Reviewerly/exHarmony huggingface dataset:
Research on Business Data Protection Strategy Based on SVM Algorithm

For modern enterprises, the development trend of information electronization means that data is an asset, and it is also the embodiment of the core competitiveness of enterprises. This article combines the advantages of support vector machine (SVM) algorithm in big data analysis and prediction, normalizes the data of intrusion detection system, log files, firewalls, network devices and so on through data fusion, and then makes further situation assessment and prediction based on these unified data, and compares different algorithms in network safety situation awareness assessment and prediction effect and data training time. The simulation results show that after many iterations, the error of network safety situation awareness assessment and prediction algorithm gradually decreases and tends to be stable. The results show that this method improves the accuracy of network safety risk estimation, has important reference value for defending network safety risks, and can provide technical support for enterprise business data protection.
Geochemical and Economic Assessment of Iron-Rich Bodies in Choman-Rayat Area, Kurdistan Region, Iraq

This paper reports the finding of potential economic iron mineralization in the ophiolite rocks system in the Choman-Rayat Area, Kurdistan, Iraq. XRD, SEM-EDS, ICP-MS, ICP-OES, and detailed optical microscopy studies indicated that these iron-rich bodies are sedimentary and formed in a hydrothermal alteration system of the abundant serpentinized peridotite in the area with possible microbial interaction. Hematite (up to 55%) is the major mineral in iron-rich bodies, along with quartz, chlorite, and some minor spinel content. In addition to Fe, other potential economic metals associated with iron-rich bodies are nickel (Ni) and cobalt (Co) mineralization. The Ni and Co concentrations ranged from 2094 to 989 ppm and 189.1 to 678.5 ppm respectively. This study adds significant data to the scarcely known or geochemically assessed mineral resources in the Iraqi Kurdistan
Penerapan Data Mining Untuk Mengukur Kepuasan Mahasiswa Terhadap Pembelajaran dengan Menggunakan Algoritma Naïve Bayes

Education is very important for life, with education can help in improving Human Resources (HR). Human Resources (HR) in universities are students. Increased competence in students carried out in higher education is carried out by learning. The process carried out in learning is very influential on the results obtained from learning both competencies and abilities possessed by students. Based on this, it is necessary to improve learning in higher education in order to support good results. Measuring the level of student satisfaction with learning can measure the extent to which the learning process has been carried out. The process of measuring the level of student satisfaction with learning is done first by collecting data. After collecting data, the data processing process is carried out to get the expected results. Errors in the data processing process, the results obtained are also not in accordance with the objectives carried out. Therefore, to solve the problem it is necessary to do it with the right process by using a separate method or technique where the method is data mining. Data mining is a method or technique used for data processing. The data processing process carried out in data mining is carried out on large data. The Naïve Bayes algorithm is an algorithm that is included in the classification of data mining techniques. Where the process in the nave Bayes algorithm is very dependent on the grouping process carried out on each attribute and also the target class of each object. The results of the study show that the probability value of PUAS is 0.034108116 and the probability value of NOT SATISFIED is 0. This indicates that the result of decision making is SATISFIED.
Review of electrooculography-based human-computer interaction: recent technologies, challenges and future trends

Electrooculography-based Human-Computer Interaction (EOG-HCI) is an emerging field. Research in this domain aims to capture eye movement patterns by measuring the corneal-retinal potential difference. This enables translating eye movements into commands, facilitating human-computer interaction through eye movements. This paper reviews articles published from 2002 to 2022 in the EOG-HCI domain, aiming to provide a comprehensive analysis of the current developments and challenges in this field. It includes a detailed and systematic analysis of EOG signal electrode arrangement, hardware design for EOG signal acquisition, commonly used features, and algorithms. Representative studies in each section are presented to help readers quickly grasp the common technologies in this field. Furthermore, the paper emphasizes the analysis of interaction design within the EOG-HCI domain, categorizing different interaction task types and modalities to provide insights into prevalent interaction research. The focus of current research in the field is revealed by examining commonly used evaluation metrics. Lastly, a user-centered EOG-HCI research model is proposed to visually present the current research status in the EOG-HCI field from the perspective of users. Additionally, we highlight the challenges and opportunities in this field.
Information, Digital, and Socio-Psychological Technologies in the Training of Specialists in the Social Sphere

The article is devoted to the application of various educational technologies in the process of training specialists in the social sphere. It is shown that one of the important aspects of professional training is the formation of a generalized orientation in the future social work specialist in terms of goals, subject, means, composition of future professional activity, and this, in turn, represents a problem, since complex types of professional activity are not amenable to rigorous analysis, always leave a person space for creative search. As a guideline on the path of addressing this problem, the importance of didactic design of the training of specialists in the social sphere is emphasized, based on the combination of socio-psychological and information-digital technologies in the educational process, including with the aim of motivating students to adopt the paradigm of life-long learning.
Blockchain Technology in Malaysian Context: Bibliometric Analysis and Systematic Review

Blockchain technology has attracted widespread attention due to its compelling features, such as decentralization, transparency, and smart contracts, which can address significant issues in various industries in developing countries such as Malaysia. However, although several studies have arisen from diverse academic backgrounds addressing blockchain in Malaysia, no studies provide a comprehensive review and classification of the research in this field. The main goal of this research is to conduct a bibliometric analysis and systematic review of all blockchain papers published in Malaysia to understand the evolution of knowledge and present state and identify prospective future research fields. Web of Science and Scopus databases searched for existing literature on blockchain in Malaysia, and 76 papers were reviewed and categorized based on study purpose/focus, domain/sectors, the methodology employed, theories applied, and level of analysis. The findings show that blockchain is under-explored in Malaysia, and most current studies focus on Blockchain adoption in specific industries such as finance and supply chain management. However, in other areas, such as healthcare and education, Blockchain conceptual progress is still in its infancy. These findings are being utilized to suggest future research paths in this discipline, such as the need for methodological improvements and a theoretical basis to study blockchain in different sectors.
Gambaran Motivasi Belajar Siswa dari Keluarga yang Broken Home

Every teenager wants to live in a complete family. However, not all teenagers are lucky to have it, such as family divorce. broken home Families can affect teenagers' learning at school. This research aims to describe the learning motivation of students from broken home families. This research uses quantitative methods with a descriptive approach. The sample for this research was 56 students using a purposive sampling technique using the criteria of young victims of broken homes and looking at the students' behavior and achievements at school. Data were collected using a learning motivation questionnaire consisting of aspects of learning preparation, aspects of following the teaching and learning process, and aspects of following up on lessons with a Likert model scale. The number of validity items is 30 items and the reliability is 0.884. To find out the description of the research variables, use the percentage formula. The results of this research found that 43.10% of students from broken home families had low learning motivation. Therefore, guidance and counseling services are needed, such as information services, content mastery services, individual counseling services and group guidance services to increase student learning motivation.
Exploring Public Sentiment: A Sentiment Analysis of GST Discourse on Twitter using Supervised Machine Learning Classifiers

A key economic move that resulted in heated disputes was India's introduction of the Goods and Services Tax (GST). Social media channels offered a widely used forum for the people to express their views on the GST, providing insightful data for gauging mood and guiding next revisions. The emotion of 5629 GST-related tweets was assessed using the VADER lexicon after being obtained using the Twitter Developer API. The tf-idf feature was used for text vectorization, with 80% of the data going toward training and the remaining 20% going toward testing. In this study, six well-known classifiers—the Ridge Classifier, Logistic Regression, Linear SVC, Perceptron, Decision Tree, and K-Nearest Neighbor—were thoroughly compared to evaluate their performance in a range of circumstances. Accuracy, precision, recall, f-score, training, and testing times were all included in the performance measurements. The study presented novel pre-processing methods and examined the training/testing times before coming to the conclusion that the Ridge Classifier performed better than the others in terms of accuracy, precision, and efficiency. In this study, six well-known classifiers—the Ridge Classifier, Logistic Regression, Linear SVC, Perceptron, Decision Tree, and K-Nearest Neighbor—were thoroughly compared to evaluate their performance in a range of circumstances. Accuracy, precision, recall, f-score, training, and testing times were all included in the performance measurements. The study presented novel pre-processing methods and examined the training/testing times before coming to the conclusion that the Ridge Classifier performed better than the others in terms of accuracy, precision, and efficiency.
Aplikasi Game Edukasi Tajwid Berbasis Android

An Android-based Tajweed Educational Game Application for Madrasah Diniyah takmiliyah, Pagar Alam City, still uses the manual method, namely the conventional method in the form of books and blackboards. This makes it sometimes difficult for children to remember and understand the material presented by the students. By doing an analysis of the existing problems, namely the absence of interesting and interactive learning media at Madrasah Diniyah takmiliyah Insan Madani, an Android-based Tajweed Educational Game Application was created. The application was built using Construct 2, and using the Development Life Cycle (GDLC) method. This application is also equipped with audio, visual and pictures of numbers, letters, with the concept of learning while playing which aims to help children more easily remember and understand Tajwid and make it easier for students to learn. students to provide the material to be delivered&#x0D; Keywords: Tajwid educational game application, Android, Construct 2, GDLC
Sentiment Analysis of Roman Text: Challenges, Opportunities, and Future Directions

The rise of social media platforms has sparked significant interest among Natural Language Processing (NLP) researchers in Sentiment Analysis over the past few decades. Numerous studies have focused on analyzing the sentiment of different languages, including Urdu, which is widely spoken in South Asia and serves as the national language of Pakistan. While considerable research has been conducted on sentiment analysis using Machine Learning (ML), Deep Learning (DL), and other methods, Roman Urdu sentiment analysis holds equal importance. This paper presents a comprehensive analysis of existing literature on sentiment analysis of various languages, with a specific focus on Roman text and approaches such as ML, DL, and others. Additionally, the paper proposes new directions for future researchers in this field.
A Probabilistic Adaptive Cerebral Cortex Segmentation Algorithm for Magnetic Resonance Human Head Scan Images

The total efficiency of Magnetic Resonance Imaging (MRI) results in the need for human involvement in order to appropriately detect information contained in the image. Currently, there has been a surge in interest in automated algorithms that can more precisely divide medical image structures into substructures than prior attempts. Instant segregation of cerebral cortex width from MRI scanned images is difficult due to noise, Intensity Non-Uniformity (INU), Partial Volume Effects (PVE), MRI's low resolution, and the very complicated architecture of the cortical folds. In this paper, a Probabilistic Adaptive Cerebral Cortex Segmentation (PACCS) approach is proposed for segmenting brain areas of T1 weighted MRI of human head images. Skull Stripping (SS), Brain Hemisphere Segmentation (BHS) and CCS are the three primary processes in the suggested technique. In step 1, Non-Brain Cells (NBC) is eliminated by a Contour-Based Two-Stage Brain Extraction Method (CTS-BEM). Step 2 details a basic BHS technique for Curve Fitting (CF) detection in MRI human head images. The left and right hemispheres are divided using the discovered Mid-Sagittal Plane (MSP). At last, to enhance a probabilistic CCS structure with adjustments such as prior facts change to remove segmentation bias; the creation of express direct extent training; and a segmentation version based on a regionally various Gaussian Mixture Model- Hidden Markov Random Field – Expectation Maximization (GMM-HMRF-EM). The underlying partial extent categorization and its interplay with found image intensities are represented as a spatially correlated HMRF within the GMM-HMRF-EM method. The proposed GMM-HMRF method estimates HMRF parameters using the EM technique. Finally, the outcomes of segmentation are evaluated in terms of precision, recall, specificity, Jaccard Similarity (JS), and Dice Similarity (DS). The proposed method works better and more consistently than the present locally Varying MRF (LV-MRF), according to the experimental findings obtained by using the suggested GMM-HMRF-EM methodology to 18 individuals' brain images.
Wave-Atom and Cycle-Spinning-Based Noise Reduction in Mammography Images

Image denoising is crucial in medical image processing. Digital mammography depends significantly on de-noising for computer-aided-detection of malignant cells like Microcalcifications. In this work, we proposed an unique hybrid approach to reduce Gaussian noise in digital mammograms by combining the wave-atom translation and cycle spinning methods. Pictures denoised by thresholding of coefficients would produce pseudo-Gibbs events because wave atoms are not translationally invariant. Circular motion is applied to keep away the artefacts. Experimental results clearly establish that the method is effective at filtering out background noise while maintaining the integrity of edges and enhancing picture quality. Mini-Mias pictures with variable quantities of Gaussian Noise are used to evaluate and analyse the performance using peak signal-to-noise ratio and structural similarity index. The provided technique outperforms several current filters in terms of evaluated results of peak signal-to-noise ratio and structural similarity index.
Simulating Bumblebot: A Simple Two-Wheeled Robot

This chapter discusses how to build a basic two-wheeled robot named “Bumblebot” in simulation. It consists of the following steps:
SECT: Sentiment-Enriched Continual Training for Image Sentiment Analysis

In recent times, pre-training models of a large scale have achieved notable success in various downstream tasks by relying on contrastive image-text pairs to learn high-quality visual general representations from natural language supervision. However, these models typically disregard sentiment knowledge during the pre-training phase, subsequently hindering their capacity for optimal image sentiment analysis. To address these challenges, we propose a sentiment-enriched continual training framework (SECT), which continually trains CLIP and introduces multi-level sentiment knowledge in the further pre-training process through the use of sentiment-based natural language supervision. Moreover, we construct a large-scale weakly annotated sentiment image-text dataset to ensure that the model is trained robustly. In addition, SECT conducts three training objectives that effectively integrate multi-level sentiment knowledge into the model training process. Our experiments on various datasets, namely EmotionROI, FI, and Twitter I, demonstrate that our SECT method provides a pre-training model that outperforms previous models and CLIP on most of the downstream datasets. Our codes will be publicly available for research purposes.
Comparative Performance of Data Mining Techniques for Cyberbullying Detection of Arabic Social Media Text

Cyberbullying has spread like a virus on social media platforms and is getting out of control. According to psychological studies on the subject, the victims are increasingly suffering, sometimes to the point of committing suicide among the victims. The issue of cyberbullying on social media is spreading around the world. Social media use is growing, and it can have useful and negative implications when you take into account how social media platforms are abused through different forms of cyberbullying. Although there is a lot of cyberbullying detection in English, there are few studies in the Arabic language. Data Mining techniques are often used to solve and detect this problem. In this study, different data mining algorithms were used to detect cyberbullying in Arabic texts.. Our study was conducted The Bullying datasets consisted of 26,000 comments written in Arabic and were collected from kaggle.com, the Cyber_2021 dataset consisted of 13,247 comments collected via github.com, and the Data 2022 dataset consisted of 47,224 comments collected via Instagram. Various extraction features CountVectorizer and Tf-Idf were used Accuracy, precision, recall, and the F1 score were used to evaluate classifier performance. In the study, Bagging Classifier achieve high results of Bullying dataset from Kaggle Accuracy 96.04, F1-Score 95.98, Recall 96.04, Precision 95.95, SVC model gave the highest results of Cyber_2021 dataset from Github an Accuracy 98.49, F1-Score 98.49, Recall 98.49, Precision 98.50, while Data 2022 dataset from (Instagram) achieving an Accuracy of 77.51, F1-Score 76.60, Recall 77.51, and Precision 77.24. Were achieved for Tf-Idf Vectorizer. Tf-Idf Vectorizer the best to all results than count Vectorizer .
Energy-Efficient Robotic Arm Control Based on Differentiable Spiking Neural Networks

Robotic arm control using deep neural networks (DNNs) has gained attention due to its high precision and flexibility, but requires significant computational resources and results in massive energy consumption. In contrast, spiking neural networks (SNNs) are energy-efficient, but their discretized activation function and gradient mismatch pose challenges. To address these issues, this paper proposes a hardware-independent control scheme for robotic arms based on differentiable SNNs (DSNNs). Our approach utilizes binary spike sequences generated by the DSNN to significantly reduce the cost of inference. The output of spiking neurons in DSNNs is refactored, enabling direct backpropagation training while gradually approaching a pulse function. In addition, a hand gesture dataset is created as the controlling input. Simulations and experiments on a virtual robotic arm platform demonstrate that our approach achieves high efficiency and accuracy in controlling the robotic arm with gestures, while consuming less energy compared to other methods.
Enhancing Adversarial Transferability from the Perspective of Input Loss Landscape

The transferability of adversarial examples enables the black-box attacks and poses a threat to the application of deep neural networks in real-world, which has attracted great attention in recent years. Regarding the adversarial example generation as the dual optimization process of model training, existing works mainly focus on better optimization algorithm and model augmentation to improve the transferability of adversarial examples. Despite the impressive performance, the explanation on the transferability improvement is still underexplored. In this paper, recalling that weight loss landscape is a widely used indicator to characterize the generalization ability of neural networks, we investigate the effect of input loss landscape on adversarial transferability. Through abundant analysis, we find a clear correlation between the flatness of input loss landscape and adversarial transferability: existing adversarial transferability improvements all implicitly flatten the input loss landscape and the better transferability one method achieves, the flatter input loss landscape it has. Motivated by this, we propose a simple yet effective Adversarial Pixel Perturbation (APP) method to explicitly flatten the input loss landscape during the adversarial example generation process. Extensive experiments demonstrate the effectiveness of the proposed method in improving the adversarial transferability. By incorporating the proposed APP into existing attack methods, we achieve a record of $$97.0\%$$ attack success rate on average against six defense models, outperforming the state-of-the-art attack method by a clear margin of $$4.0\%$$ .
Medical Misinformation in Sexual Violence

Timely and appropriate engagement with evidence-informed healthcare is essential for survivors of sexual violence. Promotion of access can lead to improved outcomes across the healthcare spectrum, including mental health and chronic conditions. There are many individual, socio-cultural, and medical misinformation factors that limit this needed access. Identification and labeling of medical misinformation as a distinct entity should be considered as a possible strategy to mitigate these concerns.
Cognition and Metacognition Development Through the Use of Educational Games: Expected Behaviors and Developed Behaviors

The article analyzed the use of educational games in the development of children's cognition and metacognition. Through the analysis of expected and developed behaviors, the research assessed the effects of educational games on the cognitive and metacognitive development of school-age children. The results indicated that educational games can contribute to the development of cognition and metacognition in children, promoting increased attention, memory, concentration, and logical reasoning. Additionally, educational games can also foster metacognitive skills, such as the ability to monitor and regulate one's own thinking and learning. Expected behaviors were identified based on theoretical studies on cognition and metacognition, while developed behaviors were observed through children's interactions with educational games. It was concluded that the use of educational games can be a valuable tool for the cognitive and metacognitive development of children, as well as a fun and engaging form of learning.