Reviewerly/exHarmony huggingface dataset:
Data privacy protection model based on blockchain in mobile edge computing

Abstract Mobile edge computing (MEC) technology is widely used for real‐time and bandwidth‐intensive services, but its underlying heterogeneous architecture may lead to a variety of security and privacy issues. Blockchain provides novel solutions for data security and privacy protection in MEC. However, the scalability of traditional blockchain is difficult to meet the requirements of real‐time data processing, and the consensus mechanism is not suitable for resource‐constrained devices. Moreover, the access control of MEC data needs to be further improved. Given the above problems, a data privacy protection model based on sharding blockchain and access control is designed in this paper. First, a privacy‐preserving platform based on a sharding blockchain is designed. Reputation calculation and improved Proof‐of‐Work (PoW) consensus mechanism are proposed to accommodate resource‐constrained edge devices. The incentive mechanism with rewards and punishments is designed to constrain node behavior. A reward allocation algorithm is proposed to encourage nodes to actively contribute to obtaining more rewards. Second, an access control strategy using ciphertext policy attribute‐based encryption (CP‐ABE) and RSA is designed. A smart contract is deployed to implement the automatic access control function. The InterPlanetary File System is introduced to alleviate the blockchain storage burden. Finally, we analyze the security of the proposed privacy protection model and statistics of the GAS consumed by the access control policy. The experimental results show that the proposed data privacy protection model achieves fine‐grained control of access rights, and has higher throughput and security than traditional blockchain.
Celebratory expressions and linguistic diversity: investigating congratulation patterns among Pakistani Facebook users commenting in English and Urdu

This study explores verbal and nonverbal congratulatory responses in the context of joyful Facebook status updates in Pakistani English and Urdu. Employing Terkourafi’s frame-based approach, quantitative and qualitative analyses were conducted on 16,165 comments and 16,230 emoji reactions from 140 Pakistani users. Elwood’s taxonomy and Allami and Pishghadam’s refined framework were used for verbal coding, while nonverbal data followed Facebook’s algorithm guidelines and Dresner and Herring’s framework. The findings reveal users’ adept expression of happiness and convivial emotions, showcasing significant language adaptation to cultural norms, especially with the prevalence of English. Pakistani users employ various congratulatory techniques, emphasizing the role of illocutionary force-indicating devices (IFID) in reinforcing positive self-image and commending success. Nonverbal elements, primarily emojis, play a crucial role in Pakistani conversations, signaling speech genres, defining social relationships and negotiating social purposes. The study suggests that future research should explore the longitudinal evolution of emoticons, investigating their potential conventionalization and transformation into established textual markers and providing insights into their linguistic and cultural significance in digital interactions.
A deep learning approach for robust traffic accident information extraction from online chinese news

Abstract Road traffic accidents are the leading causes of injuries and fatalities. Understanding the traffic accident occurrence pattern and its contributing factors are prerequisites for effective traffic safety management. The paper proposes a deep learning approach for traffic accident recognition and information extraction from online Chinese news to extract and organize traffic accidents automatically. The approach consists of three modules, including automated news collection, news classification, and traffic accident information extraction. The automated news collection module crawls news from online sources, cleans and organizes it into a general news database with different categories of news. The news classification module robustly recognizes the traffic accident news from all types of news by fusing the sentence‐wise and context‐wise semantic news information. The accident information extraction module extracts the key attributes of traffic accidents (e.g. causes, times, locations) from news text using the SoftLexicon‐BiLSTM‐CRF method. The proposed approach is validated by comparing it with state‐of‐the‐art text mining methods using Chinese news data crawled online. The results show that the approach can achieve a high information extraction performance in terms of precision, recall, and F1‐score. It improves the performance of the best benchmark model (BiLSTM‐CRF) by 18.8% in precision and 12.08% in F1‐score. In addition, the potential value of the automatically extracted accident data is illustrated from online news in complementing traditional authority accident data to drive more effective traffic safety management in practice.
Navigating legal challenges of deepfakes in the American context: a call to action

Deepfakes are a rapidly growing technological trend that is revolutionizing many digital processes, from media production to digital access. They offer various advantages for both users and creators alike. For example, they can provide entertainment value by allowing users to create humorous or satirical versions of existing media content without having to film new scenes. They can also automate production workflows by generating background characters or extras for movies. Additionally, deepfakes could be used in healthcare applications, such as virtual avatars for doctors who require assistance during consultations with patients located far from hospitals or clinics. However, deepfakes also pose a threat to society as they can be used for malicious purposes, including spreading false information or exploiting the likenesses of others for financial gain. To address these concerns, various types of regulations have been introduced across jurisdictions worldwide. These laws prohibit doctored audio recordings and visual materials aimed at influencing election outcomes. Some countries have even gone as far as requiring companies producing deepfake materials to identify themselves as such, to make audiences aware that what they are seeing may not always be authentic representations of events. In this paper, the author will discuss what deepfakes are, their potential benefits and risks, the various types of deepfakes that exist, and how the USA has attempted to regulate them. The author will also suggest some policy recommendations aimed at mitigating the risks associated with deepfakes while still preserving their creative potential.
Evaluating and analyzing student labor literacy in China's higher vocational education: an assessment model approach

Introduction This study addresses the gap in evaluating labor literacy amongst vocational students in China's higher vocational education system. It aims to develop a comprehensive framework for assessing essential labor competencies, thereby contributing to a nuanced understanding of vocational education's role in skill development. Methods Employing a multifaceted research methodology, this study integrates questionnaire surveys, econometric analyses, and the Delphi method to assess labor literacy among 749 students from three leading vocational institutions. A pioneering labor literacy assessment model is introduced: S = 0.5466 B 1 +0.1816 B 2 +0.1623 B 3 +0.1095 B 4 , where S denotes the overall labor literacy score. Here, B 1 represents labor concepts, B 2 denotes habits and qualities, B 3 signifies knowledge and skills, and B 4 encapsulates emotions and attitudes, illustrating a comprehensive approach to measuring labor literacy. Results Our findings reveal pronounced disparities in labor literacy across the identified dimensions, with particular deficiencies in labor concepts. The study also identifies six determinants—gender, political profile, academic performance, internship and training base utilization, inclination towards innovation and entrepreneurship, and labor education evaluation mechanisms—that significantly influence labor literacy outcomes. Discussion Highlighting the imperative for a contextually informed and holistic approach to labor literacy, this study's insights advocate for educational strategies that are both aligned with labor market demands and cognizant of socio-cultural nuances. The developed assessment model not only propels the theoretical discourse in vocational education forward but also provides a pragmatic guide for educators and policy makers, aiming to mitigate disparities and enhance labor competencies through refined educational practices.
Blockchain Integrated with Internet of Things-benefits, Challenges

All sectors are now using digital ways to facilitate humans. Be it health, finance, supply chain, communication, transport, IT, or education, all the sectors are now relying on technologies and the internet for providing facilities and also using them as sources of information. These sectors, when using traditional ways, faced a lot of challenges. For example, people earlier going to railway stations to book train tickets had to wait for long durations in queues, and if all the seats are filled by the time they reach or their turn comes to reserve seats, it goes all in vain to spend time traveling to the station and time in queues. Coming to the finance sector, people had to go to banks to create a bank account and for all the formalities. They had to spend time going to banks and then wait in queues to get their work done. Also, it took around 1-2 weeks for every task to complete in banks. So, the process was quite time-consuming, monotonous and unreliable in practice. Thus many sectors started looking for alternative methods to perform their daily tasks. Slowly, the sectors started digitizing, and started using computers to perform tasks, to store and update their data. They also started using the internet in their daily applications. Each organization of industry is now available on the internet. All of their information is present and one can apply for their services using their websites. Thus, IoT comes into the picture here. All the sectors using the internet to access and provide information, using the cloud to store their data are using IoT services. Internet of Things (IoT) technology will soon become an integral part of our daily lives to facilitate the control and monitoring of processes and objects and to change the way man interacts with the physical world. For all aspects of IoT to be fully functional, there are a few obstacles to overcome and important challenges to overcome. These include, but are not limited to, cyber security, data privacy, power consumption, and metrics. The dedicated Blockchain environment and its various processes provide a useful way to address these few IoT challenges.
A Dynamic Adaptive and Resource-Allocated Selection Method Based on TOPSIS and VIKOR in Federated Learning

Abstract Federated learning (FL) is a decentralized and privacy-preserving machine learning technique that protects data privacy by learning models locally and not sharing datasets. However, due to limited computing resources on devices and highly heterogeneous data in practical situations, the training efficiency and resource utilization of federated learning is low. In order to resolve these challenges, we introduce a blockchain-assisted dynamic adaptive and personalized federated learning framework (TV-FedAvg) in the presence of restricted computing power resources and data heterogeneity. After each round of local training, we utilize an improved scoring model based on VIKOR and TOPSIS to comprehensively score the devices. The scores are then utilized to choose devices for participation in global aggregation and to carry out model aggregation through blockchain consensus. Furthermore, resources are reallocated for the next round to enhance resource efficiency, model fairness, and performance. Finally, we demonstrate through experimentation that TV-FedAvg outperforms other models such as pFedMe, FedAvg, Per-FedAvg, and TOPSIS in terms of both efficiency and performance.
Intelligent visually lossless compression of dental images

Background: Tendencies to increase the mean size of dental images and the number of images acquired daily makes necessary their compression for efficient storage and transferring via communication lines in telemedicine and other applications. To be a proper solution, lossy compression techniques have to provide a visually lossless option (mode) where a desired quality (invisibility of introduced distortions for preserving diagnostically valuable information) is ensured quickly and reliably simultaneously with a rather large compression ratio. Objective: Within such an approach, our goal is to give answers to several practical questions such as what encoder to use, how to set its parameter that controls compression, how to verify that we have reached our ultimate goal, what are additional advantages and drawbacks of a given coder, and so on. Methods: We analyze the performance characteristics of several encoders mainly based on discrete cosine transform for a set of 512 × 512 pixel fragments of larger size dental images produced by Morita and Dentsply Sirona imaging systems. To control the visual quality of compressed images and the invisibility of introduced distortions, we have used modern visual quality metrics and distortion invisibility thresholds established for them in previous experiments. Besides, we have also studied the so-called just noticeable distortions (JND) concept, namely, the approach based on the first JND point when the difference between an image subject to compression and its compressed version starts to appear. Results: The rate-distortion dependences and coder setting parameters obtained for the considered approaches are compared. The values of the parameters that control compression (PCC) have been determined. The ranges of the provided values of compression ratio have been estimated and compared. It is shown that the provided CR values vary from about 20 to almost 70 for modern coders and almost noise-free images that is significantly better than for JPEG. For images with visible noise, the minimal and maximal values of produced CR are smaller than for the almost noise-free images. We also present the results of the verification of compressed image quality by specialists (professional dentists). Conclusion: It is shown that it is possible and easy to carry out visually lossless compression of dental images using the proposed approaches with providing quite high compression ratios without loss of data diagnostic value.
Virtual reality usage intention in an intradialysis exercise program: Predictive analysis through a structural equation model

In recent years several exercise programs have begun to use technology, the most used has been virtual reality (VR). Technology-based programs must first be accepted by the population, since, if they do not see the need, the probability of rejection is high. Therefore, it is necessary to first evaluate the intention to use technology to ensure the effectiveness of the intervention. The study aimed to determine the predictive capacity of variables associated with theories of human behavior, on the intention to use VR in an intradialysis exercise program. A sample of 324 hemodialysis patients (F = 140; M = 184) from Chile, filled out a questionnaire. Before this, information was provided regarding VR and the exercise program. Structural equation modeling was used to analyze results through SmartPLS. Results showed that the dependent variable had a good amount of variance explained by the model. The latent variables that strongly influenced the intention to use were perceived enjoyment, perceived utility, and social influence. It can be predicted that users will intend to use VR to perform intradialysis exercises if they consider that it´s entertaining, useful, and validated by their peers.
Disposable and Flexible Paper‐Based Optoelectronic Synaptic Devices for Physical Reservoir Computing

Abstract Health monitoring using wearable artificial intelligence (AI) sensors with sensing and cognitive capabilities has garnered significant attention. The development of self‐contained AI sensors that can operate with low power consumption, akin to the human brain, is necessary. Physical reservoir computing (PRC), which mimics the human brain using physical phenomena, offers a low‐power consumption architecture. Nevertheless, creating a flexible and easily disposable sensors using PRC capable of processing optical signals with sub‐second response times suitable for biological signals presents a challenge. In this study, a disposable and flexible paper‐based optoelectronic synaptic devices are designed, which are composed of nanocellulose and ZnO nanoparticles, for PRC. This device exhibits synaptic photocurrent in response to optical input. To assess its performance, a classification and time‐series forecasting tasks are conducted. The memory capacity of short‐term memory task, indicating the device's ability to store past information, is 1.8. The device can recognize handwritten digits with an accuracy of 88%. These results highlight the potential of the device for PRC. In addition, subjecting the device to 1000 rounds of bending do not affect its accuracy. Furthermore, the device burn in a few seconds, much like regular office paper, demonstrating its disposability.
How Depth of Field in Screen Images Affects Egocentric Distance Perception

In virtual environments (VEs) presented on flat screens, accurate egocentric distance perception is crucial for enhancing immersion. This study investigated depth of field-induced blur, typically perceived as an ordinal depth cue, to understand its role as a quantitative depth cue in VEs. We employed verbal judgment and position adjustment methods to quantify observers’ egocentric distance perception across various depth of field settings (n = 30 participants). Our analysis also examined the interplay between blur and other depth cues. Results indicated that increased blur in VEs leads to a greater underestimation of distances. Moreover, blur operates as a relatively independent depth cue, showing minimal interaction with other cues. These findings address a gap in research on the independent role of depth of field as a quantitative depth cue in VEs. This contributes to the visual design of VEs and supports the enhancement of user experience in human–computer interactions.
No‐reference image quality assessment via a dual‐branch residual network

Abstract The No‐Reference Image Quality Assessment (NR‐IQA) method can predict quality scores of distorted images without the reference image. However, due to the variability in both color and structure of images, existing NR‐IQA methods struggle to accurately predict quality scores of distorted images. Therefore, an NR‐IQA method based on a Dual‐Branch Residual Network (DBRIQA) for evaluating the quality scores of color‐distorted images is proposed. First, guided filtering is applied to the hue images in the HSV space to extract distortion information from the image's color. Then, due to significant differences in distorted images, enhancements are made to the traditional residual blocks, forming a feature extraction module that captures multi‐scale features from the image. In order to capture the global relationships in the distorted image, a Global‐Level Attention Block (GLAB) is introduced, facilitating the interaction of information among the extracted features. Experiments were conducted across four publicly available IQA datasets, including LIVE, CSIQ, TID2008, and TID2013. The experimental results demonstrate that the proposed method exhibits strong performance and generalization capabilities in predicting image quality compared to peer methods.
Fraud victimization across the lifespan: evidence on repeat victimization using perpetrator data

Older adults are thought to be more susceptible to scams, yet understanding the relationship between chronological age and victimization is limited by underreporting. This study avoids underreporting bias by merging four longitudinal databases of Americans (N = 1.33 million) who paid money in response to mail scams over 20 years. We investigate the risk of repeat victimization and victimization by multiple scam types over the life course. Victims in their 70s and 80s are 9% more likely to experience another victimization incident than those in their 50s. Those age 18 to 29 are 24% less likely to experience another victimization incident. Relative to adults in their 50s, the odds of victimization by multiple scams are greater for those in their 60s and 70s, but lower for those 80 + . This study demonstrates the research potential in using scammers' data to understand patterns of victimization. Fraud prevention efforts should target older individuals who are at higher risk of repeat victimization and suffer greater losses as a result.
The Education Leadership Challenges for Universities in a Postdigital Age

Abstract The executive leadership of universities internationally faces significant uncertainty in how to respond to ongoing technological innovation in a postdigital age. The current rapid progress in generative artificial intelligence (GAI) is exacerbating their plight. This study uses emergent research on GAI in learning and teaching to problematise the challenges of university education leadership in the face of such disturbances to core academic work. Considering the benefits and risks associated with the design of GAI, its inclusion in learning and teaching, and how it is reconfiguring the design of degree programmes and the strategies of local leaders providing support services through university organisational structures, this paper offers a principled ecosystemic approach to leadership that considers key elements for effective strategy implementation. The interrelatedness of strategy, governance, policy, management, and investment are discussed and a way forward for effective education leadership in a context of uncertainty is offered.
Copyright Law and the Ruse of Culture: ‘Traditional Cultural Expressions and Expressions of Folklore’ as a Conception of Racial Difference

‘Traditional Cultural Expressions’ and ‘Expressions of folklore’ (TCEs/folklore) have received very little critical attention within intellectual property (IP) jurisprudence. Where TCEs/folklore have been treated, it has only been to the extent that international IP does not offer sufficient protection over them as an analogous class of IP or to the extent that TCEs/folklore themselves are philosophically incompatible with IP, and accordingly fail to adequately capture the cultural expressions of indigenous peoples. This article offers a different argument regarding TCEs/folklore which neither seeks their further protection nor their legislative and policy development. Instead, this article traces the genealogy of TCEs/folklore within the imperialist foundations of international law, arguing that TCEs/folklore are the function of an already existing discursive arrangement of power that is consistent with colonial valuations of knowledge and culture.