Reviewerly/exHarmony huggingface dataset:
Object Tracking and Detection Using Convolutional Neural Networks

This paper presents Object detection algorithm which is used to identify and locate an object, Object tracking deals with tracking objects over a sequence of images. The aim of this work is to use both image and video to detect and track vehicles ahead of time for safety, self-driving, and target tracking. This study outlines a method for locating target automobiles in video under a variety of environmental situations. For vehicle recognition, we depend on temporal information of characteristics and their motion behaviors, which compensates for the difficulty in detecting vehicle forms, colors, and kind. We use a probabilistic model to model motion in the field of vision based on scene characteristics and vehicle movements. YOLO is a state-of-the-art object detection algorithm that has quickly become the industry standard for recognizing objects in computer vision. Previously, approaches such as R CNN, Fast R CNN, and Faster R CNN were utilized, but due to its speed and accuracy, YOLO has become the mainstream technology for object identification since its launch in 2015. We have used YOLOv3 model for object detection and tracking on COCO dataset for labeling the detected objects and proved that it gives accurate results with confidence scores and execution time for each and every object present in a given image or a video. We have implemented this method in various scenarios of traffic with changes of illumination, speed of the video and compared them to prove that it is best yet simple for tracking and detecting of objects.
Ultra-Short-Term Wind Power Prediction Based on Pvmd-Esma-Delm

The non-linearity and non-stationarity of wind power data have brought great challenges to the safe operation of the power system. It is particularly important to effectively improve the accuracy of ultra-short-term prediction of wind power. Therefore, we propose an ultra-short-term wind power prediction method that combines particle swarm optimization and variational mode decomposition (PVMD), improved slime mold algorithm (ESMA) for elite opposition-based learning strategy (EOBL) and deep extreme learning machine (DELM). First, the particle swarm optimization algorithm (PSO) is used to optimize the two core parameters of the variational mode decomposition (VMD) to obtain the PVMD algorithm. The PVMD is used to decompose the original wind power data into a series of stable sub-sequences, and rolling time series is used to analyze the sub-sequences decomposed by PVMD. Then the DELM predictive model is established and the input weights ( e ) and thresholds ( [[EQUATION]] ) in DELM are optimized through ESMA, and the EOBL is used to improve the diversity and population quality of the slime mold population, thereby improve the global optimization performance and convergence accuracy of slime mold algorithm (SMA), and further improve the prediction accuracy of the DELM model. Finally, each subsequence is substituted into the DELM optimized by the improved slime mold algorithm (ESMA-DELM), and the prediction components are superimposed to obtain the final prediction result. Comparing the effects of quite a few different forecasting models with the evaluation of calculation examples proves the effectiveness of the PVMD-ESMA-DELM blended forecasting model proposed in this paper, and gives a new approach for ultra-short-term wind power prediction.
Application of The Multi-feature Splicing Technology Based on Residual Network Identification

The use of computer technology to assist the restoration of cultural relics can improve the efficiency of practical work and avoid secondary damage during the research and management of cultural relics. Especially for paper recognition of debris region, the rational use of deep learning algorithm can obtain better recognition effect. However, the accuracy of shallow network identification is too low, and deep network degradation will occur. In fragment matching and splicing analysis, it is difficult to obtain excellent results by using the simplified features if the geometric contour is too complex. Therefore, on the basis of understanding the current research status of residual network identification, according to the features of multi-feature splicing technology for cultural relics restoration, this paper analyzes the splicing method based on multi-feature constraint of contour line, and selects the Terra Cotta Warriors fragment data set for empirical analysis, so as to clarify the effectiveness of multi-feature splicing technology for cultural relics restoration.
A parallel streamline visualization algorithm based on dynamic node tree

Streamline is one of the most inspiring technologies to achieve fundamental goal of scientific insight from resulting numerical simulations. Drawing on an intuitive interpretation in terms of flow field, it is an ideal tool to illustrate and describe a wide range of phenomena encountered in the study of application-domain vector fields. In this paper, we propose a parallel streamline algorithm based on Dynamic Node Tree (DN-Tree), to improve the efficiency of streamline visualization algorithm with efficient utilization of multi core computing resources. Firstly, we realize fast location of grid cells via constructs a dynamic node tree in parallel. The grid cells are divided into coarse-grained and fine-grained partitions in terms of organizing and managing the dataset, and the index structure is established to conveniently narrow the alternative areas in the integration process. Secondly, we reduce memory requirements of streamline integration by the methods of data blocks attribute extraction and employ parallel execution of seed integral tasks to leverage multicore computing resources. Finally, the performance is analysed by comparing our algorithm with existing streamline algorithms using datasets of different scales. The results show that the proposed algorithm improve the efficiency of streamline visualization algorithm and can achieve a higher parallel speedup.
The Future of Education After Covid-19, What is the Role of Technology

This article mainly focused on upcoming technology and strategical approach for the future education system. According to previous literature work about the education improvement, technology development, and implementation of Artificial Intelligence (AI) are discussed in detail. However, the factors affecting the future technology after cataclysmic are also mentioned in brief. The covid-19 plays a crucial role in the vigorous implementation of the newer technologies in the education system, due to the sudden increased demand around the world. The feasibility of future education and technology is more dominant over the traditional education culture because the new concept which was thought to be imaginary is now possible to implement. Moreover, before covid-19, there are many prestigious institutions already providing digital and online education. This article also glimpses a teacher and student relationship through the virtual mode. In subsequent years the education system provides efficient learning methodologies in the virtual mode because previously it was only a theoretical-based teaching method, nowadays the experimentation and result analysis are also performed through animation by digital mode. The paper also highlights the challenges faced by human society in the implementation of the future education system, article urges technological optimization for the educational variables and developing it more efficient for future needs.
Enhancing the RSA algorithm using DH principles towards CIA compliance

Security remains a major concern in networks and internet communication. Incidents of cyber-attacks are still increasing. Data needs to be protected from these cyber-attacks. Several approaches to data security have been proposed in the past. The existing solutions cannot provide Confidentiality-Integrity-Authenticity (CIA) at once. In this study, we explored the possibilities of incorporating the DH principles into the RSA model to enhance the RSA model towards CIA compliance. It is our desire to design a hybrid RSA model in which the original RSA model’s encrypted messages are signed using DH principles. The proposed designs are motivated by the similarity noted in the properties of the parameter e in the RSA public key (n, e) and the g in the DH public key (n, g). We concatenated the output of the RSA to DH views to give rise to a hybrid model. An experiment was administered in which the dependent variables included memory usage, CPU usage, number of live threads, number of classes, total compile time and process CPU time. We used plain text as the independent variable. Comparison of the ordinary RSA and the hybrid RSA-DH model was done using the six dependent variables. Also, we used benchmarking software to compare the two algorithms. For each of CPU usage, maximum memory usage, number of live threads, number of classes loaded, process CPU time, and total compile time, the performance of the hybrid RSA model was significantly higher than those for the ordinary RSA, with P values consistently above 0.0001. Precisely, the hybrid RSA model outperformed the ordinary RSA model in all categories measured. We concluded that the hybrid RSA is effective.
Quantum Potential Speed-Up of Combinatorial Optimization Solvers for AUV Path Planning

Quantum technology enters in the new era of Noisy Intermediate-Scale Quantum (NISQ) with 50-100 qubits quantum computers already available. It is now important to compare quantum methods with the classical approach to solve optimization problems in order to determine if there is a potential speed-up and what quantum technologies will be able to perform. The Quantum Approximate optimization Algorithm (QAOA) is a hybrid quantum-classical variational algorithm designed to tackle combinatorial optimization problems. We provide the method to apply QAOA on a shortest path problem and a study of the performance of this quantum method by using heuristic strategies based on observed patterns in optimal parameters. First, we will present the state of the art of quantum hybrid algorithms and technologies. Then we will discuss the effect of the shape of the graph, the number of valid solutions, and the number of nodes of the graph we consider on the quality of the QAOA’s results and on the patterns in optimal variational parameters.
A Survey on the Use of Computational Intelligence Techniques in Software Engineering

The growing need for complex applications in diversified fields imposes a significant challenge on the development of software systems. There has been a recent increase in interest in the applications of computational intelligence for software engineering problems. To manage uncertain and imperfect information, recent developments focus on the use of computational intelligence techniques in software engineering. This paper aims to determine the Computational Intelligence techniques that have been commonly used in the software development life cycle (SDLC). The focus of this work is on the practices used during the past decade. The results show that nature-inspired algorithms are commonly practiced by researchers and practitioners in almost every phase of SDLC.
Distributed Fault Diagnosis System of Mining Dump Truck Based on Server

In order to improve the operating performance of mining dump trucks, reduce failure maintenance time and maintenance costs. A server-based vehicle-ground distributed fault diagnosis system is designed and implemented. The system is divided into two parts: the vehicle-mounted data acquisition subsystem and the ground fault diagnosis subsystem. The vehicle-mounted subsystem collects data through the CAN communication bus, performs digital filtering on it, and uses the 4G network to send the data to the ground server. The server has a MY SQL database and a BDD-based fault tree diagnosis algorithm to store and analyze the data. The real-time operating status of the mining dump truck is displayed on the display screen in an image, digital, and tabular manner. In addition, the system also has functions such as fault alarm, fault warning, vehicle information query, and parts management. Experimental verification shows that the system can realize real-time operation monitoring and fault diagnosis of mining dump trucks.
Introducing PIVOT: Predictive Incremental Variable Ordering Tactic for Efficient Belief Space Planning

Belief Space Planning (BSP) is a fundamental technique in artificial intelligence and robotics, which is widely used in the solution of problems such as online autonomous navigation and manipulation. Unfortunately, BSP is computationally demanding, especially when dealing with high-dimensional state spaces. We thus introduce PIVOT: Predictive Incremental Variable Ordering Tactic, a novel approach to improve planning efficiency. Although variable ordering has been extensively used for the state inference problem, variable ordering specifically for planning has hardly been considered. Interestingly, this tactic can also lead to improved loop-closing efficiency during state inference. We use the approach in an active-SLAM scenario, and demonstrate a significant improvement in efficiency. This approach follows our previous work regarding efficient BSP via belief sparsification.
A Computational Framework for Organizing and Querying Cultural Heritage Archives

Now that within the humanities more and more data sources have been created, a new opportunity is within reach: the searching of patterns spanning across data sources from archives, museums, and other cultural heritage institutes. These institutes adopt various digitization strategies based on differences in selection procedures. This results in heterogeneous data sources with a huge impact on the accessibility and interoperability of data within and between these distributed collections. We identify three interrelated challenges that researchers may encounter when querying such distributed data sources, namely query formulation , source selection , and alignment of data sources . We present a multiagent architecture to overcome these challenges and discuss a prototype implementation of the architecture by developing and integrating various technologies. To measure and validate the performance of integrated technologies that meet these three interrelated challenges, we propose a methodology for setting up and conducting experiments. We take an existing data source for which we can establish a baseline query result, against which we measure the precision and recall performance, and create various sets of data sources with realistic characteristics. We report on the results of a number of experiments that show the performance of the developed and integrated technologies.
A lightweight model based on YOLOv5 for helmet wearing detection

Helmet wearing is a major concern for the safety and protection of people on the construction site. Statistic data demonstrate that injuries and accidents occur mainly due to not following prescribed procedures, i.e., not wearing helmet. Camera-based surveillance system can conduct online monitoring task to detect such abnormalities through captured images with image processing system analysis. Although deep learning-based method can achieve higher image identification performance, it requires extensive hardware support of the computational resources. Therefore, it is imperative to design a lightweight network with lower hardware requirement to address such problem. In this paper, a GhostNet, YOLOv5 and a lightweight network are combined to design a model to analyze the image for online monitoring with faster processing speed. The performance of the proposed model is compared with those of the mainstream lightweight models. Experimental results have demonstrated that the proposed model has higher detection accuracy and flexible adaptability.
Learning Media Design Based on Augmented Reality Introduction to Rare Plants

This research is motivated by the lack of technology-based learning media, namely Augmented Reality, the use of Augmented Reality can be used as an interactive and interesting learning media solution. By utilizing Augmented Reality technology, the introduction of rare plants that were originally presented in the form of images can be added with 3-dimensional information that is displayed virtually which will later use a smartphone device, so that the learning process is more interesting. The purpose of this research is to design learning media based on Augmented Reality in recognizing rare plants, this is because Augmented Reality is an application that can use a database in the form of 3-dimensional animation that affects students in learning activities. The media contained in Augmented Reality is a combination of real illustrations in the technological process.
Laser Positioning Control of a Technical Vision System Based on Stereo Depth Data Feedback

In this paper is presented a two-step methodology for robot navigation. A Technical Vision System (TVS) is integrated with two webcams in stereo vision configuration to work as two systems in collaboration for the use of their strengths. The precise and accurate spatial coordinates provided by the TVS and the optimized scanning time reached with the integration of the stereo vision. The first step is based on the fast scanning of the scene by the stereo vision to enable the selection of a Region of Interest (ROI) for the second step, which is based on the laser position control of the TVS in the ROI for high resolution measurement. Preliminary angular coordinates obtained with stereo vision reached an error of 1.269 degrees, which was reduced to 0.744 degrees with the scanning of TVS in an optimized time.
Computation of Blockchain Readiness Under Partial Information

As the blockchain ecosystem gets more mature many businesses, investors, and entrepreneurs are seeking opportunities on working with blockchain systems and cryptocurrencies. A critical challenge for these actors is to identify the most suitable environment to start or evolve their businesses. In general, the question is to identify which countries are offering the most suitable conditions to host their blockchain-based activities and implement their innovative projects. The Blockchain Readiness Index (BRI) provides a numerical metric (referred to as the blockchain readiness score) in measuring the maturity/readiness levels of a country in adopting blockchain and cryptocurrencies. In doing so, BRI leverages on techniques from information retrieval to algorithmically derive an index ranking for a set of countries. The index considers a range of indicators organized under five pillars: Government Regulation, Research, Technology, Industry, and User Engagement. In this paper, we further extent BRI with the capability of deriving the index – at the country level – even in the presence of missing information for the indicators. In doing so, we are proposing two weighting schemes namely, linear and sigmoid weighting for refining the initial estimates for the indicator values. A classification framework was employed to evaluate the effectiveness of the developed techniques which yielded to a significant classification accuracy.
Identification of Distinctive Behavior Patterns of Bots and Human Teams in Soccer

The design of human-like AI agents requires evaluation methods that check both robustness of the system and its believability. In this paper we attempt to examine whether is possible to assess similarity of play styles between different human teams and artificial teams in soccer. We rely on “behavior fingerprints” based on heat maps and their comparison using dot product. Our method shows no distinctive differences between the fingerprints of human teams, however, clearly indicates the difference between human teams and artificial teams. This approach is aimed to assist the design of human-like soccer teams but can also be useful in the domain of sports analytics.
E-commerce Clothing Review Analysis and Model Building

Understanding client conclusions is of high significance in advertising systems today. Not exclusively will it give organizations an understanding on how clients see their items as well as administrations, yet it will likewise give them an idea en route to work on their offers. This paper endeavors to know the connection of different factors in client surveys on women clothing Internet business information, and to classify every review whether it recommends the product to buy or not and if it consists of positive or negative or neutral sentiment. To understand these objectives, we utilized univariate and multivariate examinations on dataset includes separated from survey messages, which we carried out NLP procedures for suggestion and feeling arrangement. Results have shown that a suggestion might be a solid pointer of a positive opinion score and the other way around. Then again, appraisals in item surveys are fuzzy pointers of sentiment scores. We likewise discovered that the multinomial naïve Bayes was prepared to arrive at a F1-score of 0.9596 for sentiment classification and 0.928355 for opinion arrangement.
Learning event-driven switched linear systems

We propose an automata theoretic learning algorithm for the identification of black-box switched linear systems whose switching logics are event-driven. A switched system is expressed by a deterministic finite automaton (FA) whose node labels are the subsystem matrices. With information about the dimensions of the matrices and the set of events, and with access to two oracles, that can simulate the system on a given input, and provide counter-examples when given an incorrect hypothesis automaton, we provide an algorithm that outputs the unknown FA. Our algorithm first uses the oracle to obtain the node labels of the system run on a given input sequence of events, and then extends Angluin's <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L^{\ast}$</tex> -algorithm to determine the FA that accepts the language of the given FA. We demonstrate our learning algorithm on a numerical example.
Vehicle Detection and Tracking Based on Interest Points of Visual Appearance

This paper presents a unique pattern of vehicle detection with the help of fundamental and successive algorithms. The characteristics of the vehicle are the important parameters to identify vehicles. A good number of corner points is compacted inside a vehicle region which is considered as the initial requirement for an algorithm. The densely packed corner points are grouped. This grouping gives a hint of points which are associated with each vehicle and they play a key in detection of vehicles. Once the grouping is performed, the non-vehicle region is segmented. The corner points are tracked with a Lucas-Kanade algorithm in order to maintain the stability of corner points. The detection rate with the proposed method is 93.95%.
Retraction: Advanced Deep Learning Model for Future Forecasting of COVID-19 (J. Phys.: Conf. Ser. 1916 012147)

This article (and all articles in the proceedings volume relating to the same conference) has been retracted by IOP Publishing following an extensive investigation in line with the COPE guidelines. This investigation has uncovered evidence of systematic manipulation of the publication process and considerable citation manipulation. IOP Publishing respectfully requests that readers consider all work within this volume potentially unreliable, as the volume has not been through a credible peer review process. IOP Publishing regrets that our usual quality checks did not identify these issues before publication, and have since put additional measures in place to try to prevent these issues from reoccurring. IOP Publishing wishes to credit anonymous whistleblowers and the Problematic Paper Screener [1] for bringing some of the above issues to our attention, prompting us to investigate further. [1] Cabanac G, Labbé C and Magazinov A 2021 arXiv: 2107.06751v1 Retraction published: 23 February 2022