Reviewerly/exHarmony huggingface dataset:
Cyber-attacks on health-care systems

On February 20, the Lehigh Valley Health Network (LVHN; Allentown, PA, USA) disclosed in a statement that it was the target of a cybersecurity attack by BlackCat, a gang known to be associated with Russia. The hackers released three clinical images of people with cancer receiving radiation oncology treatment at the LVHN Delta Medix facility and seven documents containing patient information on the dark web. BlackCat asked for a ransom payment, but LVHN said it had refused to pay and did not disclose how much was demanded. Such attacks are known as ransomware and use malware or software to disrupt, damage, or deny access to a computer system before demanding payment to allow access. LVHN will provide notices as required to those whose information was involved while it continues to investigate the incident with law enforcement and cyber security experts. A spokesman said LVHN was not giving any interviews but referred to a March 10 statement that said “we expect this shameful tactic to continue.”
Cloud-VAE: Variational autoencoder with concepts embedded

Variational Autoencoder (VAE) has been widely and successfully used in learning coherent latent representation of data. However, the lack of interpretability in the latent space constructed by the VAE under the prior distribution is still an urgent problem. This paper proposes a VAE with understandable concept embedding named Cloud-VAE, which constructs interpretable latent space by disentangling the latent variables and considering their uncertainty based on cloud model. Firstly, cloud model-based clustering algorithm cast initial constraint of latent space into a prior distribution of concept which can be embedded into the latent space of the VAE to disentangle the latent variables. Secondly, reparameterization trick based on forward cloud transformation algorithm is designed to estimate the latent space concept by increasing the randomness of latent variables. Furthermore, variational lower bound of Cloud-VAE is derived to guide the training process to construct concepts of latent space, realizing the mutual mapping between latent space and concept space. Finally, experimental results on 6 benchmark datasets show that Cloud-VAE has good clustering and reconstruction performance, which can explicitly explain the aggregation process of the model and discover more interpretable disentangled representations.
Covariate-constrained randomization with cluster selection and substitution

Background: An ongoing cluster-randomized trial for the prevention of arboviral diseases utilizes covariate-constrained randomization to balance two treatment arms across four specified covariates and geographic sector. Each cluster is within a census tract of the city of Mérida, Mexico, and there were 133 eligible tracts from which to select 50. As some selected clusters may have been subsequently found unsuitable in the field, we desired a strategy to substitute new clusters while maintaining covariate balance. Methods: We developed an algorithm that successfully identified a subset of clusters that maximized the average minimum pairwise distance between clusters in order to reduce contamination and balanced the specified covariates both before and after substitutions were made. Simulations: Simulations were performed to explore some limitations of this algorithm. The number of selected clusters and eligible clusters were varied along with the method of selecting the final allocation pattern. Conclusion: The algorithm is presented here as a series of optional steps that can be added to the standard covariate-constrained randomization process in order to achieve spatial dispersion, cluster subsampling, and cluster substitution. Simulation results indicate that these extensions can be used without loss of statistical validity, given a sufficient number of clusters included in the trial.
A Novel Object Detection Method of Pointer Meter Based on Improved YOLOv4-Tiny

Pointer meters have been widely used in industrial field due to their strong stability; it is an important issue to be able to accurately read the meter. At present, patrol robots with computer vision function are often used to detect and read meters in some situations that are not suitable for manual reading of the meter. However, existing object detection algorithms are often misread and miss detection due to factors such as lighting, shooting angles, and complex background environments. To address these problems, this paper designs a YOLOv4-Tiny-based pointer meter detection model named pointer meter detection-YOLO (PMD-YOLO) for the goal of practical applications. Firstly, to reduce weight of the model and ensure the accuracy of object detection, a feature extraction network named GhostNet with a channel attention mechanism is implemented in YOLOv4-Tiny. Then, to enhance feature extraction ability of small- and medium-sized targets, an improved receptive field block (RFB) module is added after the backbone network, and a convolutional block attention module (CBAM) is introduced into the feature pyramid network (FPN). Finally, the FPN is optimized to improve the feature utilization, which further improves the detection accuracy. In order to verify the effectiveness and superiority of the PMD-YOLO proposed in this paper, the PMD-YOLO is used for experimental research on the constructed dataset of the pointer meter, and the target detection algorithms such as Faster region convolutional neural network (RCNN), YOLOv4, YOLOv4-Tiny, and YOLOv5-s are compared under the same conditions. The experimental results show that the mean average precision of the PMD-YOLO is 97.82%, which is significantly higher than the above algorithms. The weight of the PMD-YOLO is 9.38 M, which is significantly lower than the above algorithms. Therefore, the PMD-YOLO not only has high detection accuracy, but can also reduce the weight of the model and can meet the requirements of practical applications.
Role and Importance of Cryptography Techniques in Cloud Computing

The study has depicted the evaluation of the impact of the cryptography techniques in cloud computing in the IT sector. Secondary data collection methods and thematic data analysis process have been used in this study on the basis of peer reviewed journals published after 2019 that increase the reliability and validity of the study. Concept of cryptography and cloud computing have been focused over here to depict the importance of the cryptography in cloud computing. Different kinds of techniques under cryptography methods to secure data including storing and transmitting data have been depicted over here. Confidentiality, integrity along with authentication and authorisation of encrypted data has been highlighted here considering pros and cons of cryptography technology. Security aspects also have been focused in this study to meet the goal of the study with informative justification. Importance of cryptography in cloud computing also has been depicted in this study to prevent challenges in cloud computing prioritizing data security. Keyword : Cryptography, data, confidentiality, cloud computing.
Systematic Literature Review on Human Resource Management Effect on Organization Performance

Purpose: The current study is the outcome of a thorough evaluation of the literature on functionalism in human resource management as it relates to organizational performance. It examines the role of human resource management and looks into various issues with human resource management in firms in such situations. Research Methodology: The theoretical underpinnings are accomplished through a systematic review and subsequent thematic content analysis of literature, including reports from published literature and research papers that have been published. These were critically examined, and the best matching documents were then carefully chosen for inclusion to reach a conclusion. Result: The result emphasizes the necessity for human resource management managers to implement cutting-edge strategies for faculty and staff empowerment so they may traverse the fast-changing environment and perform to their full potential against the odds. To engage stakeholders in amicably avoiding recalcitrance, a conceptual qualitative framework is established and recommended for the functionalism of tailored human resource management. This approach can help managers of human resources find opportunities during this crisis and help them come to significant conclusions about how to address issues and create a positive culture. Limitation: Risks of bias such as selection bias, insufficient blinding, attrition bias, and selective outcome reporting may be among the limitations. Future Implications: By working together to overcome predicted difficulties, this framework can help an educational leader manage stakeholders' expectations and benefit academics and practitioners alike. Novelty: Research novelty was a brand-new concept that might result in new knowledge or contribute to already-existing knowledge. Keywords: 1. Human Resource Management 2. Functionalism 3. Organization Performance 4. Systematic Literature Review
An AI framework to support decisions on GDPR compliance

Abstract The Italian Public Administration (PA) relies on costly manual analyses to ensure the GDPR compliance of public documents and secure personal data. Despite recent advances in Artificial Intelligence (AI) have benefited many legal fields, the automation of workflows for data protection of public documents is still only marginally affected. The main aim of this work is to design a framework that can be effectively adopted to check whether PA documents written in Italian meet the GDPR requirements. The main outcome of our interdisciplinary research is INTREPID (art ficial i elligence for gdp complianc of ublic adm nistration ocuments), an AI-based framework that can help the Italian PA to ensure GDPR compliance of public documents. INTREPID is realized by tuning some linguistic resources for Italian language processing (i.e. SpaCy and Tint) to the GDPR intelligence. In addition, we set the foundations for a text classification methodology to recognise the public documents published by the Italian PA, which perform data breaches. We show the effectiveness of the framework over a text corpus of public documents that were published online by the Italian PA. We also perform an inter-annotator study and analyse the agreement of the annotation predictions of the proposed methodology with the annotations by domain experts. Finally, we evaluate the accuracy of the proposed text classification model in detecting breaches of security.
Design and Validation of Blockchain-based Human Resource Man-agement Practices Model

The present study aims to design and validate a blockchain-based human resource management practices model and to investigate how the model factors affect each other and practical solutions for the development of human resource management in the education organization.The present study was conducted with a combined approach.In the qualitative part, using the data theory method of the foundation and in-depth interviews with 12 experts, the model of blockchain-based human resource management practices was designed and validated.Then, in the second step, using the technique of confirmatory factor analysis and modeling of structural equations among 300 managers, deputies and experts of education departments of Kohkiluye and Boyer-Ahmad provinces, the resulting model was fitted, validated and tested.In the qualitative part of the model, consisting of two main themes and six sub-themes for causal conditions, one main theme and two subthemes for background conditions, one main theme and four sub-themes for the central category, one main theme and four sub-themes for the interventionist conditions, one main theme and 6 subthemes for strategies and 4 sub-themes for outcomes were obtained.The results of the quantitative section showed the significance of the relationships between the research variables.In this study, the causal conditions (information technology factors and recruitment process) were identified as a central category (strategic development of human resource management with blockchain technology); Affects and in this impact and impression; Background conditions (design of financial mechanisms and design of legal mechanisms); Interfering conditions (barriers to the use of technology); Are involved and strategies such as (area of staffing); It can help achieve outcomes (strategic, organizational, information and technological implications).
EFL Teaching in a Digital Environment

English as a Foreign Language (EFL) teaching has, similarly to all other subjects, gone through a digital transformation that was accelerated by the COVID-19 pandemic in 2020. Quarantines and subsequent distance learning led to new paths of teaching languages that opened up new chances to facilitate language learning. This web-based survey, conducted among Austrian secondary school students, aimed to identify methods and tools used in the EFL classroom, predictors for students’ digital skills and chances that can be utilized in the future. The following key results were observed: English classes during the school year 2021/2022 offered a wide variety of tools that the students worked with and improved in. Furthermore, the analysis showed that English language skills significantly influence and predict the improvement of students’ digital skills. Finally, multiple chances for working with digital devices in the EFL classroom could be extrapolated from the results of the survey.
Health24: Health-related Data Collection from Wearable and Mobile Devices in Everyday Lives

We present an empirical study of collecting healthrelated data like sensor signals and lifelog from wearables and mobile devices. We have developed a health-related data collection framework for everyday lives, called Health24, composed of collecting data in a standard IoT platform from wearables like Galaxy watch, Apple watch, and Fitbit watch and smartphones like iPhone and Android phones. Raw sensor signal is collected from accelerometers, gyroscopes, and pressure sensors, and lifelogs such as activities and sleep time are extracted from smartphone- or cloud-supported APIs. Our study is designed to collect datasets from 300 subjects for 60 days in daily activities including sleep. We expect that health-related data could help people understand what aspects of the habits or activities in everyday lives will increase risk of disease and thus prevent it.
Boosting Adversarial Attacks with Nadam Optimizer

Deep neural networks are extremely vulnerable to attacks and threats from adversarial examples. These adversarial examples deliberately crafted by attackers can easily fool classification models by adding imperceptibly tiny perturbations on clean images. This brings a great challenge to image security for deep learning. Therefore, studying and designing attack algorithms for generating adversarial examples is essential for building robust models. Moreover, adversarial examples are transferable in that they can mislead multiple different classifiers across models. This makes black-box attacks feasible for practical applications. However, most attack methods have low success rates and weak transferability against black-box models. This is because they often overfit the model during the production of adversarial examples. To address this issue, we propose a Nadam iterative fast gradient method (NAI-FGM), which combines an improved Nadam optimizer with gradient-based iterative attacks. Specifically, we introduce the look-ahead momentum vector and the adaptive learning rate component based on the Momentum Iterative Fast Gradient Sign Method (MI-FGSM). The look-ahead momentum vector is dedicated to making the loss function converge faster and get rid of the poor local maximum. Additionally, the adaptive learning rate component is used to help the adversarial example to converge to a better extreme point by obtaining adaptive update directions according to the current parameters. Furthermore, we also carry out different input transformations to further enhance the attack performance before using NAI-FGM for attack. Finally, we consider attacking the ensemble model. Extensive experiments show that the NAI-FGM has stronger transferability and black-box attack capability than advanced momentum-based iterative attacks. In particular, when using the adversarial examples produced by way of ensemble attack to test the adversarially trained models, the NAI-FGM improves the success rate by 8% to 11% over the other attack methods. Last but not least, the NAI-DI-TI-SI-FGM combined with the input transformation achieves a success rate of 91.3% on average.
A Radio Frequency Fingerprint Identification Method for Wireless Devices Based on ShuffleNet-SVM

Radio frequency fingerprint identification can be used for device authentication, network access management, malicious device tracing, and identity spoofing attacks. The feature representation and pattern recognition capabilities of deep learning algorithms are of great significance for improving the identification capability of radio frequency fingerprint-based wireless devices. Aiming at the problem of radio frequency fingerprint identification of wireless devices, this paper proposes a radio frequency fingerprint identification method for wireless devices based on ShuffleNet-SVM. Firstly, the original signal of the wireless device is processed by the constant Q transform. Secondly, the obtained CQT spectrum is input into the ShuffleNet model for training to achieve adaptive feature extraction, and then the output of the convolution layer is extracted as the RF fingerprint feature vector, which is used as the input of the support vector machine. Finally, the output of the identification result can realize the radio frequency fingerprint identification of the wireless device. Based on the Bluetooth device data set, a series of experiments verify that the proposed method is superior to the comparison models and other time-frequency processing methods in terms of accuracy and robustness.
An Improved Future Search Algorithm Based on the Sine Cosine Algorithm for Function Optimization Problems

Future search algorithm imitates the person living life. If one person finds that his life is not good, he will try to change his living life, and he will imitate a more successful person. To overcome insufficient performances of the basic Future search algorithm, this paper proposed an improved Future search algorithm based on the sine cosine algorithm (FSASCA). The proposed algorithm uses sine cosine algorithm to loop-progressive find the best solution. The searching method of the sine cosine algorithm can make the feasible solution to be re-positioned around another feasible solutions, which can make the proposed algorithm have a strong exploitation ability. Four coefficient factors are added in the basic FSA, and new update methods are introduced in the searching phase. To verify the searching and optimization performances of the proposed algorithm in this paper, this paper also gives data calculation results, Wilcoxon rank sum test, iteration figures, box plot figures, and searching path figures. Experimental results showed that FSASCA has a better iteration speed, the convergence precision, the solving accuracy, the strong competitive, and the high balance.
A continuous convolutional trainable filter for modelling unstructured data

Abstract Convolutional Neural Network (CNN) is one of the most important architectures in deep learning. The fundamental building block of a CNN is a trainable filter, represented as a discrete grid, used to perform convolution on discrete input data. In this work, we propose a continuous version of a trainable convolutional filter able to work also with unstructured data. This new framework allows exploring CNNs beyond discrete domains, enlarging the usage of this important learning technique for many more complex problems. Our experiments show that the continuous filter can achieve a level of accuracy comparable to the state-of-the-art discrete filter, and that it can be used in current deep learning architectures as a building block to solve problems with unstructured domains as well.
Quantum Computing Approaches for Vector Quantization—Current Perspectives and Developments

In the field of machine learning, vector quantization is a category of low-complexity approaches that are nonetheless powerful for data representation and clustering or classification tasks. Vector quantization is based on the idea of representing a data or a class distribution using a small set of prototypes, and hence, it belongs to interpretable models in machine learning. Further, the low complexity of vector quantizers makes them interesting for the application of quantum concepts for their implementation. This is especially true for current and upcoming generations of quantum devices, which only allow the execution of simple and restricted algorithms. Motivated by different adaptation and optimization paradigms for vector quantizers, we provide an overview of respective existing quantum algorithms and routines to realize vector quantization concepts, maybe only partially, on quantum devices. Thus, the reader can infer the current state-of-the-art when considering quantum computing approaches for vector quantization.
Improving the Token ERC-721 Implementation for Selective Receipt: Rejectable NFTs

Lots of business processes can be benefited of the use of blockchain-based technologies. The distributed nature of the blockhain overcome a part of the reliability issues related to centralised systems. Recently the use of NFTs has been spread to multiple applications. The EIP-721 standard proposal defines the interface of non-fungible tokens (NFTs). This type of token was designed with the aim of creating interchangeable tokens but with the peculiarity of being unique and non-fungible and with the impossibility to be damaged or destroyed. For this reason, the EIP-721 standard has almost limitless applications: it can represent ownership of digital or physical assets, unlock access to certain services, … In short, it can represent any single object or right that we can transfer between a sender and a receiver. In this paper we present a study on the transferability of the NFTs and its implications in the generation of secure applications. Currently, the NFTs can be transferred with the consent of the owner (by himself or by an authorised party) but the receiver of the token cannot decline the reception of the token, that will be transferred to his wallet. Therefore, we propose in this paper the enhancement of the NFT standard that allows the rejection of reception of tokens, obtaining a kind of token that can be used in a secure way in all kinds of applications that require the selective reception of the tokens.
Collection Development Policies and Practices: A Survey of Public Libraries in Punjab, Pakistan

The status of collection development in public libraries depicts the poor condition in the following four aspects: books, periodicals, rare material and digital information resources. The library staff mostly used the traditional sources as a tool for collection development i.e. Suggestion box Catalogues, Bibliographies, Book sellers/ publishers lists, Reviews of books and Journals. The existing collection development policies were not comprehensive, did not cover necessary aspects concerning to collection development. The public libraries were facing many obstacles and challenges in proceeding collection development process i.e. shortage of staff, insufficient financial resources, lack of training opportunities for library staff to update their professional skills, non-provision of internet connection and digital equipment to use the latest information communication technology, lack of funds for libraries collection development, Indifferent and neglecting attitude of the higher authorities towards libraries were burning issues and challenges to maintain the international standards of collection development within the public libraries of Punjab, Pakistan.