Reviewerly/exHarmony huggingface dataset:
Proof theory for the logics of bringing-it-about: Ability, coalitions and means-end relationship

Abstract The logic of bringing-it-about (BIAT) aims to capture a notion of agency in which actions are analysed in terms of their results: ‘An agent does something’ means that the agent brings it about that something takes place. Our starting point is the basic BIAT logic as introduced by Elgesem in the ‘90s: this logic contains only a modal operator to express BIAT statements by single agents. Several extensions have been proposed by Elgesem himself and others, notably with the capability operator, coalitions of agents and means-end BIAT statements (i.e. of the form ‘the agent does B by doing A’). We first propose a variant of the neighbourhood semantics, called bi-neighbourhood semantics, for the basic BIAT logic and the mentioned extensions, in which a world is equipped by a set of pairs or neighbourhoods. Differently from the semantics defined in the literature, this reformulation is well suited for countermodel construction. We then introduce modular hypersequent calculi for all logics considered in this work. Our calculi enjoy the fundamental property of cut admissibility, from which it follows their completeness with respect to the axiomatization. Moreover, our calculi provide at the same time a decision procedure, as well as the first practical countermodel extraction procedure: from a single failed proof it is possible to build directly a finite countermodel of the formula under verification in the bi-neighbourhood semantics. By this last result, we obtain constructive proofs of the semantic completeness of the calculi and consequently of the finite model property for all logics.
Hierarchical Dirichlet process and relative entropy

The Hierarchical Dirichlet process is a discrete random measure serving as an important prior in Bayesian non-parametrics. It is motivated with the study of groups of clustered data. Each group is modelled through a level two Dirichlet process and all groups share the same base distribution which itself is a drawn from a level one Dirichlet process. It has two concentration parameters with one at each level. The main results of the paper are the law of large numbers and large deviations for the hierarchical Dirichlet process and its mass when both concentration parameters converge to infinity. The large deviation rate functions are identified explicitly. The rate function for the hierarchical Dirichlet process consists of two terms corresponding to the relative entropies at each level. It is less than the rate function for the Dirichlet process, which reflects the fact that the number of clusters under the hierarchical Dirichlet process has a slower growth rate than under the Dirichlet process.
Knowledge tracing: A brief overview of the advances done since 2021 using deep learning techniques

This paper aims to describe briefly the direction followed by the models based on deep learning techniques attempting to solve the Knowledge Tracing (KT) task since 2021.The goal of the KT task is to model a student's knowledge level based on their responses to a series of activities (known as interactions) provided by a learning platform.A summary of the advances made during the last year within the research area will be presented in this manuscript, with a unique focus on those models that use neural networks to provide a solution.The interest in that segment of models stems from the fact that they have achieved the highest performance over solutions based on probabilistic or logistic models.This theoretical analysis facilitates the understanding of the current state of the art for new researchers of this area.Furthermore, a brief analysis of the classification will be made accompanied with a description of relevant models.The contribution includes the timeline chart followed by the KT models, a short comparison between recently proposed taxonomies for the models within the research area, a brief description of relevant models published since 2021 falling outside any category included in the taxonomies.Lastly a short discussion of findings, possible new applications of the research area and conceivable future directions are discussed.
A Textual Backdoor Defense Method Based on Deep Feature Classification

Natural language processing (NLP) models based on deep neural networks (DNNs) are vulnerable to backdoor attacks. Existing backdoor defense methods have limited effectiveness and coverage scenarios. We propose a textual backdoor defense method based on deep feature classification. The method includes deep feature extraction and classifier construction. The method exploits the distinguishability of deep features of poisoned data and benign data. Backdoor defense is implemented in both offline and online scenarios. We conducted defense experiments on two datasets and two models for a variety of backdoor attacks. The experimental results demonstrate the effectiveness of this defense approach and outperform the baseline defense method.
Privacy Preserving by Removing Sensitive Data from Documents with Fully Convolutional Networks

We present a new approach to anonymizing personal data in text files. In the conducted research, an approach was applied that enables the analysis of sentence sentences with the use of neural networks. Contrary to other currently proposed methods, the presented work analyzes the context of a fragment of the text, which enables the detection of sensitive information not only on the basis of specific words but on the basis of “understanding” the context, such as “mayor of Paris”, “son of the CEO” of a specific company. We present a proprietary solution using convolutional networks connected with glial cells, enabling the selection of the optimal size of the CNN network structure.
Design and Implementation of Simulation System for Multi-UAV Mission

Multi-UAV (unmanned aerial vehicle) mission collaboration is one of the current research hotspots in automation, artificial intelligence, and other fields. The difficulty and high cost of real flight verification have led to the problem that related knowledge learning focuses on theoretical derivation and ignores technical practice. In this paper, a multi-UAV mission simulation system is designed to show the greatest advantage of its collaborative mission planning results simulated and tested in 3D scenarios. It simultaneously reflects and records changes in UAV position, velocity, status, and other state values. The simulation program can be directly applied to real flight with only a few settings. First, the general system framework is presented. Second, according to the simulation requirements, the key modules involved in the software in the loop are given. Finally, through a path planning test for two UAVs, the effect of the system is demonstrated. The results show that the simulation verification based on the system is consistent with the real flight results in terms of functional implementation. It is applicable to the teaching process of related majors and can also provide support for the realization of many complex tasks in the future.
Edge Detection-Based Full-Disc Solar Image Hashing

We propose a content-based solar image descriptor for fast retrieving similar images. The method is divided into three main stages: active region detection by using edge detection, representation learning and hash generation. The first step uses morphological operations for active region detection and afterwards Canny edge detection. In the learning step we use an unsupervised convolutional autoencoder in order to obtain the solar image hash. This process reduces hash length more than twelve times compared to the active region image matrix. The process of reducing the hash length is significant in reference to solar image retrieval process, in which we focus on calculating the distances between hashes. The performed experiments proved the efficiency of the proposed approach. The presented method has various potential, not only solar, applications. Moreover, the problem of searching of and retrieving solar flares has a significant impact on many aspects of life on Earth and beyond.
A Streaming Approach to the Core Vector Machine

The Support Vector Machine (SVM) is a widely used algorithm for batch classification with a run and memory efficient counterpart given by the Core Vector Machine (CVM). Both algorithms have nice theoretical guarantees, but are not able to handle data streams, which have to be processed instance by instance. We propose a novel approach to handle stream classification problems via an adaption of the CVM, which is also able to handle multiclass classification problems. Furthermore, we compare our Multiclass Core Vector Machine (MCCVM) approach against another existing Minimum Enclosing Ball (MEB)-based classification approach. Finally, we propose a real-world streaming dataset, which consists of changeover detection data and has only been analyzed in offline settings so far.
Hand Gesture Recognition for Medical Purposes Using CNN

The presented paper describes implementations of the gesture recognition methods based on the convolutional neural networks. For this purpose, we adopted three CNN structures. The data was obtained from a specially prepared dataset containing images with hand gestures taken on a green screen and downloaded backgrounds depicting hospital and office conditions. During experiments, the precision of recognizing individual gesture classes was measured. Experiments were carried out that showed the performance time of the gesture recognizing in images using a GPU card.
On a Combination of Clustering Methods and Isolation Forest

This study provides a comparison of the efficiency of anomaly detection in data using Isolation Forest (IF) combined with k-Means and Fuzzy C-Means algorithms. It also presents how to determine the anomaly score from the clustering results using the triangular and Gaussian membership functions. The number of clusters, the significance of the anomaly score obtained from the clustering process, and the degree of fuzziness of the clusters are additionally taken into account when testing the efficiency of anomaly detection. Moreover, we demonstrate that in most of the examined datasets, preceding IF with clustering algorithms allows obtaining significantly better results. Furthermore, combining IF with Fuzzy C-Means produces better results than combining it with k-Means. The results discussed in this paper allow one to decide which clustering method to use when combining it with IF to detect anomalies in the data. In addition, a comprehensive analysis presented in the paper sheds the light on the procedure of a choice of the parameters of the algorithms to get possibly the best results.
Unsupervised Pose Estimation by Means of an Innovative Vision Transformer

Attention-only Transformers [34] have been applied to solve Natural Language Processing (NLP) tasks and Computer Vision (CV) tasks. One particular Transformer architecture developed for CV is the Vision Transformer (ViT) [15]. ViT models have been used to solve numerous tasks in the CV area. One interesting task is the pose estimation of a human subject. We present our modified ViT model, Un-TraPEs (UNsupervised TRAnsformer for Pose Estimation), that can reconstruct a subject’s pose from its monocular image and estimated depth. We compare the results obtained with such a model against a ResNet [17] trained from scratch and a ViT finetuned to the task and show promising results.
GHDC: a dual-centric data center network architecture by using multi-port servers with greater incremental scalability

As the volume of data keeps growing rapidly, more and more storage devices, servers, and network devices are continuously added into data center networks (DCNs) to store, manage, and analyze the data. The industry experience indicates that, instead of adding a huge number of servers into the DCNs at a time, the DCN can also be expanded gradually by adding a small number of servers from time to time. This paper proposes a new type of dual-centric data center network structure, called GHDC (Generalized Hypercube Data Center network architecture), which is constructed by using commodity switches and multi-port servers. After analyzing the shortest distance between any two vertices, a routing algorithm for GHDC is developed. To achieve incremental scalability, two incomplete GHDC structures are proposed. A small number of servers can be added into the incomplete GHDC structures while their topological properties are maintained. The analysis and experiment results show that GHDC significantly outperform the other DCN structures, such as FatTree, BCube, Platonica, FCell, FSquare, and FRectangle, in terms of the incremental scalability and robustness. The average throughput of GHDC is approximately comparable to that of FatTree, BCube, and FSquare, and is higher than that of Platonica, FCell, and FRectangle by about 130.2%, 17.45% and 25.5%. Compared with the FatTree, BCube, FCell, FRectangle, and FSquare, GHDC reduces the cost by about 68.49%, 78.04%, 10.84%, 22.85%, and 29.55%, and reduces the max energy consumption by about 69.45%, 34.48%, 11.55%, 24.31%, and 29.58%, respectively. The actual energy consumption of GHDC is much lower than that of FatTree, BCube, FCell, FRectangle, and FSquare, and is little higher than that of Platonia.
GCAT-GTCU: Graph-Connected Attention Network and Gate Than Change Unit for Aspect-Level Sentiment Analysis

Currently, attention mechanisms are widely used in aspect-level sentiment analysis tasks. Previous studies have only used attention mechanisms combined with neural networks for aspect-level sentiment classification, and the feature extraction of the model is insufficient. When the same aspect and sentiment polarity appear in multiple sentences, the semantic information sharing of the same domain is also ignored, resulting in low model performance. To address these problems, the paper proposes an aspect-level sentiment analysis model, GCAT-GTCU, which combines a Graph-connected Attention Network containing symmetry with Gate Than Change Unit. Three nodes of words, sentences, and aspects are constructed, and local and deep-level features of sentences are extracted using CNN splicing BiGRU; node connection information is added to GAT to form a GCAT containing symmetry to realize the information interaction of three nodes, pay attention to the contextual information, and update the shared information of three nodes at any time; a new gating mechanism GTCU is constructed to filter noisy information and control the flow of sentiment information; finally, the three nodes are extracted information to predict the final sentiment polarity. The experimental results on four publicly available datasets show that the model outperforms the baseline model against which it is compared in some very controlled situations.
Information and Communication Technology and Its Reflections on Human Resources Management Practices

The study dealt with two variables of great importance to organizations represented by Information Technology and Communications expressed in their scope (hardware and equipment, software, communications and users) and human resource management practices represented by (human resource planning, employment, training and development, Compensation and incentives and performance evaluation), accordingly the study aimed to determine the repercussions or impact of Technology Information and Communication on human resource management practices in the Mosul Municipality Directorate in Nineveh governorate, The questionnaire was used as a tool for data collection distributed to a sample of (100) person who representing the managers in the field of research organization under study, and several statistical methods were used in the analysis of this data, Statistician Program (SPSS, Ver.26).The study reached several conclusions that confirmed as well as the existence of interrelationships and influence between information and communication Technology and human resource management practices at The level of their whole and partial scope.
Accelerating the Process of Copy-Move Forgery Detection Using Multi-core CPUs Parallel Architecture

Copy-move forgery is a popular type of forgery in digital images. In which, one part of the image is replicated within the same image at different locations. It is easy to do using one of the powerful image processing software. Because it is too simple to be made, anyone with low skills can forge many images in no time. Many copy-move forgery detection techniques are presented every day, but the main common drawback is the high computation time that it takes to detect the final forged region due to the repetition of many complex computational processes. One of the suggested trends to solve this problem is using parallel architectures. However, it is still in its initial phase with little research presented. In this work, copy-move forgery detection methods are discussed from the standpoint of using parallel architectures. In order to solve the problem of computation time in previous related methods, it has been necessary to think of parallel solutions to accelerate the process of copy-move forgery detection. Multi-core CPUs are a powerful processing parallel architecture. Thus, a new Parallel Copy-Move Image Forgery Detection (PCMIFD) system is proposed using Multi-core CPUs. The proposed method has been tested and achieved a high speed-up rate till 7.4. As well, the results practically came close to the theoretical speedup rate.
Using Answer Set Programming to Improve Sensor Network Lifetime

Sensor network lifetime maximization can be solved using heuristic methods, but they produce only suboptimal sensor activity schedules. However, knowing the quality of these solutions, we can use methods for solving decision problems to find better solutions than these suboptimal ones. We apply an answer set programming (ASP) system to answer the question, “Is there a schedule of length k?” where k is at least one unit higher than the best schedule returned by the heuristic method. First, we convert the problem’s constraints and a particular data instance into a high-level constraint language theory. Then we use a grounder for this language and a solver for the language of grounder’s output to find a more extended schedule or determine that no such schedule exists. The paper presents the conversion rules and the experiments’ results with one of the ASP tools for selected classes of the SCP1 benchmark.
Vumark’s Method of Production Layout Designing

The paper deals with the issue of the mixed-reality usage in the design of production systems, its changes during expansion, or technological changes in the production, where it is necessary to flexibly and quickly verify the integration of a new machine into the existing layout and eliminate collision situations even before the installation of a physical machine in production. This is realized through Vumark’s design methodology, which was verified and applied in the conditions of the production environment of the Innovation and Prototyping Centre in the Faculty of Mechanical Engineering at the Technical University of Kosice. The individual parts of the paper describe the Vumark deployment procedure in the production area and the software and hardware resources that the user can apply. Three production machines and one robotic device selected from the Factory design software database were chosen for the experiment. The chosen method enables us to verify during the experiment several variants of deployment the machines in the layout and thus to find the optimal location in a very short time. The experiment showed that the chosen method is applicable in practice and brings savings of time, costs, and energy especially when changing the layout or replacing the machine in the production hall.
Review of Time Domain Electronic Medical Record Taxonomies in the Application of Machine Learning

Electronic medical records (EMRs) help in identifying disease archetypes and progression. A very important part of EMRs is the presence of time domain data because these help with identifying trends and monitoring changes through time. Most time-series data come from wearable devices monitoring real-time health trends. This review focuses on the time-series data needed to construct complete EMRs by identifying paradigms that fall within the scope of the application of artificial intelligence (AI) based on the principles of translational medicine. (1) Background: The question addressed in this study is: What are the taxonomies present in the field of the application of machine learning on EMRs? (2) Methods: Scopus, Web of Science, and PubMed were searched for relevant records. The records were then filtered based on a PRISMA review process. The taxonomies were then identified after reviewing the selected documents; (3) Results: A total of five main topics were identified, and the subheadings are discussed in this review; (4) Conclusions: Each aspect of the medical data pipeline needs constant collaboration and update for the proposed solutions to be useful and adaptable in real-world scenarios.
DNN-Based Forensic Watermark Tracking System for Realistic Content Copyright Protection

The metaverse-related content market is active and the demand for immersive content is increasing. However, there is no definition for granting copyrights to the content produced using artificial intelligence and discussions are still ongoing. We expect that the need for copyright protection for immersive content used in the metaverse environment will emerge and that related copyright protection techniques will be required. In this paper, we present the idea of 3D-to-2D watermarking so that content creators can protect the copyright of immersive content available in the metaverse environment. We propose an immersive content copyright protection using a deep neural network (DNN), a neural network composed of multiple hidden layers, and a forensic watermark.
A Model-Driven Approach for Software Process Line Engineering

It has become increasingly preferable to construct bespoke software development processes according to the specifications of the project at hand; however, defining a separate process for each project is time consuming and costly. One solution is to use a Software Process Line (SPrL), a specialized Software Product Line (SPL) in the context of process definition. However, instantiating an SPrL is a slow and error-prone task if performed manually; an adequate degree of automation is therefore essential, which can be achieved by using a Model-Driven Development (MDD) approach. Furthermore, we have identified specific shortcomings in existing approaches for SPrL Engineering (SPrLE). To address the identified shortcomings, we propose a novel MDD approach specifically intended for SPrLE; this approach can be used by method engineers and project managers to first define an SPrL, and then construct custom processes by instantiating it. The proposed approach uses a modeling framework for modeling an SPrL, and applies transformations to provide a high degree of automation when instantiating the SPrL. The proposed approach addresses the shortcomings by providing an adequate coverage of four activities, including Feasibility analysis, Enhancing the core process, Managing configuration complexity, and Post-derivation enhancement. The proposed approach has been validated through an industrial case study and an experiment; the results have shown that the proposed approach can improve the processes being used in organizations, and is rated highly as to usefulness and ease of use.