' sustainability. The survey covers a wide range of tactics, from innovative uses of IoT and machine learning, such as IronMan's doorstep laundry, to creative solutions, like "Super-Dry," which sped up services during the pandemic. LaundryMama is a complete laundry management system and on-demand mobile application integration that makes it stand out as a transparent and scalable solution. Notwithstanding hindrances such as geographical limitations and dependence on technology, the "My Door Laundry Application" fosters efficiency and employment prospects. All these technological advancements are intended to modernize the established laundry service sector, addressing persistent problems, and introducing a novel, inventive environment that places a premium on efficiency and user satisfaction


Literature Survey on College Information Chatbot

Abstract: A chatbot is software that allows users to communicate with computers in natural language, similar to human conversations. Chatbots interact with clients and provide answers based on human input. The user may believe they are conversing with a human, while in reality, they are interacting with a machine. The chat bot program enables students to inquire about college admissions from any location with an internet connection and receive prompt responses. The chatbot technology streamlines the admissions process by delivering necessary information to students and parents, reducing the manpower effort in addressing inquiries.


Goal exploration augmentation via pre-trained skills for sparse-reward long-horizon goal-conditioned reinforcement learning

Abstract: Abstract Reinforcement learning often struggles to accomplish a sparse-reward long-horizon task in a complex environment. Goal-conditioned reinforcement learning (GCRL) has been employed to tackle this difficult problem via a curriculum of easy-to-reach sub-goals. In GCRL, exploring novel sub-goals is essential for the agent to ultimately find the pathway to the desired goal. How to explore novel sub-goals efficiently is one of the most challenging issues in GCRL. Several goal exploration methods have been proposed to address this issue but still struggle to find the desired goals efficiently. In this paper, we propose a novel learning objective by optimizing the entropy of both achieved and new goals to be explored for more efficient goal exploration in sub-goal selection based GCRL. To optimize this objective, we first explore and exploit the frequently occurring goal-transition patterns mined in the environments similar to the current task to compose skills via skill learning. Then, the pre-trained skills are applied in goal exploration with theoretical justification. Evaluation on a variety of spare-reward long-horizon benchmark tasks suggests that incorporating our method into several state-of-the-art GCRL baselines significantly boosts their exploration efficiency while improving or maintaining their performance.


SR-MACL: Session-Based Recommendation with Multi-layer Aggregation Augmentation in Contrastive Learning

Abstract: Abstract Session-based recommendation (SBR) aims to predict the next item of interest in chronological order based on a given sequence of short-term behaviour of anonymous users. Due to the limited data available for short-term user interactions, its performance is more susceptible to data sparsity problems than traditional recommendation methods. Contrastive learning is often used to solve the data sparsity problem due to its ability to extract general features from the raw data. Existing session-based recommendation methods based on graph contrastive learning typically build graph contrastive learning by using information from other sessions to generate augmented views. While this avoids the problem that the use of dropout in traditional contrast learning methods can cause damage to the session context, it inevitably introduces irrelevant item information, which interferes with accurately modelling user interests and leads to sub-optimal model performance. To address these issues, we propose a new session recommendation method based on multi-layer aggregation augmentation contrastive learning, namely SR-MACL. In SR-MACL we construct a contrastive view by adding noise to the embedding representation and forming a contrastive embedding representation by multi-layer aggregation, which not only effectively solves the problem that traditional graph enhancement methods can destroy the context of the whole session, but also avoids the interference of irrelevant items. Experimental results on three real datasets have shown that SR-MACL can improve the accuracy of recommendation results and predict the user's next interaction more effectively.