Reviewerly/exHarmony huggingface dataset:
Argumentation with justified preferences

It is often necessary and reasonable to justify preferences before reasoning from them. Moreover, justifying a preference ordering is reduced to justifying the criterion that produces the ordering. This paper builds on the well-known ASPIC+ formalism to develop a model that integrates justifying qualitative preferences with reasoning from the justified preferences. We first introduce a notion of preference criterion in order to model the way in which preferences are justified by an argumentation framework. We also adapt the notion of argumentation theory to build a sequence of argumentation frameworks, in which an argumentation framework justifies preferences that are to underlie the next framework. That is, in our formalism, preferences become not only an input of an argumentation framework, but also an output of it. This kind of input-output process can be applied in the further steps of argumentation. We also explore some interesting properties of our formalism.
Comparison of Different Methods for Building Ensembles of Convolutional Neural Networks

In computer vision and image analysis, Convolutional Neural Networks (CNNs) and other deep-learning models are at the forefront of research and development. These advanced models have proven to be highly effective in tasks related to computer vision. One technique that has gained prominence in recent years is the construction of ensembles using deep CNNs. These ensembles typically involve combining multiple pretrained CNNs to create a more powerful and robust network. The purpose of this study is to evaluate the effectiveness of building CNN ensembles by combining several advanced techniques. Tested here are CNN ensembles constructed by replacing ReLU layers with different activation functions, employing various data-augmentation techniques, and utilizing several algorithms, including some novel ones, that perturb network weights. Experimental results performed across many datasets representing different tasks demonstrate that our proposed methods for building deep ensembles produces superior results.
Reachability in Simple Neural Networks

We investigate the complexity of the reachability problem for (deep) neural networks: does it compute valid output given some valid input? It was recently claimed that the problem is NP-complete for general neural networks and specifications over the input/output dimension given by conjunctions of linear inequalities. We recapitulate the proof and repair some flaws in the original upper and lower bound proofs. Motivated by the general result, we show that NP-hardness already holds for restricted classes of simple specifications and neural networks. Allowing for a single hidden layer and an output dimension of one as well as neural networks with just one negative, zero and one positive weight or bias is sufficient to ensure NP-hardness. Additionally, we give a thorough discussion and outlook of possible extensions for this direction of research on neural network verification.
Al-yousofi: A Novel Benchmark For Multi-vehicle Detection And Tracking

Vehicle detection in aerial imagery has been instrumental in a wide range of applications. Lately, as a result of the robust feature representations, convolutional neural networks (CNN) based detection methods have achieved prodigious performance in computer vision. The diversity of dataset sources that relate to vehicle images is numerous, but it is not sufficient in some cases due to the different types of vehicles from one country to another. In this paper, we propose a new dataset of vehicle images called AL-YOUSOFI taken in the Hashemite Kingdom of Jordan, Irbid city. The AL-YOUSOFI benchmark dataset consists of 40 challenging videos captured from real-world traffic scenes (over 158,000 frames with rich annotations, including vehicle type and vehicle bounding boxes) for multi-object detection and tracking. The state-of-the-art algorithms that have been previously trained on AL-YOUSOFI, has been evaluated. The results showed a variation in efficiency between the algorithms, and this is due to how each works. The full dataset is available at this URL.
Integration of blockchain in contract management for prevention of construction disputes: a systematic literature review and conceptual framework

Certainty, commitment, and communication are essential requirements which seem to be eluding the existing contracting process giving rise to claims and disputes. Contract management practices continue to be manual and traditional despite the digital and technological developments in construction sector. Blockchain technology is increasingly used in different aspects of the business world with its intrinsic qualities of transparency and security. This research conducts systematic literature reviews in three areas aiming to: analyse the chronic problem of construction disputes, investigate recent methods and tools of contractual dispute avoidance, and provide a compelling rationale for the integration of blockchain technology into the contracting process. While the ‘contract-related’ issues were found to be the leading cause of disputes, a ‘centralized’ hierarchy-based environment was established as one of the key challenges to dispute avoidance mechanisms. Discussion followed by feedback survey from industry experts further validated the literature review results. Based on the findings, an integrated blockchain-based conceptual framework is proposed illustrating a contracting process that is: (1) efficient and transparent; (2) digitally integrated; and (3) trustworthy and secure in execution. The study reviews the existing practices and challenges in contract management and offers an interdisciplinary and novel approach for future research in this domain.
Method Comparison of Two Electrolyte Testing Devices: A Data Analytics based on Python Approach

In this paper, analyze the statistical data analysis using Python. In Phyton, user can able to run multiple analyses at the single time and data visualization, analytics are more productive. In this paper, 10 data’s of sodium perform in two different devices and check correlation, t-test, chi-square test, oneway ANOVA in Python. Different proposed structures of Data Science and address the impact of statistics and data analysis.
Temporal logic of surjective bounded morphisms between finite linear processes

In this paper, we study temporal logic for finite linear structures and surjective bounded morphisms between them. We give a characterisation of such structures by modal formulas and show that every pair of linear structures with a bounded morphism between them can be uniquely characterised by a temporal formula up to an isomorphism. As the main result, we prove Kripke completeness of the logic with respect to the class of finite linear structures with bounded morphisms between them.
On the Intersection of Computational Geometry Algorithms with Mobile Robot Path Planning

In the mathematical discipline of computational geometry (CG), practical algorithms for resolving geometric input and output issues are designed, analyzed, and put into practice. It is sometimes used to refer to pattern recognition and to define the solid modeling methods for manipulating curves and surfaces. CG is a rich field encompassing theories to solve complex optimization problems, such as path planning for mobile robot systems and extension to distributed multi-robot systems. This brief review discusses the fundamentals of CG and its application in solving well-known automated path-planning problems in single- and multi-robot systems. We also discuss three winning algorithms from the CG-SHOP (Computational Geometry: Solving Hard Optimization Problems) 2021 competition to evidence the practicality of CG in multi-robotic systems. We also mention some open problems at the intersection of CG and robotics. This review provides insights into the potential use of CG in robotics and future research directions at their intersection.
MeViT: A Medium-Resolution Vision Transformer for Semantic Segmentation on Landsat Satellite Imagery for Agriculture in Thailand

Semantic segmentation is a fundamental task in remote sensing image analysis that aims to classify each pixel in an image into different land use and land cover (LULC) segmentation tasks. In this paper, we propose MeViT (Medium-Resolution Vision Transformer) on Landsat satellite imagery for the main economic crops in Thailand as follows: (i) para rubber, (ii) corn, and (iii) pineapple. Therefore, our proposed MeViT enhances vision transformers (ViTs), one of the modern deep learning on computer vision tasks, to learn semantically rich and spatially precise multi-scale representations by integrating medium-resolution multi-branch architectures with ViTs. We revised mixed-scale convolutional feedforward networks (MixCFN) by incorporating multiple depth-wise convolution paths to extract multi-scale local information to balance the model’s performance and efficiency. To evaluate the effectiveness of our proposed method, we conduct extensive experiments on the publicly available dataset of Thailand scenes and compare the results with several state-of-the-art deep learning methods. The experimental results demonstrate that our proposed MeViT outperforms existing methods and performs better in the semantic segmentation of Thailand scenes. The evaluation metrics used are precision, recall, F1 score, and mean intersection over union (IoU). Among the models compared, MeViT, our proposed model, achieves the best performance in all evaluation metrics. MeViT achieves a precision of 92.22%, a recall of 94.69%, an F1 score of 93.44%, and a mean IoU of 83.63%. These results demonstrate the effectiveness of our proposed approach in accurately segmenting Thai Landsat-8 data. The achieved F1 score overall, using our proposed MeViT, is 93.44%, which is a major significance of this work.
Discovering Non-Linear Boolean Functions by Evolving Walsh Transforms with Genetic Programming

Stream ciphers usually rely on highly secure Boolean functions to ensure safe communication within unsafe channels. However, discovering secure Boolean functions is a non-trivial optimization problem that has been addressed by many optimization techniques: in particular by evolutionary algorithms. We investigate in this article the employment of Genetic Programming (GP) for evolving Boolean functions with large non-linearity by examining the search space consisting of Walsh transforms. Especially, we build generic Walsh spectra starting from the evolution of Walsh transform coefficients. Then, by leveraging spectral inversion, we build pseudo-Boolean functions from which we are able to determine the corresponding nearest Boolean functions, whose computation involves filling via a random criterion a certain amount of “uncertain” positions in the final truth table. We show that by using a balancedness-preserving strategy, it is possible to exploit those positions to obtain a function that is as balanced as possible. We perform experiments by comparing different types of symbolic representations for the Walsh transform, and we analyze the percentage of uncertain positions. We systematically review the outcomes of these comparisons to highlight the best type of setting for this problem. We evolve Boolean functions from 6 to 16 bits and compare the GP-based evolution with random search to show that evolving Walsh transforms leads to highly non-linear functions that are balanced as well.
ConCs-Fusion: A Context Clustering-Based Radar and Camera Fusion for Three-Dimensional Object Detection

Multi-modality three-dimensional (3D) object detection is a crucial technology for the safe and effective operation of environment perception systems in autonomous driving. In this study, we propose a method called context clustering-based radar and camera fusion for 3D object detection (ConCs-Fusion) that combines radar and camera sensors at the intermediate fusion level to achieve 3D object detection. We extract features from heterogeneous sensors and input them as feature point sets into the fusion module. Within the fusion module, we utilize context cluster blocks to learn multi-scale features of radar point clouds and images, followed by upsampling and fusion of the feature maps. Then, we leverage a multi-layer perceptron to nonlinearly represent the fused features, reducing the feature dimensionality to improve model inference speed. Within the context cluster block, we aggregate feature points of the same object from different sensors into one cluster based on their similarity. All feature points within the same cluster are then fused into a radar–camera feature fusion point, which is self-adaptively reassigned to the originally extracted feature points from a simplex sensor. Compared to previous methods that only utilize radar as an auxiliary sensor to camera, or vice versa, the ConCs-Fusion method achieves a bidirectional cross-modal fusion between radar and camera. Finally, our extensive experiments on the nuScenes dataset demonstrate that ConCs-Fusion outperforms other methods in terms of 3D object detection performance.
FORMATION OF PROFESSIONAL COMPETENCES OF A BACHELOR OF CULTURAL STUDIES IN THE PROCESS OF STUDYING THE DISCIPLINES OF PROFESSIONAL TRAINING

The article analyzes the place and role of the disciplines «Museum Studies», «Fundamentals of Socio-Cultural Design», «Information Technologies for Professional Activity», and «University Education and Academic Writing» in the system of Bachelor of Cultural Studies professional training. The stages of the above-mentioned disciplines' introduction into educational and professional programs «Cultural Studies» of the first (bachelor’s) level of higher education in the specialty 034 Cultural Studies of the field of knowledge 03 Humanities of different years of introduction have been determined. The scope, form of control, and transformation of educational components have been clarified. It has been established that the introduction of such disciplines as mandatory for professional training is directly related to the approval by the State Standard of Higher Education of Ukraine for specialty 034 «Cultural Studies» for the first (bachelor’s) level of higher education.&#x0D; The study of the discipline's content shows that the topics presented in the programs have a clear practical orientation and allow to effectively form the future specialist’s professional skills and abilities. The priority importance of these disciplines in the formation of specific (professional, subject) competencies, which corresponds to SC1, SC2, SC3, SC6, SC8, SC9, SC10, SC11, and SC12 of the educational and professional program «Cultural Studies» has been proven for the first (bachelor’s) level of higher education in the specialty 034 Cultural Studies of the field of knowledge 03 Humanities.&#x0D; This approach corresponds to the goals of education stated in the standard of higher education and modern trends in the training of a competitive, high-class specialist who can actively develop his creative potential, which is necessary for solving complex multifaceted socio-cultural objectives and practical problems.&#x0D; In addition, it contributes to the formation of a specialist with an innovative type of thinking, capable not only of perceiving and adapting but also of producing changes in society, taking into account the norms of professional ethics. The formation of the professional ethos of a cultural specialist is extremely important in the context of the process of Ukraine’s integration into the European community, which requires new communication skills and activities with a fundamentally tolerant, non-aggressive approach to other traditions, views, and norms.
Technological sovereignty? Delivering a complete European digital single market

The EU does not suffer from a lack of ambition on digital policy. From ‘strategic autonomy’ to ‘technological sovereignty’, European leaders like to portray the EU as a geopolitical heavyweight on digital. In practice, however, the European digital single market continues to be exposed to many of the fundamental challenges that have plagued it since its inception. The ongoing European effort to draft the global rulebook on tech regulation remains a laudable endeavour, but this has contributed little to boosting the competitiveness of the European digital sector. Many European tech companies still struggle to offer their services outside of national borders and expand their reach to a genuinely European customer base. The EU must tackle inconsistent regulations, close infrastructure gaps, promote investment, and facilitate secure, yet speedy data flows. These issues are integral to helping to turn the digital single market into a tech hub for global business. This article puts forward a number of policy proposals for upgrading the European digital agenda as one of the main conduits for ensuring European economic growth and improved global standing.
Query-Guided Refinement and Dynamic Spans Network for Video Highlight Detection and Temporal Grounding in Online Information Systems

With the surge in online video content, finding highlights and key video segments have garnered widespread attention. Given a textual query, video highlight detection (HD) and temporal grounding (TG) aim to predict frame-wise saliency scores from a video while concurrently locating all relevant spans. Despite recent progress in DETR-based works, these methods crudely fuse different inputs in the encoder, which limits effective cross-modal interaction. To solve this challenge, the authors design QD-Net (query-guided refinement and dynamic spans network) tailored for HD&amp;TG. Specifically, they propose a query-guided refinement module to decouple the feature encoding from the interaction process. Furthermore, they present a dynamic span decoder that leverages learnable 2D spans as decoder queries, which accelerates training convergence for TG. On QVHighlights dataset, the proposed QD-Net achieves 61.87 HD-HIT@1 and 61.88 TG-mAP@0.5, yielding a significant improvement of +1.88 and +8.05, respectively, compared to the state-of-the-art method.
Assurance of Network Communication Information Security Based on Cyber-Physical Fusion and Deep Learning

The internet brings high efficiency and convenience to society; however, the issue of information security in network communication has significantly affected every aspect of the society. How to ensure the security of this network communication information has become an important research topic. This paper proposes a diagnosis and prediction method based on cyber-physical fusion and deep learning, such as LSTM and CNN, to diagnose and predict network security in a complex network environment. The experiment results showed that the accuracy of network security diagnosis of the LSTM method in the training set was approximately 80%/ After the CNN training process, it has the highest accuracy rate of 95% on the test data set. This paper analysed the nature of network security problems from the perspective of cyber-physical fusion. CNN-based method to diagnose network security can obtain results with a higher accuracy rate so that technicians can better take measures to protect network security.
Corporate Governance and Operational Performance of Banks in the MENA Region

This article aims to study the impact of corporate governance on the performance of banks operating in the MENA region over the period 2009-2020. The results, obtained using the maximum likelihood method and the system generalized method of moments (system-GMM), show that the size of the board of directors, CEO-chairman role duality, ownership concentration and the presence of remuneration and nomination committees have a significant impact on the performance of banks in MENA countries. In addition, the empirical results revealed that bank size, credit risk, capitalization ratio, economic growth and the quality of the institutional environment are indeed explanatory factors of bank performance. Our findings provide useful information to regulatory authorities to improve the governance mechanisms of banks in the MENA region.
MT-VTON: Multilevel Transformation-Based Virtual Try-On for Enhancing Realism of Clothing

Virtual 3D fashion fitting, commonly referred to as 2D virtual try-on, has garnered significant attention due to its potential to revolutionize the way consumers interact with fashion items online. This paper presents a novel approach to virtual try-on utilizing a deep learning framework built upon the concept of appearance flow. Our proposed method improves the existing state-of-the-art techniques by seamlessly integrating natural cloth folds, shadows, and intricate textures, such as letters and comic characters, into the synthesized virtual try-on images. Building upon the advancements of previous research, our approach introduces a multi-faceted transformation strategy that operates at both the pixel and image patch levels. Our method’s effectiveness is demonstrated through extensive experiments and comparisons with existing virtual try-on techniques. The results showcase a substantial improvement in the synthesis of virtual try-on images with natural-looking cloth folds, realistic shadows, and intricate textures.
Conduct of analogies in the process of formation of mathematical concepts in technical students

The article demonstrates the idea of applying the method of analogies in the process of forming mathematical concepts among students of technical specialties. Different approaches to the interpretation of the process of making analogies in the educational process are analyzed. Based on the analysis of scientific studies devoted to the specified issue, the method of conducting analogies is defined as a method of scientific knowledge that allows you to form conclusions about the researched object by comparing it with an already known object. Among other things, the main consequence of mental actions when conducting an analogy is an inference – the result of conducting an analogy, which allows you to draw a conclusion about the similarity or difference of the features (elements) of the studied objects or subjects, as well as their properties. Therefore, the result of analogizing (making an analogy) is students' acquisition of new knowledge about an unknown object (subject).&#x0D; The method of analogies has two important functions: the transition from a known information object to an unknown one, and the transition from a “simple” information object to its “complex” counterpart. An important feature of the analogy method is its potential for application in the formation of mathematical concepts. Thus, taking into account the dialectical unity of the functional properties of the method of analogies (explanatory and searching functions), the explanatory function of the method of analogies, in particular when forming concepts, is realized by building models for carrying out analogies, which will become the basis for forming an idea of a specific concept (object), searching the function of the method of analogies during the formation of concepts is based on the fact that students carry out an analysis and synthesis of the received information, build hypotheses regarding a specific information structure (concept).&#x0D; The purpose of the article is to reveal the essence of the method of analogies, to demonstrate its implementation in the process of forming mathematical concepts among students of technical specialties.&#x0D; The conducted study of the method of analogies allows us to characterize the basic factors of its implementation in the educational process.&#x0D; Given the multifacetedness of the chosen research topic, the prospects for further research include theoretical and practical developments in the implementation of the concept of the method of conducting analogies in the mathematical training of future technical specialists.