Reviewerly/exHarmony huggingface dataset:
Visual Enhancement of Underwater Images Using Transmission Estimation and Multi-Scale Fusion

The demand for the exploration of ocean resources is increasing exponentially.Underwater image data plays a significant role in many research areas.Despite this, the visual quality of underwater images is degraded because of two main factors namely, backscattering and attenuation.Therefore, visual enhancement has become an essential process to recover the required data from the images.Many algorithms had been proposed in a decade for improving the quality of images.This paper aims to propose a single image enhancement technique without the use of any external datasets.For that, the degraded images are subjected to two main processes namely, color correction and image fusion.Initially, veiling light and transmission light is estimated to find the color required for correction.Veiling light refers to unwanted light, whereas transmission light refers to the required light for color correction.These estimated outputs are applied in the scene recovery equation.The image obtained from color correction is subjected to a fusion process where the image is categorized into two versions and applied to white balance and contrast enhancement techniques.The resultants are divided into three weight maps namely, luminance, saliency, chromaticity and fused using the Laplacian pyramid.The results obtained are graphically compared with their input data using RGB Histogram plot.Finally, image quality is measured and tabulated using underwater image quality measures.
An Ontology Based Cyclone Tracks Classification Using SWRL Reasoning and SVM

Tropical cyclones (TC) are often associated with severe weather conditions which cause great losses to lives and property.The precise classification of cyclone tracks is significantly important in the field of weather forecasting.In this paper we propose a novel hybrid model that integrates ontology and Support Vector Machine (SVM) to classify the tropical cyclone tracks into four types of classes namely straight, quasi-straight, curving and sinuous based on the track shape.Tropical Cyclone TRacks Ontology (TCTRO) described in this paper is a knowledge base which comprises of classes, objects and data properties that represent the interaction among the TC characteristics.A set of SWRL (Semantic Web Rule Language) rules are directly inserted to the TCTRO ontology for reasoning and inferring new knowledge from ontology.Furthermore, we propose a learning algorithm which utilizes the inferred knowledge for optimizing the feature subset.According to experiments on the IBTrACS dataset, the proposed ontology based SVM classifier achieves an accuracy of 98.3% with reduced classification error rates.
Algorithms for Pre-Compiling Programs by Parallel Compilers

The paper addresses the challenge of transmitting a big number of files stored in a data center (DC), encrypting them by compilers, and sending them through a network at an acceptable time.Face to the big number of files, only one compiler may not be sufficient to encrypt data in an acceptable time.In this paper, we consider the problem of several compilers and the objective is to find an algorithm that can give an efficient schedule for the given files to be compiled by the compilers.The main objective of the work is to minimize the gap in the total size of assigned files between compilers.This minimization ensures the fair distribution of files to different compilers.This problem is considered to be a very hard problem.This paper presents two research axes.The first axis is related to architecture.We propose a novel pre-compiler architecture in this context.The second axis is algorithmic development.We develop six algorithms to solve the problem, in this context.These algorithms are based on the dispatching rules method, decomposition method, and an iterative approach.These algorithms give approximate solutions for the studied problem.An experimental result is implemented to show the performance of algorithms.Several indicators are used to measure the performance of the proposed algorithms.In addition, five classes are proposed to test the algorithms with a total of 2350 instances.A comparison between the proposed algorithms is presented in different tables discussed to show the performance of each algorithm.The result showed that the best algorithm is the Iterative-mixed Smallest-Longest-Heuristic (ISL) with a percentage equal to 97.7% and an average running time equal to 0.148 s.All other algorithms did not exceed 22% as a percentage.The best algorithm excluding ISL is Iterative-mixed Longest-Smallest Heuristic (ILS) with a percentage equal to 21,4% and an average running time equal to 0.150 s.
SA-MSVM: Hybrid Heuristic Algorithm-based Feature Selection for Sentiment Analysis in Twitter

One of the drastically growing and emerging research areas used in most information technology industries is Bigdata analytics.Bigdata is created from social websites like Facebook, WhatsApp, Twitter, etc. Opinions about products, persons, initiatives, political issues, research achievements, and entertainment are discussed on social websites.The unique data analytics method cannot be applied to various social websites since the data formats are different.Several approaches, techniques, and tools have been used for big data analytics, opinion mining, or sentiment analysis, but the accuracy is yet to be improved.The proposed work is motivated to do sentiment analysis on Twitter data for cloth products using Simulated Annealing incorporated with the Multiclass Support Vector Machine (SA-MSVM) approach.SA-MSVM is a hybrid heuristic approach for selecting and classifying text-based sentimental words following the Natural Language Processing (NLP) process applied on tweets extracted from the Twitter dataset.A simulated annealing algorithm searches for relevant features and selects and identifies sentimental terms that customers criticize.SA-MSVM is implemented, experimented with MATLAB, and the results are verified.The results concluded that SA-MSVM has more potential in sentiment analysis and classification than the existing Support Vector Machine (SVM) approach.SA-MSVM has obtained 96.34% accuracy in classifying the product review compared with the existing systems.
Optimal Deep Belief Network Enabled Cybersecurity Phishing Email Classification

Recently, developments of Internet and cloud technologies have resulted in a considerable rise in utilization of online media for day to day lives.It results in illegal access to users' private data and compromises it.Phishing is a popular attack which tricked the user into accessing malicious data and gaining the data.Proper identification of phishing emails can be treated as an essential process in the domain of cybersecurity.This article focuses on the design of biogeography based optimization with deep learning for Phishing Email detection and classification (BBODL-PEDC) model.The major intention of the BBODL-PEDC model is to distinguish emails between legitimate and phishing.The BBODL-PEDC model initially performs data pre-processing in three levels namely email cleaning, tokenization, and stop word elimination.Besides, TF-IDF model is applied for the extraction of useful feature vectors.Moreover, optimal deep belief network (DBN) model is used for the email classification and its efficacy can be boosted by the BBO based hyperparameter tuning process.The performance validation of the BBODL-PEDC model can be performed using benchmark dataset and the results are assessed under several dimensions.Extensive comparative studies reported the superior outcomes of the BBODL-PEDC model over the recent approaches.
Improved Genetic Algorithm for English Information Education and Teaching Quality Impact Analysis

With the increase of the number of college students, in order to improve the quality of teaching, the professional setting is more and more detailed, the course content is more and more rich, the information teaching system will be increasingly perfect. This paper proposes an improved genetic algorithm for English informatics education scheduling system to address the problem of the contradiction between the rapidly increasing number of students and the limited teaching resources in the current English scheduling in universities. The system first proposes an improved parallel mechanism which computes the probability of population changing evolutionary strategies according to the algebra of population maintaining the optimal solution invariant. The evolution strategy needed by the population can be obtained by fuzzy reasoning according to the evolution degree and individual difference of the population so as to get rid of the local extreme value and search for the global optimal value. Secondly, an improved algorithm initialization method, fitness transformation method, and competition strategy are proposed to adjust the balance between convergence speed and population diversity. Finally, based on Baldwin evolution theory, a new local search strategy is proposed. The algorithm has been tested on famous international data sets and a school’s English curriculum. Experimental results show that this algorithm has higher efficiency and quality than other algorithms with better performance, and the proposed teaching system can improve the teaching quality of English information education.
Predicting Fraudulence Transaction under Data Imbalance using Neural Network (Deep Learning)

The number of financial transactions has the potential to cause many violations of the law (fraud). Conventional machine learning has been widely used, including logistic regression, random forest, and gradient boosted. However, the machine learning can work as long as the dataset contains fraud. Many new financial technology companies need to anticipate the potential for fraud, which they have not experienced much. This potential for a crime can also be experienced by old service providers with a low frequency of previous fraud. With the data imbalance, traditional machine learningis likely to produce false negatives so that they do not accurately predict potential fraud. This study optimizes the machine learning approach based on Neural Networks to improve model accuracy through the integration of KNIME and Python Programming with KERAS and TensorFlow models. The study also conducts a comparative analysis to scrutinize the performance of Adam and Adamax Optimizer. Using data from European cardholders in 2013, this study proves that workflows and neural network algorithms can detect with up to 95% accuracy even with a very small fraud sample of only 0.17% or 492 of 284,807 transactions. In addition, the Adam optimizer performs higher accuracy than the Adamax optimizer. The implication is that this supervisory technology innovation can be developed to minimize transaction crimes in the financial services sector.
Lexicons of Key Terms in Scholarly Texts and Their Disciplinary Differences: From Quantum Semantics Construction to Relative-Entropy-Based Comparisons

Complex networks are often used to analyze written text and reports by rendering texts in the form of a semantic network, forming a lexicon of words or key terms. Many existing methods to construct lexicons are based on counting word co-occurrences, having the advantage of simplicity and ease of applicability. Here, we use a quantum semantics approach to generalize such methods, allowing us to model the entanglement of terms and words. We show how quantum semantics can be applied to reveal disciplinary differences in the use of key terms by analyzing 12 scholarly texts that represent the different positions of various disciplinary schools (of conceptual change research) on the same topic (conceptual change). In addition, attention is paid to how closely the lexicons corresponding to different positions can be brought into agreement by suitable tuning of the entanglement factors. In comparing the lexicons, we invoke complex network-based analysis based on exponential matrix transformation and use information theoretic relative entropy (Jensen-Shannon divergence) as the operationalization of differences between lexicons. The results suggest that quantum semantics is a viable way to model the disciplinary differences of lexicons and how they can be tuned for a better agreement.
Application and Exploration of VR and AI Technology in College English Teaching

In order to improve the effect of college English teaching, this article applies VR and AI technology to college English teaching. According to the pixel structure of English teaching image and its array drive design, this article proposes a semiactive address-driven pixel structure. Moreover, this article controls the scanning rate by adjusting the period of the clock signal CLK generated by the computer or FPGA, which determines the speed of image transmission. In addition, in order to determine the output DC value of the cascode structure, the negative feedback loop is biased, and an intelligent teaching system is constructed. According to the teaching evaluation research, it can be seen that the college English teaching method based on VR technology and AI technology proposed in this article can effectively improve the teaching effect of college English.
A Deep Learning Method for Intelligent Analysis of Sports Training Postures

With the further research of artificial intelligence technology, motion recognition technology is widely used in posture analysis of sports training. However, the interference of light, Angle, and distance in real life makes the existing model unable to focus on the expression of human movements. Aiming at the above problems, this paper proposes a motion training attitude analysis method based on a multiscale spatiotemporal graph convolution network. Firstly, the spatiotemporal image of the skeleton is constructed, and then the convolution operation is performed on the spatiotemporal image of the skeleton. Finally, the convolution results are linearly weighted and fused to capture the characteristics of action types with different time lengths. At the same time, the algorithm increases the processing of some important information loss and increases the randomness of the data set. Experimental results show that the proposed algorithm can adapt to the behavior changes of different complexity, and the model performance and recognition accuracy are significantly improved.
Wisdom Tourism Management Mode in the Background of Big Data of the Internet of Things

Since the rapid development of the global economy, tourism has become an indispensable entertainment and relaxation item in people’s lives. However, the traditional tourism management mode is slow in conveying tourism information, and there is no reasonable planning of tourism projects for tourists, and it is even more difficult to accurately locate the travel preferences of travelers and make specific travel plans, so that travel resources cannot be fully utilized. Therefore, the Internet of Things big data technology and travel management are combined. For this reason, a wisdom tourism management model based on the big data background of Internet of Things (IoT) is developed. In order to timely arrange and adjust work and travel plans, wisdom tourism makes use of cloud computing, IoT, and other new technologies, with the help of portable mobile terminal devices, so that the tourists can receive timely information about tourism resources, tourism activities, and tourism surroundings to achieve intelligent perception of tourism information, timely arrangement effect. Wisdom tourism can make use of undeveloped tourism resources as much as possible and drive the development of surrounding industries, and promote the economic development of tourist areas. This paper mainly compares the smart tourism management mode with the traditional tourism management mode. The experiment shows that the development degree of the wisdom tourism management mode accounts for 82.8% of the total tourism resources. The traditional tourism management model is only 70.1%, and the GDP of the economy around the tourist area in the wisdom tourism management model in one year is 1.4 times higher than that of the traditional tourism management model. In terms of tourism experience, the wisdom tourism management model is also much better than the traditional tourism management model. And the lower the average age of the experimenter, the higher the favorability of wisdom tourism, up to 98%. It highlights the advantages of the wisdom tourism management model in personalized tourism, tourism development, and surrounding economic growth.
A Longitudinal Study of Children’s Perceived Usability: Methodology and Measurements

This study examines longitudinal changes in children’s perceived usability based on two aspects. First, we developed a child-friendly usability questionnaire, which used cartoons to express the questionnaire response options. This approach provides an easy-to-understand five-point scale and a filling process using magnetic blocks, which together lead to highly reliable results. Additionally, we designed a longitudinal study to investigate the children’s perceived usability according to two measurement methods (immediate and retrospective). The children’s usability increased with longer durations of usage (i.e., increased repetitions of exercises). The short-term retrospective assessments depended on the most recent experience, whereas the long-term retrospective assessments were generally more positive.
Blockchain-Based Intelligent Interconnection System Optimization Decision

This article conducts a comprehensive analysis of the relevant content of blockchain; blockchain is a new field that has only emerged in recent decades, and it has a high level of new energy that cannot be compared with other fields. This article proposes the advantages and disadvantages of blockchain and the current factors that trigger network security defense. A multimicrogrid customer transaction system under the blockchain technology is also proposed. We will illustrate the compatibility between multimicrogrid and blockchain, which is self-adaptive and can be adjusted in time with market changes. Third, in order to ensure the normal operation of the distribution network, a smart contract dynamic constraint scheme is designed for power flow constraints and voltage offset problems. Multimicrogrid system as energy interconnection research on multiagent competitive game on multimicrogrid system is crucial to the development of energy Internet technology. Blockchain-based decision-making systems exist based on a comprehensive analysis of the needs and interests of microgrid operators, large users, and distributed aggregators. For the complex problem of multimicrogrid system competition game model, a suitable solution method (IACO) is proposed. The model and solution algorithm established by the experiment are accurately analyzed, and the simulation result is that the optimal electricity price strategy in the subdivision field can effectively balance the interests of different market players and achieve win-win and coordinated development. Experiments show that IACO can deal with the multiple problems of blockchain.
Fine‐grained analysis of dependency cycles among classes

Abstract Dependency cycles have been claimed to incur negative impacts on software quality and make a system difficult to test, maintain, and reuse. Previous studies have investigated dependency cycles' evolution and proposed various methods to detect and break dependency cycles. While these studies provide valuable inputs for understanding dependency cycles, we do not know how developers address dependency cycles in their daily activities and whether dependency cycles' resolution follows certain patterns. Therefore, in this paper, using the data from 18 open‐source projects, we extracted dependency cycles along with their changes from each commit and studied how dependency cycles evolved in a fine‐grained level. Our results demonstrate that, 28.4% of dependency cycles have been continuously changing through different status: aggregating, breaking, newly‐forming, and disappearing. Furthermore, we found that dependency cycles in different shapes present their specific evolution characteristics, for example, star and circle dependency cycles tend to preserve their original shape during aggregation or breaking. In addition, we discovered several recurring patterns that developers applied in fully breaking star and circle dependency cycles. We believe the recurring patterns we summarized have the potential to be automated in breaking dependency cycles in practice.
Vehicle Classification and Counting from Surveillance Camera Using Computer Vision

Over the last decade, the number of automobiles has skyrocketed. There are more than one billion active automobiles in the universe, including 70–80 million in India. Handling such traffic conditions and providing enough parking spaces is a difficult task. Vehicle counting and classification on congested routes will assist authorities in obtaining traffic flow data as well as understanding and studying traffic patterns, allowing for the most efficient traffic management. As a result, the aim of this work is that a cost-effective vision-based vehicle counting and classification system that is mainly implemented in OpenCV utilising Python programming and some methods of image processing.
Supervised Deep Learning Approach for Generating Dynamic Summary of the Video

In this growing digital world, videos are one of the important forms of data. Daily, millions of users watch videos on the Internet, and hence, video summarization will be handy in generating small and concise videos that will reduce the time of users. By video summarization, users can decide about the usefulness of a video. In this paper, supervised deep learning approach using convolutional neural networks (CNNs) is used for generating the dynamic summary of the video. The proposed methodology is implemented on Tour20 dataset, and summary of the video is generated. Experimental results were evaluated against the Tour20 dataset and were satisfactory.
Video Keyframe Extraction Based on Human Motion Detection

Due to the substantial growth of CCTV surveillance data, it is very hard to cumulate the crime scene information from a long durable video collection as frames. Keyframe extraction is used to eradicate the non-essential frames in order to reduce the processing time of an entire video. Still, keyframe extraction lags to gain more accuracy on determining the crime scene with human detection, thus the spatiotemporal feature extraction approaches the human motion detection phase using the HOG descriptor along with the SVM classifier was reviewed from the existing methods. In this study, two methods are implemented by a combination of frame difference method with HOG along SVM on various edge detection methods, predicts the optimization of human motion detected keyframes. These extracted human detected keyframes are sustaining the local features as keyframes for depicting the crime scene as a clear summarized report. Finally, the experimental result shows that spatiotemporal feature extracted keyframe through Canny edge detection achieves 98.73% as recognition accuracy.