Reviewerly/exHarmony huggingface dataset:
Extended Object Tracking with Embedded Classification

This paper proposes a novel extended object tracking (EOT) approach with embedded classification. Traditionally, for extended objects, only tracking is addressed without considering classification. This has serious defects: On the one hand, some practical EOT problems require classification as an embedded subproblem; on the other hand, with the assistance of classification, the tracking performance can be improved. Therefore, we propose a systematic EOT method with embedded classification, which is desired to satisfy the practical demands and also enjoys superior tracking performance. Specifically, we first formulate the EOT problem with embedded classification by kinematic models and attribute models. Then, we explore a random-matrix-based, multiple model EOT method with embedded classification. Two strategies are creatively provided in which soft classification and hard classification are embedded, respectively. Especially for the EOT with hard classification, a sequential probability ratio-test-based classification scheme is explored due to its nice properties and adaptability to our problem. In both methods, classification assist tracking is used. The simulation results demonstrate the superiority of the proposed EOT method with embedded classification, which can not only satisfy the practical requirements for classification but can also improve the tracking performance by utilizing the assistant of classification.
A Multipopulation Dynamic Adaptive Coevolutionary Strategy for Large-Scale Complex Optimization Problems

In this paper, a multipopulation dynamic adaptive coevolutionary strategy is proposed for large-scale optimization problems, which can dynamically and adaptively adjust the connection between population particles according to the optimization problem characteristics. Based on analysis of the network evolution characteristics of collaborative search between particles, a dynamic adaptive evolutionary network (DAEN) model with multiple interconnection couplings is established in this algorithm. In the model, the swarm type is divided according to the judgment threshold of particle types, and the dynamic evolution of collaborative topology in the evolutionary process is adaptively completed according to the coupling connection strength between different particle types, which enhances the algorithm's global and local searching capability and optimization accuracy. Based on that, the evolution rules of the particle swarm dynamic cooperative search network were established, the search algorithm was designed, and the adaptive coevolution between particles in different optimization environments was achieved. Simulation results revealed that the proposed algorithm exhibited a high optimization accuracy and converging rate for high-dimensional and large-scale complex optimization problems.
Performance of Deep Learning pickers in routine network processing applications

&amp;lt;p&amp;gt;In recent years there have been a great progress in earthquake detection and picking arrival times of P and S phases using Deep Learning algorithms. However, the general adoption of these methods for the routine processing of monitoring networks has been held back by factors such as the availability of well documented software, computational resources, and a gap in knowledge of these methods. We have analyzed recent available Deep Learning pickers, comparing the results against data picked by a human operartor and against non-Deep Learning programs. We have used data recorded in several locations, with different characteristics and triggering mechanisms, such as volcanic eruptions, induced seismicity and local eartquakes, recorded using different types of instruments. We have found that the Deep Learning algorithms are able to achieve results comparables to a human operator, and several times better than a classical program, specially in data with a low signal to noise ratio. They are very efficient at ignoring large amplitude transient noise and at picking S waves, a task that is often difficult even for experienced analysts, and they require very few parameters to tune (often only the probability threshold) so an in-depth knowledge of neural networks is not required. (This research has been funded by &amp;amp;#160;&amp;lt;span&amp;gt;Spanish Ministry of Science and Innovation MICINN/AEI/10.13039/501100011033 grants&amp;amp;#160;&amp;lt;/span&amp;gt;&amp;lt;span&amp;gt;CGL2017-88864-R and PRE2018-084986).&amp;lt;/span&amp;gt;&amp;lt;/p&amp;gt;
Hubungan Perhatian Orang Tua dengan Motivasi Belajar Siswa Kelas V SD

This study aims to determine the relationship between parental attention and learning motivation of fifth grade students of SD Cluster XVIII, Lilirilau District, Soppeng Regency. The approach used is a quantitative approach. The type of research used is descriptive research. The population in this study amounted to 58 students. Data collection was carried out by means of a questionnaire. The data analysis technique used is the person product moment correlation technique. The results showed that the parents' attention and learning motivation of fifth grade elementary school students in Gugus XVIII, Lilirilau District, Soppeng Regency were included in the medium category and there was a significant relationship based on the results of tcount greater than ttable, so the alternative hypothesis (H1) was accepted while the hypothesis (Ho ) rejected.
Innovative Research on Urban Community Governance Decision-Making Relying on Distributed High-Performance Computing Blockchain Key Algorithms

Since the country began to go global, the country’s economy has developed rapidly, cultural exchanges between countries have become more and more frequent, and foreign cultures have begun to gradually spread to the country. At the same time, through the absorption of foreign community governance experience, domestic research on community governance has also begun to be put on the agenda. This paper aims to study the innovative exploration of urban community governance mechanisms through key high-performance computing algorithms. To this end, this paper proposes a combination of the Bayesian algorithm and distributed high-performance computing to analyze and explore the governance and management methods of the community through its efficient and stable computing power, and derive the most suitable community governance mechanism. An experiment was also set up for comparative analysis. The experimental results show that the community governance mechanism derived from the key distributed high-performance computing algorithm improves the community governance capability by 19.4%, effectively improving the community governance and management issues.
Compact Finite Field Multiplication Processor Structure for Cryptographic Algorithms in IoT Devices with Limited Resources

The rapid evolution of Internet of Things (IoT) applications, such as e-health and the smart ecosystem, has resulted in the emergence of numerous security flaws. Therefore, security protocols must be implemented among IoT network nodes to resist the majority of the emerging threats. As a result, IoT devices must adopt cryptographic algorithms such as public-key encryption and decryption. The cryptographic algorithms are computationally more complicated to be efficiently implemented on IoT devices due to their limited computing resources. The core operation of most cryptographic algorithms is the finite field multiplication operation, and concise implementation of this operation will have a significant impact on the cryptographic algorithm's entire implementation. As a result, this paper mainly concentrates on developing a compact and efficient word-based serial-in/serial-out finite field multiplier suitable for usage in IoT devices with limited resources. The proposed multiplier structure is simple to implement in VLSI technology due to its modularity and regularity. The suggested structure is derived from a formal and systematic technique for mapping regular iterative algorithms onto processor arrays. The proposed methodology allows for control of the processor array workload and the workload of each processing element. Managing processor word size allows for control of system latency, area, and consumed energy. The ASIC experimental results indicate that the proposed processor structure reduces area and energy consumption by factors reaching up to 97.7% and 99.2%, respectively.
Analysis of the critical infrastructure cyber security policy

Critical infrastructures are complex operating environments that often require special protection and security.A successful security strategy design should adhere to the principles of durability, integrity, and regularity.In the European Union, there is a strong interest in the security of critical infrastructures, especially those with interdependence.Given the fact that critical infrastructures play an essential role in a country's economy, it makes them even more vulnerable.The main aim of this article is to analyze the critical infrastructures' cyber security policy.The creation of a security strategy requires identification of the needs for equipment, mode of operation, and required security level.It has to establish rules for precise operation and handling of situations.The article tackles the issues of security strategy for critical infrastructures to protect sensitive areas and sectors.In addition, a cybersecurity policy as a countermeasure is discussed.
Construction of a Family of Maximally Entangled Bases in ℂd ⊗ ℂd′

In this paper, we present a new method for the construction of maximally entangled states in Cd&otimes;Cd' when d'≥2d. A systematic way of constructing a set of maximally entangled bases (MEBs) in Cd&otimes;Cd' was established. Both cases when d' is divisible by d and not divisible by d are discussed. We give two examples of maximally entangled bases in C2&otimes;C4, which are mutually unbiased bases. Finally, we found a new example of an unextendible maximally entangled basis (UMEB) in C2&otimes;C5.
Image SPAM Detection Using ML and DL Techniques

Abstract Since e-mail is one of the most common places to send messages, spammers have, in recent years, targeted it as a preferred way of distributing undesired messages (spam) to several users to spread viruses, cause destruction, and obtain user's information. Spam images are considered one of the known spam types. The spammer processes images and changes their characteristics, especially background colour, font type, or adding artefacts to the images to spread spam. In this paper, we proposed a spam detection model using Several ML (Random-Forest (RF), Decision-Tree (DT), KNearest Neighbor (KNN), Support-Vector Machine (SVM), NaïveBays (NB), and Convolutional Neural Network (CNN)). Several experiments evaluate the efficiency and performance of the (ML) algorithms for spam detection. Using the Image Spam Hunter Dataset extracted from real spam e-mails, the proposed model achieved over 99% accuracy on spam image detection. Keywords: SPAM, Machine Learning, Image Classification, Feature Extraction, Deep Learning.
The Implementation of K-Means Clustering in Coal-fired Power Plant Performance

Performance monitoring for power plant industry is important to know current condition and determined the prior way for improvement strategy. This study aims to classified performance index which are Equivalent Availability Factor (EAF) and Equivalent Forced Outage Rate (EFOR) in various coal-fired power plant in Java Bali, Indonesia. The results of this plants grouping possible to be applied as one of the supports systems in real-time operational decisions to determine plant maintenance priorities. The proposed clustering model is possible to be applied for power plant’s control centre unit in Indonesia in grouping power plants based on performance indexes. The result show that there were 4 clusters of the power plant performance. The clustering drives to identify which plant that must be prioritized for improvement. The clustering method has a model accuracy rate of 87% which indicates that model was valid and applicable. For further research, a decision support system design was required for operational validation. Keywords—Decision-making, Decision support system, Kmeans clustering, Performance, Power plant.
Leadership Practices: Impact on Brigada Eskwela Level of Implementation

This study aimed to investigate and explore the impact of leadership practices in the level of implementation of brigada eskwela.To facilitate such concern, two (2) sets of questionnaires were utilized by the researcher.One (1) is for the inventory of leadership practices of the school head and the second one is for the inventory on the level of implementation of brigada eskwela.Frequency, Mean, standard deviation, and T-test were the statistical tools used in this research.The results revealed that the three (3) prevailing leadership practices were democratic, transformational and delegative.The Brigada Eskwela implementation stages namely: Pre-implementation stage, implementation stage and postimplementation stage were all rated at very high level.Moreover, significant difference was also registered on the impact of leadership practices in all stages of the implementation of brigada eskwela program.Thus, managerial skills are a key factor in conducting an activity or program like the brigada eskwela.
Semantic Web Seni Pertunjukan Indonesia

Documentation of performance arts is an effort of cultural heritage to maintain the dignity and nobility of a nation. Semantic web is a promising alternative that support the objective. From the user side, the website will be more informative, while from the technical side, the website has ontology (a well-defined formal knowledge which is highly potential to be related to other websites). This research is an effort to developed a performance arts ontology using Methontology method. To enrich the ontology, two ontologies were merged . Linked-data principal was implemented by the use of dbpedia. The ontology was evaluated based on 4 parameters (consistency, completeness, verification, and validation) and proved to be effective while implemented in a website.
Multi-camera System Depth Estimation

For the problem of multi-camera image depth measurement, a multi-camera depth estimation system based on semantic segmentation is proposed in this paper. The system first obtains the depth map between neighboring cameras by multi-camera calibration and stereo matching, then stitches multi-camera images by feature extraction and matching, then uses semantic segmentation network to identify and segment objects, and establishes an object height library, and finally estimates the object distance in the non-overlapping region based on the similar triangle principle and the object height library. The object depth obtained by the method of this paper has certain reference value and basically meets the distance measurement requirements of SLAM.
Design and Development of Android Based Animal Healthcare Application

Abstract: This Paper represents the work related to the Design &amp; Development of Android Based Animal Healthcare Application provided a simple interface for user to find nearby veterinary hospitals and easily consult with veterinary doctor about their pets. In this paper we propose a Medical services Application and online conference for Creatures. This Animal Healthcare application contains all relevant information regarding animals, veterinary Doctors and animals common diseases with their symptoms and basic precautions. In the some cases of emergency, users or Persons cannot able to treat sick animals immediately and they not able to contact immediately to right doctors and specialist. This issue is the main source of death in said creatures. Therefore, developers have the concept to develop an Android based application system which keeps record of authenticate doctors, proper detail, location etc. The android application focused on the high quality specialist based on their treatment and suitable timing for users. Based on review system users are able to find and decide which doctor will be best for them. Admin can monitor all the registered doctors, if the doctors getting continuously negative responses from the users, admin will suspend their account as well. Users will get only reputed doctors and quality assurance here will helps a lot for easy and fast treatment. Keywords: Mobile Application, Firebase, Admin Portal, User Portal, Doctor Registration.
Learning soft threshold for sparse reparameterization using gradual projection operators

Deep neural networks (DNNs) have achieved great success in the field of computer vision in recent years. While being high-precision, the characteristic of over-parameterization impedes DNNs from being applied to lightweight devices. To obtain parameter-efficient networks, a large body of work based on uniform sparsity or heuristic non-uniform sparsity techniques has been explored. However, these sparsity techniques offer limited improvement in inference speed (FLOPs) and prediction accuracy. To make further progress, we propose a novel gradual projection operators (GPO) to learn the soft threshold for sparse reparameterization. GPO approaches the soft-threshold operator with a family of projection operators, which progressively reduces the gradient of weights to be pruned during training, to gently learn the pruning thresholds. Experiments on ImageNet show that ResNet-50 with the proposed training algorithm achieves 76.52% top-1 validation accuracy at the sparsity 81.62%, which has merely a 0.5% accuracy gap to its dense counterpart. Additionally, the non-uniform budgets learned by GPO can reduce the FLOPs by up to 10% compared to the state-of-the-arts, which is superior to the popular heuristics methods, thus yielding an effective mechanism for sparse reparameterization.1
The Using Communicative Language Teaching in the Online Class through Soundcloud, Zoom, and Google Classroom Applications

The schools have been implementing online learning during the pandemic. Teachers are required to be able to do online learning where these activities require the role of information and communication technology (ICT). Teachers cannot ensure that every student will understand the material taught during online learning. Therefore, the teacher must make several effective learning methods and approaches which can make students enthusiastic in learning and understanding the material presented. One approach that can be used by teachers is CLT (Communicative Language Teaching). Social interactions used in communicative language teaching can be done through dialogue, discussion, role-playing, etc. Nowadays, teachers implement many applications to support the online learning process, such as SoundCloud, Zoom, and Google Classroom. The use of those applications is to assist students in improving their skills. CLT can be used to teach many things such as vocabulary development, communication among instructors and learners, use of specialist language to a minimum, use of language (in the context of communication in general), use of Information Technology, information search skills, personal viewpoint argumentation abilities, nonverbal skills, and group networking skills.
Image Segmentation Based on Color Dissimilarity

This study aims to develop a segmentation technique that can be used to identify objects in an image. The concept used is to imitate the human concept of recognizing an object based on its color difference. A color is considered different if it has different R, G or B values. Humans can only distinguish two colors clearly if they both have a DeltaE value of at least 8. The difference in DeltaE values is obtained from differences in the values of R, G or B, either alone or in pairs. The application of this concept to the segmentation technique has shown good results. This technique is able to give the same results and is good for images with JPG, PNG and BMP file types. The results of this segmentation will be very suitable for the process of identifying objects in an image.
RANCANG BANGUN APLIKASI SKRINING KESEHATAN MENTAL REMAJA BERBASIS WEB DI RSUD DR.DRADJAT PRAWIRANEGARA DENGAN MENGGUNAKAN METODE ANALYTIC HIERARCHY PROCESS (AHP)

The development of information systems is now very fast and rapid, one form of information system that is easy to develop is web-based. The use of information systems in the medical world is very much, but there are still some who have not utilized the information system to help the work of a medical worker, especially in helping the development of mental health. The system used to help improve mental health in dr.Dradjat Prawiranegara Hospital is now still conventional, namely face to face between medical personnel and patients, this is what makes teenagers who have problems with their mental health have a variety of reasons not to visit a psychiatrist. The web-based adolescent mental health screening application is designed using php programming language, MySQL database and decision making system using Analytic Hierarchy Process (AHP) method. By implementing Web- Based Adolescent Mental Health Screening Application Using Analytic Hierarchy Process (AHP) method is expected to help teenagers know their mental health independently and provide information and education about mental health disorders.
HyCASTLE: A Hybrid ClAssification System based on Typicality, Labels and Entropy

Traditional supervised classification models aim to approximate the functional mapping between instance attributes and their class labels. These models, however, do not consider the interdependence between instances and global characteristics of data and thus often they lead to poor classification results. In this work, we present a novel hybrid classification model – named HyCASTLE – designed to solve the main shortcomings of hybrid models that employ topological information through clustering in order to improve classifiers performances: they make hypotheses on the underlying data distribution and do not consider the effect of noise. HyCASTLE utilises a non-parametric estimator to capture the underlying data distribution and creates entirely data-driven shape-free clusters. HyCASTLE then refines this cluster configuration using both data topology and available labels through an iterative cluster aggregation and separation process. We evaluated HyCASTLE performance on 37 datasets and compare it with both traditional and hybrid classification models. Our results show that HyCASTLE has comparable or better performance than the other models and results to be more resilient to class noise.
Gamification in Efl Class using Quizizz as an Assessment Tool

The teaching and learning process today is no longer carried out traditionally using books only, but many technologies are used to obtain a better teaching and learning process. The application of information, communication, and technology (ICT) tools in the field of education is increasingly widespread. This article discusses about the use of Quizezz as one of the learning media which can be used as an assessment tool. It has particular characteristics and advantages which can help students in reducing the anxiety in doing the exam or text. Therefore, the use of gamification, particularly Quizizz, can beused as the assessment tool during the classroom activity.
Yolov5-DP: A New Method for Detecting Pedestrian Aggregation

In this paper, a novel network Yolov5-DP (Yolov5-DBSCAN-P) is proposed.Deep separable convolution and ACON-C activation function are added into Yolov5 network to improve the detection accuracy of pedestrians.Firstly, DBSCAN-P is used as the clustering detector to detect pedestrians in the area.Secondly, the depth-separable convolution is used to replace the common convolution in Yolov5.Finally, the loss function Swish is improved to ACON to increase the model speed and reduce the model size.The Yolov5-DP network is tested on the public dataset MOT20Det.The experimental results show that good detection results and accurate aggregation detection results are obtained.