Reviewerly/exHarmony huggingface dataset:
Partitioned Gaussian Process Regression for Online Trajectory Planning for Autonomous Vehicles

Gaussian process regression and ordinary kriging are effective methods for spatial estimation, but are generally not used in online trajectory-planning applications for autonomous vehicles. A common use for kriging is spatial estimation for exploration. Kriging is limited by the necessary covariance matrix inversion and its computational complexity of O(n<sup>3</sup>), where <tex>$n$</tex> represents the number of measurements taken in a sparsely-sampled field. Using the Sherman-Morison matrix inversion lemma, the complexity can be reduced to O(n<sup>2</sup>). This work focuses on further improving the computational time required to conduct spatial estimation with partitioned ordinary kriging (POK) for online trajectory-planning. A recursive algorithm is introduced to quickly subdivide a field for local kriging, reducing the computation time. We show computational time decreases between ordinary kriging with a regular inverse (OK), the iterative inverse ordinary kriging (IIOK), and POK with the iterative inverse method. Computation times are also compared between OK, IIOK, and POK methods for trajectory planning using a highest variance criterion and linear trajectory segments.
Web-based Project and Service Search “O-ser” Application

The purpose of this study is to build an application that provides a list of relevant jobs for job seekers according to their preferences and especially those required by freelance workers in Indonesia. The research method used includes three main parts, namely literature study, observation of similar applications, and surveys. Literature study includes four main parts, namely literature study, observation of similar applications, prototyping, and evaluation. Literature study includes reading and studying books, previous theses, literature related to the topic, and drawing conclusions from all references. Observation of similar applications is carried out by observing several similar applications that have similar functions and analyzing the advantages and disadvantages of each of these applications as a reference. A working application prototype is built based on the prior requirement gathering. The evaluation was conducted using the System Usability Scale survey which is carried out by providing an online form to the public. The results achieved from this study based on the reliability test show that this research instrument is reliable or trustworthy. The validity test shows that all question items can be used as a means of collecting data on the aspects studied. The results of the calculation of the System Usability Scale (SUS) method from a total of 40 participants and 10 SUS questions, obtained an average score of 75.56 and the O-ser application got a percentile rank score for SUS class B with a score of &#x2A7E;74. In conclusion, the O-ser application makes it easier for users, especially workers, to find jobs according to their desired preferences, and job providers make a list of the jobs needed.
Path Planning for Underwater Glider Based on Ant Colony Algorithm Guided by Artificial Potential Field

This paper studies the path planning of underwater gliders in a global static environment, and proposes an improved algorithm based on ant colony algorithm, which eliminates the problem of slow convergence due to the lack of initial pheromone in path planning of ant colony algorithm. Firstly, according to the operating characteristics of the glider, the grid method is used to model the known marine environment; Secondly, the improved artificial potential field method is introduced to construct the heuristic information function to speed up the convergence speed of the ant colony algorithm; Finally, the ant colony algorithm is used to search Path, improve the original pheromone update rule of the ant colony algorithm. The simulation results show that the algorithm in this paper is effective and superior.
Contextual Networks and Unsupervised Ranking of Sentences

We construct a contextual network to represent a document with syntactic and semantic relations between word-sentence pairs, based on which we devise an unsupervised algorithm called CNATAR to score sentences, and rank them through a bi-objective 0-1 knapsack maximization problem over topic analysis and sentence scores. We show that CNATAR outperforms the combined ranking of the three human judges provided on the SummBank dataset under both ROUGE and BLEU metrics, which in term significantly outperforms each individual judge’s ranking. Moreover, CNATAR produces so far the highest ROUGE scores over DUC-02, and outperforms previous supervised algorithms on the CNN/DailyMail and NYT datasets. We also compare the performance of CNATAR and the latest supervised neural-network summarization models and compute oracle results.
Deformable deep networks for instance segmentation of overlapping multi page handwritten documents

Digitizing via scanning the physical artifact often forms the first primary step in preserving historical handwritten manuscripts. To maximally utilize scanner surface area and minimize manual labor, multiple manuscripts are usually scanned together into a scanned image. Therefore, the first crucial task in manuscript content understanding is to ensure that each of the individual manuscripts within a scanned image can be isolated (segmented) on a per-instance basis. Existing deep network based approaches for manuscript layout understanding implicitly assume a single or two manuscripts per image. Since this assumption may be routinely violated, there is a need for a precursor system which extracts individual manuscripts before downstream processing. Another challenge is the highly curved and deformed boundaries of manuscripts, causing them to often overlap with each other. To tackle such challenges, we introduce a new document image dataset called IMMI (Indic Multi Manuscript Images). To improve the efficiency of dataset and aid deep network training, we also propose an approach which generates synthetic images to augment sourced non-synthetic images. We conduct experiments using modified versions of existing document instance segmentation frameworks. The results demonstrate the efficacy of the new frameworks for the task. Overall, our contributions enable robust extraction of individual historical manuscript pages. This in turn, could potentially enable better performance on downstream tasks such as region-level instance segmentation within handwritten manuscripts and optical character recognition.
Generating Multi-agent Patrol Areas by Reinforcement Learning

In this paper, we designed reinforcement learning environment for distributed patrolling agents. In the partially observable environment, the agents take actions for each one's interest and the non-stationary problem in multi-agent setting encourages the agents not to invade other agent's region. In our environment, the patrolling routes for the agents are generated implicitly. We suggested different types of the environments and evaluated with different initial positions of the agents. We also show how the reinforcement learning algorithm changes the distribution of agents as training time goes.
Solving Truss Structures Problem by Size Optimizing using Red Deer Algorithm

This paper presents an approach to minimize the truss structures weights with discrete and continuous variables using Red Deer Algorithm (RDA). The RDA is one of the most recent meta-heuristic algorithms presented to solve optimization problems in 2016 by Amir and Mostafa. This algorithm mimics the breeding season of Scottish red deer animal, starting from roaring and ending with mating. In this paper, the RDA has been evaluated on three truss structure problems and has been compared with other six meta-heuristic algorithms. The results conclude that the RDA had a good performance for the 10-bar truss structure, and could enhance the compatibility equivalence constraints to minimize the weight of the bar design, and accelerate the convergence rate effectively. On the other hand, the study demonstrates that the RDA algorithm was effectively used to achieve a good enough solution for the 25-bar problem, and ranked as the fifth algorithm for the 72-bar truss structure.
Applying Knowledge Map System for Sharing Knowledge in an Organization

Knowledge management (KM) is one of the most important systems that need to be applied in an organization. It has benefits for improving efficiency and productivity in a company to achieve organizational goals. One of the tools that will make finding knowledge easier for employees is the knowledge map. It allows for visualizing the knowledge and link them by creating relationships between domains. The selected organization has several branches in different locations where it hard for the employees to reach the knowledge. The studied organization has applied knowledge management practices however there are issues with the current system. The four main problems related to KM are less communication with decision-maker, no standards for applying KM, not all staff apply KM practices, no real process of converting tacit to explicit. The suggested solutions are to apply the SECI model which is the act of socialization, externalization, combination, and internalization, and implement knowledge mapping. The paper will provide steps for implementing the suggested solutions.
A novel structure automatic-determined Fourier extreme learning machine for generalized Black–Scholes partial differential equation

This paper focuses on the numerical solutions of generalized Black–Scholes partial differential equations and proposes a novel structure automatic-determined Fourier extreme learning machine (SAD-FELM) to solve the partial differential equations. In the SAD-FELM, the Fourier extreme learning machine (FELM) is designed. The product of two Fourier polynomials is used to serve as basis functions in the hidden layer. The optimal sampling frequencies of two Fourier polynomials depend on the distribution of data samples. Then, the improved extreme learning machine algorithm is used to solve the linear equation systems derived from the generalized Black–Scholes partial differential equations. Furthermore, to determine the optimal sampling frequencies and the number of neurons, this paper designs a structure automatic-determined algorithm based on metaheuristic algorithms. The research conducts comparative experiments among the SAD-FELMs based on different metaheuristic algorithms. The experimental results show that the numerical solutions obtained by the SAD-FELM based on the hunger games search algorithm are the most accurate. Finally, compared with other methods, the superiority and feasibility of the SAD-FELM are proved.
Toxic Comment Detection: Analyzing the Combination of Text and Emojis

Detection of toxicity in online commentary is a growing branch of Natural Language Processing (NLP). Most research in the area rely only on text-based toxic comment detection. We propose a machine learning approach for detecting the toxicity of a comment by analyzing both the text and the emojis within the comment. Our approach utilizes word embeddings derived from GloVe and emoji2vec to train a bidirectional Long Short Term Memory (biLSTM) model. We also create a new labeled dataset with comments with text and emojis. The accuracy score of our model on preliminary data is 0.911.
Basketball Foul Model and Judgment System Proposal

Recently, the practical applications of artificial intelligence (AI) in various fields have increased owing to advancements in AI technologies, such as image recognition and machine learning. Video judgment is often used in ball games and athletics for arbitration, e.g., the Hawk-Eye system in tennis. However, bad decisions are a frequent problem in sports, and the arbitration results tend to differ based on the subjectivity of the human referees; hence, a system that ensures objective judgment is required. In this paper, we propose a solution to judge fouls in basketball automatically. First, the players are designated as research objects; then, the objects in basketball fouls are classified, and recognizable motions are defined for each category. Next, a basketball foul model was designed, and current motion information of each object was extracted from real-life basketball match videos. Finally, a basketball foul judgment system was designed to extract the offending objects and display them in the video in real time.
Pervasive Healthcare Computing: Applications, Challenges and Solutions

Pervasive computing allows evolving the notion of autonomous systems that could probably incorporate varied technologies and electronic devices in view of the environmental setting to accomplish a set of rule-based actions and message exchanging. The area of the use of pervasive computing is very big. Evolving pervasive computing expertise is suitable in various aspects of lifetime such as healthcare system, pervasive learning and sport. In simple terms, pervasive healthcare can be precise as healthcare to everyone, every time as well as everywhere by eliminating locality, time and additional limitations although growing mutually its area coverage and overall quality. The wide-ranging definition consists of healthcare maintenance, prevention and check-ups and small-term observing, long-time observing, individual healthcare observing, incidence recognition and supervision as well as emergency mediation and conveyance and proper treatment. This chapter discusses a number of pervasive healthcare uses, relevant challenges as well as related solutions. An incorporated wireless structural design that is proposed to use the abilities of existing and progressing wireless as well as mobile networks for locality supervision, patient monitoring, intellectual emergency system and moveable telemedicine applications is presented and discussed.
Why Harmful Posts on Social Media should be regulated

Harmful content over the internet is going viral nowadays on most of the social media platforms, which has negative effects on both adults and children, especially, with the increasing usage of social media tools during the Covid-19 situation. Therefore, social media’s harmful posts should be regulated. Through the recent legislative efforts, societies are still suffering from the influence of these posts. We observe that the people who share harmful posts often hide behind the First Amendment right and the Freedom of Expression of the American Constitution. This paper focuses on suggesting possible regulations to strike down social media’s harmful content regardless of the platforms it was posted on, to safeguard society from their negative effects. In addition, it highlights the attempts by Qatar’s government to regulate social media crimes and aims to assess if these efforts are enough. Also, it will take a general look at the situation in the United States and how it is dealing with this issue.
Neurodegenerative diseases categorization by applying the automatic model selection and hyperparameter optimization method

Neurodegenerative diseases affect a large part of the population in the world and also in Mexico, deteriorating gradually the quality of patients’ life. Therefore, it is important to diagnose them with a high degree of reliability. In order to solve it, various computational methods have been applied in the analysis of biomarkers of human gait. In this study, we propose employing the automatic model selection and hyperparameter optimization method that has not been addressed before for this problem. Our results showed highly competitive percentages of correctly classified instances when discriminating binary and multiclass sets of neurodegenerative diseases: Parkinson’s disease, Huntington’s disease, and Spinocerebellar ataxias.
2D3D Medical Image Registration Based on Fully Convolutional Network Approved by Attention Mechanism

2D3D medical image registration is very vital in the fields of surgical navigation and radiotherapy. In order to solve the problems of being time-consuming and trapping in local extreme value in previous methods, a 2D3D medical image registration method based on fully convolution network and attention mechanism was proposed. In this method, a large number of digital reconstructed radiological images were input as training data to convolutional neural network, and image features were automatically extracted, so as to predict the registration transformation parameters. The network structure was based on the modified VGGNet-16 approved by an attention mechanism, and a global average pooling layer was applied to replace the fully connected layer. The experimental results showed that the average offset error of the proposed method was 0.08 mm, and the average angular error was 0.05°. Its time consumption was approximately 42ms, which was much less than the traditional method's of 305.81s. The proposed method meets the real time and precision requirements of medical image registration, avoids the iterative process of traditional methods, and effectively improves the registration efficiency.
Efficient Multi-Party Contact Tracing

Since the beginning of the Covid-19 Pandemic, many Countries adopted Contact Tracing Apps as an automatic way to trace if someone has been in contact with a Covid-19 patient. However, most existing solutions consider two party settings only. Since many people meet at the same time in real-life scenarios, one cannot extend current schemes to multi-party settings because they will not scale well and burden the device. In this paper, we propose a new contact tracing protocol that works in a multi-party setting. We evaluate our scheme to show its efficiency.
Map Marker: a Multi-Agent Pathfinder for Cohesive Groups in Real-Time Strategy Games

Multi-agent pathfinding algorithms are essential to studies on graph navigation with more than one agent. Their importance is especially evident in Real-Time Strategy (RTS) games, as even modern implementations can still lead to unintended agent behaviors that cause frustration. One such problem occurs if a group of agents breaks military formation when given the order around a big obstacle, becoming more vulnerable to enemies. To mitigate this problem, we propose Map Marker, an extension of the A * algorithm that takes in concepts from ant colony optimization to improve group cohesion. To evaluate the new algorithm we constructed three scenarios that stress the separation of groups. These scenarios were simulated with different parameters and the best results were then recreated inside the engine of the game StarCraft II and compared to A *. As a result, we found that the new algorithm effectively maintained group cohesion in all scenarios, resulting in fewer casualties and less time needed to reach the destination when the group is ambushed by enemies, in addition to being 2.8 faster than A * when computing multiple paths.
Application of Data Mining Algorithms in Data Analysis of Information Education Evaluation

Abstract In the new century, China has attached great importance to informatization work. Based on the development of education and teaching, the necessary infrastructure for informatization must be gradually improved. At the same time, we should make reasonable and effective use of information technology in the analysis of educational evaluation data, so that informatization teaching becomes an indispensable part of education. The purpose of this article is to study the application of data mining algorithms in data analysis of information education evaluation. This article will give full play to the interactive, dynamic and open advantages of the teaching resources of the network platform, combined with the ability of teachers to use information technology, rationally optimize the education evaluation data analysis system, use existing information technology to build an evaluation system, and evaluate the participating teachers. Ensure that the evaluation results are objective and effective. According to the overall framework and demand analysis of the system, the teacher information technology application ability assessment system is divided into different functional modules, each module is analyzed in detail according to user needs, and the corresponding database is designed. The evaluation system has an intuitive user interface, easy operation, and stable operation. It is suitable for first-line teachers and school administrators in primary and secondary schools to make a fair assessment of the teaching of teachers. Through the evaluation and analysis report, teachers can optimize and comprehensively understand their own teaching situation, see the problems in their teaching, and change the teaching method in time; appropriately summarize and analyze to improve the teaching level. Understand the teaching work of teachers, and realize the in-depth integration of informatization and teaching. Experimental investigations have shown that students have a great lack of experimental ability. The other four abilities are not far apart, all of which are above 70%. They are in the upper middle and upper reaches. Among them, the analysis and application tools are about 5% higher than the grade level. In general, the experimental ability must be strengthened to enhance. Let students achieve all-round development. Through the evaluation and analysis report, teachers can find their own shortcomings in teaching more clearly based on their optimization, and change the teaching methods in time; appropriately summarize and analyze to improve the teaching level.
Vzgliady uchenykh pedagogov i psikhologov na nekotorye voprosy intellektual'nogo razvitiia

This chapter examines the views of educational scientists, psychologists, methodologists on the issues of intellectual development. The learning process at school includes not only the assimilation of a certain system of knowledge, but also, mainly, the intellectual development of students. All cognitive processes make up a single system that simultaneously provides perception, memorization, discovery, and the formulation of something new.
Distributed Multitarget Tracking via Heterogeneous Fusion with 3D Radars and Infrared Sensors

This paper addresses distributed multitarget tracking over a network of heterogeneous sensors. We consider a 3D heterogeneous track-to-track fusion (T2TF) problem using radars and infrared (IR) sensors. The radar trackers use the Cartesian state vector with 3D position and velocity. The IR trackers use modified spherical coordinate (MSC) for the state vector, since use of MSC leads to improved state estimation accuracy compared with Cartesian coordinate. In order to fuse state estimates, the state estimates and associated covariances in MSC are first converted to corresponding quantities in Cartesian coordinates via one-to-one mapping function and the unscented transformation (UT). Different from the single target tracking scenario, the T2TF for multitarget tracking scenario is faced with the problem of associating tracks from different sensors. To this end, we propose a track-to-track association method, at the core of which is a 2D assignment problem taking the generalized covariance intersection (GCI) divergence as the cost function. Results of Monte Carlo simulations highlight the accuracy of the proposed distributed heterogeneous T2TF.
Feature Extraction of QAR Data via Sequence-Parameter Attention Based Convolutional Autoencoder Model

Quick Access Recorder (QAR) data contains a large number of flight parameters used to record flight information during flight, and is increasingly utilized to analyze and evaluate flight safety. Feature extraction of QAR data is a prerequisite for further analysis in many applications and studies and therefore an important research topic. In this study, a sequence-parameter attention based convolutional autoencoder (SPA-CAE) model is proposed for feature extraction of QAR data. Data landed at Changshui Airport in Kunming were used as experimental data and the results show that our model is better able to perform feature extraction of QAR data and discover the distribution pattern of extracted features.