Reviewerly/exHarmony huggingface dataset:
A multi-objective effort-aware defect prediction approach based on NSGA-II

Effort-Aware Defect Prediction (EADP) technique sorts software modules by the defect density and aims to find more bugs when testing a certain number of Lines of Code (LOC). The existing EADP methods ignore the number of required inspected modules and thus resulting in more testing cost. Therefore, we propose a multi-objective effort-aware defect prediction approach based on NSGA-II named MOOAC for EADP, which aims to maximize the Proportion of the found Bugs (PofB@20%) and minimize the Proportion of Module Inspected (PMI@20%) when inspecting the top 20% LOC. MOOAC firstly trains a random forest classification model. Then, it builds a logistic regression model, and utilizes the NSGA-II algorithm to generate the coefficient vector of the model by maximizing the PofB@20% value and minimizing the PMI@20% value simultaneously. In the model prediction phase, MOOAC firstly employs the built random forest classifier to decide whether modules are defective. Next, the predicted defective modules are first inspected based on the ratio between the predicted defect probability by the logistic regression model and LOC, which can make testers to find more bugs and test as fewer LOC as possible. The clean modules are then inspected to reduce the Initial False Alarms (IFA), if there is still the testing budget left. The results show that MOOAC exhibits the best overall performance on the PofB@20% and PMI@20%. In other words, MOOAC enables testers to identify more bugs per 1% module.
Multi-chain blockchain based secure data-sharing framework for industrial IoTs smart devices in petroleum industry

Industry 4.0 efficiently enhances the manufacturing process in many aspects such as increasing system performance, productivity, cost reduction, and building large-scale systems with enable autonomous operation. Numerous heterogeneous devices and sensors are connecting to the internet network through the utilization of revolutionary techniques that transfer the manufacturing paradigm to intelligent processes. The traditional schemes adopted centralized architectures and complex encryption algorithms that suffer from several cyber-security threats and latency making them unsuitable for resource-constrained IoTs devices. In our work, we proposed a decentralized, low-power consumption, fast, scalable, secure, privacy-preserving, and trusting architecture-based private blockchain network/smart contract and interplanetary file system for the petroleum industry in Iraq. Our scheme improves blockchain performance by adopting the incremental aggregator subsector commitment instated of Merkle tree and a multi-chain proof of rapid authentication as consensus algorithms, which are specially designed for industrial IoTs applications. It provides a high level of security authentication and privacy preservation, P2P communication, remote access, immutability, traceability, and information backtracking. In addition to addressing the blockchain storage limitation and eliminating third-party dependencies by adopting an interplanetary file system (IPFS). Also provides an efficient data encryption mechanism at the perceptual layer by adopting a lightweight encryption algorithm with internet friendly ARM Cortex-M33 Microcontroller as clear in the experimental results. Moreover, our architecture succeeds in merging blockchain technology, IoTs, and an IPFS with the petroleum industry as an industrial application area, and provides an efficient framework that resists common cyber-security attacks by achieving data integrity, availability, and confidentiality.
Information Management Technology as a Tool for Making Effective Management Decisions

Information Management Technology (IMT) refers to using of technology and software for manage and analyze data in order to make informed and effective decisions. IMT provides decision-makers with timely, accurate, and relevant information to support planning, monitoring, and evaluating of organizational processes and operations. IMT can be used to collect, store, process, analyze, and disseminate data, which helps managers to identify patterns, trends, and insights that are critical to the decision-making process. IMT also can be used to facilitate communication and collaboration among stakeholders, automate routine tasks, and provide real-time feedback to managers. It was proposed the IMT, which can help decision-makers to improve the quality of their decisions by providing them with a range of tools and techniques to analyze data and evaluate the potential impact of different courses of action. Some examples of IMT tools include business intelligence software, data mining and analysis tools, predictive analytics, and simulation software. Overall, IMT plays a critical role in enabling effective management decision-making by providing decision-makers with the information they need to make informed decisions, improving the quality and speed of decision-making, and enabling collaboration and communication among stakeholders. By leveraging the power of IMT, organizations can make more effective and efficient use of their resources, improve their competitive position, and achieve their strategic objectives.
Research on the Blockchain Digital Capability Platform for “No Proof City”

In order to address the issues of insufficient data sharing, low recognition of electronic certificates, and inadequate interdepartmental coordination in the construction process of “No Proof City, ” a blockchain capability platform was built using Hyperledger Fabric and the Inter Planetary File System. The Raft consensus algorithm was utilized to formulate transaction endorsement policies, while smart contracts were used to supervise and certify electronic certificates. Decentralized applications were also developed to facilitate the entire process. To verify the feasibility of the system, performance testing was conducted using JMeter. In the experimental environment, the throughput of the system remained stable at 250tps for write operations and 550tps for query operations. The success rate of blockchain transactions was 100 percent. The results indicate that the system meets the requirements of practical performance and can facilitate secure data sharing and interdepartmental coordination.
Implementation and Analysis of Feistel and SPN Structured Ciphers -CLEFIA and PRESENT

The investigation of interconnected devices, sensors, and various network-connected facilitates communication and information exchange. These devices are susceptible to security threats related to privacy and data exchange, especially considering the billions of inter-connected devices. Lightweight block ciphers offer a viable solution for power-constrained devices, including Radio-frequency identification (RFID) tags, ubiquitous computing, wireless sensor networks, aggregation networks, and the Internet of Things (IoT). This study implements two lightweight block ciphers, namely CLEFIA and PRESENT, both of which have FIESTEL and SPN structures respectively. Feistel structure has simple encryption and decryption algorithms and SPN is simple and easy to analyze. These ciphers are implemented using the Xilinx and Cadence tool. Implemented ciphers performance is evaluated in terms of power, area, and delay. Our findings demonstrate that PRESENT surpasses CLEFIA in terms of memory usage and security, while CLEFIA provides superior throughput compared to PRESENT.
Pose Estimation and Virtual Gym Assistant Using MediaPipe and Machine Learning

This research paper demonstrates a virtual fitness trainer, using Google's Mediapipe library, which was made for various multimodal machine learning and deep learning pipelines. By monitoring the user's every step with posture estimation algorithms, the suggested system can provide useful feedback and insights as they work out. Mediapipe's deep algorithms and posture estimation module are utilized to develop this novel system, which captures user movements by identifying unique body landmarks for each exercise. The system also keeps track of the total amount of reps quite precisely. The system determines the user's performance by computing angles and landmarks and feeding this data into a machine-learning model, which then classifies the correct posture and the number of repetitions of the exercise. The system uses many fitness datasets in addition to user-supplied data for thorough training and assessment. A detailed comparison of machine learning models like Logistic Regression, Support Vector Machine, Naive Bayes, Decision Tree, and Artificial Neural Network is included in this research. The findings prove that our suggested method provides accurate and individualized feedback, which improves exercise performance and lessens the likelihood of damage.
Adaptive Mayfly Algorithm for UAV Path Planning and Obstacle Avoidance in Indoor Environment

This paper presents the Adaptive Mayfly Algorithm (AMA) as a solution for path planning and obstacle avoidance in unmanned systems. Path planning is a critical function for autonomous navigation in complex environments. The AMA combines the global search capabilities of the Mayfly algorithm with the local search capability of obstacle avoidance approaches to provide an effective and efficient solution. For obstacle avoidance, the AMA employs a gradient descent approach to find an alternative path that avoids the obstacle. This adaptive approach ensures that the algorithm can handle complex situations and dynamically adjust the paths based on the environment. Simulation and experimental validation demonstrate that the AMA gives optimal path length and navigational time it also outperforms existing algorithms in terms of finding the best pathways and effectively avoiding obstacles in complicated scenarios. The obtained percentage of deviation between simulation and experimental results are less than 5.5%.
Enhancing Object Detection with Mask R-CNN: A Deep Learning Perspective

Object identification is a critical task in the field of machine learning, focusing on locating and recognizing specific elements of interest within an image. A mask-based R-CNN model using the ResNet-50-FPN backbone, which has been already trained on the data set COCO as a feature extractor is used in this study of author's innovative method for object detection. In order to train several neural network algorithms for object recognition, which include CNN, VGG-16, and Inception Net, pertinent features from the input photos are first extracted using the pre-trained models. The proposed method improved accuracy in object detection, as well as the ability to efficiently extract relevant features using pre-trained models. The three frameworks were assessed on two different datasets: Pascal VOC and COCO. For both the datasets the VGG-16 model achieves high accuracy 89.9% and 95.4% respectively. The results of the experiment show that the suggested approach is beneficial, with the model achieving of higher accuracy in object detection compared to other existing methods. This research contributes to the development of efficient and adaptable frameworks in order to recognize and separate things in photographs which are essential for the automation of machine vision systems.
Classification of Parasite Malaria in Schizon Stage with GoogleNet and VGG-19 Pre-Trained Models

The development of artificial intelligence technology has currently given benefit for humans in various fields. In the medical field, artificial intelligence was developed to help medical experts to classify various diseases using medical images, including malaria. Early detection of malaria parasites is important to save the patients, thus this study developed a detection system for some malaria parasites (P. falciparum, P. vivax, and P. malariae) in the schizont stage. This system uses deep learning methods using GoogleNet and VGG 19 pre-trained models. This study performs accuracy, running time, and analysis based on the confusion matrix for testing result. The best training result is performed by the GoogleNet pre-trained model, with an average running time of 7 minutes 14 seconds and an average accuracy of 98.53% $\pm 1.27\%$. The best model for classifying malaria image in the blood is the GoogleNet model with an accuracy value of 97.41%, precision 100%, recall 93.75%, specificity 100% and f-score 99.53%.
Spam Detection in Short Message Service (SMS) Using Naïve Bayes, SVM, LSTM, and CNN

Short Message Services (SMS) has been most people’s habits. The numbers of SMS usage have been growing rapidly since text messages are more effective than emails. However, there is a problem that people often encounter while using SMS which is spam. Many researchers have discussed what are the best methods to avoid spam, especially in SMS. They attempt to avoid spam by making an SMS filtering machine using various algorithms. In this study, we’re aiming to compare the accuracy percentage of SMS spam detection using various machine learning algorithms, such as Naive Bayes, Support Vector Machine (SVM), Long-Short Term Memory (LSTM), and Convolutional Neural Network (CNN). These algorithms have been implemented and tested over a dataset consisting of 5.574 records. This testing gave specific results of mean, which are 96.95% by using the Naive Bayes Algorithm, followed by 97.93% by using Support Vector Machine, 98.57 by Convolutional Neural Network, and 99.1% by Long-Short Term Memory leads in this testing.
An Improved Abnormal Power Consumption Detection System Based on Federated Learning

In the power business scenario, the existing abnormal power consumption detection system based on federated learning generally uses a single encryption algorithm to encrypt information during data transmission, which has certain limitations in efficiency and security. In this paper, we propose an improved abnormal power consumption detection system based on federated learning, which optimizes the encryption algorithm for the actual power business scenario, and adds the mixed use of multiple algorithms. The results show that in the actual power business scenario, compared with the traditional abnormal power consumption detection system based on federated learning, the system can better improve the efficiency of model training.
Enhancing BISINDO Recognition Accuracy Through Comparative Analysis of Three CNN Architecture Models

This paper aims to develop an effective and efficient system for hand gesture recognition of Indonesian Sign Language (BISINDO) using a comparative analysis of three CNN architectures. The Python programming language is used as the development environment, together with the free edition of Google Colab. Google Colab provides a free online platform for coding and experimentation. PyTorch is used as a framework for training models, exploiting its Python programming language capabilities. and comprised 8,792 pictures of 200 by 200 pixel dimensions, divided A CNN-based technique is used, consisting of convolutional layers with ReLU activation, a comparison of three possible CNN designs from the detected image, and max pooling to constantly minimize dimensions, parameters, and computations within the network. The acquired findings show the CNN model is accurate after training and testing, with different accuracy rates.
Semantic Literature Review on Non-Fungible Token: Expansion Area of Usage &amp; Trends

Blockchain, Metaverse & NFT are technologies that were booming during the Pandemic. As a derivative product of blockchain, the Non-Fungible Token (NFT) is one of the technologies that has attracted the most interest from investors and the public because of its potential benefits and has always been closely associated with the Metaverse as a main purposes research of the NFTs. This paper is to explore the possibilities and provide an overview of the current use of NFT technology so that it can provide insight for further research & gap with the big picture that it is trying to present in this paper. However, after exploring further related to NFT in this research, it was found that the problem that most people are trying to solve through NFT is related to the topic of Decentralization & Web3 not as a Metaverse main backbone.
Analysis and Improvement of JPEG Compression Performance with Discrete Cosine Transform and Convolution Gaussian Filtering

Although computers and smartphones now have graphical capabilities for conveying data information, there are limitations on the size of the file used to store the data. As a result, it takes longer to transmit data to another location, which in turn affects the required time. This paper presents a compression method that utilizes the Discrete Cosine Transform to minimize the number of coefficients needed to represent the input data. The quantization and masking processes then remove relatively small coefficients without causing visual distortion while maximizing compression and maintaining image quality. Furthermore, post-processing techniques are employed to improve the quality of compressed or degraded images resulting from the DCT transformation process. The Gaussian Filter method is used to smooth the image and remove normally distributed noise. Consequently more compact information is obtained, which reduces the transmission time when it is sent to another location.
Revealing Market Dynamics Pattern of DeFi Token Transaction in Crypto Industry

The decentralized financial (DeFi) market has witnessed remarkable growth in recent years, transforming how people access, manage, and exchange digital financial services. DeFi essentially refers to decentralized economic activities facilitated by blockchain technology. The tokenization of real-world assets contributes to the popularity of digital financial services, enabling unlimited sets of innovative ways to represent assets and interchange them. The DeFi peer-to-peer nature has been seen as a disruptor for traditional financial services due to its flexibility, openness, programmability, financial inclusion, censorship-resistant, transparency, and cost-effectiveness. To better understand the evolving DeFi ecosystem and its potential, analyzing transaction data using social network analysis (SNA) can offer valuable insights. The main goal of this research is to identify influential participants and uncover groups of related transactions, providing valuable information to market participants, regulators, and innovators on how to enhance the stability of DeFi and reduce risks. Three DeFi token-based Ethereum protocols have been analyzed: Dai (DAI), Uniswap (UNI), and Wrapped Bitcoin (WBTC). The study unveiled key players associated with each token by applying centrality calculations. Additionally, the modularity metric is employed to identify transaction clusters formed within each token, shedding light on the patterns and relationships within the network of the token economy.
Deep Dive into Hotel Reviews Analysis Using Convolutional Neural Networks Algorithm

Tourism industry has been instrumental in the growth of hotels globally, and the rise of online booking systems has led to a surge in online hotel reviews. Currently, travelers are keen to share their experiences, making online hotel reviews a valuable source of information. However, reading and understanding these reviews can be time-consuming and challenging, as human language is complex, and sentiment analysis requires high accuracy. This paper focuses on the development and evaluation of a sentiment analysis model for hotel reviews using Convolutional Neural Networks (CNN). The objective of the model is to assist hotel staff in gaining insights from online reviews. Traditional sentiment analysis algorithms have limitations and have reached a plateau in their performance. The experiment was conducted on six hotel reviews in Langkawi using Word2vec in Python generates word embeddings, and vector representations of words that enable effective natural language processing. The parameters used in the CNN algorithm, such as batch size, filter size, dropout, and data split between training and testing, were varied that need to be experimented with to find the high accuracy of CNN model. The results of the experiments showed that the CNN model achieved high accuracy of more than 90%, making it an effective text classification method. Moreover, the models with a dropout rate of 0.1 consistently outperformed the models with a higher dropout rate of 0.5 in terms of accuracy. The study used the CNN model to create a prototype for predicting the sentiment of hotel reviews. Results showed that the CNN algorithm is a reliable tool for sentiment analysis in the hotel industry, as it provides high accuracy and valuable insights from online reviews.
A Framework for Piano Online Education Based on Multi-Modal AI Technology

Piano online teaching under the epidemic is faced with the problems that students can't correct mistakes by practicing alone, the error prone points are hard to correct, and the playing status can't be continuously tracked and recorded. However, rapid development of multi-modal AI technology brings opportunities to the reform of the piano online education mode in the new era. This paper proposes a framework for piano online teaching based on multi-modal AI technology whose characteristics are below: first, multi-modal speech denoise and video processing is used for recording and correct online homework and learning processes of students; second, the individual learning model is constructed by means of knowledge graph to provide personalized guidance for students. Finally, through background data to track record, the periodic learning “portrait” of each student is depicted, providing comprehensive teaching evaluation. Therefore, teaching interaction is enhanced, and independent learning ability of students are cultivated. Through this way, online teaching effectiveness of music course is continuously improved, and the goal of improving the quality of online piano teaching is finally achieved.
Using Eye Tracking to Map Attention in an EEG-Based Brainwave Graphic Visualization System

This article describes the incorporation of eye tracking in a brainwaves visualization and analysis system, based on electroencephalography (EEG). The visualization system was developed in Python, using a Emotiv Insight headset, to analyze the fruition of audiovisual content. During the tests, there was a need to identify whether the reactions mapped by the EEG were in fact related to the enjoyment of the content or whether they originated from external elements, with the individual looking away from the screen and, consequently, losing his attention. Based on the Design Science Re-search methodology, eye tracking was incorporated into the system architecture. For validation, tests were performed with 10 users, using a movie trailer. Analyzing the generated data, it was possible to identify the correlation between the information presented by the EEG and the gaze of the individuals. In this way, it is possible to increase certainty about the origin of emotions during the fruition of audiovisual content.