Reviewerly/exHarmony huggingface dataset:
Efficient CNN Hardware Architecture Based on Linear Approximation and Computation Reuse Technique

Large deep neural network (DNN) models pose significant computational and memory challenges, particularly when deploying them on edge devices. To address this, techniques such as pruning, quantization, data sparsity, and data reuse have been applied to DNNs, mitigating memory and computational complexity at the cost of some accuracy loss. This paper introduces an efficient hardware accelerator tailored for Convolutional Neural Networks (CNNs). The proposed architecture is the result of a co-optimized approach encompassing both algorithms and hardware. It leverages linear approximation of pre-trained network weights with minimal accuracy loss. A novel computational reuse method is presented to curtail the number of multiplication and addition operations and memory accesses, seamlessly integrated into the dedicated elements within the CNN design. To validate the effectiveness of this architecture, we conducted experiments on a gem5-based RISCV simulator, employing the VGG16 model for the CIFAR 100 dataset and the AlexNet model for the TinyImageNet dataset. The results showcased an impressive speedup of approximately $2\times$ on AlexNet compared to the reference model. Additionally, our proposed CNN design was successfully implemented on the Xilinx Kintex 7 Field Programmable Gate Array (FPGA), achieving a notable reduction in hardware resource utilization compared to prior research efforts. This work serves as a versatile framework for evaluating diverse trade-offs involving accuracy, latency, power consumption, and cost across different CNN architectures.
Virtual reality in welding training and education: A literature review

The welding workforce is facing a deficit of skilled welders, highlighting the need to develop more efficient and effective welding training methods. Virtual reality (VR) technologies have been adopted to create VR welding simulations for educational and training purposes. The purpose of this literature review was to collect and analyze peer-reviewed research, published between 2012-2022, regarding VR technology applications in welding training. The concept of Engagement in Blended Learning Environments served as our research framework. In the context of welding, VR welding training is a relatively new approach that functions as a blended learning environment. During data collection, we identified 31 articles; however, following analysis and coding, 13 were excluded, for a final N=18. Through a process of triangulation, we identified themes across existing research to highlight trends, recommendations for real-world welding practice, and recommendations for future research. Four main research themes emerged from the literature: 1. Comparison of Approaches, 2. VR as a Teaching Tool, 3. System Development, and 4. System Testing. Six future research recommendations were identified and grouped, including the recommendation to compare virtual weld performances to live weld performances. Four practitioner-based recommendation themes were identified, including the recommendation for instructors to develop their own knowledge and skills related to VR welding technology. From this review, we identified gaps in the existing literature and made recommendations for future research on VR welding training.
Generalized Segmentation Algorithm for Dissimilar Script Languages

Optical Character Recognition is considered one of the fastest methods of data entry. OCR converts the text image representation of x and y coordinates representing pixel information to be converted into text data in a particular language. OCR as a field of pattern recognition and document image understanding, OCR requires a challenging job once a different language text is available on the image. Difference in language script will pose different challenges for OCR which requires entirely different approaches and algorithms. Latin scripts require a different approach whereas the Arabic adopted language scripts require a different approach. In this regard, various solutions have been proposed for different languages. Segmentation is considered one of the important tasks in the process of OCR. A good segmentation will definitely increase the accuracy of an OCR. Segmentation includes the segmentation of text lines from text images which are further divided into words. These segmented words are further divided into characters which are to be recognized. A single segmentation algorithm to segment various scripts of the languages is proposed in this study which checks the script and then segments the text image for the further processing in OCR. The proposed generalized algorithm will check the style, direction and other properties of the script and then adopts the segmentation process to segment text lines, words and characters of the language. The proposed algorithm segments more than ten languages of three scripts and segments for their OCRs. These images can be further processed for feature extraction and classification further. The process of OCR for selected languages will be made easier to recognize. Multiple scripts, languages and images were experimented, and the proposed algorithm successfully segmented 32,833 images of text line, words and character image. The algorithm provides 97% accuracy while segmenting these images and can be extended to further languages as well as scripts.
Classification models comparison from the user’s level of interpretability

The effective deployment of classification models generated with the help of relevant machine learning algorithms also affects the ability of target users to understand the given models in the context of the solved task, especially in the domain of healthcare provides or medical diagnostics. Various interpretation methods and interpretability assessment metrics have been proposed to provide insight into the decision-making processes of these models. Therefore, we decided to propose an approach that, based on the user’s preferences entered at the input, will evaluate individual models and sort them according to the preferences of traditional or interpretability metrics. Our approach uses the TOPSIS method for evaluation and the Saaty’s matric to represent the user’s preferences. The first testing with public available Breast Cancer Wisconsin dataset brough plausible results for the future work.
Solving two-machine flow shop scheduling problem with total weighted number of on-time tasks maximization criterion on D-Wave quantum computer

The work deals with the NP-hard problem of scheduling tasks on two machines with the sum-cost criterion. In this problem, each task must be performed sequentially on the first and then on the second machine. Each task is associated with: execution time, due date, and weight (revenue) related to completing the task on-time (i.e. before the due date). The goal is to determine the order of task execution, the same on both machines, that maximizes the total weighted number of on-time tasks (maximum profit). We propose a hybrid algorithm combining quantum and classical methods based on the branch and bound method. We propose to formulate the problem under consideration as a Constrained Quadratic Model (CQM), due to which we can compute upper bounds (using Lagrange relaxation) and lower bounds on the D-Wave quantum machine. Although the D-Wave quantum computer performs probabilistic calculations, the solution determined by the proposed algorithm is optimal.
Lightweight Blockchain Simulation with Transaction Graph Visualizer

In this paper, a lightweight blockchain simulation and transaction graph visualization application is presented, crafted to augment the identification of pivotal nodes within blockchain networks. Harnessing sophisticated data visualization techniques, this application provides an extensive and easy to use environment for delving into and scrutinizing intricate transaction data within the blockchain. By incorporating principles from graph theory, this tool empowers users to distinguish critical nodes within the blockchain, unveiling the key participants, addresses, or entities involved in diverse transactions. This dual-functionality, featuring both blockchain simulation in C# and visualization in JavaScript, holds substantial potential for enhancing the analysis of blockchain networks. It promises to furnish valuable insights for researchers, cryptocurrency enthusiasts, and cybersecurity professionals, facilitating their endeavors to comprehend and fortify blockchain ecosystems.
Hardware Mitigation and Verification For Rogue In-Flight Data Load Attacks

Rogue In-Flight Data Load (RIDL) is a microarchitecture security attack that exploits store-to-load forwarding in the line fill buffer. Several microarchitecture-level mitigations have been proposed for defense against RIDL. However, errors and/or Trojans in the implementations of these mitigations can be exploited to render the microarchitecture vulnerable to RIDL. We propose a formal verification methodology that can be used to guarantee that line fill buffer implementations are immune to RIDL attacks. We also propose a hardware-mitigation for RIDL, inspired by our verification approach, that allows the use of store-to-load forwarding. We have demonstrated the efficacy of our approach using several memory pipeline benchmarks.
Contextual-Based Learning Videos for Sixth-Grade Elementary School Indonesian Language Content

Students still need to learn independently; this is due to the unavailability of interactive learning media. This research aimed at creating contextual-based learning videos for grade VI elementary school Indonesian language content. This type of research was developed using the ADDIE model. The research subjects included content, design and learning media experts, and individual and small group tests. Data collection methods in this development research used questionnaire methods and test methods. Data analysis methods and techniques were quantitative descriptive analysis techniques and t-test inferential statistics. Based on the evaluation results from content experts, the percentage was 96.66% of design expert, 92.85% of media expert, 92% of individual trial, and 99.33% of Small Group Trials. The effectiveness of developing contextual-based learning videos obtained an average posttest result of 90.54, which was in a very good qualification, more significant than the average pretest result of 46.90, which was in the poor stuff. Based on the t-test results, it was obtained that the tcount was equal to 26.448, and the t table was similar to 2.018 for db equals 42 from a significance level of 5%. The results showed that there were significant results between before and after the use of contextual-based learning videos. The research concluded that learning videos effectively improves student learning outcomes in Indonesian language content for class VI elementary school.
A Study on Development Plans to Expand the Area of Disaster Psychological Support for Firefighters : Focusing on the Cases of the Firefighting Peer Psycholgical Counseling System and the Central Firefighting Psychological Support Group

This study seeks to expand the area of fire safety services to the public by establishing a firefighting colleague psychological support system for firefighters and a disaster psychological support system for the public. we examined the National Fire Agency's mental health support policy and the status of PTSD(posttraumatic stress disorder) management among domestic and foreign firefighters. In addition, as a result of analyzing the effectiveness of the firefighting colleague counseling and the operation cases of the Central Firefighting Psychological Support Group, it was confirmed that it has a positive effect on the mental health management of firefighters. In order for the Central Fire Protection Psychological Support Group to develop into a focal point for not only the mental health of firefighters but also the mental health of disaster victims, it is necessary to establish a legal basis and to reorganize the systematic organization and budget. Additionally, programs to foster internal experts are desperately needed. The results of this study are intended to contribute to boosting firefighting morale and expanding the scope of firefighting activities by discovering disaster psychological support policies for firefighters.
A Study on the Class Linked with the Extracurricular Program for strengthening character competencies - Focused on Reading Classics and Writing -

The purpose of the study was to theoretically explore how to connecting class and extracurricular program to strengthen university students' character competencies. As the theoretical basis of the study, we have focused on the core competencies and ideals of future society, relationship between liberal arts and character education, Possibility of character education through class linked extracurricular program. Next, on the basis of the theoretical foundation, The possibility of strengthening the character competencies of college students was confirmed through the operation of ‘classical reading and writing’ as a extracurricular program linked to liberal arts class. The summary of the study result is as follows. Firstly, it was confirmed that linking extracurricular program in liberal arts classes is a way to effectively cultivate the core competencies of college students. Secondly, we presented the possibility of using ‘classical reading and writing’ as a method to strengthening the character competencies of college students and design of the teaching and learning process. In conclusion, the utilization of has a theoretical basis and can be considered to be applicable in the field application. In conclusion, the utilization of ‘classical reading and writing’ can be used has a theoretical basis development of extracurricular program in connection with class to strengthening the character competencies of college students. As a subject of follow-up research an evaluation study was proposed to measure the effect of character competencies of participants in the ‘classical reading and writing’programs.
Explainable artificial intelligence models for enhancing classification reliability of ground weapon systems

This study focused on the development of a reliable artificial intelligence (AI) model to enhance the classification reliability of ground weapon systems for surveillance and reconnaissance applications. The proposed AI model overcomes the limited data availability of military objects such as tanks, canons, and multiple-launch rockets by leveraging transfer learning and fine-tuning techniques. A comprehensive evaluation of 35 deep learning models using the publicly available Military-Vehicles dataset on Kaggle identified MobileNet as the most suitable model for ground weapon system classification. The selected MobileNet model achieved an average F1 score of 92% when tested on a dataset comprising five types of ground-weapon systems. In addition, the application of the explainable AI technique Grad-CAM provided insights into the decision-making process of the proposed model and verified its reliability. Real-world evaluations using frames extracted from training videos demonstrated promising accuracy for tanks, canons, and multiple-launch rockets. However, challenges related to object occlusion and the absence of target objects in the images were observed, which resulted in misclassifications. Overall, this study contributes to the development of explainable and reliable AI models for enhancing the performance of ground surveillance and reconnaissance systems.
Ridge Extraction Using Adaptive Direction Information and Tracking Techniques

The ridge is a type of edge. However, unlike most types of edges, the ridge acts like a Laplacian-of-Gaussian (LoG) function as a step function. However, since using the LoG is similar to the second-order differentiation, the result is largely affected by noise, and the detail is over-stressed. To solve the problem, a robust ridge extraction algorithm was developed in this study. First, the adaptive direction information was generated and refined by an iterative extension algorithm. Moreover, with accurate information, a tracking method connected the broken ridge line. Experiments showed that, with the proposed algorithm, the ridge was extracted even if the image was highly interfered with by noise. Regardless that the ridge lines were too close or crossed with one another, the ridge was extracted well.
Application of Environmental Noise Monitoring System Integrated with Visual Recognition Technology

Environmental noise is an imperceptible problem in daily life and has a significant impact on human health and quality of life. Thus, noise abnormalities need to be monitored. The cause of the noise is directly related to the environment and human activities. Because of the rapid development of micro-processing machines, image-processing equipment has become easily accessible so visual recognition technology with computer vision and machine learning rapidly develops. Facial recognition is one of the most developed technologies and gradually becomes applicable to various situations such as emotional awareness and safety monitoring. Long-term noise anomaly data and invasion data were collected using cloud edge flatbed integration. The abnormal time data and noise anomaly data were used to build an optimal model of feature detection.
Malicious Website Detection Method Based on BGResNet Multi-feature Fusion

Website security detection is important for Internet security. Existing machine learning-based malicious URL detection methods have a low accuracy and weak generalization ability. Thus, we proposed a new multi-feature fusion malicious website detection model BGResNet by integrating the advantages of Bidirectional Encoder Representations from Transformers (BERT), Graph Convolutional Network (GCN), and Residual neural network (ResNet). The method integrates three features: Uniform Resource Locator (URL) creator, rule features, and website titles. First, we used BGResNet to process URL characters and website titles separately, transforming them into vector representations. Then, these two vectors were fused with URL rule vectors. Finally, we employed the softmax function to realize the detection of malicious websites. The experimental results showed that the proposed model exhibited significant superiority in detecting malicious websites, providing a new and effective method for malicious URL detection in Internet security.
Research on Extraction Model of Parallelism Sentences in Writing Text Based on Co-occurrence Word Template

Computer-independent creation is important in computer science. Most of the technology is based on specific templates to create relatively independent text in writing, without explaining and introducing the specific meaning of the text regarding the main body. At the same time, there is a lack of description of the effect of text modification. To write independently attractive articles, comparison terms used in real-life creation were designed to a certain degree of similarity based on the title to make the article more vivid and interesting. Generally, methods such as word segmentation, part-of-speech setting, punctuation matching, co-occurrence word matching, and neatness checking were used to automatically identify the phenomenon of inter-paragraph and inter-sentence parallelism in standard documents. Experiment results showed that the accuracy of the information extracted from parallel sentences by this method was over 90.3%, which greatly improved the accuracy compared to similar technologies.
Manage Website Template That Using Content Management System Joomla

In this paper we introduce the important concepts how to manage website template that using content management system Joomla. We mean the features, functions and common applications of content management system Joomla which is used to build most popular websites in the world. After that we focus on group of codes and procedures and techniques on how to manage and customize our websites especially on template.
Modularized Quantum Many-body Dynamics Simulator

We propose a quantum dynamics simulator for light-irradiated matter. As the coherent control of the quantum states of matter is an intriguing problem in realizing quantum technology, the quantum properties of light-matter complexes have attracted much attention. Theoretical calculations corresponding to individual experimental configurations are required to understand these properties, and simulation methods that flexibly adapt to various experimental configurations are required. In this paper, we propose a quantum dynamics simulation method that encompasses program modules corresponding to light sources, optical components, material systems, and the evaluation of physical properties. We demonstrate the entanglement generation dynamics between two spin-chains via irradiated photons and show that a flexible and expandable quantum dynamics simulator can be constructed.
The Study of Educational System: A Thematic Study in Select Works of Chetan Bhagat

The prime thing for every people in their life is education. Education can change a person and help each and every part of his life. There are some conflicts and unethical things happening in the education sector. The young generation is facing numerous conflicts and hurdles in the educational system. Some of the conflicts and prime issues in the Indian educational system are shown by Chetan Bhagat through his novels. His writings throw light on how the younger generation gets pressure and expectations from their family and how they overcome the current educational system. This paper shows the thematic evolution of the education system and how the younger generation overcomes the traditional system of learning in the select works Five Point Someone, Half Girlfriend, and Revolution 2020. There are some flaws in the education system such as learning methods, lack of innovation and creativity, deciding the student's ability by grade points, and language problems. In the educational sector, corruption also takes place to obtain permissions and approvals. Bhagat also gives some career advice and solution for the conflicts faced by the younger generation through his works.
Evidence on the role of institutions in economic growth: A panel data study of developing countries

The paper examines the role of various types of institutions on economic growth through capital formation and technological progress. The Solow residual is taken as a proxy of technological progress. The study uses panel data from twenty-one developing countries from the International Development Association. The sample period extends from 1990 to 2013. The institutions are categorized as economic, financial, political, and social institutions. The Solow growth model is the basic reference point of this study. The GMM panel estimation technique is applied due to the problem of endogeneity. The relationship between GDP per labor and institutions is explored through technology and stock of capital per labor. The results of this study show a significantly positive relationship between economic growth and economic, political, social, and financial institutions. Moreover, based on empirical results this study concludes that to achieve economic growth in developing countries, the government should strengthen its institutions and control the corruption, ethnic tension, injustice, terrorism, and intolerance in the society. The governments of developing countries should strengthen the financial and economic institutions to enhance growth via increasing investment in the country.