Reviewerly/exHarmony huggingface dataset:
Privacy and Surveillance in Digital Era

Authors:
raw_author_name: Vikram Singh
raw_affiliation_string: Ch. Devi Lal University, Sirsa, India

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

Authors:
raw_author_name: Keshav Kumar
raw_affiliation_string: Haldia Institute of Technology, India
raw_author_name: Krishan Kundan Kumar
raw_affiliation_string: Haldia Institute of Technology, India
raw_author_name: Ritam Chatterjee
raw_affiliation_string: Haldia Institute of Technology, India
raw_author_name: Santanu Koley
raw_affiliation_string: Haldia Institute of Technology, India
raw_author_name: Pinaki Pratim Acharjya
raw_affiliation_string: Haldia Institute of Technology, India

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

Authors:
raw_author_name: Junchi Chen
raw_affiliation_string: High School Affiliated to Shanghai Jiao Tong University, Shanghai, 200439, China

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.
Research on the sketch recognition and automatic plotting of deep learning

Authors:
raw_author_name: Xulun Cheng
raw_affiliation_string: University of Virginia, 852 West Main Street, Charlottesville, Virginia

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

Authors:
raw_author_name: M. Sirajul Islam
raw_affiliation_string: Alfaisal University, College of Business, Department of OPM/MIS, Riyadh, Saudi Arabia

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

Authors:
raw_author_name: Peng Yu
raw_affiliation_string: School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan, People’s Republic of China

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.
Secure Sharing of Electronic Medical Records Based on Blockchain

Authors:
raw_author_name: Song Luo
raw_affiliation_string: College of Computer Science and Engineering, Chongqing University of Technology, Chongqing, China
raw_author_name: Tan Hu
raw_affiliation_string: College of Computer Science and Engineering, Chongqing University of Technology, Chongqing, China
raw_author_name: YuHua Qian
raw_affiliation_string: School of Artificial Intelligence, Chongqing University of Technology, Chongqing, China

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

Authors:
raw_author_name: Erdal Güvenoğlu
raw_affiliation_string: Faculty of Engineering and Natural Sciences, Department of Computer Engineering, Maltepe University, Istanbul, Turkey

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

Authors:
raw_author_name: Junrui Liu
raw_affiliation_string: Faculty of Information Technology, Beijing University of Technology, 100124, China
raw_author_name: Tong Li
raw_affiliation_string: Faculty of Information Technology, Beijing University of Technology, 100124, China
raw_author_name: Zhen Yang
raw_affiliation_string: Faculty of Information Technology, Beijing University of Technology, 100124, China
raw_author_name: Di Wu
raw_affiliation_string: Faculty of Information Technology, Beijing University of Technology, 100124, China
raw_author_name: Huan Liu
raw_affiliation_string: Computer Science and Engineering, Arizona State University, Tempe, 878809, AZ, USA

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

Authors:
raw_author_name: Banoth Thulasya Naik
raw_affiliation_string: Department of Electronics and Communication Engineering, National Institute of Technology, Warangal, India
raw_author_name: Mohammad Farukh Hashmi
raw_affiliation_string: Department of Electronics and Communication Engineering, National Institute of Technology, Warangal, India
raw_author_name: Aditya Gupta
raw_affiliation_string: Department of Information and Communication Technology, University of Agder, Grimstad, Norway

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

Authors:
raw_author_name: Yingqiu Xiong
raw_affiliation_string: School of Management, Hefei University of Technology, Hefei, Anhui 230009, China
raw_author_name: Yezheng Liu
raw_affiliation_string: National Engineering Laboratory for Big Data Distribution and Exchange Technologies, Shanghai 200436, China; School of Management, Hefei University of Technology, Hefei, Anhui 230009, China
raw_author_name: Yang Qian
raw_affiliation_string: Intelligent Interconnected Systems Laboratory of Anhui Province (Hefei University of Technology), Hefei, Anhui 230009, China; Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei, Anhui 230009, China
raw_author_name: Yuanchun Jiang
raw_affiliation_string: Key Laboratory of Philosophy and Social Sciences for Cyberspace Behaviour and Management, Hefei, Anhui 230009, China; School of Management, Hefei University of Technology, Hefei, Anhui 230009, China
raw_author_name: Yidong Chai
raw_affiliation_string: Key Laboratory of Philosophy and Social Sciences for Cyberspace Behaviour and Management, Hefei, Anhui 230009, China; School of Management, Hefei University of Technology, Hefei, Anhui 230009, China
raw_author_name: Haifeng Ling
raw_affiliation_string: Key Laboratory of Philosophy and Social Sciences for Cyberspace Behaviour and Management, Hefei, Anhui 230009, China; School of Management, Hefei University of Technology, Hefei, Anhui 230009, China

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

Authors:
raw_author_name: Henderik A. Proper
raw_affiliation_string: TU Wien, Vienna, Austria

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

Authors:
raw_author_name: Giuliano De Stefano
raw_affiliation_string: Engineering Department, University of Campania, Aversa, 81031, Italy

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.