Reviewerly/exHarmony huggingface dataset:
Authorship identification methods in student plagiarism detection

In the modern educational context the problem of plagiarism is urgent and requires the development of effective methods of detection and prevention of this phenomenon. The application of authorship identification methods in the field of student plagiarism detection is considered. Different check, detect and analyze plagiarism approaches in various works are investigated. Both classical methods, which include text comparison and similarity search, and modern methods based on machine learning algorithms, as well as their combination and potential modifications, are considered. The advantages and limitations of each method are also discussed, and recommendations are given for choosing one or another approach according to the specific requirements of the research. Special attention is paid to such modern methods as metadata analysis and the application of neural networks. Stylistic analysis reveals authorial peculiarities such as word choice, preferred wording, and even punctuation. Lexical and syntactic models are used to identify repetitive phrases and structures that may indicate plagiarism. Statistical methods can identify anomalies in the use of words and phrases, and machine learning can create models to calculate the probability of plagiarism based on large amounts of data. Ultimately, an comparison of authorship identification techniques in the field of student plagiarism detection is provided, which aims to provide valuable information about different approaches and their applicability, and to help researchers and educators develop effective strategies for detecting and preventing plagiarism in educational environments.
An improved hybrid whale optimization algorithm for global optimization and engineering design problems

The whale optimization algorithm (WOA) is a widely used metaheuristic optimization approach with applications in various scientific and industrial domains. However, WOA has a limitation of relying solely on the best solution to guide the population in subsequent iterations, overlooking the valuable information embedded in other candidate solutions. To address this limitation, we propose a novel and improved variant called Pbest-guided differential WOA (PDWOA). PDWOA combines the strengths of WOA, particle swarm optimizer (PSO), and differential evolution (DE) algorithms to overcome these shortcomings. In this study, we conduct a comprehensive evaluation of the proposed PDWOA algorithm on both benchmark and real-world optimization problems. The benchmark tests comprise 30-dimensional functions from CEC 2014 Test Functions, while the real-world problems include pressure vessel optimal design, tension/compression spring optimal design, and welded beam optimal design. We present the simulation results, including the outcomes of non-parametric statistical tests including the Wilcoxon signed-rank test and the Friedman test, which validate the performance improvements achieved by PDWOA over other algorithms. The results of our evaluation demonstrate the superiority of PDWOA compared to recent methods, including the original WOA. These findings provide valuable insights into the effectiveness of the proposed hybrid WOA algorithm. Furthermore, we offer recommendations for future research to further enhance its performance and open new avenues for exploration in the field of optimization algorithms. The MATLAB Codes of FISA are publicly available at https://github.com/ebrahimakbary/PDWOA .
Blockchain privacy: Fundamental aspects and challenges for the future Internet data sharing

Abstract Privacy concerns the majority of individuals, governments, and organizations that share data over dissimilar networks of nodes. Every kind of participant requires awareness of the data journey that unfolds inside a blockchain network with its own trustworthy rules of data management and accessibility. This paper provides a methodological approach on privacy for data sharing within blockchain environments. Specific technological aspects of blockchains that relate to on‐chain privacy such as network nature, party join, smart contracts, blockchain states, transactions, and ledger flows, are analysed with respect to the involvement and impact of privacy in distributed ledgers. A high‐level architectural approach is suggested that is intended to address significant privacy concerns on data sharing among different kinds of users in the context of blockchain networks deployment and the broader Web 3.0 ecosystem building. Simultaneously, many pertinent challenges are discussed regarding network nature, configurable privacy, ownership, confidentiality, secured computation, and data monetization that appear in the field and ought to be carefully tackled for the future mass adoption of privacy‐matured distributed ledgers that interconnect delivering the future Internet.
An Analysis of the Impact of Industrial Robots on Employment of College Students

With the development of artificial intelligence technology and the promotion and application of industrial robots, it has had a profound impact on economic behavior and reform, as well as on the job market. As an important part of the labor market, the impact of AI and industrial robots on the employment situation of college students is also a topic worth studying. This paper investigates the impact that AI and industrial robots will have on the overall employment number as well as the employment number of college students through the data of the employment market in 30 Chinese provinces from 2015 to 2019 with the OLS and fixed effect model. The empirical research results prove that the development of AI technology and the promotion and application of industrial robots will have the impact of the promotion effect on the overall employment number as well as the employment number of college students.
Personalized and Diversified: Ranking Search Results in an Integrated Way

Ambiguity in queries is a common problem in information retrieval. There are currently two solutions: Search result personalization and diversification. The former aims to tailor results for different users based on their preferences, but the limitations are redundant results and incomplete capture of user intents. The goal of the latter is to return results that cover as many aspects related to the query as possible. It improves diversity yet loses personality and cannot return the exact results the user wants. Intuitively, such two solutions can complement each other and bring more satisfactory reranking results. In this paper, we propose a novel framework, namely PnD to integrate personalization and diversification reasonably. We employ the degree of refinding to determine the weight of personalization dynamically. Moreover, to improve the diversity and relevance of reranked results simultaneously, we design a reset RNN structure (RRNN) with the “reset gate” to measure the influence of the newly selected document on novelty. Besides, we devise a “subtopic learning layer” to learn the virtual subtopics, which can yield fine-grained representations of queries, documents, and user profiles. Experimental results illustrate that our model can significantly outperform existing search result personalization and diversification methods.
Home-made polyp-detector: Deep-learning based polyp detector using Android smartphones

Aims I investigate whether detecting and classifying polyps using an ordinary smartphone is feasible. I also investigated which types of data are better suited for this kind of deep learning.
Kids ScreenTime: Mobile Application for Internet Usage Monitoring

The pandemic of Covid-19 transforms our daily life by bringing new norms which makes Internet as an essential component in our everyday routine. With regard to this, both parents and kids are also fully depended on the Internet for their online classes, assessments and for work purposes. Unfortunately, the kids lose track of time while accessing Internet, which seem to be more entertaining. Currently, majority parents were found struggling to monitor their kid’s Internet usage. Thus, this project proposes a mobile application for Internet usage monitoring aiming to assist parents who are concern on their kid’s Internet usage. This project involved Rapid Application Development (RAD) methodology for developing the mobile application. The outcomes of this project are beneficial for parents not only for monitoring purposes but also as a training platform for their kids during this transition period.
Sequential Minimal Optimization Algorithm for One-Class Support Vector Machines With Privileged Information

One of the powerful techniques in data modeling is accounting for features that are available at the training stage, but are not available when the trained model is used to classify or predict test data — <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Learning Using Privileged Information</i> paradigm (LUPI, Vapnik and Vashist [1]). <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Sequential Minimal Optimization</i> (SMO) method has been already developed for supervised Support Vector Machines (SVM) in Platt [2] and Keerthi et al. [3], for unsupervised (one-class) SVM in Schölkopf et al. [4], and for SVM with privileged information (SVM+) in Pechyony and Vapnik [5]. As can be seen, the missing brick in this research has long been a one-class SVM with privileged information (OC-SVM+). In this paper, we propose SMO algorithm for OC-SVM+ that significantly outperforms non-sequential algorithms for training the OC-SVM+ model. Its finite-time convergence is established. The experiments show how privileged information affects a descriptive domain in the space of original features. Comparative benchmark tests demonstrate that our algorithm is superior over interior point algorithms.
LANGUAGE OF HATE THROUGH COMMUNICATION IN SOCIAL NETWORKS

Work on the theme of hate speech used in the media and mass media for the importance and complexity that this topic represents, can hardly be neglected, especially since today the whole world is under the influence of the development of modern technology. In our society, where there is a lack of adequate information of the citizens about the impacts that derive from the use of hate speech, when it comes to the content of these messages, it is noticed that they are tainted by the most diverse, regardless of the status, gender or other extra-linguistic factors. The Internet revolution has redefined the concept of communication. The main objective of this article is to study the impact of the use of hate speech in the media in Albania, the examination and linguistic interpretation of the lexicon, drawn from the speech of public figures, as well as the evidence of characteristics in the relevant speech. The main method is the research, classification and analysis of the selection of the "language" of hate from the speech of public figures in written and spoken discourse. Through this statement, we must come to the conclusion that the use and promotion of hate speech in the media has a profound psychological, spiritual, but also physical impact, which can go up to fatal consequences. Based on this analysis, we can say that it remains the duty of state institutions through legal acts, even media, etc.
Free-Space Optical Spiking Neural Network

Neuromorphic engineering has emerged as a promising avenue for developing brain-inspired computational systems. However, conventional electronic AI-based processors often encounter challenges related to processing speed and thermal dissipation. As an alternative, optical implementations of such processors have been proposed, capitalizing on the intrinsic information-processing capabilities of light. Within the realm of optical neuromorphic engineering, various optical neural networks (ONNs) have been explored. Among these, Spiking Neural Networks (SNNs) have exhibited notable success in emulating the computational principles of the human brain. Nevertheless, the integration of optical SNN processors has presented formidable obstacles, mainly when dealing with the computational demands of large datasets. In response to these challenges, we introduce a pioneering concept: the Free-space Optical deep Spiking Convolutional Neural Network (OSCNN). This novel approach draws inspiration from computational models of the human eye. We have meticulously designed various optical components within the OSCNN to tackle object detection tasks across prominent benchmark datasets, including MNIST, ETH 80, and Caltech. Our results demonstrate promising performance with minimal latency and power consumption compared to their electronic ONN counterparts. Additionally, we conducted several pertinent simulations, such as optical intensity to-latency conversion and synchronization. Of particular significance is the evaluation of the feature extraction layer, employing a Gabor filter bank, which stands to impact the practical deployment of diverse ONN architectures significantly.
DACBERT: Leveraging Dependency Agreement for Cost-Efficient Bert Pretraining

Building on the cost-efficient pretraining advancements brought about by Crammed BERT, we enhance its performance and interpretability further by introducing a novel pretrained model Dependency Agreement Crammed BERT (DACBERT) and its two-stage pretraining framework - Dependency Agreement Pretraining. This framework, grounded by linguistic theories, seamlessly weaves syntax and semantic information into the pretraining process. The first stage employs four dedicated submodels to capture representative dependency agreements at the chunk level, effectively converting these agreements into embeddings. The second stage uses these refined embeddings, in tandem with conventional BERT embeddings, to guide the pretraining of the rest of the model. Evaluated on the GLUE benchmark, our DACBERT demonstrates notable improvement across various tasks, surpassing Crammed BERT by 3.13% in the RTE task and by 2.26% in the MRPC task. Furthermore, our method boosts the average GLUE score by 0.83%, underscoring its significant potential. The pretraining process can be efficiently executed on a single GPU within a 24-hour cycle, necessitating no supplementary computational resources or extending the pretraining duration compared with the Crammed BERT. Extensive studies further illuminate our approach's instrumental role in bolstering the interpretability of pretrained language models for natural language understanding tasks.
Fast Marching Tree applied to geodesic trajectories in presence of uncertain wind: a day of flights in Europe study

This paper addresses the generation of aircraft cruise optimal trajectories in the presence of wind using the FMT* algorithm. Proven efficient in robotics, it is adapted to take account of wind and great-circle distances. After presenting the problem handled, the adaptation of the algorithm to solve it is presented. In particular, how to deal with great circle distances without increasing too much the computation time is detailed. The proposed method is evaluated on benchmarks and is shown as appropriate to solve this problem. A case study on European flights is also given. This case study shows that this method obtains good results on most European origin-destination pairs. Uncertainties are then considered and tested on a transatlantic flight case. This work is a preliminary work to show that FMT* can be used for aircraft cruise trajectories planning in the presence of wind.
Application of the K-Means Clustering Algorithm to Determine Student Academic Ability Levels during Covid-19

Since the existence of the COVID-19 pandemic in Indonesia, various ways and efforts have been made by the government to prevent the spread of the corona virus. One of them is by implementing an online learning system, so that it causes differences in the learning process that is usually carried out by lecturer face to face with students. Changes in the learning process are expected to be carried out effectively and efficiently. The purpose of this research is to find out how the level of students' academic ability is during online learning in the Informatics Engineering study program at Universitas Malikussaleh and aims to produce a system model that can cluster the level of student academic ability using the K-Means Clustering Algorithm. The variables used in this study are 3 parameters, namely the teaching and learning process, facilities and infrastructure and learning outcomes. The stages carried out in this study were collecting questionnaire data filled out by 200 students. The data obtained would be processed using the K-Means Clustering Algorithm and forming 3 clusters, namely low, medium and high. The clustering results of 200 data obtained 27 students having low academic ability levels (Cluster 1), 87 students having moderate academic ability levels (Cluster 2) and as many as 86 students having high academic ability levels (Cluster 3). The contribution of this research is to help the management of the Informatics Engineering Study Program at Universitas Malikussaleh to find out how the level of student academic ability is during the online learning period during the COVID-19 pandemic, so that evaluation of learning can be carried out online.
Academician Glushkov’s legacy: Human capital in the field of cybernetics, computing, and informatics at Igor Sikorsky Kyiv Polytechnic Institute

The role of academician Viktor Glushkov in the creation of scientific schools in the field of cybernetics, computing, and informatics at the Igor Sikorsky Kyiv Polytechnic Institute, which became a powerful national center for training specialists in this field, is considered. The significant influence of academician Hlushkov’s ideas on the formation of generations of scientists, who to this day continue to build a digital society in Ukraine and far beyond its borders, is shown.
Creating JATS XML from DOCX

JATS XML (Journal Article Tag Suite) is an adopted XML format used for publishing scientific and scholarly content. It is an alternative way of publishing contemporary scientific and professional journals, which has multiple advantages over traditional publishing methods.One of the key advantages of JATS XML is its ability to enhance the discoverability and accessibility of journal articles. This is achieved through the use of metadata tags, which provide a standardized way of describing content and make it easier for search engines to index and retrieve articles. Additionally, the use of JATS XML allows for better preservation of content and ensures that articles are machine-readable and easily integrated with other scholarly resources.Another significant advantage of JATS XML is its ability to facilitate publishing workflows, but unfortunately although it should be the case, currently that ability is not fully realized. Despite providing a standardized structure for content, which theoretically makes it easier for publishers to manage the publication process, some challenges still exist in the practical implementation of JATS XML. Creating JATS XML requires a lot of time if the editor writes XML from scratch or significant financial resources that need to be invested in purchasing professional tools or outsourcing this work. There are almost no free and effective tools for creating complete and correct JATS XML.The team from SRCE – University of Zagreb, University Computing Centre behind the HRČAK portal decided to adapt the universal document converter called Pandoc primarily for editors and publishers in HRČAK, but with the intention that in the future it can also be used by the wider scientific and professional community. At this workshop, we will introduce the process of creating JATS XML as part of HRČAK which will allow journal editors and publishers to create JATS XML more easily. The goal of is to allow users to create complete and correct JATS XML files for their journals through a simple interface without any prior knowledge of XML.
Kernel-based Algorithms for Image Classification: A Review

Abstract The field of kernel learning methods has made significant strides since their inception in the 1990s. These methods involve mapping data from the original input space to a higher-dimensional feature space using an implicitly determined nonlinear mapping through a kernel function. In the feature space, a linear model is constructed, enabling the fusion of statistical information and the geometric structure of data samples. Kernel methods have demonstrated their effectiveness in various computer vision applications, including remote sensing image analysis, face recognition, and online scene classification. However, traditional kernel methods face challenges in image classification, such as computational complexity and the design of appropriate kernel functions for complex image features. To address these limitations, we explore two approaches: online kernel learning and the integration of deep learning architectures with kernel methods. We present advancements and applications in image classification tasks, aiming to provide a comprehensive overview of kernel learning methods and identify future research directions in this domain.
Visual search and real-image similarity: an empirical assessment through the lens of deep learning

The ability to predict how efficiently a person finds an object in the environment is a crucial goal of attention research. Central to this issue is the "search surface" proposed by Duncan and Humphreys (1989), a representation that outlines how the similarity between target and distractor objects (TD) and between distractor objects themselves (DD) affect search efficiency. However, the search surface shape lacks direct quantitative support, being a summary approximation of a wide range of lab-based results poorly generalisable to real-world scenarios. This study exploits convolutional neural networks to predict human search efficiency from computational estimates of similarity between objects populating, potentially, any visual scene. Our results provide ecological evidence supporting the main principles underlying the search surface: search performance continuously varies across tasks and conditions and improves with decreasing TD similarity and increasing DD similarity. Furthermore, our results reveal a crucial dissociation: TD and DD similarities mainly operate at two distinct layers of the network; DD similarity at the intermediate layers of coarse object features, TD similarity at the final layers of complex features used for classification. This suggests that these different similarities exert their major effects at two distinct perceptual levels and demonstrates our methodology’s potential to offer insights into the depth of visual processing on which the search relies. By combining computational techniques with visual search principles, this approach aligns with modern trends in other research areas and fulfils longstanding demands for more ecologically valid research in the field of visual search.