Reviewerly/exHarmony huggingface dataset:
DEVELOPMENT OF DIGITAL COMPETENCE UNDER THE CONDITIONS OF DIGITALIZATION OF EDUCATION

The development of the digital environment covers almost all spheres of life. Today, every person needs a wide range of digital knowledge, abilities, and skills in the intelligent use of the latest technologies for education, professional activities, and participation in society. Special attention is paid to providing the educational process of institutions of higher military education with appropriate digital technologies that would meet modern challenges and ensure the support and development of modern digital competencies of teachers. The article presents the results of an empirical study of the peculiarities of the development of digital competences among scientific and scientific-pedagogical employees of institutions of higher military education. It was established that in the conditions of digital challenges, the role of a teacher of a higher military education institution as a provider of digital transformations and the formation of his digital competence becomes important. It was noted that the majority of scientific and scientific-pedagogical workers paid due attention to the issue of systematic application of information technologies in professional activities. It was established that teachers need the development of digital competence related to the work of gamification tools, content visualization for solving practical, professional and general educational goals. It was determined that the digital competence of scientific and scientific-pedagogical workers of institutions of higher military education should be considered as the newest type of literacy, which is associated with special knowledge, skills, and practical skills for activities in a digital educational environment. Mandatory structural components of a teacher's digital competence are computer literacy, information literacy, communicative literacy, and safety in the digital environment. The development of digital competences of teachers will contribute to the establishment of interaction with students, overcoming the digital gap between participants in the educational process, the development of digital literacy of students, and their conscious use of digital technologies for their own realization in the conditions of a digital society.
A comparative analysis of recurrent neural network and support vector machine for binary classification of spam short message service

Over the years, communication through Short Message Service (SMS) has been a primary tool for mobile subscribers. SMS has varied applications in health, industry, finances, education and social networking among others. The growth of mobile devices and SMS usage has consequently increased the attack surface for cyber-criminals culminating to the proliferation of malicious activities introduced using SMS spam, phishing, spyware, malware etc. Ham messages are normal messages people trade with one another and are usually not unwanted by the recipient, while spam messages are unsolicited junk and redundant messages that may be sent to a large number of people at once and are usually unwanted. Various spam detection models have been developed using various traditional machine and deep learning techniques. However, most studies where comparison between deep and traditional machine learning algorithms is done, have unfortunately omitted K-Nearest Neighbors and Support Vector Machine (SVM) which are empirically deemed as the most popular traditional machine learning algorithms. In this study, therefore, we develop a deep learning model based on Recurrent Neural Network (RNN) for Spam and Ham SMS classification and compare its performance against SVM model for the same University of California (UCl) SMS dataset. The results show that RNN has a slightly higher training and validation accuracy of 0.98 compared to SVM at 0.94, however, the false positive rate of SVM is marginally lower. Exploring application of deep learning with better optimization algorithms such as RNN improves accuracy, reduces computational complexities i.e. memory consumption and speed, and thus minimizing false positive rates. For future work, we suggest the use of varied performance metrics to validate the model in a distributed dataset environment.
Breast cancer histopathology image-based gene expression prediction using spatial transcriptomics data and deep learning

Abstract Tumour heterogeneity in breast cancer poses challenges in predicting outcome and response to therapy. Spatial transcriptomics technologies may address these challenges, as they provide a wealth of information about gene expression at the cell level, but they are expensive, hindering their use in large-scale clinical oncology studies. Predicting gene expression from hematoxylin and eosin stained histology images provides a more affordable alternative for such studies. Here we present BrST-Net, a deep learning framework for predicting gene expression from histopathology images using spatial transcriptomics data. Using this framework, we trained and evaluated 10 state-of-the-art deep learning models without utilizing pretrained weights for the prediction of 250 genes. To enhance the generalisation performance of the main network, we introduce an auxiliary network into the framework. Our methodology outperforms previous studies, with 237 genes identified with positive correlation, including 24 genes with a median correlation coefficient greater than 0.50. This is a notable improvement over previous studies, which could predict only 102 genes with positive correlation, with the highest correlation values ranging from 0.29 to 0.34.
Formation and Development of Scientific Knowledge and Research to the Use of Natural Technologies

The human race came from one blood (Acts 17: 26). The abilities of the soul and spirit of each person are laid down by the Creator for research. The human spirit reflects and fixes the qualitative properties of the perceived environment. The human mind cognizes and explores the observed reality. Through linguistic interpretation and functional formalization within the framework of a theory built with the help of axioms and concepts, it captures the results of research. The functional law of thinking Dao, Chinese philosophers proposed as a law of formalization and quantitative representation of the material world as follows: Dao generates 1, 1 generates 2, and so on; The Dao gives rise to plus, minus, multiply, divide, and other functions. Linguistic thinking generates an interpretation of what is happening in the observed reality. Functional thinking is formed and developed within the framework of axiomatic theory. A theory is built on the basis of axioms and concepts. The theory describes the phenomena, processes, essence of the material world and provides an opportunity to conduct research based on its facts and results. When a person focuses his attention on the relevant research, this indicates that he has the knowledge and skills to work in this area, perceiving the living information of nature and the noosphere. Complex formalization and interpretation of the surrounding reality have led to artificial intelligence, which develops new practical results for a person. Interdisciplinary research provides a more holistic view of the Universe. Actual research at the current time is the construction of the theory, detection, identification and use of natural technologies.
Realizing all-to-all couplings among detachable quantum modules using a microwave quantum state router

One of the primary challenges in realizing large-scale quantum processors is the realization of qubit couplings that balance interaction strength, connectivity, and mode confinement. Moreover, it is very desirable for the device elements to be detachable, allowing components to be built, tested, and replaced independently. In this work, we present a microwave quantum state router, centered on parametrically driven, Josephson-junction based three-wave mixing, that realizes all-to-all couplings among four detachable quantum modules. We demonstrate coherent exchange among all four communication modes, with an average full-iSWAP time of 764ns and average inferred inter-module exchange fidelity of 0.969, limited by mode coherence. We also demonstrate photon transfer and pairwise entanglement between module qubits, and parallel operation of simultaneous iSWAP exchange across the router. Our router-module architecture serves as a prototype of modular quantum computer that has great potential for enabling flexible, demountable, large-scale quantum networks of superconducting qubits and cavities.
TOWARDS CURBING CYBER-BULLYING IN MALAYSIA BY AUTHOR IDENTIFICATION OF IBAN AND KADAZANDUSUN OSN TEXT USING DEEP LEARNING

Online Social Network (OSN) is frequently used to carry out cyber-criminal actions such as cyberbullying. As a developing country in Asia that keeps abreast of ICT advancement, Malaysia is no exception when it comes to cyberbullying. Author Identification (AI) task plays a vital role in social media forensic investigation (SMF) to unveil the genuine identity of the offender by analysing the text written in OSN by the candidate culprits. Several challenges in AI dealing with OSN text, including limited text length and informal language full of internet jargon and grammatical errors that further impact AI's performance in SMF. The traditional AI system that analyses long text documents seems inadequate to analyse short OSN text's writing style. N-gram features are proven to efficiently represent the authors' writing style for shot text. However, representing N-grams in traditional representation like Tf-IDF resulted in sparse and difficult in grasping the semantic information from text. Besides, most AI works have been done in English but receive less attention in indigenous languages. In West Malaysia, the supreme languages that transcend ethnic boundaries are Iban of Sarawak and KadazanDusun of Sabah, which both are inherently under-resourced. This paper presented a proposed workflow of AI for short OSN text using two Under-Resourced Language (U-RL), Iban and KadazanDusun tweets, to curb the cyberbullying issue in Malaysia. This paper compares Tf-Idf (sparse) and SoA embedding-based (dense) feature representations to observe which representations best represent the stylistic features of the authors’ writing. N-grams of word, character, and POS were extracted as the features. The representation models were learned by different classifiers using machine learning (Naïve Bayes, Random Forest, and SVM). The convolutional neural network (CNN), a SoA deep learning model in sentence classification, was tested against the traditional classifiers. The result was observed by combining different representation models and classifiers on three datasets (English, Iban, and KadazanDusun). The best result was achieved when CNN learned embedding-based models with a combination of all features. KadazanDusun achieved the highest accuracy with 95.76%, English with 95.02%, and Iban with 94%..
OWAdapt: An adaptive loss function for deep learning using OWA operators

In this paper, we propose a fuzzy adaptive loss function for enhancing deep learning performance in classification tasks. Specifically, we redefine the cross-entropy loss to effectively address class-level noise conditions, including the challenging problem of class imbalance. Our approach introduces aggregation operators, leveraging the power of fuzzy logic to improve classification accuracy. The rationale behind our proposed method lies in the iterative up-weighting of class-level components within the loss function, focusing on those with larger errors. To achieve this, we employ the ordered weighted average (OWA) operator and combine it with an adaptive scheme for gradient-based learning. Through extensive experimentation, our method outperforms other commonly used loss functions, such as the standard cross-entropy or focal loss, across various binary and multiclass classification tasks. Furthermore, we explore the influence of hyperparameters associated with the OWA operators and present a default configuration that performs well across different experimental settings.
Influence Sustainable Development on Management Risk in the NPL Portfolio in the Western Balkan Countries

Concept sustainable of banking is based on principle realization growth and profit for the bank and shareholders, but only with achievement social goals, as and care for conservation life environment. Providing new ones banking service and creation new ( green ) banking jobs, except for _ profitable, they achieve and spreads social and ecological goals, chances are with creation competitive advantages, both in countries Western Balkans observed in the work, so and in all countries of the world In this one work we will focus on analysis macroeconomic parameters in countries Western Balkans works tests influence selected indicators on performance banking system, with special in retrospect on social and component protection life environment. The results indicate that rate unemployment the most significant indicator potentially risky events for bankers, as and banks they are not recognized in national conservation strategies _ life environment which are in WB countries currently on strength. Accordingly, the former is necessary to carry out education foreign affairs, and in parallel to promote green loans like they live and in the long term more favorable of traditional loans, with update the existing one regulatory frame in the domain protection life environment.
Polyglot Modal Models through Lingua Franca

Complex software systems often feature distinct modes of operation, each designed to handle a particular scenario that may require the system to respond in a certain way. Breaking down system behavior into mutually exclusive modes and discrete transitions between modes is a commonly used strategy to reduce implementation complexity and promote code readability. The work in this paper aims to bring the advantages of working with modal models to mainstream programming languages, by following the polyglot coordination approach of Lingua Franca (LF), in which verbatim target code (e. g., C, C++, Python, Typescript, or Rust) is encapsulated in composable reactive components called reactors. Reactors can form a dataflow network, are triggered by timed as well as sporadic events, execute concurrently, and can be distributed across nodes on a network. With modal models in LF, we introduce a lean extension to the concept of reactors that enables the coordination of reactive tasks based on modes of operation.
Energy-Based Detection of Adverse Weather Effects in LiDAR Data

Autonomous vehicles rely on LiDAR sensors to perceive the environment. Adverse weather conditions like rain, snow, and fog negatively affect these sensors, reducing their reliability by introducing unwanted noise in the measurements. In this work, we tackle this problem by proposing a novel approach for detecting adverse weather effects in LiDAR data. We reformulate this problem as an outlier detection task and use an energy-based framework to detect outliers in point clouds. More specifically, our method learns to associate low energy scores with inlier points and high energy scores with outliers allowing for robust detection of adverse weather effects. In extensive experiments, we show that our method performs better in adverse weather detection and has higher robustness to unseen weather effects than previous state-of-the-art methods. Furthermore, we show how our method can be used to perform simultaneous outlier detection and semantic segmentation. Finally, to help expand the research field of LiDAR perception in adverse weather, we release the SemanticSpray dataset, which contains labeled vehicle spray data in highway-like scenarios.
Breast Cancer Progression Prediction for Care Treatment Efficiency and Intelligent Adaptation, Based on AI Algorithm Classification

In recent years death rates is extremely increasing due to breast cancer. Currently, it is the most commonly diagnosed malignancy among women around the globe. Despite the studies made early detection of breast cancer there is a need for predicting factors and biomarkers of treatment response. Early monitoring of the effectiveness of treatment can thus recognize non responding tumors and provide to adapt the therapy and select the most beneficial therapy for individual patient, also it helps to calculate the improvable toxicity. Our main goal is to make a significant contribution toward improving the quality of healthcare. This work strives to create a predictive model to predict the eventual therapeutic response of breast cancer patients after first cycle of treatment which could help the doctors to select easier the suitable therapeutic protocol using machine learning methods. Machine learning techniques are more used and are extremely promising in medicine field and can improve the treatment process and predict treatment outcomes. This paper describes briefly the recent applications and various machine learning algorithms including SVM, random forest, logistic regression, and NN, and their use in our study to predict treatment response based on synthetic data.
Meet-in-the-Middle Differential Fault Analysis on ITUbee Block Cipher

Differential fault analysis (DFA) was introduced by Biham and Shamir. It is a powerful analysis technique to retrieve the secret key by injecting fault into an internal state and utilizing the differences between the correct ciphertexts and the faulty ciphertexts. Based on the idea of meet-in-the-middle, some differential characters can help to recover the key of some symmetric ciphers. At CHES 2011, this technique was utilized to give analyses on AES. In this article, we propose several DFA schemes on ITUbee, a software-oriented block symmetric cipher for resource-constrained devices based on the meet-in-the-middle idea. Our attacks are efficient enough and more powerful than previous works. Furthermore, the attacks in this article break the protection countermeasure, meaning we have to review the protection method on devices for ITUbee.
SL-Swin: A Transformer-Based Deep Learning Approach for Macro- and Micro-Expression Spotting on Small-Size Expression Datasets

In recent years, the analysis of macro- and micro-expression has drawn the attention of researchers since they provide visual cues of an individual&amp;rsquo;s emotions for a broad range of potential applications such as lie detection and criminal detection. In this paper, we address the challenge of spotting facial macro- and micro-expression from videos and present compelling results by using a deep learning approach to analyze the optical flow features. Different from other deep learning approaches that are mainly based on Convolutional Neural Networks (CNNs), we propose a Transformer-based deep learning approach that predicts a score indicating the probability of a frame being within an expression interval. Unlike other Transformer-based models that achieve high performance by being pre-trained on large datasets, our deep learning model, called SL-Swin, which applies Shifted Patch Tokenization and Locality Self-Attention to the backbone network Swin Transformer, effectively spots macro- and micro-expressions by being trained from scratch on small-size expression datasets. Our evaluation outcomes surpass the MEGC 2022 spotting baseline result, obtaining an overall F1-score of 0.1366. Additionally, our approach also performs well in the MEGC 2021 spotting task with an overall F1-score of 0.1824 and 0.1357 on CAS(ME)2 and SAMM Long Videos, respectively. The code is publicly available on GitHub (https://github.com/eddiehe99/pytorch-expression-spotting).
AraSpell: A Deep Learning Approach for Arabic Spelling Correction

Abstract Spelling correction is the task of identifying spelling mistakes, typos, and grammatical mistakes in a given text and correcting them according to their context and grammatical structure. This work introduces "AraSpell," a framework for Arabic spelling correction using different seq2seq model architectures such as Recurrent Neural Network (RNN) and Transformer with artificial data generation for error injection, trained on more than 6.9 Million Arabic sentences. Thorough experimental studies provide empirical evidence of the effectiveness of the proposed approach, which achieved 4.8% and 1.11% word error rate (WER) and character error rate (CER), respectively, in comparison with labeled data of 29.72% WER and 5.03% CER. Our approach achieved 2.9% CER and 10.65% WER in comparison with labeled data of 10.02% CER and 50.94% WER. Both of these results are obtained on a test set of 100K sentences.
Artificial Intelligence Utilization in Libraries

Artificial intelligence is becoming more and more crucial each passing day. This study initially will present artificial intelligence utilization in libraries. In 1950, Alan Turing came up with the idea that computers might be able to imitate human behavior. According to Turing, artificial intelligence could analyze texts, make modelling the knowledge to help decision-making, reproduce a standard reasoning and use this information to make decisions and to produce knowledge thanks to machine learning. In this research, a literature review is conducted concerning different aspects of the subject. This study will lead to better focus on different scientific point of view about the artificial intelligence. Keywords: artificial intelligence, libraries, technology, digital transformation, smart libraries
Image Processing with Python Libraries

Today, computer vision is considered one of the most important sub-fields of artificial intelligence, due to the variety of its applications and capabilities to transfer the human ability to understand and describe a scene or image to the computer, so that it becomes able to recognize objects, shapes, colors, behavior and other capabilities of understanding the image. Image processing is one of the branches of computer science, and it is a way to perform some operations on an image in order to obtain an improved model for this image or extract some useful information from it. Often the data that is collected is primary data, meaning that it is not suitable for direct use in applications, so its need to analyze or pre-process it and then use it. For example: to build a data set that has been used in a model that classifies images as to whether they contain a house or not, depending on an image as input for this program. Our first step will be to collect hundreds of house images, but the problem is that these images will not be of the same dimensions, for example, so it’s to Change its dimensions, i.e., processing it in advance before submitting it to the model. The above is just one of the many reasons why image processing is important for any computer vision application