Reviewerly/exHarmony huggingface dataset:
Research on tracking and filtering technology of high-speed target in near space

The emergence of HTV-2, X-51A and other near space hypersonic vehicles poses a new challenge to radar target tracking. Near space hypersonic vehicle has the characteristics of high speed and high maneuverability, but the tracking performance of conventional tracking algorithm is low, which can not meet the operational requirements. In this paper, the technical difficulties of near space target tracking are described. By analyzing the motion characteristics of near space hypersonic vehicle, the dynamics model of HTV-2 is established, and a tracking method based on interacting multiple model (IMM) for near space vehicle is proposed. At the same time, considering the unscented Kalman filter (UKF) algorithm has a better filtering effect on strong nonlinear object. The simulation results show that, the interact multiple model－unscented Kalman filter (IMM-UKF) algorithm is better than the single model EKF algorithm.
Deep Learning Approach for Bullying Classification on Twitter Social Media with Indonesian Language

Cyberbullying is usually through social media intermediaries. The victim of cyberbullying will feel very depressed because of the wide spread of social media that can be seen and accessed by many people and also the privacy of the victim has no meaning, even all the shame and ugliness of the victim can be accessed by many people. The purpose of this study was to analyze the text documents on social media and then classify them into two classes, namely indications of bullying or cleanliness. Word2Vec and LSTM (Long Short Term Memory) will be combined in this classification model. Based on the testing phase, it can be concluded that there is still a lot of bullying on social media, especially on Twitter. This is evident from a large amount of Twitter data that 81.6% contains bullying words or sentences. The results of this study can be used as a basis for social media managers to take decisive action against bullies.
Solver-Aided Multi-Party Configuration

Configuring a service mesh often involves multiple parties, each of whom is responsible for separate portions of the overall system. This can result in miscommunication, silent and sudden errors, or a failure to meet goals.
Research on the Construction of Construction Engineering Cost construction Information System Based on BIM Technology

Abstract This subject is based on cost construction and BIM technology, researches the project cost construction system based on BIM technology, analyses the system’s related business processes and system processes; through analysis of the existing cost construction system, finds the existing system Existing problems, it is concluded that applying BIM technology to the project cost construction system can directly solve many problems faced by traditional systems. It analyses that the two systems can exchange data through the data interface. Finally, a cost construction model based on BIM technology is constructioned. Through the research in this article, I hope to apply it to the actual production and life of the enterprise, bring benefits to the enterprise, promote the informationization and systemization of the cost construction of the enterprise, and provide accurate data support for the project decision makers of the enterprise in the future.
A New Classification Technique Based on the Combination of Inner Evidence

Data with high uncertainty and ambiguity is challenging for the classification task. EKNN is a popular evidence theory-based classification method developed for handling uncertainty data. However, as a distance-based technique, it also suffers from the problem of high dimensionality as well as performs ineffectively with mixed distribution data where closed data points originated from different classes. In this paper, we propose a new classification method that can softly classify new data upon each separate cluster which can remedy the overlapping data problem. Moreover, pieces of evidence induced from the trained clusters are combined using Dempster’s combination rule to yield the final predicted class. By defining the mass function of evidence with the weight factor based on the distance between new data points and clusters’ centers, it helps reduce the computational complexity which is also a problem in distance-based k-NN alike methods. The classification experiment conducted on various real data and popular classifiers has shown that the proposed technique has the results comparable to state-of-the-art methods.
Nonlinear Function Optimization Based on Adaptive Genetic Algorithm

Genetic algorithm is widely used to solve complex optimization problems especially for the optimization of multimodal function, due to the independence, strong robustness, strong global selection and global searching ability. In order to overcome the shortcomings that standard genetic algorithm has such as relatively weak local searching ability and premature convergence is prone to occur, adaptive genetic algorithm combined with nonlinear programming method is employed into the optimization process of nonlinear functions in this paper. Simulation performance shows that the algorithm can adaptively achieve the global optimal solution and obtain more optimal solution faster than traditional genetic algorithm.
Research on Path Planning Based on Artificial Potential Field Algorithm

Artificial potential field (APF) algorithm is widely used in path planning research because of its simple structure, good real-time performance and smooth path generated to solve the problem of obstacle avoidance in task space environment. Aiming at the problem of inaccessibility and local minimum of artificial potential field algorithm, a hybrid algorithm of piece wise repulsive potential function and virtual target point obstacle filling is proposed. The results show that improved APF algorithm can solve problems of traditional APF algorithm to a certain extent.
An Analytics Toolbox for Cyber-Physical Systems Data Analysis: Requirements and Challenges

The fast improvement in telecommunication technologies that has characterised the last decade is enabling a revolution centred on Cyber-Physical Systems (CPSs). Elements inside cities, from vehicles to cars, can now be connected and share data, describing both our environment and our behaviours. These data can also be used in an active way, by becoming the tenet of innovative services and products, i.e. of Cyber-Physical Products (CPPs). Still, having data is not tantamount to having knowledge, and an important overlooked topic is how should them be analysed. In this contribution we tackle the issue of the development of an analytics toolbox for processing CPS data. Specifically, we review and quantify the main requirements that should be fulfilled, both functional (e.g. flexibility or dependability) and technical (e.g. scalability, response time, etc.). We further propose an initial set of analysis that should in it be included. We finally review some challenges and open issues, including how security and privacy could be tackled by emerging new technologies.
WITHDRAWN: Electronic music creation based on complex embedded system

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn at the request of the Editor in Chief. Subsequent to acceptance of this special issue paper by the responsible Guest Editor Sundhararajan Mahalingam, the integrity and rigor of the peer-review process was investigated and confirmed to fall beneath the high standards expected by Microprocessors & Microsystems. There are also indications that much of the Special Issue includes unoriginal and heavily paraphrased content. Due to a configuration error in the editorial system, unfortunately the Editor in Chief did not receive these papers for approval as per the journal's standard workflow.
Optimization of Fuzzy Controllers for Autonomous Mobile Robots Using the Stochastic Fractal Search Method

This paper aims at measuring the improvement in performance and efficiency of fuzzy controllers after being optimized. The Stochastic Fractal Search (SFS) method is used to optimize the membership functions of a fuzzy controller for an autonomous mobile robot. The experimentation is based on getting the method to reduce the error that exists between the desired trajectory and the real trajectory of the robot. First, the efficiency of the SFS method is tested with benchmark functions, which aims at finding the overall minimum for each of the functions. The SFS method is inspired by fractals, a kind of fragmented structure, which grows at every scale with self-similarity. After testing with different dimensions in the Unimodal functions and Multimodal functions, we proceed with the optimization of the fuzzy controller. Simulation results show the advantages of SFS in fuzzy control optimization.
Question answering system for agriculture domain using machine learning techniques: literature survey and challenges

Natural language processing (NLP) is a part of artificial intelligence and computer science. Question answering system that provides an interaction between computers and human languages, it is role to program the computers to process and analyse the large amounts of human language data. It is one of the important aspects in all domains and helps in every domain to satisfy the people requirements. Now a day's almost all people are literate and using the mobile phone to receive the up to date information as per their requirement. QAS can be used to provide succinct information for the questions that are being asked by user and it provides answers to users based on some rules which are stored in the data base. This survey paper details about what is question answering system and its previous related work with respect to methods, technologies or approaches that were used. It provides research gaps and future scope to the researches in the reviewed papers, which helps researchers to choose a suitable solution to their problems wherein available comparative analyses have been provided.
PEMBANGUNAN APLIKASI BORANG AKREDITASI PRODI BERBASIS APS 4.0 SUB KEGIATAN PENGELOLAAN BASIS DATA

Abstrak Automatic First Arrival Picking adalah sistem yang bisa mendapatkan P-Wave atau gelombang pertama yang datang pada gelombang gempa bumi. Karena P-Wave adalah gelombang yang pertama datang maka dibutuhkan penelitian yang dapat mendapatkan kedatangan P-Wave secara otomatis. Pada penelitian ini memiliki tujuan untuk membuat sistem Automatic First Arrival Picking dan menguji performansi metode yang nantinya akan mendapatkan hasil Picking P-Wave dan akurasi dari metode klasifikasi Support Vector Machine (SVM). Data sampel gempa bumi harus melalui tahap Feature Extraction terlebih dahulu agar hasilnya dapat digunakan sebagai masukan pada metode klasifikasi SVM. Pada data sampel penelitian ini S-Wave dan Noise dianggap sebagai No P-Wave, jadi hanya ada dua klasifikasi pada SVM yaitu P-Wave dan No P-Wave. Hasil pada penelitian tugas akhir ini mendapatkan sistem Automatic First Arrival Picking dengan performansi accuracy sebesar 88.00%, precision sebesar 90.00%, recall sebesar 73.50%, f1-score sebesar 78.00%. Kata Kunci : Picking, First Arrival Picking, P-Wave, SVM Abstract Accreditation is a formal recognition given by an accreditation organization to institutions or organizations. Simply accreditation is the value of the campus or school in several criterias. Therefore, accreditation is an important point of an institution or organization. To get the assessment, tertiary institutions must meet the nine criterias set by the BAN-PT or the National Accreditation Board for Higher Education. At the time of making accreditation forms currently using the manual method. It causes the accreditation process to be long and complicated. This problem gives an idea to create a tool to facilitate the compilation of data forms more quickly and efficiently. Information system is a system that provides information for information technology in an organization. The rapid development of technology in the field of information technology makes efficiency in disseminating information from one party to another quickly. The Final project conducted by this author about the application of APS 4.0 accreditation forms, using the normalization database, database design using relational databases, this project expected to help produce application compilation of files and data forms to facilitate accreditation based on APS 4.0 . Using the relational database method in database development, and the use of functionality, structural and black box methods for testing the study program form application can be said to have run perfectly and meet the overall system requirements. The use of the black box method to see the functionality and structural data base that has been created, creating a database in this study program form application can reduce data redundancy that is not needed on the system. Keywords: Accreditation, Forms, normalization database, relational database.
Capability Maturity Model for Agricultural Supply Chain Management Software

Compared to the supply chain management of other business domains, agricultural supply chain management is affected with issues such as diversity of production and demand, the bulkiness of produce, perishability, seasonality, harvest uncertainty, and climate complexity. These issues are more prominent in rural agricultural sector. Availability of mature supply chain management processes and systems can enhance the productivity of rural agricultural communities. This chapter proposes a five-stage capability maturity model for the implementation and maintenance of supply chain management processes in farm management information systems. The capability maturity model is a valuable aid to determine the digitized supply chain process' ability to consistently and continuously achieve improvement and organizational objectives. The model is proposed based on the findings of the analysis of 121 supply chain management software in the farming sector, the Capability Maturity Model by the Software Engineering Institute, and the Supply Chain Process Management Maturity Model.
WITHDRAWN: A novel secure and high-entropy hardware password manager

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn as part of the withdrawal of the Proceedings of the International Conference on Emerging Trends in Materials Science, Technology and Engineering (ICMSTE2K21). Subsequent to acceptance of these Proceedings papers by the responsible Guest Editors, Dr S. Sakthivel, Dr S. Karthikeyan and Dr I. A. Palani, several serious concerns arose regarding the integrity and veracity of the conference organisation and peer-review process. After a thorough investigation, the peer-review process was confirmed to fall beneath the high standards expected by Materials Today: Proceedings. The veracity of the conference also remains subject to serious doubt and therefore the entire Proceedings has been withdrawn in order to correct the scholarly record.
Construction and Application of Computer-based English Teaching Management System

Abstract Nowadays, teaching and learning based on computer information technology is becoming more and more popular and will undoubtedly become a standardized teaching mode in the post-popular era. This article discusses in detail online English teaching based on the Internet: First, it defines the online online English teaching; then it explains some principles and characteristics, summarizes several common online online teaching models; finally, it points out the advantages and advantages of the current online online English teaching limitation. At the same time, the article also studies the construction strategy of a computer-based college English teaching management system in order to provide help for college English teaching management.
Evaluation of Machine Learning Algorithms for Automated Management of Wireless Links

Machine learning, a subfield of artificial intelligence, has been widely used to automate tasks usually performed by humans. Some applications of these techniques are understanding network traffic behavior, predicting it, classifying it, fixing its faults, identifying malware applications, and preventing deliberate attacks. The goal of this work is to use machine learning algorithms to classify, in separate procedures, the errors of the network, their causes, and possible fixes. Our application case considers the WiBACK wireless system, from which we also obtained the data logs used to produce this paper. WiBACK is a collection of software and hardware with auto-configuration and self-management capabilities, designed to reduce CAPEX and OPEX costs. A principal components analysis is performed, followed by the application of decision trees, k nearest neighbors, and support vector machines. A comparison between the results obtained by the algorithms trained with the original data sets, balanced data sets, and the principal components data is performed. We achieve weighted F1-score between 0.93 and 0.99 with the balanced data, 0.88 and 0.96 with the original unbalanced data, and 0.81 and 0.89 with the Principal Components Analysis.
Non-native vs Native English Speakers User Experience in HMD vs Desktop Immersive Training

During previous years, researchers from TUKE (Technical University of Kosice) and MSU (Mississippi State University) collaborated on building a virtual immersive training environment for law enforcement officers. The training tool was designed for operation in Virtual Reality (VR) using HMD (Head Mounted Display) and 3D Desktop modes. The goal was to control a Jaguar V4 robot in a virtual environment using both joystick and voice commands, because not all functions could be accessed by hand controllers alone. The speech recognition was designed for English speakers. Firstly it was tested at MSU with five US participants and later the same tests were repeated with 16 students at TUKE, who were non-native English speakers. This article provides an overview of the experiment and a comparison of cognitive aspects and user acceptance levels resulted from these trials on different continents and different English speakers (native and non-native ones).
A Study on Decision Making by Estimating Preferences Using Utility Function and Indifference Curve

To get the desired goal, in various fields, decision making is being employed, which has been gaining attention in current decades, for which efficient evaluation of individual preferences should be done. Preferences are vital elements of decision making to estimate the effects regarding individual behavior and their social status. The main objective of the paper is to present various quantitative techniques regarding preference relation in a decision making process. This paper shows how the decision making process can be enhanced through preferences and utility function. The indifference curve is also incorporated for decision making involving uncertainty. This study presents the existing studies by investigating the relationship between the choices, preferences and indifference curve in the decision making process.
Subjective Metrics-Based Cloud Market Performance Prediction

This paper explores an effective machine learning approach to predict cloud market performance for cloud consumers, providers and investors based on social media. We identified a set of comprehensive subjective metrics that may affect cloud market performance via literature survey. We used a popular sentiment analysis technique to process customer reviews collected from social media. Cloud market revenue growth was selected as an indicator of cloud market performance. We considered the revenue growth of Amazon Web Services as the stakeholder of our experiments. Three machine learning models were selected: linear regression, artificial neural network, and support vector machine. These models were compared with a time series prediction model. We found that the set of subjective metrics is able to improve the prediction performance for all the models. The support vector machine showed the best prediction results compared to the other models.
Quantum Neural Networks and Quantum Intelligence

The concept of quantum artificial intelligence (QAI) combines the machine learning methods and the ideas of quantum information processing, which enables an exponential quantum acceleration of the learning and the recognition processes thanks to the quantum parallelism of information processing. In this contribution, we consider both the results in quantum generalization of the machine learning methods, and the existing experimental implementations of quantum neural networks (QNN), also known as adiabatic quantum computers, made on SQUID magnetic flux qubits or optical devices. We also consider the recent progress of artificial intelligence due to the flourish of deep learning methods and their quantum generalizations related to feature extraction in Hilbert spaces. While the present commercial implementations of quantum neural networks made on SQUIDs operate at extremely low temperatures, less than 0.1 K, the construction of future portable QAI devices, operating at room temperature, implies the use of open quantum system theory, where the interaction with fluctuating environment is essential. The relevance of the quantum information processing by open quantum system to real work of the brain is discussed.
Feature Fusion Based Background Model Learning for Video Object Detection

In this paper, a novel feature fusion based strategy is proposed to model complex background to effectively detect the foreground in a real world scene. In order to take care of bad weather conditions of a scene having low visibility and dynamic entities in the background, we have proposed “m-smoothing” filter to obtain the local features. This local feature is fused with the Local Binary Pattern (LBP) based feature to generate a new feature frame for background modeling. Background model learning takes place in the fused feature space to extract the foreground. The proposed method is tested with change detection data sets (Blizzard and Wet Snow) and the performance of our proposed method outperform LBP, STLBP and C-EFIC based methods.