Reviewerly/exHarmony huggingface dataset:
A Model for Trust Decision, Data Analysis, and Evaluation to Identify Quality Web Services

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)

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

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

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

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

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

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

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

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

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

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 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

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

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

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

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

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

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.