Reviewerly/exHarmony huggingface dataset:
A generic model for security algorithms in mobile electronic payment systems

At a time when communication network technology is developing rapidly, the security of the Internet has aroused widespread concern in the academic community. In particular, electronic payment, which has been booming in recent years, has great security risks and people are prone to information leakage and property loss in the process of payment. In order to solve these problems, this study adopts the AES to encrypt the information in the payment process, introduces the RSA Public Key System (RSA) and the SM4 packet cipher algorithm for comparison, and analyses their encryption (decryption) speed, decryption (decryption) speed and encryption speed respectively. (decryption) speed, as well as PR curve, Loss function and sensitivity. The results show that the AES algorithm has a faster encryption (decryption) speed compared to the other two algorithms. In the PR curve, the AES algorithm has an AP value of 0.9988, which is significantly higher than the other two algorithms, and has a better balance between accuracy and recall, and better performance. In the sensitivity analysis, the AES algorithm can have the highest sensitivity of 97.88%. This is significantly higher than the 93.47% and 96.59% of the other two algorithms. Moreover, as shown by the Loss function of the AES algorithm, it converges faster and the Liss value varies between 0.1 and 0.9. In summary, the AES algorithm has a faster encryption speed and is better in terms of accuracy, convergence speed and security factor in all aspects. This also demonstrates the feasibility and effectiveness of this algorithm for information security protection in electronic payment processes.
Deteksi Wajah Bermasker Menggunakan Deep Neural Network dan Tensorflow

The use of face masks is an important part of life in the midst of the Covid-19 pandemic which has been designated as a global pandemic, people are urged to cover their faces when in public areas to avoid the spread of the virus. The use of these face masks has raised serious questions regarding face detection systems. This study used Deep Neural Network and Tensorflow methods to detect faces both using masks and without masks. This study used two datasets, a face collection dataset without a mask and a face group using a mask. The result of this study was the detection of faces with masks trained on the dataset achieved 98% accuracy in training, and for testing got 100% accuracy on faces without masks, and 99.99% accuracy for face with mask.
A Framework for Analyzing and Improving ISP Compliance

Even though abundant research has been done on the factors that may impact employee information security policy (ISP) compliance intention, the results are mixed. To more fully capture the dimensions which may influence employee ISP compliance, this paper introduces a framework that is based on the synthesis of the theories of motivation and behavior. The framework was tested with survey data. Key findings of the study are 1) employee ISP compliance intention is influenced by factors involving four distinct dimensions of action: awareness, motivation, capability, and organizational culture; and 2) The factors from the capability and organizational culture dimension may significantly moderate the strength of the relationship between motivation and ISP compliance intention. The implications of the study regarding the theoretical and practical contributions of this study are discussed.
Challenges and Path Ahead for Artificial Intelligence-aided Human Resource Management

Introduction: The application of artificial intelligence (AI) can substantially enhance both short- and long-term decision-making in human resource management (HRM) practices. However, academic research fails to address the dark side of AI in confluence with HRM and primarily paints a bright picture of the advantages of AI.Purpose: The current research emphasises the challenges faced in the HRM domain in applying AI in HRM practices and further discusses the future path to maximise the effect of AI on HRM.Methodology: The study rigorously surveyed secondary sources like the journal papers, consultant reports and other databases to critically examine the challenges encountered in applying AI in HRM practices.Findings: Analysis of the above-mentioned sources shows that AI algorithm might bring routinisation of work. HRM ethics, data safety and integrity, biased algorithm from the programmer, fewer data to train the AI model, lack of technical skills of HR executive, neglecting values, and ignoring the creative thinking by employees are a few aspects that might cause difficulty in the adaptation of AI in the HRM domain. As a consequence, there could be unnecessary extra monitoring of employee behaviour, which in turn could lead to loss of workplace well-being and trimming of the human element in HRM.Practical Implications: This study adds value by focusing on the challenges and suggests the path for robust HRM practices; because, the biased decision-making by AI could potentially lead to improper decision-making by the top management, and in turn, the sustainability of a firm could be at stake.
Formalism: Noam Chomsky and his Generative Grammar

For six decades generative concept dominated syntactic theory. The work on generative rules cannot simplify the concepts of the normative principles of the rules, but rather that the subject matter of the rules be considered normative. Grammar is a way to express phrases in their correct form. Grammar rules are accurate by the way they are formulated in a specific type that does not include generative grammar. The term "generative" is directly related to Noam Chomsky's tradition of grammatical research. This term and its formalities and terminology have been studied extensively within the Chomsky tradition.
Prediction model of regional economic development potential based on data mining technology

Abstract Due to the differences between regions, the regional economy is also affected by the development of the real economy and the environment in a specific region. Thus, it is necessary to predict the development potential of the regional economy to ensure the smooth operation of the economy. The continuous upgrading of technology enables the country to grasp the development trend of the regional economy more accurately than ever before. However, the existing prediction techniques still have large errors. Therefore, this paper constructed a prediction model based on data mining technology to predict the development potential of the regional economy to enhance the accuracy of predictive models. The experiments showed that the prediction accuracy of the prediction model constructed in this paper could reach 96.78%, which could provide an accurate reference for the development of the regional economy to ensure the stable development of the regional economy, and has important application value.
Adoption of digital technologies amidst COVID-19 and privacy breach in India and Bangladesh

This article problematizes the institutional void caused by the lack of accountable digital regulation in India and Bangladesh regarding the adoption of public health-related digital technologies during the COVID-19 pandemic. Findings from literature review and preliminary interviews illustrate an emerged pattern in these countries that intersect governmentality and materiality with an absence of oversight. The findings further indicate an absence of privacy laws that leave citizens vulnerable to privacy breach. As surveillance becomes a social norm, authorities appear to turn a blind eye toward human rights while public remain unaware and uninformed. The article recommends that consumer-centric governmentality is needed to ensure the privacy and protection of consumers and citizens in India and Bangladesh.
A novel hybrid consensus-based variational Bayesian filter with position uncertainty in multi-agent system

Most existing distributed state estimation approaches assume that the position of agent in multi-agent system (MAS) is precisely known. However, in target tracking applications, the position of agent is noisy, which makes traditional distributed state estimation methods suffer from accuracy reduction. This paper focuses on developing a novel hybrid consensus-based variational Bayesian filter (HCVB) for target tracking with position uncertainty in nonlinear MAS. To improve the nonlinear approximation accuracy, high-degree cubature information filter framework is used. To cope with agent position uncertainty, the measurement update is derived by using variational Bayesian (VB) method to approximate the joint posterior distribution of the target state and agent position, which can estimate the state and agent position jointly. To decrease the required consensus iterations, a hybrid consensus method is proposed, which can achieve global consistency of all agents with a few consensus iterations. Simulations on UAV tracking in MAS verify the superiority of the proposed method.
Estimating random effects in a finite Markov chain with absorbing states: Application to cognitive data

Abstract Finite Markov chains with absorbing states are popular tools for analyzing longitudinal data with categorical responses. The one step transition probabilities can be defined in terms of fixed and random effects but it is difficult to estimate these effects due to many unknown parameters. In this article we propose a three‐step estimation method. In the first step the fixed effects are estimated by using a marginal likelihood function, in the second step the random effects are estimated after substituting the estimated fixed effects into a joint likelihood function defined as a h‐likelihood, and in the third step the covariance matrix for the vector of random effects is estimated using the Hessian matrix for this likelihood function. An application involving an analysis of longitudinal cognitive data is used to illustrate the method.
Towards a catalog of composite refactorings

Abstract Catalogs of refactoring have key importance in software maintenance and evolution, since developers rely on such documents to understand and perform refactoring operations. Furthermore, these catalogs constitute a reference guide for communication between practitioners since they standardize a common refactoring vocabulary. Fowler's book describes the most popular catalog of refactorings, which documents single and well‐known refactoring operations. However, sometimes, refactorings are composite transformations, that is, a sequence of refactorings is performed over a given program element. For example, a sequence of Extract Method operations (a single refactoring) can be performed over the same method, in one or in multiple commits, to simplify its implementation, therefore, leading to a Method Decomposition operation (a composite refactoring). In this paper, we propose and document a catalog with eight composite refactorings. We also implement a set of scripts to mine composite refactorings by preprocessing the results of refactoring detection tools. Using such scripts, we search for composites in a representative refactoring oracle with hundreds of confirmed single refactoring operations. Next, to complement this first study, we also search for composites in the full history of 10 well‐known open‐source projects. We characterize the detected composite refactorings, under dimensions such as size and location. We conclude by addressing the applications and implications of the proposed catalog.
Information Retrieval and Optimization in Distribution and Logistics Management Using Deep Reinforcement Learning

Resource balance is one of the most critical concerns in the existing logistic domain within dynamic transport networks. Modern solutions are used to maximize demand and supply prediction in collaboration with these problems. However, the great difficulty of transportation networks, profound uncertainties of potential demand and availability, and non-convex market limits make conventional resource management main paths. Hence, this paper proposes an integrated deep reinforcement learning-based logistics management model (DELLMM) to increase and optimize the logistic distribution. An optimization approach can be used in inventors and price control applications. This research methodology gives the fundamentals of information retrieval and the scope of blockchain integration. The conceptual framework of use cases for an efficient logistic management system with blockchain has been discussed. This research designs the deep reinforcement learning system that can boost optimization and other business operations due to impressive improvements in generic self-learning algorithms for optimal management. Thus, the experimental results show that DELLMM improves logistics management and optimized distribution compared to other methods with the highest operability of 94.35%, latency reduction of 97.12%, efficiency of 98.01%, trust enhancement of 96.37%, and sustainability of 97.80%.
Discretized skew‐<i>t</i> mixture model for deconvoluting liquid chromatograph mass spectrometry data

In this paper, new statistical algorithms for accurate peak detection in the metabolomic data are proposed. Specifically, liquid chromatograph‐mass spectrometry data are analyzed. The discretized skew‐ t mixture model for peak detection is proposed. It shows great flexibility and capability in fitting skewed or heavy‐tailed peaks. The methodology is further extended to cross‐sample peak alignment for identifying the true peaks. A measure of peak credibility is provided through the assessment of misclassification probabilities between two cross‐sample peaks. The proposed algorithms are applied to spike‐in data with promising results.
The Security Algorithm ES-BR22-001 Used to Secure the Health Data

The data age information is considerably more significant in open life, since individuals’ well-being information just concluded regardless of whether COVID-19 impacted, and furthermore connected with all medical problems information. These information used to examine and anticipate the medical problems information by Machine Learning Algorithm, and afterward anticipated information need greater security. In this way, we applied the current strategy ChaCha technique and that strategy zeroed in as it were “encryption execution” so security is less. In this paper, to apply the new ES-BR22-001 strategy, this technique has 7 stages. The 1st stage is finding the K value. The 2nd stage is applying the K value in Eq. (1). The 3rd stage is finding the Sk values by using Eq. (1). The 4th stage is applying the Sk values in the sparse matrix. The 5th stage is sparse matrix values are converted into single line. The 6th stage is pairing all the values. The final stage is all paired values will be applied in the matrix. The new ES-BR22-001 method provides security and performance is good while compared to ChaCha method.
User Experience Analysis in Obstacle Clearance Tasks in Virtual and Mixed Reality Environments

Lower body tracking and user experience analysis are ongoing challenges in immersive technologies and biomechanics research, respectively. Particularly, in immersive technologies like mixed reality, there are limited research applications in user comfort and experience, and locomotion. In this paper, we aimed to conduct a cross-disciplinary research (by combining the elements of Biomechanics and human–computer interaction) in understanding the impact of user experience and immersive technologies induced symptoms in virtual and mixed reality environments for obstacle clearance tasks. For this, we created obstacle clearance applications for Oculus Quest and Microsoft HoloLens. Also, we conducted a user study with 12 participants under two conditions (virtual reality and mixed reality) in obstacle clearance behavior of varying heights. The results from our study indicated that the levels of immersion and enjoyment were significantly higher in virtual reality compared to mixed reality. In terms of virtual reality-induced symptoms, the participants found the mixed reality environment to be slightly more comfortable than virtual reality.
Bibliometric Analysis on the Smart University Concept

The smart university concept seeks to improve the quality of life by applying information technologies in a comprehensive, intensive, and sustainable way. It is an emerging concept, where little attention has been paid so far to the quantitative description of its evolution. The main objective of this article is to present the results of a bibliometric analysis around the emerging concept of smart university. It includes citation analysis, keyword networks, journal co-citation networks, references, and most cited authors. For this paper, a computer tool was used as a support to identify and visualize the intellectual structure of the smart university concept and its relationships retrieved from the Dimensions.ai. platform.
Text Classification on Customer Review Dataset Using Support Vector Machine

Customer review is constantly generated in the form of text based on customer opinion concerning specific products in the electronic (e)-commerce space. With the proliferation of data, the classification of customers’ opinions is a major issue in e-commerce. However, merchants frequently require customers to input their opinion about a product, in order to harvest customer experiences. The text classification technique helps customers and merchants to know if a product has a positive or negative review. This will enable merchants to improve the customer experience and also improve the revenue of the company. Sentiment analysis was executed on a customer product review dataset using support vector machine (SVM) in this study. The experimental results obtained show an accuracy of 86.67% in classifying customers’ opinions on selected products. The findings will guide merchants to know customer feedback and identify their needs to improve sales culminating in increased revenue.
Decoupled contributed attribute-object composition detection

Object recognition is quite a well known task in computer vision. Objects are often associated with attributes. It becomes further challenging to correctly classify the object and associated attribute as a composition. Most of the methods for attribute-object pair detection involve discriminating approach that detects the attribute and object separately. Such approaches fail to consider some important facts regarding the composition viz. appearance of attributes is dependent on object and that of an object changes with the attribute. Making use of this interdependence, we propose a model, ContribNet to learn attribute-object composition representation. The model uses the semantic linguistic features to learn robust visual composition while highlighting the importance of component features in identifying its counterpart of the composition. The factors responsible for model performance are also discussed.
Using virtual reality to implement disability studies’ advocacy principles: uncovering the perspectives of people with disability

One central aim of disability studies is to shift understandings of disability, such that disability comes to be understood as about the social disadvantage/oppression that people face when society does not cater to impairment of body/mind. Nevertheless, there remains a need for more practical tools for disability advocacy, through which to transmit disability studies’ ideas of disability to the general community. Drawing on a qualitative study of the perspectives of 23 people with physical and sensory impairments, this paper proposes virtual reality as an advocacy tool to communicate the principles and beliefs of disability studies. The findings highlight that, due to the nature of the technology, participants feel virtual reality has clear potential as a disability advocacy tool that can facilitate empathy, perspective-taking and positive social change, with a particular focus on how it is the environmental barriers and social attitudes around people that disables them.Points of interestMore practical advocacy or informal education tools are needed that align with the principles of disability studies.This research conducted in-depth interviews with 23 people with physical and sensory impairments.The research finds that virtual reality has clear potential as a disability advocacy tool.The participants reported that virtual reality can facilitate empathy, perspective-taking and positive social change.The research finds that virtual reality focuses on how environmental barriers and social attitudes around people with disability disable them, rather than focusing on impairment experiences.
A restricted subset selection procedure for selecting the largest normal mean under heteroscedasticity

This article considers the goal of selecting the population with the largest mean among k normal populations when variances are not known. We propose a Stein-type two-sample procedure, denoted by RBCH, for selecting a nonempty random-size subset of size at most m (1≤m≤k−1) that contains the population associated with the largest mean, with a guaranteed minimum probability P*, whenever the distance between the largest mean and the second largest mean is at least δ*, where m, P*, and δ* are specified in advance of the experiment. The probability of a correct selection and the expected subset size of RBCH are derived. Critical values/procedure parameters that are required for certain k, m, P*, and δ* are obtained by solving simultaneous integral equations and are presented in tables.
Study on angle-only relative navigation for unmanned aerial vehicle formation in GPS-denied environment

To satisfy the autonomous relative navigation demand of unmanned aerial vehicle (UAV) formation in a GPS-denied environment, an angle-only relative navigation algorithm is proposed. First, the relative states in 3-dimension space are decoupled into relative motion along the horizontal plane and altitude by applying the relative azimuth between the north-east-down global frame and the body-fixed horizontal frame belonging to UAVs. Second, a relative dynamic and measurement model considering the measurement deviation of sensors are established. This solves the problem of colored noise in a majority of sensors used in practical engineering. Third, a consensus constraint model of relative states is established by introducing a geometric constraint among formation members. Additionally, a consensus-extended Kalman filter is designed. Finally, the angle-only relative navigation method is validated using standard Monte Carlo simulations. The simulation results show that all the states of the system are observable when the observability criterion is satisfied. The relative position accuracy achieved using gyros with a drift rate of 50°/h, accelerometers with 1 mg deviation, and optical cameras with 1° Gaussian white noise is within 10 m (3σ). Additionally, the method proposed in this study can be used to estimate the sensor measurement deviations effectively. Furthermore, it can be potentially applied to improve the absolute navigation accuracy of UAVs.