Reviewerly/exHarmony huggingface dataset:
Explanation by Automated Reasoning Using the Isabelle Infrastructure Framework

In this paper, we propose the use of interactive theorem proving for explainable machine learning. After informally motivating our proposition, we illustrate it on the dedicated application of explaining security attacks using the Isabelle Infrastructure framework and its process of dependability engineering. This formal framework and process provides the logics for specification and modeling. Attacks on security of the system are explained by specification and proofs in the Isabelle Infrastructure framework. Existing case studies of dependability engineering in Isabelle are used as feasibility studies to illustrate how different aspects of explanations are covered by the Isabelle Infrastructure framework. Finally, we propose a research agenda on how first-class explanation integrated with automated reasoning will solve the problem.
Multiparallel MMT: Faster ISD Algorithm Solving High-Dimensional Syndrome Decoding Problem

The hardness of the syndrome decoding problem (SDP) is the primary evidence for the security of code-based cryptosystems, which are one of the finalists in a project to standardize post-quantum cryptography conducted by the U.S. National Institute of Standards and Technology (NIST-PQC). Information set decoding (ISD) is a general term for algorithms that solve SDP efficiently. In this paper, we conducted a concrete analysis of the time complexity of the latest ISD algorithms under the limitation of memory using the syndrome decoding estimator proposed by Esser et al. As a result, we present that theoretically nonoptimal ISDs, such as May-Meurer-Thomae (MMT) and May-Ozerov, have lower time complexity than other ISDs in some actual SDP instances. Based on these facts, we further studied the possibility of multiple parallelization for these ISDs and proposed the first GPU algorithm for MMT, the multiparallel MMT algorithm. In the experiments, we show that the multiparallel MMT algorithm is faster than existing ISD algorithms. In addition, we report the first successful attempts to solve the 510-, 530-, 540- and 550-dimensional SDP instances in the Decoding Challenge contest using the multiparallel MMT.
Cognitive Architectures Based on Natural Info-Computation

At the time when the first models of cognitive architectures have been proposed, some forty years ago, understanding of cognition, embodiment and evolution was substantially different from today’s. So was the state of the art of information physics, information chemistry, bioinformatics, neuroinformatics, computational neuroscience, complexity theory, self-organization, theory of evolution, as well as the basic concepts of information and computation. Novel developments support a constructive interdisciplinary framework for cognitive architectures based on natural morphological computing, where interactions between constituents at different levels of organization of matter-energy and their corresponding time-dependent dynamics, lead to complexification of agency and increased cognitive capacities of living organisms that unfold through evolution. Proposed info-computational framework for naturalizing cognition considers present updates (generalizations) of the concepts of information, computation, cognition, and evolution in order to attain an alignment with the current state of the art in corresponding research fields. Some important open questions are suggested for future research with implications for further development of cognitive and intelligent technologies.
Systematic and Flexible Genetic-Algorithm-Based Feature Reduction for Decision Tree ML-Validation

In this paper, we propose a systematic genetic-algorithm-based feature reduction method. It has a high design flexibility based on 5-tuple parameter adjustment. The users can decide these 5 parameters to satisfy the demands of making the focus on accuracy or reduced feature amount. The proposed algorithm is verified by decision-tree models with different data sets. As for the data set, ala, the number of features is reduced from 123 to 53 while the accuracy performance has an increase of 4.2%. In addition, for other data sets, the maximum accuracy loss is no more than 3.1% while the feature reduction ratio achieves 41.9%. Its advantage is to provide a design trade-off between accuracy and reduced feature amount.
Saliency Map Based Data Augmentation

Data augmentation is a commonly applied technique with two seemingly related advantages. With this method one can increase the size of the training set generating new samples and also increase the invariance of the network against the applied transformations. Unfortunately all images contain both relevant and irrelevant features for classification therefore this invariance has to be class specific. In this paper we will present a new method which uses saliency maps to restrict the invariance of neural networks to certain regions, providing higher test accuracy in classification tasks.
The Effect of Content Moderation on Online and Offline Hate: Evidence from Germany's NetzDG

Social media companies are under scrutiny for the prevalence of hateful content on their platforms, but there is scarce empirical evidence of the consequences of regulating such content. We study this question in the context of the ``Network Enforcement Act'' (NetzDG) in Germany, which mandates major social media companies to remove hateful posts within 24 hours. Using a difference-in-differences strategy, we find that the law was associated with a statistically significant reduction in toxic posts by far-right social media users. Further, we show that the NetzDG reduced anti-refugee hate crimes in towns with more far-right Facebook users. Together, these findings suggest that online content moderation can curb online hate speech and offline violence.
Toward the analysis of graph neural networks

Graph Neural Networks (GNNs) have recently emerged as an effective framework for representing and analyzing graph-structured data. GNNs have been applied to many real-world problems such as knowledge graph analysis, social networks recommendation, and even COVID-19 detection and vaccine development. However, unlike other deep neural networks such as Feedforward Neural Networks (FFNNs), few verification and property inference techniques exist for GNNs. This is potentially due to dynamic behaviors of GNNs, which can take arbitrary graphs as input, whereas FFNNs which only take fixed size numerical vectors as inputs.
Comparison of Free Android Mobile 3D Modeling Tools for AR Apps

User expectations regarding the functionality of mobile devices have led to the development of applications with digital representations very close to reality that require the least amount of resources. Until recently, three-dimensional modeling required the compulsory participation of an expert in the field, increasing two valuable resources: time and money. Augmented reality offers the possibility of recreating reality on your smartphones through 3D objects. Currently there are applications that generate three-dimensional objects using photogrammetry. The objective of this work was to compare free Android mobile applications, to identify one that generates a good enough result to be used in augmented reality. The investigation corresponds to the quantitative model with experimental design, the tests were carried out in: 3D Escáner Live, 3DScanLink and Qlone; the results were tested on a prototype AR app. For the analysis, the quantitative evaluation methodology of textured models was used with the metrics: density, in which the 3D Escáner Live app generated a higher value in two of its three objects, with an equivalent to 66.67%; as well as in the difference of forms and comparison of contours where it obtained the highest percentage with 90.23%; and in the variation of the curvature in which it reached an average of 62.61% similarity in contours and edges; while in the number of textures over the number of visible points, all the applications obtained 100%. In conclusion, 3D Escáner Live is the one that generates a 3D model of better quality.
Blockchain adoption in supply chain finance: a research model for empirical investigation in the electricity industry

The purpose of this study is to study the behavioral intentions and influencing factors of blockchain adoption in the context of supply chain finance in the electricity industry. We propose a new model by drawing on the strength of the UTAUT model's ability to identify individual behavioral factors in organizations and the TOE framework's ability to identify external influences on organizations. This paper is empirically validated by distributing a questionnaire to employees of companies in the electric industry. This study will provide theoretical and practical contributions to the adoption of blockchain technology in electricity industry.
A Technique for Altering Face Attributes Based on Differential Structure

The task of face attribute editing refers to the implementation of specified attribute editing by integrating encoder-decoder and generative adversarial networks. The majority of current codec architectures carry out attribute editing by providing the generator with both the source face attribute vector and the destination face attribute vector. Nevertheless, using the entire target attribute vector as input more often hurts the editing outcomes, leading to modifications in unnecessary areas and blurring of image features. This paper suggests an attribute difference method that uses the difference between the target attribute and the source attribute vector as the encoder-decoder input to improve the flexible editing ability of attributes, taking into account that a particular editing task is only related to the attribute to be changed. Constrained picture reconstruction quality, detailed information preservation, and effective attribute editing are achieved simultaneously by combining the reconstruction loss, adversarial loss, and attribute classification loss as the algorithm's loss expression. Experiments demonstrate that the suggested approach successfully address the issue of blurring the details in the generated image as well as precisely alter the face attributes.
Modifications of grover’s quantum algorithm and approaches to their implementation

Modern variants of Grover›s algorithm and approaches to their implementation are considered. The definition of Grover›s algorithm given, its main three stages are indicated. A generalization of Grover›s algorithm «amplitude amplification» is given. A description of the quantum circuit of Grover›s algorithm is given. An example of a complete Grover quantum circuit for two qubits is considered. The characteristic of quantum search in d-dimensional system with the help of qutrits and qudits is given. Examples of implementation of a number of variants of Grover›s algorithm using IBM Quantum Composer tools are proposed. Descriptions of the latest modifications of Grover›s algorithm are given.
Scheduler Architecture and Key Features in Distributed Computing

The cluster scheduler is an important component of modern distributed computing infrastructure, and it has evolved significantly in the last few years, so it has got much more attention by the industry and enterprises. With the development of information technology and the increasing demand of distributed applications, the cluster scheduler has been very complex and diversified. Among several resource scheduling system architecture, the design and work mechanism of their scheduler are very different. When we need to change the resource scheduling technology, from the built environment, the basic use, and then to customized modification according to the different business scenarios, each step may take us a lot of effort and cost. Therefore, we need to understand the architecture principles and comprehend their advantages and disadvantages, so as to use the rules and experiences flexibly to optimize our system. This paper first states the classification and meaning of the core performance indicators of scheduling systems, and then summarizes the mainstream scheduler architectures of distributed systems, compares their differences and expounds their characteristics, and then gives a description of the key characteristics of schedulers. In short, it gives a more comprehensive analysis of schedulers.
Mixed Emotions: Evaluating Reactions to Dynamic Technology Feedback with NeuroIS

Technology is increasingly leveraged in the healthcare context to provide instant and personalized feedback that can help users improve their well-being. However, technologies sometimes evoke negative emotions, which can reduce information technology (IT) use. These emotions may be related to the manner in which technologies provide feedback. It is hypothesized that emotional ramifications of feedback can be limited by providing feedback that is empathetic and supportive. An experiment is proposed to evaluate emotional reactions to different types of feedback provided by a chatbot in real-time. The chatbot will generate combinations of outcome, corrective, and personal feedback. Emotions will be continuously monitored throughout the chatbot interaction using emerging NeuroIS tools. The results will indicate how technologies that provide feedback can be designed to optimize emotions and promote continuous IT use.
Improvement of Mechanisms to Counteract Internet Fraud

The study of Internet fraud is of undoubted interest, primarily because it is a type of cybercrime. As evidenced by statistics on this problem, the crimes of the designated group increased due to the economic crisis and the COVID-19 pandemic. The main ways of committing fraudulent actions using information and telecommunication technologies are investigated. The positions of scholars and researchers in terms of countering cyber-fraud attacks are analyzed. The authors put forward proposals for the most effective counteraction to Internet fraud.
Joint interference sources separation and geolocation for vehicular systems using Bayesian inference

This paper investigates the joint interference source separation and geolocation problem, where the receiver is a vehicle with a known trajectory. We consider communications on a public band where contention-based protocols ensure that two interference sources close to each other do not transmit concurrently. No other prior knowledge on the interference sources is known. The vehicle collects the received power from several positions along its trajectory, and observes a random mixture of the interference sources, which can be modeled by a Dirichlet mixture. A Machine Learning technique relying on Bayesian inference is designed to jointly solve the interference source separation and geolocation. On the one hand, the source separation can be done by three approaches: decision making technique, sampling technique and hybrid decision-sampling technique. On the other hand, the geolocation is carried out via bayesian updates of the posterior distribution of interference sources' positions. The proposed method has the advantage of addressing various situations thanks to a non-parametric modeling.
Structure-Aware Flow Generation for Human Body Reshaping

Body reshaping is an important procedure in portrait photo retouching. Due to the complicated structure and multifarious appearance of human bodies, existing methods either fall back on the 3D domain via body morphable model or resort to keypoint-based image deformation, leading to inefficiency and unsatisfied visual quality. In this paper, we address these limitations by formulating an end-to-end flow generation architecture under the guidance of body structural priors, including skeletons and Part Affinity Fields, and achieve unprecedentedly controllable performance under arbitrary poses and garments. A compositional attention mechanism is introduced for capturing both visual perceptual correlations and structural associations of the human body to reinforce the manipulation consistency among related parts. For a comprehensive evaluation, we construct the first large-scale body reshaping dataset, namely BR-5K, which contains 5,000 portrait photos as well as professionally retouched targets. Extensive experiments demonstrate that our approach significantly outperforms existing state-of-the-art methods in terms of visual performance, controllability, and efficiency. The dataset is available at our website: https://github.com/JianqiangRen/FlowBasedBodyReshaping.
Large Remote Sensing Image Super Resolution Using Self Attention Conditional Coordinate Network

Optical imagery is one of the most important sources for model remote sensing. Super resolution of the optical data could be very useful for industry and academia. Conventional solutions from computer vision only offers super resolution on small images describing relatively smaller scene and optimized towards human perception. In this paper, we proposed a new deep learning model for large remote sensing image super resolution. By using conditional coordinate and self-attention, the model could achieve arbitrary large image super resolution task with special focus on correctness of the details. Numerical evaluation of the model is carried out based on real satellite remote sensing data. Result shows a promising performance with PSNR <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$&gt; 33\ \text{dB}$</tex> and SSIM <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$&gt; 0.93$</tex> .
Computational Intelligence

Implementing systems that can exhibit human-like and more biological capabilities (e.g., learning and summarizing information) is a current discussion topic. Having such systems could enable diverse tasks to be performed more efficiently and reliably. Some authors refer to computational intelligence as a branch of Artificial Intelligence that implements biologically inspired models algorithmically. For Kramer (2009) Computational intelligence: Eine Einführung. Springer, Berlin and Keller et al (2016) Fundamentals of computational intelligence: neural networks, fuzzy systems, and evolutionary computation. Wiley, New York, the main approaches that lead computer systems to simulate nature-inspired ways of reasoning include Neural Networks, Evolutionary Algorithms, Swarm Intelligence, Fuzzy Logic, and Reinforcement Learning. Scholars by Abele and D’Onofrio (2020). Artificial intelligence–the big picture. In: Cognitive Computing. Springer, Wiesbaden, pp. 31–65, on the other hand, defined computational intelligence as an interplay of the approaches mentioned above. For the author of this book, using more nature-inspired methods in the solution of problems in our cities is a coherent way of addressing the issues of currently used methods. For instance, machine learning methods are not necessarily developed following digital ethics considerations and humanistic values (e.g., respecting the privacy of users and considering environmental aspects such as energy consumption). Therefore, in this chapter, a selection of concepts of Fuzzy System, Linguistic Summarization, and Swarm Intelligence are introduced in Sects. 3.1 and 3.2 to provide a theoretical basis for the development of this book. The outcome of this review of concepts provided the basis to answer the RQ2.
Digitalization of Modern Educational Processes and Improvement of Inclusive Programs

This study examines the impact of digitalization on the educational process. It analyzes the results of scientific research and statistical data in the field of ensuring the right to affordable education for all citizens, regardless of their physical, intellectual, social, emotional, linguistic, and other characteristics. It considers the use of a unified electronic information and educational environment for the workflow improvement. The article also highlights the features of digitalization that can affect the specifics of the abilities of modern students.
Acquiring the Cultural Characteristics of English Online Courses Based on Wavelet Transformation

In recent years, China’s online English curriculum has received extensive attention, not only because it is more in line with the educational development background of the new era, but also because this teaching mode has stronger flexibility, coupled with the assistance of information technology, which further improves the extraction effect of English curriculum cultural character. Therefore, the research on the extraction method of English online curriculum cultural character based on wavelet transform. Set the theoretical objectives and construction principles of cultural character of English online course, build a multi-level feature extraction model under wavelet transform, and extract the cultural character of the course by wavelet phase transform extraction method. The final test analysis shows that under the same test environment, compared with the traditional Gabor correlation extraction test group, the extraction test group of wavelet transform finally obtains the higher accuracy of cultural character extraction of English online course, which shows that it has better effect on cultural character extraction of English online course and has practical application significance.
Algorithmic difficulty of solving examples from Slovak language

Creating e-learning materials may seem like a simple thing at first glance, but the opposite is true. Not only do the materials themselves have to meet the curriculum of the specific subject, but the electronic version of the examples or exercises has to meet the requirements of a good sequence of steps leading to the solution of a given task. Therefore, the participation of a subject matter expert as well as a programming language expert is a necessity in the creation of examples and exercises. In our paper we will focus on the suitability of solving Slovak language examples based on algorithms, specifically using flowcharts.
NDNET: A Unified Framework for Anomaly and Novelty Detection

We introduce NDNET ( https://novelty-detection.net/p/ndnet ), an anomaly and novelty detection library that implements various detection algorithms adjusted for online processing of data streams. The intention of this library is threefold: 1) Make experimentation with different anomaly and novelty detection algorithms simple. 2) Support the development of new novelty detection approaches by providing the mCANDIES framework. 3) Provide fundamentals to analyze and evaluate novelty detection algorithms on data streams. The library is freely available and developed as open-source software.
Fine-grained Cars Recognition using Deep Convolutional Neural Networks

Population growth and the high concentration of vehicles on urban roads have negatively impacted urban mobility and the global environment since the primary transportation modes occupy a lot of space on the streets and are one of the main polluting gas emitters. In this context of inefficient urban mobility and unsustainability, the Intelligent Transportation Systems (ITS) aims to solve or minimize urban traffic issues. ITS are also widely used in applications focused on traffic safety, such as vehicle recognition related to a traffic or law violation. For this task, the fine-grained vehicle classification technique is mainly used by computer vision and deep learning advances. However, identifying vehicles by the model can be problematic because the same vehicle can be easily misclassified when observed from different perspectives, with different colours, or by similar models. Knowing these inherent issues from vehicle recognition tasks, Deep Convolutional Neural Networks (DCNNs) are commonly used due to their ability to extract features from images. In that regard, the goal of this paper is to evaluate some state of art DCNNs architectures, conducting experiments with three different datasets to identify which architectures have the best performance metrics in the refined car classification task within ITS context.