Reviewerly/exHarmony huggingface dataset:
Outlier Robust Disease Classification via Stochastic Confidence Network

Accurate and timely diagnosis and classification of diseases using medical imaging data are essential for effective treatment planning and prognosis. Yet, the presence of outliers, which are rare and distinctive data samples, can result in substantial deviations from the typical distribution of a dataset, particularly due to atypical or uncommon medical conditions. Consequently, outliers can significantly impact the accuracy of deep learning (DL) models used in medical imaging-based diagnosis. To counter this, in this work, we propose a novel DL model, dubbed the Stochastic Confidence Network (SCN), designed to be robust to outliers. SCN leverages image patches and generates a decoded latent matrix representing high-level categorical features. By performing a stochastic comparison of the decoded latent matrix between outliers and typical samples, SCN eliminates irrelevant patches of outliers and resamples outliers into a typical distribution, thereby ensuring statistically confident predictions. We evaluated the performance of SCN on two databases for diagnosing breast tumors with 780 ultrasound images and Alzheimer’s disease with 2,700 3D PET volumes, with outliers present in both databases. Our experimental results demonstrated the robustness of SCN in classifying outliers, thereby yielding improved diagnostic performance, compared with state-of-the-art models, by a large margin. Our findings suggest that SCN can provide precise and outlier-resistant diagnostic performance in breast cancer and Alzheimer’s disease and is scalable to other medical imaging modalities.
The analysis of use of information and communication technologies in higher education institutions in the function of the students' scientific field

Modern ICTs are increasingly used in educational institutions whose main role is to facilitate and improve the teaching process. In order for the educational system to be successful, it must follow modern trends in the application of these technologies. The degree of application of these technologies in education is influenced by numerous factors, which is why their application differs from country to country, as well as between different educational institutions within a country. For this reason, thirteen higher education institutions that carry out their educational activity in the northern part of AP Kosovo and Metohija were selected for the subject of this research. Considering that the application of these technologies is influenced by numerous factors, the aim of this paper is to analyze the attitude of students from different scientific fields according to the degree of application of ICT in their higher education institutions. The results of the research were obtained by collecting data through student surveys, as well as their statistical processing using the appropriate statistical program.
The use of the ChatGPT language model in creation of malicious programs

ChatGPT is a new chatbot developed by the company OpenAI and is an interface for the language model (Large Language Model). Although its primary function is to mimic a human interlocutor in a conversation, ChatGPT is capable of interpreting unusual responses as well. It can generate computer programs, answer exam questions, write poetry and song lyrics. The analysis of multiple hacking communities shows that there are already cases of cybercriminals using ChatGPT to develop malicious tools. This paper deals with determining the possibility of generating malicious programs using the ChatGPT language model. The authors have shown that it is possible to exploit ChatGPT to generate a script that can be used for bruteforce attacks.
Experiences of cultural heritage digitalization in different institutions: City Library in Sombor, Church Library in Apatin and Sombor Singing Society

The goal of this work is to show the scope, possibilities and limitations of digitalization of cultural heritage in libraries, institutions or associations, which possess library-information material of homeland and significant interest for citizens and the history of the place where they are located. The research was carried out in the Sombor City Library "Karlo Bijelicki", the ChurchNative Library of the Danubian Germans in Apatin and the Sombor Singing Society. The perceived problem is how to preserve old and rare books, periodicals, records or documents in smaller communities through continuous digitization and what kind of support is necessary for that. The result of the conducted research refers to different institutions in a similar problem connected by a variety of native materials and the same goal, which is to reach as many users and funds as possible in order to start the digitization process, preserve the rich heritage and give it to the reading public in digital form. The conclusion is that it is necessary to: continuously provide funding for digitalization in all cultural institutions that have cultural and native structures, allow better compensation for employees and enable the visibility of those associations or organizations that can apply equally for digitization projects regardless of how they are administratively registered in the system. The described problems are the result of the analysis of transcripts of unstructured interviews with respondents from the mentioned institutions.
Advanced Attention for Causality Classification of Verb Nodes of Knowledge Graph

In this paper, the causal relationship between verb nodes in knowledge graph is determined according to the verb nodes and the sentences in which they are located. For the operation of paying attention to the whole sentence, the verb is not studied specifically, which is disadvantageous to the judgment of causality. Therefore, this paper proposes an advanced attention method based on attention mechanism to judge the causality of knowledge graph. It can not only focus on the extraction of verb related information, but also learn the whole sentence features. The experiment verifies the method, and the results show that the method in this paper has advantages.
Multiplicative Gaussian Noise Removal using Partial Differential Equations and Activation Functions: A Robust and Stable Approach

Multiplicative noise poses challenges in image and signal processing due to its nonlinear and signal-dependent nature. Removing it while preserving information requires specialized techniques. We propose a novel approach for denoising images corrupted by multiplicative Gaussian noise using partial differential equations and activation functions. With a focus on learning the kernels, we address the crucial problem of denoising images with multiplicative noise, an area that has received relatively limited attention compared to additive noise. Our methodology leverages explicit schemes with a gray-level indicator matrix and explores various activation functions to improve denoising performance. Through extensive experimentation and evaluation, we achieved higher Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Indicator Measure (SSIM) values, thereby advancing the state-of-the-art in multiplicative noise removal.
Enhancing Banking Security through Intelligent Models for Advanced Fraud Prevention

Over the years, financial organizations have strived to enhance their fraud detection methods. However, as financial systems have become increasingly complex and the rules governing them have grown exponentially intricate, constructing and maintaining such systems has become a daunting challenge. With the rapid growth of technology, the internet, and online services, fraud against financial institutions has persisted. To address this, financial organizations have transitioned from rule-based monitoring systems to embrace modern machine learning algorithms. These machine learning-based systems can efficiently process vast volumes of data, connecting seemingly unrelated data points to detect suspicious patterns. This paper details the development of a deep learning model for classifying financial transactions, implementing five classifiers: Decision Tree, Logistic Regression, KNN, AdaBoost, and Random Forest algorithms. The incorporation of dropout regularization enhances classification performance. The results demonstrate promising accuracy and quick training times, with the Random Forest model exhibiting superior performance.
Improved Grey Wolf Optimization Algorithm Based on Local Derivative Inertia Weights

Under the background of the development of big data and Internet economy, the effective prediction of Consumer Confidence Index (CCI) can ensure the formulation of relevant policies. In this study, to solve the problem that the Grey wolf optimizer (GWO) algorithm has low optimization accuracy and slow convergence speed, the Improved Grey Wolf Optimization Algorithm Based on Local Derivative Inertia Weights (LGWO) is proposed. LGWO is a nonlinear inertia weight of derivative local formula based on information entropy and an improved Grey wolf optimization algorithm that updates the position of population self-adaptively, which improves the optimization efficiency of GWO. By comparing the optimization performance of LGWO with other classical swarm intelligence algorithms through 7 typical test functions, it is confirmed that the convergence speed and solution accuracy of LGWO proposed in this paper are greatly improved compared with other classical algorithms. Subsequently, this paper establishes LGWO-BP model to predict CCI, and the simulation analysis results show that LGWO-BP model has higher prediction accuracy than ordinary BP model and GWO-BP model.
An Improved Teaching-Learning-Based Optimization With Adaptive Moment Estimation for Global Optimization

The Teaching and Learning Optimization Algorithm (TLBO) simulates the two main stages of traditional classroom teaching. It is divided into the "teaching stage" and the "learning stage". However, the traditional teaching process is a relatively complex process, and the simulation process of this algorithm is relatively simple. Therefore, this study introduces two new educational concepts and educational methods, "group teaching" and "adaptive learning", into the simulation process of traditional classroom teaching, and proposed an improved teaching-based optimization algorithm (TLBO-Adam) based on adaptive moment estimation. This study introduces group teaching in the "teaching stage" and uses Fuzzy C-means clustering (FCM) algorithm in the field of machine learning, and introduces Adaptive learning in the "learning stage" and uses an adaptive moment estimation algorithm with the characteristics of a goal-oriented mechanism to solve the problem of premature convergence and slow convergence of the TLBO algorithm. Finally, through designing comparative experiments, experimental results show that the improved algorithm TLBO-Adam is effective in 9 test functions and can find the global optimal solution quickly and efficiently compared with other swarm intelligence algorithms.
Research on Handwritten Test Paper Score Recognition Algorithm based on LSTM model

In order to solve the problems that the excessive parameters of the network model in the existing handwritten character recognition methods increase the consumption of computing and storage resources, the target recognition rate is difficult to meet the real-time requirements, and it is easy to cause parameter explosion, gradient loss and over-fitting. Based on the traditional digital image processing module, a long-term and short-term memory network (LSTM) model is embedded to identify the handwritten score template of a specific test paper. This method performs normalization, noise reduction, tilt correction, and binarization preprocessing on the handwritten digital test paper image. The Hough line detection is used to cut the horizontal line, and the LSD algorithm erases the vertical line to obtain the target image. Finally, a new digital recognition algorithm is constructed based on the LSTM model. Through experimental testing and verification, the results show that the method effectively improves the rate of recognizing handwritten digit scores in pictures, reduces the number of model parameters and resource consumption, accelerates the convergence speed of the model, and meets the actual use requirements of identifying specific test paper picture templates under certain conditions.
Preprocessing-based Adversarial Defense for Object Detection via Feature Filtration

Deep Neural Network (DNN)-based object detection achieved great success in a variety of scenarios. However, adversarial examples can cause catastrophic mistakes in DNNs. Despite the adversarial examples with human-imperceptible perturbations can completely change the predictions of the networks in the decision space, few defenses for object detection are known to date. In this paper, we proposed an end-to-end input transformation model to defend adversarial examples, which is motivated by research on feature representations under adversarial attacks. The proposed model consists of an Autoencoder (contains an encoder and a decoder) and a critic network only used in training. Both benign and adversarial examples are used as training sets for the proposed model. The critic network can force the encoder to eliminate the distribution divergence between benign and adversarial examples in the latent space, to filter out the non-robust features and adversarial perturbations. Finally, the decoder is used to reconstruct the input examples from preserved feature vectors into a clean version, which is then fed to the trained detector. Extensive experiments on the challenging PASCAL VOC dataset demonstrate that the proposed method can significantly improve the robustness of various detectors against unseen adversarial attacks, and it has better performance and lower time cost than previous works.
Person Attributes Recognition Based on Hierarchical Classification

The parallel classification method is usually used to identify pedestrian attributes in the current monitoring field, but ignoring the inherent hierarchical structure information. Resulting in an uneven property identification performance. Therefore, we proposed an end-to-end structured learning algorithm for person attributes recognition. The algorithm uses deep convolution neural network for pedestrian location detection, and uses hierarchical classification to integrate pedestrian attribute information into deep convolution neural network. Relying on the person attributes tree type structure, we designed a multi-task and multi branch network based on shared layer. A training algorithm based on associated multi-task classifier is used to train all branches networks cooperatively, At the same time, we added structured constraint items effectively suppresses the error diffusion among different layers. In Market-1501 and Duke datasets, the algorithm has 1.98%∼2.7% improvement compared with the existing algorithm.
KONX: A Dynamic Approach for Explainable AI in Higher Education

In the era of data-driven decision-making, Higher Education Institutions (HEIs) can greatly benefit from the potential of explainable Artificial Intelligence (XAI) to provide transparent and interpretable insights. This paper presents the KONX (CONNECTS) approach, a methodology that leverages semantic web technologies to create a dynamic and comprehensive knowledge graph for advanced predictive models in academic advising. The KONX methodology focuses on harmonizing heterogeneous educational data sources, enabling seamless data querying and manipulation. By incorporating a feedback mechanism, the KONX approach remains adaptable to changes in the academic domain, continuously updating and maintaining its knowledge representation. To practically apply and evaluate the proposed methodology, a prototype was implemented and tested on an experimental case study concerning student outcomes prediction. The implemented prototype includes a graphical SPARQL generator interface to streamline the construction of SPARQL queries in an integrated way. In this way, this paper proposes both a comprehensive XAI methodology and a holistic technological infrastructure for applying the methodology in real-time scenarios. By bridging the gap between AI decision-making and human-comprehensible explanations, the KONX approach enhances transparency and user trust in AI-driven systems in the education sector.
Effect of Tunnel Form Building Under 10 Past Earthquake Records Analyzed Using Ruaumoko 2D

Countries that experience regular earthquakes with magnitudes greater than 7 on the Richter Scale are currently recognized to suffer significant destruction. These unpredicted calamities can have detrimental effects on both people and structures. However, by identifying the threat that might be damaging the current structures, the effect of seismic can be lessened. As a result, the effectiveness of tunnel form building (TFB) has been evaluated under seismic events of varying intensities. To establish the safety level of a tunnel form building, ten numbers from previous ground vibrations are chosen. Six previous earthquake records were chosen from East and West Malaysia, while four previous earthquake records were chosen from nations outside of Malaysia. The maximum lateral displacement and lateral loading capacity of the prototype building have been modeled and studied to determine the structural deformation of the TFB at peak ground acceleration for a few historical earthquake histories. It may be concluded that while this sort of structure survives local earthquakes, mass earthquake that originates in other countries than Malaysia causes severe damage or even cause the building to collapse.
Quality Education for Sustainable Development: A Comparative Study of Public and Private Schools of Pokhara

This paper aims to explore the performance of two distinctly established education institutions, private and public schools and compare the quality of educational services provided by them in the perception of parents. The study has been carried out using the qualitative research design. With the use of relevant secondary data sourced from relevant literature reviews and official reports, the informants like parents, teachers and school administrators from the purposively sampled schools of Pokhara Metropolitan City Ward no. 13 were taken as the sources of primary data. The analysis of the data followed a dual approach. Initially, responses from the participants were systematically organized and quantitatively summarized. Subsequently, thematic analysis was employed to identify recurring themes and patterns within the qualitative data. This comprehensive analysis aimed to derive meaningful insights and an understanding of the research questions. The study has tried to investigate the reasons behind school’s selection by the parents and to assess whether the deviation in enrollment in private and public schools is due to differences in quality between them. Although, Nepal is among the leading countries in South Asia in term of accessibility to education, there are numerous challenges when it comes to quality. It concludes that private and public schools should not be seen as competitors of each other, rather they should be regarded as the supplements as both of provide equal contribution in education sector of the country.
IMPLEMENTASI ALGORITMA LEVENBERG–MARQUARDT DALAM PREDIKSI DINI PENYAKIT KARDIOVASKULAR

Heart disease or also known as cardiovascular disease are all diseases that occur due to impaired heart function. Heart disease is the result of plaque buildup in the coronary arteries, which blocks blood flow to the heart and increases the risk of heart attack and other complications. Cardiovascular disease due to atherosclerosis is a challenge in itself because this type of disease is a progressive disease that can be modified by various actions and only causes few symptoms until the end of the course of this disease but when this disease is clinically proven, there is only a short duration between the time of onset of symptoms and pain, disability, even death. Risk factors for cardiovascular disease begin at a young age, even when a person has no symptoms and is not aware of the consequences of these risk factors. This Early Cardiovascular Disease Prediction application implements the Levenberg Marquardt algorithm in a cardiovascular disease grouping system so that the search process requires a relatively fast time. This Cardiovascular Disease Early Prediction application can be accessed easily using a smartphone so that users can access it quickly and efficiently.
Effectiveness analysis of virtual practicum in basic physics course using PhET simulation

In order for the practicum to still be carried out, alternative practicum learning media are needed. An interactive application based on research of physics phenomena is PhET (Physics Education Technology) simulation. This study aims to determine the level of effectiveness of virtual basic physics practicum and the level of student understanding of basic physics courses through virtual practicum with PhET simulation. Interactive learning and allowing students to practice independently, limited practicum equipment facilities and inadequate laboratory space are the main contributing factors. In order to compare the differences in student learning outcomes between experimental and control courses, this study used a Pre test-Post test Control Group Design with one experimental class or treatment class and one control class to test the difference in student learning outcomes between experimental and control classes using data processing procedures using the True Experiment method. Based on the research, the effectiveness of using PhET simulation application media has an N-gain value of 0.66 and can increase understanding of lecture material by 52%.
Node Classification of Graph Neural Networks Based on Graph Degree-Symmetry

In order to accurately reflect the information of each node's neighbours and the topological relationship of the graph structure, the graph neural network node classification task aims to treat each node and all of its neighbouring nodes as a subgraph and update the feature representation of each node through information transfer. However, a crucial aspect of network symmetry is disregarded in the current research of the graph neural network node classification job, resulting in redundant data in the training dataset. Consequently, this research suggests a strategy for classifying nodes in a graph neural network based on graph degree-symmetry (The same abbreviation later is GDS). The next two steps make up the majority of the method: Using the nauty technique to retrieve the network's track information, calculating the similarity between nodes according to the degree-symmetry, then applying the softmax function for normalization, assigning weights to each neighboring node; Lastly, neighborhood node information is combined, the node information is updated using weighted summation to produce graph data with more useful node characteristics. The revised network is then re-entered into the graph neural network for convolution, which increases the accuracy of the classification task. In this paper, GDS is equipped with SIGN, MixHop, SGC models to improve the performance. Three publicly accessible citation network datasets were used for the experimental analysis of GDS fitted with the three graph neural network models mentioned above. The findings of the analysis confirmed the efficacy of GDS, showing that GDS can more effectively utilize the semantic information of graphs to synthesize more reasonable node feature representations to improve node classification.