Reviewerly/exHarmony huggingface dataset:
Real-Time Identification of Medical Equipment Using Deep CNN and Computer Vision

Sign language is a way of communication in which hand gestures and symbols are used to connect with each other. Communication provides interaction among people to exchange feelings and ideas. Similarly, when it comes to the handling of medical equipment using a robot, sign language should not be a barrier to carrying out such applications. The purpose of this work is to provide a real-time system that can convert Sign Language (ISL) to text format. Most of the work is based on the handcrafted feature. This paper concentrates on introducing a deep learning approach that can classify the signs using the convolutional neural network. First, we make a classifier model using the signs, then using Kera's implementation of convolutional neural network using python we analyze those signs and identify the surgical tools. Then we process another real-time system that uses skin segmentation to find the Region of Interest in the frame. The segmented region is fed to the classifier model to predict the sign. The predicted sign would gradually identify the surgical tool and convert the sign into text.
An Active Vibration Isolation and Compensation System for Improving Optical Image Quality: Modeling and Experiment

Optical detection equipment (ODE) is subjected to vibrations that hamper the quality of imaging. In this paper, an active vibration isolation and compensation system (VICS) for the ODE is developed and systematically studied to improve the optical imaging quality. An active vibration isolator for cameras is designed, employing a dual-loop control strategy with position compensation and integral force feedback (IFF) control, and establishing the mapping relationship between vibration and image quality. A performance metric for evaluating images is also proposed. Finally, an experimental platform is constructed to verify its effectiveness. Based on the experimental results, it can be concluded that the proposed VICS effectively isolates vibrations, resulting in a reduction of 13.95 dB in the peak at the natural frequency and an 11.76 Hz widening of the isolation bandwidth compared with the system without it. At the same time, the experiments demonstrate that the image performance metric value increases by 46.03% near the natural frequency.
TECHNOLOGIES INTEGRATED TO THE CLASSROOM

This article presents a theoretical context on technologies integrated into the classroom, linked to quality in schools. The purpose of this is to show the importance of the process of using technologies integrated into classrooms as an advantage to achieve the quality of learning within the school. Faced with the demanding educational world and a globalized society, technological tools in classrooms must be designed with quality. The future of an educational institution depends on the technologies integrated into the classroom and can be able to satisfy the requirements of educational quality. These integrated technologies can provide students, today, more than ever, with a passport to the student’s digital development, where it is a topic to think about and reflect on the growth of learning acquired at school, where all this belongs to the use of effective educational tools. So-called digital teachers and students are the only ones capable of self-direction and development, as they are the people who make things happen, who drive learning, produce progress and provide services in an exceptional way. And to maintain good quality learning as a differential, the school must seek to keep its staff always well-prepared and technologically advanced.
The Faces of Hacktivism by the Anonymous Collective in the Context of Russian War Against Ukraine. Comparison Between 2014 and 2022

Hacktivism is a social phenomena which evokes different social assessments. The definitions of a term differ in many respects. This theoretical model of hacktivism has not yet been implemented into an empirical strategy for sociological research. The paper describes the the main initiatives taken by Anonymous collective during the 2014 conflict in Ukraine, which is considered by many researchers to be the first stage of preparation for the war triggered by Russia in 2022. Author analyses the collective’s activity in 2022, after the war started, in order to identify similarities and differences in the creation of information messages about the situation. The comparative analysis covers information published in Anonymous’ tweets and selected online news services. She asks the question about the possible consequences of Anonymous actions in the open cyber field for the social moods around the world. To what extent these media messages and their construction have reflected the social perception and/or social attitudes towards Russia’s aggression? The theoretic explorations were embedded mainly on two methods: criticism of writing and the analytical and comparative one.
On Minimum Covering Energy of Semigraph

A compromise between the concept of graph and hypergraph is semigraph. This paper introduced the concept of minimum covering matrix and minimum covering energy of a semigraph G. The minimum covering energy is the summation of singular values of the minimum covering matrix. Upper and lower bounds for minimum covering energy are established and also derive some relationship between minimum covering energy and energy of semigraph G.
Vulnerability Analysis in the Business Organization

An analysis of the vulnerability of the organization's assets is one of the basic tools for managing security risks. The MS Center Configuration Manager tool was mainly used for the risk analysis, which was based on the ISO/IEC 27005 international norm approach and list of vulnerabilities provided by Cybersecurity & Infrastructure Security Agency. In order to determine the severity of the vulnerability and its impact on the company's operation, a standard scale of 1–10 was used as part of the investigation, where 1 represents minimal impact and vulnerabilities 8–10 represent critical vulnerabilities. In addition, in the research, we also monitor type vulnerabilities in the analyzed applications. Another variable that is monitored in the vulnerability analysis is the time for which the vulnerability exists in the information system without treatment. The results of the analysis showed the use of unauthorized software, a large number of vulnerabilities immediately after the installation of the SW and thus the bad use of the additional installation of security patches. Only 22% of secure computers were identified in the organization.
Faster quantum state decomposition with Tucker tensor approximation

Researchers have put a lot of effort into reducing the gap between current quantum processing units (QPU) capabilities and their potential supremacy. One approach is to keep supplementary computations in the CPU, and use the QPU only for the core of the problem. In this work, we address the complexity of quantum algorithms of arbitrary quantum state initialization. QPUs do not outperform classical machines with existing precise initialization algorithms. Hence, many studies propose an approximate but robust quantum state initialization. Cutting a quantum state into a product of (almost) independent partitions with the help of CPU reduces the number of two-qubit gates, and correspondingly minimizes the loss of state fidelity in the quantum part of the algorithm. To find the least entangled qubits, current methods compute the singular value decomposition (SVD) for each qubit separately using the CPU. In this paper, we optimize CPU usage and memory resource bottlenecks. We consider Tucker tensor decomposition as an alternative to the CPU-based SVD in a single low-entangled qubit detection task without loss of solution quality. An iterative implementation of Tucker tensor decomposition replaces explicit applications of SVD as proposed in Araujo et al. (2021). This improvement gives both a theoretical and practical time complexity reduction for the circuit-preparation part of quantum algorithms working with vector data. We propose two implementations of our method; both of them outperform the SVD in time and memory for systems of at least ten qubits. We achieve an order faster implementation and two orders less memory usage for a system of 15 qubits.
Information Security: Identification of Risk Factors Through Social Engineering

The use of technology has increased exponentially thanks to the facilities it provides in academic, personal, social, and business activities. Therefore, security information is important to guarantee information privacy. Social engineering is one of the most used techniques by cybercriminals to exploit different risk factors. They take advantage of ignorance and false confidence to evade security mechanisms to access information systems and obtain private information. The aim of this study was to evaluate the behavior of users exposed to fraud and risk factors that can affect a university information system in order to propose methods to avoid future incidents. Participants answered a pre-test to evaluate their knowledge about the risks of social networks. Five social engineering attacks were implemented on university students using computational and non-computational techniques under controlled scenarios. Website attack vectors, infectious media generator, QR code generator, shoulder surfing and vishing were used. Overall, results showed that 57% of the participants were victims of at least one of social engineering attacks. Consequently, it is advisable to apply different techniques to increase students’ awareness and knowledge of information security to help reduce future attacks.
Tutoring as a Tool for Academic Performance Improvement During the Covid-19 Pandemic

The Covid-19 crisis not only had a global impact on the sanitary services, but also had economic and social repercussions, especially at the educational system; this last apex is to be dealt with in relation to the Ecuadorian higher education level reality. As it is so, this chapter's aim is to collect the tutoring experience as part of the shared and formative evaluation (F&amp;SA) during the pandemic. For this process the phenomenological methodology of research was used, managing to consolidate teachers and university students focus groups, who made a contribution with derivate features from the university educational reality, coming to consider tutoring as a necessary tool to strengthen those weaknesses given during synchronous connections. In this way, it provides an evaluation that promotes participative training process, where favorable learning environments are generated.
Distributivity of a segmentation lattice

Closure spaces, namely the finite ones, with closed singletons are studied on the level of segmentations — partitions of the space into closed subsets. Segmentations form a lattice and we study spaces for which this lattice is distributive. Studying these spaces may help understanding mathematical background for segmentation of a digital image. A crucial notion is that of connectively irreducible sets which can be defined in any finite closure space. The paper provides several equivalent conditions for segmentational distributivity in terms of triples of closed sets, connected systems of closed sets, property of induced closure operator on down-sets of connectively irreducible sets, and finally by restriction (or disability) of existence of certain sublattices.
Human action recognition in complex live videos using graph convolutional network

Despite its high computation costs, human activity recognition is one of the most widely researched areas in computer vision. Deep learning based Human activity recognition in the complex live video has been focused on in this work. Various works stated that end-to-end models are adequate for learning features of video inherently. However, it is a challenging method to capture effective features to recognize the activity. To alleviate this problem, we identify the proposals where the actual event is happening amongst the sequence of frames. In this paper, deep CNN and Stacked LSTM are used to select the deep features for action and maintain the long-term dependencies (i.e., previous information) of the action features, respectively. Also, we proposed Graph Convolutional Network (G-ConvNET), which encodes the features from the graph to label the activity. The proposed method is tested on the THUMOS14 dataset, which comprises twenty complex sports field activities. The experimental results show a significant improvement of 8.6% in modernized methods with mean Average Precision (mAP).
The importance of graph databases and graph learning for clinical applications

The increasing amount and complexity of clinical data require an appropriate way of storing and analyzing those data. Traditional approaches use a tabular structure (relational databases) for storing data and thereby complicate storing and retrieving interlinked data from the clinical domain. Graph databases provide a great solution for this by storing data in a graph as nodes (vertices) that are connected by edges (links). The underlying graph structure can be used for the subsequent data analysis (graph learning). Graph learning consists of two parts: graph representation learning and graph analytics. Graph representation learning aims to reduce high-dimensional input graphs to low-dimensional representations. Then, graph analytics uses the obtained representations for analytical tasks like visualization, classification, link prediction and clustering which can be used to solve domain-specific problems. In this survey, we review current state-of-the-art graph database management systems, graph learning algorithms and a variety of graph applications in the clinical domain. Furthermore, we provide a comprehensive use case for a clearer understanding of complex graph learning algorithms. Graphical abstract.
Smart Contract – Problems with Taking Evidence in Polish Civil Proceedings in the Light of European Regulations

In recent years, we have observed an amazing development of new technologies; many contracts come into effect without paper documents being signed. New possibilities have appeared, for example, the smart contract (also known as the digital contract or blockchain). In some cases, there is a dispute between the participants in the smart contract, e.g., as to the manner of its implementation. A court case might be necessary to resolve the dispute. As in any dispute, evidence proceedings will have to be conducted. The smart contract should appear as a proof. However, due to its unusual nature and complicated status under substantive law, as well as the fact that it is produced by new technological solutions, it is essential to determine its admissibility as evidence. The procedural law regulates in detail only traditional evidence. The smart contract has not been regulated in procedural regulations, therefore, its status needs to be established in the context of the existing documentary evidence. This article aims to contribute to the discussion on the status of smart contracts in civil court proceedings. Primarily, it should be determined whether the smart contract can be considered a document within the meaning of procedural law. In the Polish legal system, the document is defined as an information carrier whose content can be read. Accordingly, the smart contract meets the definition criteria. However, in the absence of provisions governing the manner of taking documentary evidence, it may be difficult to actually take such evidence and establish its value. The article also draws attention to Regulation (EU) No 910/2014 of the European Parliament and of the Council on electronic identification and trust services for electronic transactions in the internal market and repealing Directive 1999/93 / EC. Its art. 46 refers to the legal effectiveness of electronic documents and prohibits discrimination against evidence from such documents, which should undoubtedly contribute to the acceptance of a smart contract as evidence in civil proceedings.
APPLYING DESIGN THINKING IN TEACHING PHYSICS THROUGH STEM ACTIVITIES

The new general education curriculum has set obvious goals for developing the competencies needed for students in the 21st century. Design Thinking is a new educational approach that aims to maximize students abilities and direct students to solve real-life problems.The flexible application of Design Thinking in STEM activities helps students to approach and come up with clear solutions to the problems posed. The article presents research results on the approach Design Thinking in teaching Physics in general, teaching STEM topics in particular, and how to implement it in teaching in high schools.
Comparison of two coefficients of variation: a new Bayesian approach

The coefficient of variation is a useful indicator for comparing the spread of values between datasets with different units or widely different means. In this paper we address the problem of investigating the equality of the coefficients of variation from two independent populations. In order to do this we rely on the Bayesian Discrepancy Measure recently introduced in the literature. Computing this Bayesian measure of evidence is straightforward when the coefficient of variation is a function of a single parameter of the distribution. In contrast, it becomes difficult when it is a function of more parameters, often requiring the use of MCMC methods. We calculate the Bayesian Discrepancy Measure by considering a variety of distributions whose coefficients of variation depend on more than one parameter. We consider also applications to real data. As far as we know, some of the examined problems have not yet been covered in the literature.
Power-Based Side-Channel Attacks on Program Control Flow with Machine Learning Models

The control flow of a program represents valuable and sensitive information; in embedded systems, this information can take on even greater value as the resources, control flow, and execution of the system have more constraints and functional implications than modern desktop environments. Early works have demonstrated the possibility of recovering such control flow through power-based side-channel attacks in tightly constrained environments; however, they relied on meaningful differences in computational states or data dependency to distinguish between states in a state machine. This work applies more advanced machine learning techniques to state machines which perform identical operations in all branches of control flow. Complete control flow is recovered with 99% accuracy even in situations where 97% of work is outside of the control flow structures. This work demonstrates the efficacy of these approaches for recovering control flow information; continues developing available knowledge about power-based attacks on program control flow; and examines the applicability of multiple standard machine learning models to the problem of classification over power-based side-channel information.
A Machine-Learning-Based Motor and Cognitive Assessment Tool Using In-Game Data from the GAME2AWE Platform

With age, a decline in motor and cognitive functionality is inevitable, and it greatly affects the quality of life of the elderly and their ability to live independently. Early detection of these types of decline can enable timely interventions and support for maintaining functional independence and improving overall well-being. This paper explores the potential of the GAME2AWE platform in assessing the motor and cognitive condition of seniors based on their in-game performance data. The proposed methodology involves developing machine learning models to explore the predictive power of features that are derived from the data collected during gameplay on the GAME2AWE platform. Through a study involving fifteen elderly participants, we demonstrate that utilizing in-game data can achieve a high classification performance when predicting the motor and cognitive states. Various machine learning techniques were used but Random Forest outperformed the other models, achieving a classification accuracy ranging from 93.6% for cognitive screening to 95.6% for motor assessment. These results highlight the potential of using exergames within a technology-rich environment as an effective means of capturing the health status of seniors. This approach opens up new possibilities for objective and non-invasive health assessment, facilitating early detections and interventions to improve the well-being of seniors.
A SIMPLIFIED PROOF OF THE EPSILON THEOREMS

Abstract We formulate Hilbert’s epsilon calculus in the context of expansion proofs. This leads to a simplified proof of the epsilon theorems by disposing of the need for prenexification, Skolemisation, and their respective inverse transformations. We observe that the natural notion of cut in the epsilon calculus is associative.
The research on an electronic evidence forensic system for cross-border cybercrime

In response to cross-border cybercrime, investigative organs have adopted cross-border criminal forensic measures such as online public extraction, remote electronic data inspection and real-time monitoring, which may violate the principles of data sovereignty and judicial assistance. While promoting the establishment of a cross-border cooperation mechanism, China should establish a new cross-border electronic evidence criminal forensics model by promoting the procedural justification of cross-border search and monitoring measures. To better regulate cross-border criminal forensics activities, it is necessary to improve the cross-border cybercrime procuratorial organs system to intervene in advance and guide investigations.
FMGAN: A Filter-Enhanced MLP Debias Recommendation Model Based on Generative Adversarial Network

In recommendation models, bias can distort the distribution of user-generated data, leading to inaccurate representation of user preferences. Failure to filter out biased data can result in significant learning errors, ultimately reducing the accuracy of the recommendation model. To address this issue, this paper proposes a Generative Adversarial Network (GAN) model comprising a filter-enhanced Multi-Layer Perceptron (MLP) generator and a linear discriminator to mitigate bias and improve the accuracy of the recommendation. The proposed model leverages the GAN architecture, where the filter structure in the generator enhances the data distribution before model training, allowing for the generation of more precise recommendation lists. The discriminator learns from the skew-corrected user review list to extract user features, which are then used alongside the recommendation list generated by G in an adversarial process. This adversarial process enables each component to optimize and improve itself while strengthening the correction effect. To enhance the accuracy of G generation, we evaluate the influence of three different input lists on the filter effect. Finally, we validate our model on two real-world datasets by comparing the effect of filter-augmented MLP and pure MLP generators. Our results demonstrate the effectiveness of filters, and our model achieves better recommendation accuracy than other baseline models.
EVALUATING STUDENT INTERNSHIP EXPERIENCE AND ITS IMPACT ON CURRICULUM IMPROVEMENTS: A CASE STUDY FOR SOFTWARE ENGINEERING STUDENTS

EVALUATING STUDENT INTERNSHIP EXPERIENCE AND ITS IMPACT ON CURRICULUM IMPROVEMENTS: A CASE STUDY FOR SOFTWARE ENGINEERING STUDENTS