Reviewerly/exHarmony huggingface dataset:
Developing the Connection of Batik Lasem's E-Commerce Platform and Metaverse

Since the Covid 19 pandemic, Batik Lasem's sales have been taking a plunge since they rely heavily on tourists. Combined with the new highway cutting through Lasem, the sales of Batik Lasem has never been lower. Competing against bigger and more established Batik corporate in online sales is not easy, therefore Batik Lasem needs a new method of showcasing and selling Batik virtually through metaverse. Objective of this research is to develop a direct connection system between metaverse scene and e-commerce website. The connection is achieved using Woocommerce as the e-commerce site with payment gateway plugin and 2D objects inside the metaverse scene as a vessel to attach the product URL. The research found that the connection between metaverse and e-commerce site is feasible but not very optimal on some devices. User experience on VR head mount is not optimal for doing transaction but PC and mobile devices do not give the most immersive ambience for users.
An Efficient Rating System Using Blockchain for Recommender Systems

The last few years have seen an increase in the research of Blockchain (BC)-based recommender systems (RSs). Indeed, to overcome the many shortcomings of traditional RSs, BC has emerged as one of the best candidates to guarantee the new needs of users in terms of privacy protection. As a decentralized system, BC has, nevertheless, several drawbacks, such as scalability and performance. For a recommendation system to provide accurate suggestions, it mainly needs the ratings that users deliver. In real-world scenarios, since the methods required to add users’ ratings or aggregate an item’s ratings are frequently called, a rating system may suffer from a decrease in performance when storage and computation are performed on-chain. The goal of this paper is to introduce a counter-based rating system. The latter uses the BC in the form of smart contracts (SCs). The main distinction between the proposed system and other comparable approaches is the use of counters to store item-related evaluations, as opposed to storing raw ratings. The concepts used are theoretically detailed before being compared in terms of performance to other systems in the literature. Significant resource consumption and computational time improvements are perceived.
Deep Learning Based Framework for Reliable Sri Lankan Currency Authentication and Counterfeit Prevention

Digital image processing plays a crucial role in enabling efficient and precise analysis, manipulation, and enhancement of images. In this study, researchers address challenges faced by individuals with visual impairments in recognizing currency denominations and identifying counterfeit banknotes. The researchers propose "Blind Trust," an IoT device that utilizes an Arduino Uno and a camera module to capture images of banknotes. To achieve these objectives, researchers utilize pre-processing techniques using the OpenCV and TensorFlow libraries to extract the notes/coins' characteristics. Custom datasets are developed for training Convolutional Neural Network (CNN) models, which are then used to identify currency denominations and detect counterfeit currency. To enhance the model's performance, various preprocessing techniques are employed, resulting in high accuracy rates for both tasks. The currency notes identification model achieves an impressive 99% accuracy when tested on 25% of the data, while the currency coins identification model achieves 93% accuracy using InceptionV3. Additionally, the counterfeit currency detection model, created using VGG16, achieves an accuracy rate of 97% on a dataset comprising genuine and counterfeit currency images. Moreover, the note placement detection model attains 93% accuracy. "Blind Trust" holds great potential for enhancing financial security and accessibility for individuals with visual impairments. Its accuracy, speed, and ease of use contribute significantly to the development of new technologies aimed at improving their quality of life.
Multigranularity Pruning Model for Subject Recognition Task under Knowledge Base Question Answering When General Models Fail

In general knowledge base question answering (KBQA) models, subject recognition (SR) is usually a precondition of finding an answer, and it is a common way to employ a general named entity recognition (NER) model such as BERT-CRF to recognize the subject. However, in previous researches, the difference between a NER task and a SR task is usually ignored, and a wrong entity recognized by the NER model will certainly lead to a wrong answer in the KBQA task, which is one bottleneck for KBQA performance. In this paper, a multigranularity pruning model (MGPM) is proposed to answer a question when general models fail to recognize a subject. In MGPM, the set of all possible subjects in the Knowledge Base (KB) is pruned by 4 multigranularity pruning submodels successively based on the constraint of relation (domain and tuple), string similarity, and semantic similarity. Experimental results show that our model is compatible with various KBQA models for both single-relation and complex questions answering. The integrated MGPM model (with the BERT-CRF model) achieves a SR accuracy of 94.4% on the SimpleQuestions dataset, 68.6% on the WebQuestionsSP dataset, and 63.7% on the WebQuestions dataset, which outperforms the original model by a margin of 3.6%, 8.6%, and 5.3%, respectively.
High Performance Computing Applied to Logistic Regression: A CPU and GPU Implementation Comparison

We present a versatile GPU-based parallel version of Logistic Regression (LR), aiming to address the increasing demand for faster algorithms in binary classification due to large data sets. Our implementation is a direct translation of the parallel Gradient Descent Logistic Regression algorithm proposed by X. Zou et al. [12]. Our experiments demonstrate that our GPU-based LR outperforms existing CPU-based implementations in terms of execution time while maintaining comparable f1 score. The significant acceleration of processing large datasets makes our method particularly advantageous for real-time prediction applications like image recognition, spam detection, and fraud detection. Our algorithm is implemented in a ready-to-use Python library available at : https://github.com/NechbaMohammed/SwiftLogisticReg.
Implementation K-Nearest Neighbor Algorithm Skin Diseases Classifier

Skin diseases are skin disorders caused by germs, fungi, parasites, viruses, or due to infections that can attack anyone, skin diseases can attack part or all of the skin of the sufferer which of course can endanger the condition. The health of the sufferer is not treated seriously. Several factors can cause skin disorders, namely living factors, environment, unhealthy living habits, allergies, and climate including one factor that is often encountered, and there are many other factors. This study will analyze and classify data using existing variables using mining fund algorithms and will be processed with machine learning. The method used by researchers is K-Nearest Neighbor. In the classification of K-Nearest Neighbor, there is a good k value for the algorithm depending on the amount of data. A high k value will generally reduce the effect of noise on classification. And the results of the classification are used as a reference for making skin disease detection applications.
Evaluation of Service Quality in Human Resource Information Systems Using the ISO/IEC 25010

Currently, the use of information systems is very important to increase the effectiveness and efficiency of a service process. Surabaya State University has implemented a Human Resource Information System to facilitate service processes and monitoring in the field of human resources online. Given the importance of using the Human Resource Information System, it is necessary to evaluate the quality of the system based on functional suitability and usability, which aims to determine the advantages and disadvantages of the system from user experience so that it can be used as material for evaluating the quality of information systems. ISO/IEC 25010 is an international standard for evaluating information systems, using ISO/IEC 25010 to evaluate the quality of information systems using product quality, where the process refers to the characteristics of an information system product. The purpose of this study was to evaluate the quality of the Human Resource Information System based on ISO/IEC 25010. The research method used is a descriptive quantitative approach. Assessment is carried out on five main menus: Proposals for Further Studies, Proposals for Leave, Promotions, Proposals for Inclusion of Degrees, and Proposals for Retirement. The assessment process is based on a questionnaire designed using the functional and usability sub-characteristics of the aspects of the system being assessed. The results showed that the highest score for each sub-characteristic, namely, the Completeness, Correctness, and Appropriateness sub-characteristics, was obtained on the Leave Proposal menu by 92%, 83%, and 90%. In contrast, the Learnability, Operability, and User Interface Aesthetics sub-characteristics were obtained on the Retirement Proposal menu by 81%, 88%, and 83%.
Two Methods for Analyzing Material Uniformity in the Process of Feeding

The online medication dosing methods in large-scale pig farms are diverse. Ensuring the uniformity of solid material dosing based on specific ratios can be a challenging task. This paper examines two approaches to assess the uniformity of such equipment: weighing and image processing. The traditional weighing method employed for material quantification analysis can lead to significant time spans and is susceptible to erroneous data due to material accumulation and collapse. In contrast, the image analysis strategy based on the Canny algorithm offers a non-contact approach, effectively reducing analysis time and remaining unaffected by material accumulation, thereby providing a novel perspective on equipment dosing uniformity.
Exploring the mechanisms of AI message generation: A chatbot development activity for students

Courses Health Communication, Public Communication Campaigns, Public Relations, Introduction to Communication.
Tune Up Multi Layer Perceptron From Sentiment Analysis with Natural Language Processing

Sentiment analysis is frequently worn for opinion mining of giving data or corpora a name or label (high quality, bad, impartial) into information or corpus. It is possible to say that NLP (Natural Language Processing) is used for preprocess data and clean text so it can be comprehended by machines. Text classification is worn to handle data and corpora that are entered into the multi-layer perceptron model of the classification engine, which will then result in a projection of exactness percentage > 90%. (better). A supervised learning process is the figure of the data used to convey the output of the machine learning model. The accuracy of the machine model that was drill is improved by the use of model selection, which employs the Monte Carlo cross-validation model. The accuracy of the drilled machine model is improved by using model selection, which employs the Monte Carlo cross-validation model. The experiment's effects on the gadget constructed received an evaluation of up to 95.29%
Application of Improved KAZE Algorithm in Image Feature Extraction and Matching

Intelligent navigation and recognition technology have continuously improved the field of image matching, so how to achieve more efficient and accurate feature matching is the key to image processing. This study provides a detailed introduction to the calculation methods, advantages and disadvantages of various algorithms such as SIFT, ORB and KAZE. Finally, the KAZE algorithm was selected because it can better handle edge information. However, this algorithm still has drawbacks such as high computational cost. Therefore, starting from the feature description and similarity calculation, the study introduced the concepts of second-order degree values, circular rotation characteristics, and expected distance and intercept distance to improve it, which is called the algorithm. Theoretically, this algorithm can greatly reduce the computational burden of the algorithm. To further validate the effectiveness of the improved algorithm, the study selected five types of image groups from the Mikolajczyk standard image database, including changes in image scale, brightness, and perspective. Simulation experiments were conducted on the improved algorithm and the remaining five algorithms. The experimental results show that compared to the original algorithm, Li-KAZE reduces the overall running time by 27.9% and improves the average matching rate by 0.12%. Compared to other SIFT and other algorithms, Li-KAZE also has the best performance among all algorithms. When faced with changes in compression values and extensive changes in graph groups, its highest repeatability reaches 99%. In summary, the comprehensive performance of the Li-KAZE algorithm has achieved the goal of improvement.
Deep Stochastic Logic Gate Networks

This paper introduces a novel regularization approach aimed at improving generalization performance by perturbing deterministic logical expressions. We incorporate logical inference into deep neural networks using logic gates and propose stochastic sampling to select appropriate logic gates from a predetermined set at each node, resembling sampling from a categorical distribution. While the Gumbel softmax relaxation facilitates effective sampling learning, the independence of perturbation from the maximum index operation (argmax) poses challenges in maintaining consistent sampling and preserving the original categorical probability order. To address this issue, we introduce scaled noise in the Gumbel process, followed by normalization to unnormalized probabilities. By leveraging randomness and introducing stochastic learning into deterministic logical transformations, we demonstrate enhanced classification accuracy. Extensive evaluations on publicly available datasets, including UCI (adult and breast cancer), MNIST, and CIFAR-10, establish the superiority of our method over softmax-based logical gate networks. Our contributions significantly advance the training of logic gate-based networks, inspiring further developments in deep logic gate network training.
A visual recognition method for the automatic detection of distracted driving behavior based on an attention mechanism

For distracted driving behavior in real driving scenarios there are problems such as complex detection backgrounds, different target scales, and weak anti-interference ability. In this paper, an improved YOLOv5 model is proposed. First, the PAN in the neck network layer of the model is modified to a Bidirectional Feature Pyramid Network (BiFPN), which improves the ability of feature fusion of the model. Next, three Convolutional Block Attention Modules (CBAMs) are introduced into the detection layer to enhance the extraction of valid information from the model, and the traditional NMS is replaced with DIoU-NMS, which improves the correspondence between the generated frames and the target frames, reduces the missed detection rate of the model, and further improves the detection performance of the model. Then, a dataset containing 14 types of distracted driving was built and augmented using data augmentation to improve the generalization capability of the model. Finally, experimental results show that the improved model achieves precision and mean average precision of 91.6% and 89.2%, respectively, which are 7.5% and 1.6% higher than the original model. This demonstrates that the improved model can effectively improve the detection performance of distracted driving behavior.
Random Number Generators: Principles and Applications

In this paper, we present approaches to generating random numbers, along with potential applications. Rather than trying to provide extensive coverage of several techniques or algorithms that have appeared in the scientific literature, we focus on some representative approaches, presenting their workings and properties in detail. Our goal is to delineate their strengths and weaknesses, as well as their potential application domains, so that the reader can judge what would be the best approach for the application at hand, possibly a combination of the available approaches. For instance, a physical source of randomness can be used for the initial seed; then, suitable preprocessing can enhance its randomness; then, the output of preprocessing can feed different types of generators, e.g., a linear congruential generator, a cryptographically secure one and one based on the combination of one-way hash functions and shared key cryptoalgorithms in various modes of operation. Then, if desired, the outputs of the different generators can be combined, giving the final random sequence. Moreover, we present a set of practical randomness tests that can be applied to the outputs of random number generators in order to assess their randomness characteristics. In order to demonstrate the importance of unpredictable random sequences, we present an application of cryptographically secure generators in domains where unpredictability is one of the major requirements, i.e., eLotteries and cryptographic key generation.
Assessing the Feasibility of Integrating Renewable Energy: Decision Tree Analysis for Parameter Evaluation and LSTM Forecasting for Solar and Wind Power Generation in a Campus Microgrid

The world has embarked on a road to sustainable energy production. As a result, countries have turned to microgrid developments. This article aims to study the feasibility of renewable sources such as solar PV and wind power for integrating a microgrid campus, taking the example of a case in East Africa, precisely the case of the University of Djibouti. We applied the weather parameters to evaluate the solar and wind potential with the Decision Tree method for analyzing and classifying the degrees of solar radiation and the consistency of wind speed. These data are spread over eight years to establish and capture seasonal changes and prove the accessibility of renewable sources in a specific site. The results were compared to Random Forest, Logistic Regression, K-Nearest Neighbors, Support Vector Machine, and Naïve Bayes classifiers, which showed that the performance of classifying the Decision tree outperformed all other methods with an accuracy of 0.99321. The second work of this article explored the forecasting of the possible powers predicted with the LSTM deep learning method by the generation of the Solar PV array and wind turbines which were simulated on PVLib and Windpowerlib. The results are favorable, and the LSTM has performed well on the different hyperparameters. With the combination of machine learning and deep learning, it was possible to theoretically conclude the integration of renewable energies since we investigated all the potential possibilities in evaluating meteorological parameters and power predictions. Finally, decision scores from the Decision Tree architecture and the LSTM features were integrated to form a hybrid Tree-LSTM fusion method. It introduces a novel architectural concept that can effectively address sequential data and harness the non-linear capabilities of decision trees. The proposed model was validated by tuning the hyperparameters. Enhancing the maximum depth of the model increases the performance at a certain point, and conversely, reducing the minimum sample split improves the model performance. These contributions will help to create sustainable energy systems and increase the transition to a clean CO2 environment.
LucidiTEE: Scalable Policy-Based Multiparty Computation with Fairness

Motivated by recent advances in exploring the power of hybridized TEE-blockchain systems, we present $$\textsf{LucidiTEE} $$ , a unified framework for confidential, policy-compliant computing that guarantees fair output delivery. For context: Summarizing, $$\textsf{LucidiTEE} $$ enables multiple parties to jointly compute on private data, while enforcing history-based policies even when input providers are offline, and fairness to all output recipients, in a malicious setting. $$\textsf{LucidiTEE} $$ uses the ledger only to enforce policies; i.e., it does not store inputs, outputs, or state on the ledger, letting it scale to big data computation. We show novel applications including a personal finance app, collaborative machine learning, and policy-based surveys amongst an apriori-unknown set of participants.
Federated Learning Model for Early Detection of Dementia Using Blood Biosamples

Alzheimer’s disease (AD) is a serious, long-term health problem that causes much pain and loss for the person with it and their family. Its early and accurate detection might result in a substantial reduction of the disease outcomes and consequences. Blood biosamples are a simple and inexpensive technique in medical testing. This paper proposes diagnostic models for blood biosamples based on federated learning (FL) and its modifications to detect AD early. Our experiments used blood biosample data sets from the ADNI website to evaluate our models. Our performance analysis indicates that our algorithms are more accurate and achieve an accuracy of 87% for early detection.
DiffusionVID: Denoising Object Boxes With Spatio–Temporal Conditioning for Video Object Detection

Several existing still image object detectors suffer from image deterioration in videos, such as motion blur, camera defocus, and partial occlusion. We present DiffusionVID, a diffusion model-based video object detector, that exploits spatio-temporal conditioning. Inspired by the diffusion model, DiffusionVID refines random noise boxes to obtain the original object boxes in a video sequence. To effectively refine the box from the degraded images in the videos, we used three novel approaches: cascade refinement, dynamic core-set conditioning, and local batch refinement. The cascade refinement architecture effectively collects information from object regions, whereas the dynamic core-set conditioning further improves the denoising quality using adaptive conditional guidance based on the spatio-temporal core-set. Local batch refinement significantly improves the refinement speed by exploiting GPU parallelism. On the standard and widely used ImageNet-VID benchmark, our DiffusionVID with the ResNet-101 and Swin-Base backbones achieves 86.9 mAP @ 46.6 FPS and 92.4 mAP @ 27.0 FPS, respectively, which is state-of-the-art performance. To the best of the authors’ knowledge, this is the first video object detector based on a diffusion model. The code and models are available at https://github.com/sdroh1027/DiffusionVID.