Reviewerly/exHarmony huggingface dataset:
Analysis of Enterprise Accounting Informatization Risk and Prevention Under Data Mining Technology

This paper aims to analyze the risk and prevention of enterprise accounting informatization under data mining technology. It focuses on the analysis of enterprise business processes, information systems and management. This paper analyzes the risks of information security, privacy protection and data quality brought by enterprise accounting informatization; Propose solutions to these risks; It also discusses how enterprises can prevent them from happening.
The physical body as a computing interface: Theoretical conceptualization of embodied affordances and empirical validation

Despite the growing interest in immersive virtual reality (IVR) for collaboration and the rising prevalence of commercial applications, the extant body of knowledge on IVR is still in a nascent stage. A paucity of empirical studies have investigated the affordances engendered for collaboration, resulting in a lack of theoretical underpinnings for comprehending the impact of IVR on collaboration outcomes. In an effort to address these lacunae in research, this study explores how IVR affordances for collaboration influence task performance and collaboration satisfaction. Drawing on the metaverse framework for collaboration against the backdrop of the corporeal embodiment concept, this study develops a research model that investigates the interplay between IVR affordances for collaboration, collaborative behavior enactment, and collaboration outcomes. The model was tested using data collected from 168 subjects who participated in a virtual collaboration using IVR in a laboratory setting. The results of the study showed that avatar customizability was a key antecedent to embodied affordances, among which embodied communication and embodied team processing jointly influenced collaborative behavior enactment, which, in turn, influenced collaboration outcomes (task performance and collaboration satisfaction). This study contributes to the IVR literature by conceptualizing novel affordances for collaboration facilitated by IVR and empirically scrutinizing the manner in which perceived affordances precipitate their actualization, subsequently affecting collaboration outcomes. With respect to practice, the findings of this study provide useful insights for organizational managers and IVR developers who seek to harness the benefits of IVR for effective collaboration.
Using HVS Dual-Pathway and Contrast Sensitivity to Blindly Assess Image Quality

Blind image quality assessment (BIQA) aims to evaluate image quality in a way that closely matches human perception. To achieve this goal, the strengths of deep learning and the characteristics of the human visual system (HVS) can be combined. In this paper, inspired by the ventral pathway and the dorsal pathway of the HVS, a dual-pathway convolutional neural network is proposed for BIQA tasks. The proposed method consists of two pathways: the "what" pathway, which mimics the ventral pathway of the HVS to extract the content features of distorted images, and the "where" pathway, which mimics the dorsal pathway of the HVS to extract the global shape features of distorted images. Then, the features from the two pathways are fused and mapped to an image quality score. Additionally, gradient images weighted by contrast sensitivity are used as the input to the "where" pathway, allowing it to extract global shape features that are more sensitive to human perception. Moreover, a dual-pathway multi-scale feature fusion module is designed to fuse the multi-scale features of the two pathways, enabling the model to capture both global features and local details, thus improving the overall performance of the model. Experiments conducted on six databases show that the proposed method achieves state-of-the-art performance.
Deep Learning: A Detailed Analysis Of Various Image Augmentation Techniques

Deep learning has been performing reasonably well in computer vision tasks that call for a high volume of photos, although gathering images is often expensive and challenging. Different picture augmentation techniques have been put forth as practical and efficient solutions to this problem Understanding current algorithms is critical when developing new processes or determining the best approaches for a certain task. With deep learning, some of the data pre-processing that is typically required for machine learning is avoided. Unstructured text and visual data can be handled by these algorithms, which can also automate feature extraction and lessen the need for human experts. With a brand-new taxonomy of usable data, we undertake a complete survey of picture augmentation for deep learning in this work. We discuss the difficulties in computer vision tasks and vicinity distribution to give you a fundamental understanding of why we want picture augmentation. Based on the study, we think that our survey provides a clearer knowledge that may be used to select the best techniques or create original algorithms for real-world uses.
A model for the design of immersive learning enactments for students with disabilities

Immersive technologies offer benefits with great potential in special education. A limited number of publications have been found regarding the design of immersive educational environments for special education and especially for students with disabilities. This study proposes MILES-D, a model for the design of effective immersive educational environments for students with disabilities. MILES-D comprises two dimensions by incorporating the affordances of immersive technologies and the Universal Design for Learning model, to propose a series of learning tasks afforded in immersive environments for students with disabilities. A systematic literature review regarding students with intellectual disability revealed that researchers do not seem to use any model for the design of their educational environments. Nevertheless, studies incorporating certain affordances, develop immersive environments which follow the proposed by MILES-D tasks. This is a first indication that MILES-D works and empirical studies are needed to validate it.
Application of Profiled Analysis to ADC-Based Remote Side-Channel Attacks

Remote side-channel attacks are emerging attack vectors that allow attackers to perform side-channel attacks (SCAs) without gaining physical access to the target devices. In particular, remote SCAs using analog-to-digital converters (ADCs) pose a major threat to the embedded devices used in sensor networks. In a previous study, ADC-based SCAs were evaluated using a non-profiled attack technique called correlation power analysis (CPA), which recovers only six out of 16 bytes of Advanced Encryption Standard (AES)-128 keys among 10 million ADC traces. However, when the attack target is an embedded device or some other device that is readily available to attackers, more powerful profiled attacks should be evaluated. In this paper, we introduce a profiled analysis to an ADC-based SCA and show the successful recovery of all 16-byte AES-128 keys from approximately 300k ADC traces under the best measurement condition.
Semantically Rich Differential Access to Secure Cloud EHR

Existing Cloud-based Electronic Health Record (EHR) services face challenges in handling heterogeneous data and maintaining performance with large records since they often use a relational database or only partially store information in a graph database.We have developed a novel approach that allows fine-grained field-level security for Cloud EHRs to protect patient privacy and data security.Our graph-based EHR has been developed by integrating Attribute-based Encryption (ABE) with ontology reasoning using Semantic Web technologies.The novelty of our approach lies in providing differential access to an EHR by using a comprehensive knowledge graph that stores all medical data as encrypted nodes, thereby handling heterogeneous patient data while preserving good performance.In this paper, we describe our system in detail, along with the results demonstrating that the system maintains consistent data retrieval performance with different data sizes and allows realtime updates on the data while supporting queries.
New Tricks to Old Codes: Can AI Chatbots Replace Static Code Analysis Tools?

The prevalence and significance of web services in our daily lives make it imperative to ensure that they are – as much as possible – free from vulnerabilities. However, developing a complex piece of software free from any security vulnerabilities is hard, if not impossible. One way to progress towards achieving this holy grail is by using static code analysis tools to root out any common or known vulnerabilities that may accidentally be introduced during the development process. Static code analysis tools have significantly contributed to addressing the problem above, but are imperfect. It is conceivable that static code analysis can be improved by using AI-powered tools, which have recently increased in popularity. However, there is still very little work in analysing both types of tools' effectiveness, and this is a research gap that our paper aims to fill. We carried out a study involving 11 static code analysers, and one AI-powered chatbot named ChatGPT, to assess their effectiveness in detecting 92 vulnerabilities representing the top 10 known vulnerability categories in web applications, as classified by OWASP. We particularly focused on PHP vulnerabilities since it is one of the most widely used languages in web applications. However, it has few security mechanisms to help its software developers. We found that the success rate of ChatGPT in terms of finding security vulnerabilities in PHP is around 62-68%. At the same time, the best traditional static code analyser tested has a success rate of 32%. Even combining several traditional static code analysers (with the best features on certain aspects of detection) would only achieve a rate of 53%, which is still significantly lower than ChatGPT's success rate. Nonetheless, ChatGPT has a very high false positive rate of 91%. In comparison, the worst false positive rate of any traditional static code analyser is 82%. These findings highlight the promising potential of ChatGPT for improving the static code analysis process but reveal certain caveats (especially regarding accuracy) in its current state. Our findings suggest that one interesting possibility to explore in future works would be to pick the best of both worlds by combining traditional static code analysers with ChatGPT to find security vulnerabilities more effectively.
Massively Parallel Genetic Optimization Through Asynchronous Propagation of Populations

Abstract We present , an evolutionary optimization algorithm and software package for global optimization and in particular hyperparameter search. For efficient use of HPC resources, omits the synchronization after each generation as done in conventional genetic algorithms. Instead, it steers the search with the complete population present at time of breeding new individuals. We provide an MPI-based implementation of our algorithm, which features variants of selection, mutation, crossover, and migration and is easy to extend with custom functionality. We compare to the established optimization tool . We find that is up to three orders of magnitude faster without sacrificing solution accuracy, demonstrating the efficiency and efficacy of our lazy synchronization approach. Code and documentation are available at https://github.com/Helmholtz-AI-Energy/propulate/ .
A Convex Programming Approach to Multipoint Optimal Motion Planning for Unicycle Robots

An important extension of point-to-point motion planning deals with navigating a robot optimally through a sequence of waypoints. The resulting multipoint problem is typically nonconvex due to the requirement to optimize the waypoint passage time, which has hindered the determination of globally optimal solutions. In this paper, a new motion planning technique is presented for multipoint problems involving unicycle robots. Our main finding is that, by adopting a cost function that weights a combination of path length and maneuver time, together with a suitable tightening of the control input constraints, such type of problems can be cast as a convex program. This can be solved in polynomial time to obtain a global optimum. The proposed motion planner is compared to time-optimal trajectory generation scheme based on nonlinear programming, and it can find better trajectories than this method while being computationally faster.
Validating Social Service Robot Interaction Trust (SSRIT) Scale in Measuring Consumers’ Trust Toward Interaction With Artificially Intelligent (AI) Social Robots With a Chinese Sample of Adults

Research on consumers’ trust toward interaction with Artificially Intelligent (AI) social robots in service delivery has gained much more interest due to the outbreak of COVID-19 pandemic. However, this topic has not been widely invesgiated in China. To provide a psychometrically sound instrument in diverse cultural contexts, this study was to validate a scale of Social Service Robot Interaction Trust (SSRIT) that measures consumers’ trust toward interaction with AI social robots in service delivery in a Chinese sample of adults. The results showed that the Chinese version of the SSRIT was validated with reliability and validity, suggesting that the Chinese version of the SSRIT could be used as an effective tool to assess trust in AI social robots in service delivery within the Chinese context. The implications of the findings were also discussed.
An improved hybrid defuzzification method for fuzzy controllers

This paper deals with fuzzy logic controllers. Our interest is in studying different techniques of defuzzification methods. In particular, we propose a generalization of the WABL method to the multiple output system controller. We give a numerical realization, and we show the advantages of this method compared to other defuzzification methods (MOM, COG, WAF,\ldots) through an application for intelligent control of air conditioners.
Development of a research environment for the operational and computational architecture of central bank digital currency software

Objectives . The development and implementation of information and computing architecture and information support for a state central bank digital currency (CBDC) is based on the selection of a software and hardware platform, including technologies and methods for supporting interaction between the elements of the computing complex. The implementation of CBDC technologies significantly depends both on the operational and computing architecture, as well as on the technological characteristics of the means for implementing digital currency information support, which determines the need to develop an appropriate research environment. Thus, the present study sets out to develop an infrastructure for the experimental research environment of the operational and computing architecture used to provide information support for the CBDC. Methods . Digital technologies required for forming an CBDC implementation stack are under development in many countries of the world. The basis for the formation of a software and hardware complex for providing CBDC information support is comprised of theoretical and experimental studies into contemporary digital transaction management tools. Results . The main architectural and technological components that make up the CBDC operational and computing environment comprise operational and computing architectures, blockchain technologies, consensus algorithms, and various forms of digital currencies. Five CBDC operational and computing architecture options are presented. Information models of interaction between the participants in transactions of the central bank digital currency were studied with the aim of establishing the effects of an architectural solution to the characteristics of the computing complex used to provide information support. Features of various digital currencies in the form of accounts and tokens were analyzed. Conclusions . A research environment infrastructure for the CBDC operational and computing information support architecture has been developed. The prerequisites for a comprehensive analysis of the technological characteristics of the CBDC operational and computing environment are set out along with a comparison of operational and computing architecture variants. As a result of the analysis, a summary list of the characteristics of the studied architectures is drawn up. This provides for selecting the optimal operational and computing architecture depending on the requirements imposed on the CBDC.
A Framework to Include and Exploit Probabilistic Information in SHACL Validation Reports

The Shapes Constraint Language (SHACL) is a W3C recommendation which allows to represent constraints in RDF– shape graphs –, and validate RDF data graphs against these constraints. A SHACL validator produces a validation report whose result is false for a shape graph as soon as there is at least one node in the RDF data graph that does not conform to the shape. This Boolean result of the validation of an RDF data graph against an RDF shape graph is not suitable for discovering new high-potential shapes from the RDF data. In this paper, we propose a probabilistic framework to accept shapes with a realistic proportion of nodes in an RDF data graph that does not conform to it. Based on this framework, we propose an extension of the SHACL validation report to express a set of metrics including the generality and likelihood of shapes and we define a method to test a shape as a hypothesis test. Finally, we present the results of experiments conducted to validate a test RDF data graph against a set of shapes.
Generating Multiple 4D Expression Transitions by Learning Face Landmark Trajectories

In this work, we address the problem of 4D facial expressions generation. This is usually addressed by animating a neutral 3D face to reach an expression peak, and then get back to the neutral state. In the real world though, people show more complex expressions, and switch from one expression to another. We thus propose a new model that generates transitions between different expressions, and synthesizes long and composed 4D expressions. This involves three sub-problems: (i) modeling the temporal dynamics of expressions, (ii) learning transitions between them, and (iii) deforming a generic mesh. We propose to encode the temporal evolution of expressions using the motion of a set of 3D landmarks, that we learn to generate by training a manifold-valued GAN (Motion3DGAN). To allow the generation of composed expressions, this model accepts two labels encoding the starting and the ending expressions. The final sequence of meshes is generated by a Sparse2Dense mesh Decoder (S2D-Dec) that maps the landmark displacements to a dense, per-vertex displacement of a known mesh topology. By explicitly working with motion trajectories, the model is totally independent from the identity. Extensive experiments on five public datasets show that our proposed approach brings significant improvements with respect to previous solutions, while retaining good generalization to unseen data.
Digital tools for managing the process of methodological skills formation

The article discusses the question on expediency of using digital tools for managing the process of forming complex, multi-component skills, which comprise skills that are part of the methodological competence. Methodical skills are realized through methodical actions, where invariant and specific parts can be distinguished. The analysis of work experience and existing approaches to the management of the didactic system showed that at various stages of students' mastering design schemes of methodological actions invariants, it is necessary to use different management models. At the first stage, when the approximate basis of methodological actions is built by the teacher, it is advisable to use a distracted, cyclic, automated and non-automated type of control. At the stage of joint construction of the approximate basis of professional action by the teacher and the student, the most effective model will be a delayed management one, accompanied by independent control of each step according to the developed scheme of action. At the stage of the student's independent allocation of the approximate basis of the methodological action, it is advisable to use a delayed, directed, non-automated control model. Organising mutual control at the last stage will allow filling the design schemes of the invariants of methodological actions with a large number of ‟images”. The article justifies the choice of digital tools for implementing these control models, provides examples of training methodological tasks and the corresponding elements of the Moodle course.
Open Science Training in TRIPLE

This case study focuses on the online training activities on Open Science delivered within the H2020 project Transforming Research through Innovative Practices for Linked Interdisciplinary Exploration (TRIPLE, Grant Agreement 863420). The project is dedicated to building a discovery platform for the Social Sciences and Humanities (SSH) and is committed to promoting and supporting the uptake of Open Science within research practices. In order to address SSH research and training communities' needs for enhanced competencies on Open Science and for stronger support in the Findable, Accessible, Interoperable, Reusable (FAIR) management of digital training materials, two reusable outputs were produced. The work carried out is presented as a novel approach to tackle the issues related to FAIRifying research and training practices and to create training resources whose reusability and relevance reaches beyond the project lifetime and framework. The case study presents the methods by which the results were produced so as to encourage and enable their future adaptation and reuse. The TRIPLE Open Science training series (result 1) targets SSH researchers, research support personnel and infrastructure developers in need of practical tools and specific skills to integrate Open Science practices in their workflows. The training series provides 12 competence-oriented online training events in Open Access whose training materials are available as Open Educational Resources (OER). The TRIPLE Training Toolkit (result 2) targets training organisers and research performing organisations who wish to design and manage training events as OERs and increase the impact of their training following good practice. The Toolkit is an easily reproducible workflow designed to help trainers minimise the time they spend in managing training events following FAIR practice. The workflow follows a FAIR-by-design method to address the frequent findability and reusability issues related to the management of digital training resources .