Reviewerly/exHarmony huggingface dataset:
Formation of critical thinking personality in the context of the value-sense educational paradigm

The article is devoted to the problem of the formation of critical thinking of an individual in the context of the value-sense paradigm of education. The authors provide a thorough description of the essence of the value-sense educational paradigm, the leading ideas of which are defined as: the natural urge of the individual to learn based on internal meanings without presenting ready-made knowledge and facts, but through meaningful learning and “discovery” of knowledge; personality is unique and unique with its own set of values and meanings, and also, in fact, personality produces values and in the educational process there is a kind of mutual exchange of values; mutual exchange of values, and, accordingly, their formation/correction is possible during subject-subject interaction between participants of the educational process, which is what the value-sense paradigm is focused on; the purpose of the educational process is to form a person’s ability to successfully model and, on this basis, realize his future based on values and meanings; the formation of the necessary competences set by society through the legal framework occurs naturally if the individual recognizes the necessary values as meaningful. In turn, the critical thinking of an individual is a type of thinking that cannot be formed in the conditions of outdated (technocratic) paradigms that are focused purely on the knowledge component. After all, critical thinking involves independent “discovery” of knowledge through analysis, reflection, generalization, revision of the formed point of view if necessary. The authors prove that the leading features of critical thinking (meaningfulness and awareness, reflexivity, purposefulness, reasonableness, self-control, independence, flexibility) can be formed only in such an educational environment that is meaningful for the individual. The implementation of the value-sense paradigm in education allows to form the critical thinking of an individual naturally, taking into account the value orientations of a person, by saturating the educational process with interactive forms and methods of work based on dialogic and partner interaction.
Time optimal quantum state transfer in a fully-connected quantum computer

Abstract The speed limit of quantum state transfer (QST) in a system of interacting particles is not only important for quantum information processing, but also directly linked to Lieb–Robinson-type bounds that are crucial for understanding various aspects of quantum many-body physics. For strongly long-range interacting systems such as a fully-connected quantum computer, such a speed limit is still unknown. Here we develop a new quantum brachistochrone method that can incorporate inequality constraints on the Hamiltonian. This method allows us to prove an exactly tight bound on the speed of QST on a subclass of Hamiltonians experimentally realizable by a fully-connected quantum computer.
Combinatorial Algorithms for Integer Syndrome Decoding Problem

Message recovery attacks against the Classic McEliece proposal to the NIST standardization process, have recently made use of the Integer syndrome decoding problem. It was demonstrated the one can modify or find extra information about the encrypted data, by means of physical attacks. The question raised by these modifications gave birth to the integer syndrome decoding problem. Here, we propose an algorithm that works as an optimized exhaustive-search, and thus finds all the solutions to the aforementioned problem. The key idea in the complexity gain is to split the binomial coefficient into product of smaller binomial coefficients. We show that this can be achieved using a permutation decomposition of the input matrix. Simulations are provided for small length and dimension matrices.
Realisation of the transient dynamics of dimension-varying control systems

The quotient space method provides a novel idea for modelling the transient dynamics of dimension-varying control systems. This paper investigates the realisation problem of the transient dynamics of dimension-varying control systems. By revealing the structure of the controllable subspace for the linear system on quotient space, we propose the condition for the realisation of the transient dynamics of dimension-varying control systems, based on which a new scheme for modelling the transient dynamics is given. Moreover, the proposed scheme justifies the existing result for the strategy for modelling the transient dynamics. A numeric example is given to illustrate our theoretical results.
On Detecting and Measuring Exploitable JavaScript Functions in Real-World Applications

JavaScript is often rated as the most popular programming language for the development of both client-side and server-side applications. Because of its popularity, JavaScript has become a frequent target for attackers who exploit vulnerabilities in the source code to take control over the application. To address these JavaScript security issues, such vulnerabilities must be identified first. Existing studies in vulnerable code detection in JavaScript mostly consider package-level vulnerability tracking and measurements. However, such package-level analysis is largely imprecise as real-world services that include a vulnerable package may not use the vulnerable functions in the package. Moreover, even the inclusion of a vulnerable function may not lead to a security problem, if the function cannot be triggered with exploitable inputs. In this paper, we develop a vulnerability detection framework that uses vulnerable pattern recognition and textual similarity methods to detect vulnerable functions in real-world JavaScript projects, combined with a static multi-file taint analysis mechanism to further assess the impact of the vulnerabilities on the whole project (i.e., whether the vulnerability can be exploited in a given project). We compose a comprehensive dataset of 1,360 verified vulnerable JavaScript functions using the Snyk vulnerability database and the VulnCode-DB project. From this ground-truth dataset, we build our vulnerable patterns for two common vulnerability types: prototype pollution and Regular Expression Denial of Service (ReDoS). With our framework, we analyze 9,205,654 functions (from 3,000 NPM packages, 1892 websites and 557 Chrome Web extensions), and detect 117,601 prototype pollution and 7,333 ReDoS vulnerabilities. By further processing all 5,839 findings from NPM packages with our taint analyzer, we verify the exploitability of 290 zero-day cases across 134 NPM packages. In addition, we conduct an in-depth contextual analysis of the findings in 17 popular/critical projects and study the practical security exposure of 20 functions. With our semi-automated vulnerability reporting functionality, we disclosed all verified findings to project owners. We also obtained 25 published CVEs for our findings, 19 of them rated as “Critical” severity, and six rated as “High” severity. Additionally, we obtained 169 CVEs that are currently “Reserved” (as of Apr. 2023). As evident from the results, our approach can shift JavaScript vulnerability detection from the coarse package/library level to the function level, and thus improve the accuracy of detection and aid timely patching.
The Renewal of the Webcomic in the Era of Platformization: The Case of 9Gag on Instagram

This article addresses the place that the webcomic occupies as valuable content within the repertoire of consumable options in digital social media platforms. For the new digital generations, these new media spaces of light entertainment are spaces of encounter and interaction. Taking as a case study the Instagram profile of 9Gag, this article analyzes the level of engagement of 9Gag audiences with the webcomics published in that profile during the year 2022. The hypothesis defended in this text is to consider that these new spaces of consumption and interaction between users and content are especially relevant when considering the reach that the webcomic has as a format or content of interest for younger audiences. The results obtained after the data analysis show, even considering its possible exploratory nature of the study, that the webcomic is an attractive content for centennial audiences, although its formal and discursive concreteness remains far from the linguistic innovations that the digital comic promoted in its first manifestations.
Online Signature Verification Using Deep Learning Approach

With the advent of advanced technology and its availability at a low price nowadays the biometric verification is getting a common method of person authentication. Online signature recognition is a class of biometric recognition in which a person is identified with the help of its signature. In this paper, a novel deep learning model using shortcut connections has been proposed for online signature recognition. The model has been designed with the modification of the original ResNet model. As the original ResNet model has been designed for the image data so after performing various experiments the modifications in the original ResNet model have been performed that are essential to process the text-based data instead of images. The proposed model has been trained and tested on a custom collected dataset of 4200 online signatures from 280 subjects to achieve an accuracy of 98.6%. It is evident from the results that the proposed model outperforms the state-of-the-art models.
CryptoCircle eXchange – An Advanced Cryptocurrency Exchanges Solution Build on Top of Ethereum Blockchain

The emergence of the Blockchain technology has opened new doors for innovation in many industries, because of the key features it provides: transparency, traceability, speed and enhanced security. The ecosystem behind most projects build on the blockchain is usually sustained by utility coins that can be either generated within the ecosystem or purchased and traded on a designated exchange. In this paper we propose a new exchange solution build on top of Ethereum Blockchain. The paper lays a different approach by offering solutions to problems found in existing exchanges by developing a new robust algorithm provided by GoLang programming language. The proposed solution is entitled CryptoCircle eXchange [1] and it offers high performance trading with over 10 million transactions per second. A few key features provided by this exchange are: a bot powered by artificial intelligence, auto-trading, copy trading, professional charts with technical analysis, anti-market manipulation and trading alerts. All these features are wrapped up in a secure, user-friendly and responsive interface and also in a specially designed mobile app.
Robot Identification using Modern Pattern Recognition Techniques

Computer vision is becoming more and more prevalent in recent years and is increasingly being used in various applications. One of these fields is RoboCup Soccer Small Size League (SSL). This report investigates solutions to recognize individual robots on the field-of-play and their limitations. The first of these classification methods is based on the Circle Hough Transform. This method utilizes edge detection in an image to determine the location and size of circles present in an observed image. The color within circles identified to belong to an individual robot is used to assign the robot with a unique ID. The second approach utilizes Principal Component Analysis to reconstruct a given image of a robot, comparing this reconstructed input image to the original. This approach is like that used commonly in facial recognition. The difference between the original and reconstructed image is used to assign an ID. Lastly, a Convolutional Neural Network is investigated. This method, which is increasingly being used in pattern recognition, utilizes massive data sets to train a classifier on various features. The performance of each classification method is obtained by attempting to classify a set of test images with variation in the height of image capture, the angle of image capture, and the lighting conditions- all of which are areas of concern in the implementation of a computer vision system in this application. The testing results in a clear distinction in robustness of the system, with the Convolutional Neural Network having a perfect performance in the testing data. Implementation in the actual system is discussed, reasoning that the Circle Hough Transform has an advantage in terms of simplicity.
Denoising of Surface Plasmon Resonance (SPR) Spectra Using the Generalized S-transform and the Bald Eagle Search (BES) Algorithm

To obtain accurate resonance peaks from surface plasmon resonance (SPR) spectra, a denoising approach based on generalized S-transform optimized by a new iterative algorithm of bald eagle search (BES) is reported and applied. First, a fiber SPR sensing system is used to collect the original noisy spectra, and the generalized S-transform is performed to obtain the corresponding S-domain spectrum. Next, the denoising threshold (λn) is optimized by the BES algorithm to denoise and reconstruct the SPR reflection spectrum. Finally, the original SPR reflection and the denoised reflection spectra are used to evaluate the fitness function until the optimal denoising threshold (λn) and denoising effect are obtained. The experimental results show that the developed method maintains a relatively stable denoising for SPR reflection spectra as the average values of root mean square error (RMSE) and signal to noise ratio (SNR) are 0.27 and 23.6, respectively. This method overcomes the problem of arbitrary selection of basic functions or thresholds in conventional denoising methods, improves the accuracy of SPR sensor, and provides a new approach for spectral denoising.
A Systematic Approach to Generate TOGAF Artifacts Founded on Multiple Data Sources and Ontology

Enterprise Architecture (EA) standards are widely used in industry and recognized as a lingua franca between project' stakeholders. However, standards are most of times very extensive, requiring specialized knowledge and consuming a large effort if instantiated manually. Furthermore, the risk of producing inconsistent artifacts increases with project' complexity, e.g., changing the name of a business actor requires propagation to all artifacts using that entity. Moreover, EA generation consumes data from multiple sources, e.g., excel or BPMN files, that need to be normalized, classified, and consistently referenced in the artifacts. This paper proposes a systematic approach where the conceptual understanding of a project is shared using an ontology which in turn supports the entire EA artifacts automatic generation. Results show that there are no similar solution available in the literature. In addition, the usage of our systematic approach in four different EA projects evidences a bounded linear increase in effort as artifacts increase in complexity.
Anomaly Detection Using an Ensemble of Multi-Point LSTMs

As technologies for storing time-series data such as smartwatches and smart factories become common, we are collectively accumulating a great deal of time-series data. With the accumulation of time-series data, the importance of time-series abnormality detection technology that detects abnormal patterns such as Cyber-Intrusion Detection, Fraud Detection, Social Networks Anomaly Detection, and Industrial Anomaly Detection is emerging. In the past, time-series anomaly detection algorithms have mainly focused on processing univariate data. However, with the development of technology, time-series data has become complicated, and corresponding deep learning-based time-series anomaly detection technology has been actively developed. Currently, most industries rely on deep learning algorithms to detect time-series anomalies. In this paper, we propose an anomaly detection algorithm with an ensemble of multi-point LSTMs that can be used in three cases of time-series domains. We propose our anomaly detection model that uses three steps. The first step is a model selection step, in which a model is learned within a user-specified range, and among them, models that are most suitable are automatically selected. In the next step, a collected output vector from M LSTMs is completed by stacking ensemble techniques of the previously selected models. In the final step, anomalies are finally detected using the output vector of the second step. We conducted experiments comparing the performance of the proposed model with other state-of-the-art time-series detection deep learning models using three real-world datasets. Our method shows excellent accuracy, efficient execution time, and a good F1 score for the three datasets, though training the LSTM ensemble naturally requires more time.
Enhancing Disaster Resilience: Evaluating the Implementation of an Early Warning System through Table Top Exercises

This research investigates the periodic implementation of an Early Warning System (EWS) using the Table Top Exercise (TTX) method to assess its effectiveness in enhancing community preparedness for disasters. Specifically, it analyzes the utilization of the Early Warning System as a disaster communication medium through the Table Top Exercise method and identifies challenges faced within the Disaster Risk Reduction Forum in Gung Pinto Village, Kec. Namanteran District, Karo. The study focuses on the People-Centered Early Warning System and the incorporation of Local Wisdom in the Early Warning System. The research employs the Knowledge Construction Theory as its theoretical framework. Data is gathered through interviews, observations, and document analysis, with qualitative analysis methods applied. The study concludes that the implementation of an Early Warning System (EWS) can significantly mitigate risks and safeguard communities against disaster impacts. Additionally, it highlights challenges such as limited information accessibility, resource constraints, and time limitations in the process.
Using Knowledge Graphs to Generate SQL Queries from Textual Specifications

In this paper, we present a tool for querying relational DBs that uses a KG as an approach to generate SQL queries from NL specifications. In this approach, we argue that a KG representation of a relational DB schema can become an auxiliary tool in the translation process. Furthermore, we propose to automate the process of generating such a KG. Our approach to provide an NL interface for relational DBs comprises two major tasks: (1) generation of a KG from a relational DB schema and (2) translation of NL queries to SQL based on the semantics provided by the respective KG. We study the effectiveness of our approach using a benchmark dataset containing 82 NL query examples from the Spider dataset, considering the domain of Formula 1. Our approach is able to correctly translate these queries, which is verified against the expected results provided by our benchmark.
Unveiling Knowledge Organization Systems’ Artifacts for Digital Agriculture with Lexical Network Analysis

This article presents a bibliometric and terminological study of a corpus composed of abstracts and titles of 278 articles retrieved by a review protocol planned for surveying initiatives on building artifacts for modeling knowledge related to agricultural production systems. The original corpus comprised a 53,379-word linguistic extract filtered to 111 interconnected major terminologies by combining AntConc and VOSViewer tools. The reduced data were imported into the Gephi tool for analysis of lexical network graphs. Emergent clusters and their central terms underscore the thematic areas that prominently shape the landscape of agricultural Knowledge Organization Systems (KOS) and highlight the interplay between technological advancements, semantic enrichment, and domain-specific challenges. Our analysis of term occurrences and clusters contributes to a broader understanding of these concepts, inferring their significance, roles, and interconnections within the agricultural landscape. It also sheds light on the roles played by KOS in Digital Agriculture.
Recognition of Noisy Digital Images Using the Asymmetric Coupling Semiconductor Chaotic Lasers Network

In this work, we construct a model of an asymmetrically coupled network of semiconductor chaotic lasers in order to recognize noisy digital images of digits 0–9, derived from different samples in the digital image sets 0–9 found within the MNIST dataset. Here, the lasers network consists of eight asymmetrically coupled semiconductor lasers. The chaotic lasers network is driven by the external inputs, which encode one noise digital image to be recognized. The outputs of the chaotic lasers network driven by a total of 40 samples from the digital image sets 0–9 are utilized as ten sets of reference signals. The output of the chaotic lasers network induced by one noisy digital image is used as a test signal. By judging the maximum of the correlations of the test signal with the ten sets of reference signals, all noisy digital images 0–9 can be recognized well under different noises. Moreover, we further explore the recognition rate for each noisy digital image under different noises and a fixed injection strength. It is found that all noisy digital images can be recognized well under a certain low injection strength. The recognition-rates of all noisy digital images can further decrease to a certain extent under higher noise and a fixed the injection strength. The injection strength has little influence on the recognition rate of one noise digital image target with lower noise. The recognition rate under higher noise maintains a higher value (more than 0.9) when the injection strength is smaller than a certain value, but for the larger injection strength, the recognition rate exhibits further decrease. The modeled chaotic lasers network can play the role of photonic accelerators for the recognition of the noisy digital images.
Auditing Customer Identity and Access Management

Customer identity and access management (CAIM) is an emerging field that is a subset of traditional identity and access management. Authenticating, authorizing, and granting access to company systems to employees is the foundation of traditional IAM. Contrarily, CIAM is a specialist field that allows organizations to authenticate, authorize, and provide access to their customers/consumers for on-premises or cloud-based organizational systems. By granting customers access, it becomes necessary to control the degree of access granted to customers; as a result, auditing is a crucial component of CIAM. Auditing aids in directing businesses towards establishing control environment, assessing risk, developing, and monitoring relevant IT controls; and keeps an eye on laws that are imposed by governing authorities. The chapter begins with an introduction to audit and customer identity access management. It addresses the risks associated with CIAM, strategies for implementing security controls and the importance of implementing these controls.