Reviewerly/exHarmony huggingface dataset:
Pseudo-ground Truth Trajectory From Contaminated Data of Object Tracking Using Smoothing Algorithms

Object tracking is a study area of great interest to various researchers whose main objective is to improve the trajectory estimation for object tracking. In practical applications, the information available that allows the application of algorithms to improve the tracking process sometimes is missing. One of the main obstacles is obtaining ground truth, which takes a long processing time. There are manual methods and applications of reference algorithms. On the other hand, in most cases, the tracking information obtained using a camera is contaminated with noise during the acquisition process. In this paper, we applied smoothing algorithms to compute a pseudo-ground truth achieving lower estimation errors and higher precision than the measurement data. The test results showed that the proposed algorithms with the highest performance are q-lag UFIR and q-lag ML FIR. These smoothing algorithms can be useful in practical applications in object-tracking tasks.
The Psychological and Pedagogical Foundations of the Organization of Creative Activity of Students

The article reveals the psychological and pedagogical foundations of the creative activity of university students. The object of the analysis of the article were the concepts and theories of creative activity developed in pedagogy and psychology. The purpose of the article is to determine the conceptual psychological and pedagogical ideas of creativity development that are relevant for the organization of students’ creative activity. Some domestic and foreign theories of creativity are characterized, various approaches to the study of creativity as a special type of human creative activity aimed at obtaining a unique result in the conditions of solving a complex task or problem are analyzed. Some psychological and pedagogical approaches to the organization of creative activity of students in the system of higher education are revealed. A brief description of cognitive processes (lateral and divergent thinking) is given. The main psychological and pedagogical conditions for the effective organization of creative activity of university students are determined.
A Journey through the Potential of Research to Drive Innovation in University Education

This essay aims to explore effective ways to utilize research in improving the quality of university education and address challenges that impede its potential. The authors conducted a multivocal literature review and approached six subtopics, including the role of interdisciplinary research and collaboration, the impact of research on teaching practices and student outcomes, the potential of emerging technologies, the importance of diversity, equity, and inclusion, fostering a culture of research and innovation, and the role of funding and resources. They recommend investment in research infrastructure, the promotion of collaboration, engagement, and ethical principles, and building a culture of research. Implementing these recommendations will enable universities to utilize research effectively in order to enhance the quality of university education, drive progress, and contribute to more inclusive, equitable, and sustainable societies.
Leadership and Management Strategies in Jordanian Media Organizations and Their Relationship to Good Performance: Towards a Comprehensive Media Strategy by the Concepts of Modern Media: Focus Study

Objectives: To shed the light on Jordanian media and the problems facing it and to identify mechanisms and strategies for its development by analyzing: the status of the state-owned and private sector Jordanian media, as well as the new media statutes, and the media content and ways to improve it, in addition to media freedoms and legislation and the issues of improving staff training.&#x0D; Methods: The study adopted the analytical approach and used the descriptive methodology, through in-depth focus groups with a sample of opinion leaders, politicians, and academic media practitioners to arrive at a better analysis of Jordanian media.&#x0D; Results: There is a lack of confidence in the Jordanian media, especially the official media, high licensing fees, poor advertising returns, multiple media references, and poor training care.&#x0D; Conclusions: A new media strategy is needed, that build on the previous strategy, which was adopted between the years (2011-2015). The new media strategy should be developed by experts, academics, and media practitioners. In addition, the study call for amending the law guaranteeing the right of access to information
Implementation of Communicative Language Teaching (CLT) on educational robot for English language learning

English has become an important language for everyone to master. Therefore, innovative methods are needed to increase students&apos; interest in learning English. Robot-Assisted Language Learning (RALL) uses robots to improve motivation in language learning. One of the applications is Communicative Language Teaching (CLT) using machine learning approaches such as Automatic Speech Recognition and Natural Language Processing (NLP). This paper proposes an educational robot designed for CLT implementation, which uses Speech-to-Text (STT), Text-to-Speech (TTS), and NLP to demonstrate the robot as a conversation partner. The robot has two training modes, which are training based on a scenario and training without using scenario. The training mode based on a scenario uses conversations that have been provided to train the user&apos;s ability to speak. The training mode without using scenario uses a Deep Learning-based NLP model trained using conversation datasets from Touchstone and Speak Now books. With a small number of pairs of question and answer, the NLP model can answer basic questions within the scope of the dataset but has a limited understanding of each vocabulary.
From Pixels to Deposits: Porphyry Mineralization With Multispectral Convolutional Neural Networks

Mineral exploration is essential to ensure a sustainable supply of raw materials for modern living and the transition to green. It implies a series of expensive operations that aim to identify areas with natural mineral concentration in the crust of the Earth. The rapid advances in artificial intelligence and remote sensing techniques can help in significantly reducing the cost of these operations. Here, we produce a robust intelligent mineral exploration model that can fingerprint potential locations of porphyry deposits, which are the world's most important source of copper and molybdenum and major source of gold, silver, and tin. We present a deep learning pipeline for assessing multispectral imagery from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) with the objective of identifying hydrothermal alterations. Our approach leverages a Convolutional Neural Network (ConvNet) to analyze the high-resolution images, overcoming computational challenges through a patch-based strategy that involves an overlapping window for partitioning the images into fixed-size patches. Through the utilization of manually labeled patches for image classification and identification of hydrothermal alteration areas, our results demonstrate the remarkable ability of ConvNet to accurately detect hydrothermal alterations. The technique is adaptable for other ore deposit models and satellite imagery types, providing a revolution in satellite image interpretation and mineral exploration.
Dimensional Accuracy and Tearing Strength of Alginate Impression Material Containing Different Concentrations of Natural Miswak Powder: An In-Vitro Study

Background:&#x0D; Alginate impression material is an irreversible hydrocolloid due to its chemical setting reaction. Hydrocolloids dimensional stability depends on the presence of water, as water makes up around 70% of dental alginate. One of the Salvadoraceae families, Salvadora persica, is the source of miswak. The Arak tree, or Salvadora persica, is often referred to as the "toothbrush tree" as it is known in English. It is evergreen form develops like a shrub or small tree with a twisted trunk. The researchers mixed miswak powder with the alginate to examine the dimensional accuracy and tearing strength.&#x0D; Materials and Methods:&#x0D; Sixty alginate specimens were prepared. 30 specimens for dimensional accuracy and 30 specimens for tearing strength, each of them is divided into three groups: The control group, Group A % 17 miswak powder, and Group B % 34 miswak powder. The dimensional stability and tearing strength were tested.&#x0D; Results:&#x0D; There is no dimensional change in group B, lesser dimensional change was revealed in group A, according to control group. Group B is more resistant to tearing strength when compared with the control group and group A, and group A is more resistant than the control group.&#x0D; Conclusion:&#x0D; Adding a suitable amount of miswak powder to alginate is beneficial for reducing and preventing shrinkage, as well as improving tearing strength.
A comprehensive review of pedestrian re-identification based on deep learning

Abstract Pedestrian re-identification (re-ID) has gained considerable attention as a challenging research area in smart cities. Its applications span diverse domains, including intelligent transportation, public security, new retail, and the integration of face re-ID technology. The rapid progress in deep learning techniques, coupled with the availability of large-scale pedestrian datasets, has led to remarkable advancements in pedestrian re-ID. In this paper, we begin the study by summarising the key datasets and standard evaluation methodologies for pedestrian re-ID. Second, we look into pedestrian re-ID methods that are based on object re-ID, loss functions, research directions, weakly supervised classification, and various application scenarios. Moreover, we assess and display different re-ID approaches from deep learning perspectives. Finally, several challenges and future directions for pedestrian re-ID development are discussed. By providing a holistic perspective on this topic, this research serves as a valuable resource for researchers and practitioners, enabling further advancements in pedestrian re-ID within smart city environments.
Security System Exploitation on Domains and Subdomains of UIN Syarif Hidayatullah Jakarta

A computer is a machine that can be used to process any information. In the computer world, crime often occurs. Thus security is needed, for computer systems to avoid various attacks. In the world of computers, especially on the Internet, virus attacks often occur, such as worms, trojans, software piracy and credit card theft problems. Despite the many offers of computer sophistication in processing information, there are still weaknesses that can be exploited by irresponsible parties. This can be detrimental to the rightful owner of the information. So that it can cause small-scale to large-scale damage. Therefore, it is very important for us to know about security exploits so that we can prevent such exploitation and avoid abuse from certain parties. although no system is completely secure. at least we have minimized the possibility by knowing security exploits. Forms of security exploitation generally use various techniques, therefore a security system is needed that is able to overcome these forms of exploitation. This study discusses how to exploit a system and its techniques with the object used is a security system in the domain and subdomain of UIN Syarif Hidayatullah Jakarta. It is hoped that with this research the reader can know and understand the exploitation techniques used so that they can prevent or treat when this exploitation technique is used. The research method used consists of several paths, namely foot printing, scanning, DOOS, Acess, Filtering, finally Covering tracks and creating a backdoor. The research output is the display of the University's network security and its exploitation process.
Effectiveness of Android-Based Mobile Learning (M-Learning) on Molecular Shape Sub-Materials to Improve Students' Visual-Spatial Intelligence

The factor causing students difficulties in understanding the shape of molecules is that students need to imagine the 3D structure of a molecule with images in 2D, so a visualization tool specifically designed to overcome problems related to this is needed. This study aims to determine the Effectiveness of Android-based M-Learning in the molecular shape sub-material to improve students' visual-spatial intelligence. This study used the 4D development model proposed by Thiagarajan (Define, Design, Develop, and Disseminate) modified by Ibrahim so that it was only carried out until the development stage. The trial of M-Learning using a One-group pretest-posttest design. The subject in this research is class IX F-3 with a number of 27 students. The effectiveness of Android-based M-Learning in terms of the increase in student learning outcomes as measured by the value of the molecular shape pretest-posttest which contains 10 multiple choice questions related to the molecular shape sub-material and visual-spatial pretest-posttest which contains 6 multiple choice questions related to spatial-visual intelligence. Research results show the molecular shape test and visual-spatial, each obtained an N-Gain of 0.63 – 1 with moderate and high feature scores. which is effective in terms of increasing student learning outcomes
Employee Education and Training Recommendations using the Apriori Algorithm

The Ministry of Finance (MoF) aims to enhance employee performance through suitable education and training opportunities. Based on the data on the implementation of education and training in 2022 in the MoF Central ICT Department, only 27.35% of the employees participated in education and training according to the proposed needs for both positions and individuals. This is partly due to mandatory training that must be attended by some or all employees, urgent needs in the current year, or substitute participants who are not from the same team or function. To address this issue, the association method of data mining techniques can be utilized to analyze historical data of employees. The study used the a priori algorithm to analyze historical data on employee positions, organizations, and education and training from 2011 to 2021. This research involved comparing various minimum support values, assuming that employees attended at least 2, 3, and 4 training courses, to calculate the corresponding minimum support values. The evaluation results of the model show that the best rules are generated with a minimum support value of 0.013 and a minimum confidence value of 0.6, which is a total of 10 rules. One of the training recommendations is that if an employee has taken the Enterprise Service Bus (ESB)-API Management training, they will take the ESB API Integration Platform training. Furthermore, it can be used by the Human Resources Unit to provide education and training aligned with organizational needs and improve employee competency in line with their duties and functions, leading to better overall organizational performance.
Implementation of Gradient Boosted Tree, Support Vector Machinery and Random Forest Algorithm to Detecting Financial Fraud in Credit Card Transactions

According to Google Trends data, machine learning-based credit card identification has grown over the last five years, at the very least, across all nations. In order to detect credit card fraud in this study, the authors will use machine learning methods such random forests, support vector machines, and gradient-boosted trees. The authors used the Synthetic Minority Oversampling Technique (SMOTE) and Random Under Sampling (RUS) sampling methods in each algorithm to compare because there was a class imbalance in this investigation. The research findings demonstrate that the author's algorithm and sample technique were successfully used, as shown by the AUC values obtained for each being &gt; 0.7. The top score in RUS was 0.7835 using the Random Forest algorithm, whereas the greatest score in SMOTE was 0.73 with the Gradient Boosted Trees approach. The Random Forest algorithm and the Random Under Sampling (RUS) technique are developed as a result of this research, and they are useful for identifying fraudulent credit card transactions.
PREDICTING TUBERCULOSIS MORBIDITY RATE IN INDONESIA USING WEIGHTED MARKOV CHAIN MODEL

In this work, the Weighted Markov Chain (WMC) model for time series data forecasting is examined. The Markov Chain model has been generalized in this model. In order to forecast the morbidity rate in 2021, the WMC model was used to data on tuberculosis (TB) morbidity rates in Indonesia from 2000 to 2020. The WMC model's output takes the form of a state that is represented by the interval that contains the expected morbidity. In the first stage, the simulation results of the WMC model are analyzed, with an emphasis on the number of states and the biggest step in the Markov chain. In this research, the maximum step and the number of states were combined in 10 different ways. The analysis's study revealed that the maximum step and the number of states had no impact on the predictive value of the morbidity rate. The WMC model's projections for the morbidity rate in 2021 are presented in the second stage. These forecasts are then verified by the predictions from the Simple Exponential Smoothing (SES) approach, and it is concluded that these predictions are fairly consistent.
Mobile Application Architectures

This chapter describes the concepts of mobile application architectures, including the specific features of mobile platforms which lead to specialised forms of architecture. We consider Android and iOS app architectures in detail, and specific architectural patterns for the mobile domain.
Development Methods

This chapter describes the use of architectural design within model-driven engineering (MDE) and agile development processes. As an example of MDE, we give an MDE-based approach for the architectural design of simple business systems. We also discuss the issues involved in using architectural design in an agile development approach.
Web Application and Enterprise System Architectures

This chapter will explain some fundamental enterprise architecture styles and patterns, and show how these can be applied to organise web application and EIS architectures. We will also explain the key concepts of Java EE/Jakarta, the main alternative EIS platform to .NET.
Study on the influence of social support on the effect of science education in rural primary schools

This paper analyses the influence of different social support on the effect of science education in rural primary schools, based on the data of rural primary school teachers in 5 re¬gions of Heilongjiang Province in China. The study found that the independent effects of the five social supports such as economic support, the support of science education venues, the guidance of science education and teaching on-site, improvement training of science teacher quality, serve as a science education counsellor on the effect of science education in rural primary schools were significant. The improvement training of science teacher quality and the guidance of science education and teaching on-site still has a great influence on the effect of science education in rural primary schools under the background of different school characteristics, and under sufficient and insufficient capital of science teaching. However, the impact of economic support, the support of science education venues, serving as a science education counsellor is not significant. Therefore, in order to better provide high-quality social support for science education in rural primary schools, a multi-subject science education alliance, led by the county educational administrative departments, should be established to improve coordination and matching degree of social support resource. A science education demand evaluation system, should be established in rural primary schools, to enhance the accuracy of social support services. The quality improvement of science teachers should be taken as the core of social support to clarify the direction of social support.
Compound Components and Complex Connectors

This chapter extends the idea of a software component by defining subsystems as compound components with an internal architectural structure. Such constructions need new kinds of connectors: delegation connectors, which link external provided/required interfaces of a subsystem to corresponding interfaces within the subsystem. We also consider different communication models for connectors: publish-subscribe and asynchronous connectors.
Service-Oriented Architectures

This chapter will look at architectural design in the context of cloud-based services, web services and microservices, which can all be considered to be forms of service-oriented architecture (SOA). We will describe the purpose and functionalities of cloud services, web services, microservices and SOA, and use UML component diagrams to specify SOA.
Architectural Design for Machine Learning Systems

This chapter considers the architectural design of machine learning (ML) applications, and identifies factors which can help to ensure the maintainability and quality of these systems.
Architectural Styles

Architectural styles express recurring patterns of architectural design. They are the analogue at the architecture level of design patterns at the code design level, and help to guide the construction of architectures for new systems based on what architectures have been effective for similar types of system. An architectural style consists of: (i) A vocabulary of architectural elements, such as ‘Filter’ or ‘Controller’; (ii) A set of configuration rules for connecting components in characteristic ways; (iii) A semantic interpretation explaining the role and meaning of elements within the architecture. In this chapter we focus on the following domain-independent styles: Adapter; Pipes and filters; 2-tiered (Client-Server); N-tiered; Layered; Black-board; Model-View-Controller (MVC).
InitialGAN: A Language GAN With Completely Random Initialization

Text generative models trained via maximum likelihood estimation (MLE) suffer from the notorious exposure bias problem, and generative adversarial networks (GANs) are shown to have potential to tackle this problem. The existing language GANs adopt estimators, such as REINFORCE or continuous relaxations to model word probabilities. The inherent limitations of such estimators lead current models to rely on pretraining techniques (MLE pretraining or pretrained embeddings). Representation modeling methods (RMMs), which are free from those limitations, however, are seldomly explored because of their poor performance in previous attempts. Our analyses reveal that invalid sampling methods and unhealthy gradients are the main contributors to such unsatisfactory performance. In this work, we present two techniques to tackle these problems: dropout sampling and fully normalized long short-term memory network (LSTM). Based on these two techniques, we propose InitialGAN whose parameters are randomly initialized in full. Besides, we introduce a new evaluation metric, least coverage rate (LCR), to better evaluate the quality of generated samples. The experimental results demonstrate that the InitialGAN outperforms both MLE and other compared models. To the best of our knowledge, it is the first time a language GAN can outperform MLE without using any pretraining techniques.
Excitonic metamaterials for actively tunable photonic platforms

I will focus on the subject of strong light-matter coupling in excitonic 2D and 1D semiconductors. I will then discuss opportunities for light conversion and modulation using superlattices and metastructures made using excitonic materials and also magnetic semiconductors if time permits.