Reviewerly/exHarmony huggingface dataset:
Development of Innovation-Based Learning and Teaching Model for Technology Education in Thailand 4.0 Era

This article presents the development of an innovation-based learning and teaching and learning model for technology education in the Thailand 4. 0 era. The research conceptual framework is to study the goals of the development of Thailand 4.0, the development of teaching and learning models, and the expected competencies of graduates that the labor market needs. The research tools consisted of 1) the needs assessment form for analyzing the gap in innovative thinking competency, 2) the questionnaire assessing the quality of the DLAAP Innovation-based learning and teaching model for teaching technology and engineering. Research results were found that 1) systematic integration and linkage of knowledge, innovative product development, initiative, and innovative thinking must be developed, 2) developed DLAAP innovative learning and teaching model is very appropriate (mean = 4.31 and S.D.=0.29) that can promote knowledge, skills, and attitudes to be able to work practicably in the industrial sector in Thailand 4.0 era.
IMPLEMENTATION OF AN INTEGRATIVE MODEL IN AN INNOVATIVE SCHOOL – INTERIM RESULTS

In the scholastic year 2021/2022, the implementation of an integrated learning model, which is a joint initiative of 125th Secondary school – Sofia and the Institute of Mathematics and Informatics at the Bulgarian Academy of Sciences, was launched in the school. The strategy of the school management includes organization of project-oriented learning activities in small groups of students with increased interest in certain topics, considered separately in two (or more) academic subjects. In the pilot stage of the implementation a qualification course was organized. Subsequently were formed teams of teachers with an active positive attitude to research of topics included in the curriculum of various school subjects. According to the model, procedural matrices were developed for the project-oriented initiatives and individual work was carried out with the students. The presentation of their works took place at school seminars, where classmates and teachers were actively involved. The paper presents interim results that give us a reason to expand the scope of activities in subsequent stages of implementation.
Role of Artificial Intelligence Techniques in Active Safety using Image Processing for Autonomous Driving Vehicles

This paper presents a study on the importance of using Artificial Intelligence methods in developing self-driving vehicles. Advances in Artificial Intelligence are one of the key enablers of the Autonomous Vehicles development. There are several ways to increase the level of autonomy of a vehicle and make it capable to avoid or prevent crashes. Advanced Driver Assistance Systems will help autonomous vehicles become a reality. Image processing of various traffic scenarios can be drastically improved by the use of the superior degrees of computer processing and computer vision techniques. With the help of convolutional neural networks (CNNs), not only that a single object can be detected and tracked in a sequence, but all relevant objects can be detected and classified for further processing. CNN s, the current state-of-the art for efficiently implementing deep neural networks for vision, are more efficient because they reuse a lot of weights across the image. Also, an introduction in synthetic data generation field is presented as a way to overcome the lack of labeled datasets for training networks.
Relation Extraction on the Wargame based on Pre-trained Models

It is of great significance to study the data of military chess deduction to improve the level of machines in military chess deduction software. Currently, the research on the data of wargame has not yet started. Data mining is carried out around the military chess deduction data. This paper mainly focuses on the relation extraction task. In this paper, relation extraction is regarded as a sequence tagging task, and a weighted fusion method based on pre-trained model is proposed. The experimental corpus is used to verify the feasibility of the proposed method. The experimental F1 value is 0.79, which verifies the feasibility of the method. The research work of this paper has accumulated experience for the follow-up research on data mining of the wargaming.
Initial studies into camera-based modal force reconstruction

Much work has been done toward the reconstruction of an unknown force using measured response. Modal Force Reconstruction (MFR) attempts a solution by using measured modes and vibrations at some number of degrees of freedom. While developed by way of traditional sensors (e.g., accelerometers), the full-field nature of cameras may be exploited for such tasks. In particular, the spatial density of an image can re-establish the traditionally underdetermined problem into one that is overdetermined. Not only does this make the reconstruction well-posed, but the modal matrix being that which needs inversion establishes a niche solution in certain force reconstruction scenarios. This talk will explore the proposed theory behind camera-based MFR and present proofs of concept using analytical models. Limitations and points of error will also be discussed, helping to further establish credibility and understanding. Initial results suggest camera-based MFR to be a viable reconstruction technique with high spatial and temporal accuracy.
Human Activity Recognition Using 1-Dimensional CNN and Comparison with LSTM

The practice of recognizing different forms of human activities is known as human activity recognition (HAR). We are using two deep learning architectures LSTM and CNN for recognizing activities using smartphones sensors data and then, comparing them with each other because they are best suited for time series data which is generated from sensors. Our proposed architecture, a lightweight CNN model, achieved an accuracy of 96.06%, outperforming more complex and cumbersome algorithms like fast and robust deep convolutional neural network structure (FR-DCNN) and residual LSTM (Res-LSTM). Further, we compared our model to the LSTM model, in terms of accuracy, time taken for training, f1-scores of each activity, precision, recall values, and resources consumed. Our CNN architecture performed better than the LSTM architecture in all the mentioned terms. Hence, it is concluded that for real-world applications, CNN models should be adopted for this purpose.
Design-Engineers’ Selection of Agency: Harm Mitigation in Ambient Intelligent Environments

Abstract The computing paradigm where sensor and actuator technology work in tandem to track and act on events in real Euclidean space, known as ambient intelligence (AmI), is likely to become increasingly common due to the rapid maturation of computing technology. Installing AmI in the built environment creates ambient intelligent environments (AmIE), which strive to make the places we inhabit (invisibly) sensitive and responsive to our presence, needs, wants, and preferences. Given that built environments and the goings-on therein are complicated in an of them selves, implementing AmI for (increasingly) complicated tasks in (increasingly) complicated scenarios, increases the difficulty of managing the outcomes in AmIEs. Our previous research indicates that industry practitioners attribute the agency of AmI artifacts as responsible for these outcomes; especially when harm perpetuation is (one of) the outcome(s), which we codified as the Agency/Intelligence Axis [1]. Due to the nascence of AmI, research on best practices for the design-engineering of AmI is still emerging. This research seeks to add to this literature by evaluating our formerly identified Agency/Intelligence Axis in the context of AmIE through a case study of VyZee, a retail company working on transitioning their retail stores to “smart” stores. Our findings highlight that while VyZee seems largely unaware of any relationship between agency and perpetuating un-anticipated/-desired outcomes, they do implement an array of levels of AmI agency in their retail stores, and their justifications for their choices are presented in the discussion. Finally, coding the data revealed more nuance then previously documented in the Agency/Intelligence Axis, and a new Ambient Intelligent Agent Model, which suggests that AmI agents have six properties, is proposed.
Graph-Based Multi-Camera Soccer Player Tracker

The paper presents a multi-camera tracking method intended for tracking soccer players in long shot video recordings from multiple calibrated cameras installed around the playing field. The large distance to the camera makes it difficult to visually distinguish individual players, which adversely affects the performance of traditional solutions relying on the appearance of tracked objects. Our method focuses on individual player dynamics and interactions between neighborhood players to improve tracking performance. To overcome the difficulty of reliably merging detections from multiple cameras in the presence of calibration errors, we propose the novel tracking approach, where the tracker operates directly on raw detection heat maps from multiple cameras. Our model is trained on a large synthetic dataset generated using Google Research Football Environment and fine-tuned using real-world data to reduce costs involved with ground truth preparation.
Automated Assessment and Grading System for Short Answers

Automatic short answer grading is a task that uses computational algorithms to analyse responses written in natural language. Several years ago, there was a computer-aided assessment. This work has been done mostly for the English language in a variety of languages. Despite this, due to the complexity of the Indian natural language, not much study has been done on Indian languages. The goal of this research is to provide a comprehensive overview of ASGS (Automatic Short Answer Grading System). The methods and procedures utilised in the automatic short answer grading system for English and other languages are discussed in this study.
Developing an E-Flipbook on Environmental Education to Promote Digital Literacy among Elementary School Students and Teachers in Rural Areas in Indonesia

despite abundant evidence of the significance of digital literacy in academic advancement, the coverage of digital learning media has not reached rural areas in Indonesia. The development of an e-book that is suitable for rural areas is extremely needed. This paper aimed to describe our work in advancing our previous paper-based environmental book to be a digital-based flip-book in strengthening digital literacy among elementary school students and teachers in rural areas. The method used in this work consisted of development research and using many tools such as Flip PDF Pro and Canva. As the result, the e-flipbook has many interesting features such as videos, popup pictures, games, online worksheets, etc. It is also very interesting for both teachers and students, easy to use, and has the potential to help digital literacy through a learning process. However, this e-flipbook needs improvement. It is suggested that the instructions on some features such as buttons and popup pictures need to be clarified, and the book needs to be supported by clues or answers to questions for confirmation to support independent learning activities. Thirdly, the design needs to be improved to make it more attractive.
Southern dialects of United States English and automatic speech recognition

Automatic Speech Recognition (ASR) technology is designed to provide more human to machine communication options. Emergent research has observed variant performance of ASR for African American English (AAE) and other minority dialects. Word prediction was observed to be better for AAE compared to majority dialects. The opposite was true for word identification accuracy. The researchers hypothesize the higher word error rate for AAE is related to phonetic factors of vowel duration, consonant production, rhythm, pitch, and syllable accent. This work evaluates that hypothesis. Recordings of two AAE and two White AE speaking women from North Carolina reading Comma Gets a Cure were submitted for transcription to the Microsoft ASR program. Several common transcription errors were noted across speaker groups with a greater variety of errors noted for the AAE women. Acoustic analyses of vowel duration, and the spectral acoustics of vowel and consonant production by talker and group will be completed. The results of this analysis will be useful to describing the specific aspects of AAE speech that may perturb the tested ASR. These data provide insight into the speech tasks and the sub and supra-segmental acoustic data that should be included in future ASR programming iterations.
Synthesizing Light Field Video from Monocular Video

The hardware challenges associated with light-field (LF) imaging has made it difficult for consumers to access its benefits like applications in post-capture focus and aperture control. Learning-based techniques which solve the ill-posed problem of LF reconstruction from sparse (1, 2 or 4) views have significantly reduced the need for complex hardware. LF video reconstruction from sparse views poses a special challenge as acquiring ground-truth for training these models is hard. Hence, we propose a self-supervised learning-based algorithm for LF video reconstruction from monocular videos. We use self-supervised geometric, photometric and temporal consistency constraints inspired from a recent learning-based technique for LF video reconstruction from stereo video. Additionally, we propose three key techniques that are relevant to our monocular video input. We propose an explicit disocclusion handling technique that encourages the network to use information from adjacent input temporal frames, for inpainting disoccluded regions in a LF frame. This is crucial for a self-supervised technique as a single input frame does not contain any information about the disoccluded regions. We also propose an adaptive low-rank representation that provides a significant boost in performance by tailoring the representation to each input scene. Finally, we propose a novel refinement block that is able to exploit the available LF image data using supervised learning to further refine the reconstruction quality. Our qualitative and quantitative analysis demonstrates the significance of each of the proposed building blocks and also the superior results compared to previous state-of-the-art monocular LF reconstruction techniques. We further validate our algorithm by reconstructing LF videos from monocular videos acquired using a commercial GoPro camera. An open-source implementation is also made available ( https://github.com/ShrisudhanG/Synthesizing-Light-Field-Video-from-Monocular-Video ).
Research on Non-contact Monitoring Method of Human Knee Joint Movement State

As people's awareness of sports health continues to increase, the risk of related sports injuries is also increasing. Nearly 70% of running depth enthusiasts have experienced or are suffering from running injuries, and the incidence of knee pain is about 30%. Therefore, the detection of knee joint health is of great significance. However, the traditional wearable sensor monitoring the health of the human knee joint has disadvantages such as the need for medical equipment assistance and difficulty in operation by non-medical personnel. In response to this situation, this article designed a non-contact real-time monitoring of the health of the human knee joint based on deep learning. system. The results show that the non-contact system can monitor the state of motion that is highly in line with the real situation. Comparing the evaluation effect of the traditional medical knee joint scoring scale, the non-contact system evaluation result achieved a performance with an error of 5.95%, which finally verified the practical value of the non-contact human knee joint health monitoring system.
Theoretical Framework for Blockchain Technology Adoption in Public Sector Organizations: A Transaction Cost Theory Perspective

AbstractThe Indian economy, driven by multiple digital initiatives in the past couple of years, is considered as a fast-growing economy among developing countries. In digital initiatives, Blockchain is one of the promising technologies with potential to transform organizations in particular public sector organizations. There are concerns related to the nature of Blockchain technology and benefits offered by the same in organizational settings. This study attempts to identify critical factors influencing Blockchain technology adoption in public sector organizations. This research attempts to develop a theoretical framework for value based Blockchain technology adoption by integrating task-technology-fit theory, transaction cost theory, and the unified theory of acceptance and use of technology (UTAUT) in Indian public sector organizations. The proposed research framework may provide insights to public sector organizations, technology service providers, academicians and researchers. Researchers may collect data from public sector organizations to test the proposed research framework using structural equation modeling or other statistical models. This is a working paper and intends to further test the proposed theoretical model as the ongoing work. KeywordsBlockchain technologyTask-technology-fit theoryUTAUTTransactions cost theoryIndia
A Systematic Review of Anomaly detection using Machine and Deep Learning Techniques

Anomaly detection identifies objects or events that do not behave as expected or correlate with other data points. Anomaly detection has been used to identify and investigate abnormal data components. Detecting anomalous activities is challenging due to insufficient data size of anomalous reality, ground training data, factors related to differences in environmental conditions, working position of capturing cameras, and illumination situations. Anomaly detection has enormous applications that include (but not limited to) industrial damage prevention, sensor network, health-care services, traffic surveillance, and violence prediction. Machine learning techniques, particularly deep learning has enabled tremendous advancements in the area of anomaly detection. In this paper, we sort out an all-inclusive review of the up-to-date research on anomaly detection techniques. We seek to serve as an extensive and comprehensive review of machine and deep learning anomaly detection techniques throughout the foregoing three years 2019-2021. Particularly, we discuss both machine learning and deep learning anomaly detection applications, performance measurements, and anomaly detection classification. We also point out various datasets that have been applied in anomaly detection along with some fairly new real-world datasets. Finally, we investigate current challenges and future research prospects in this area.
Library mathematics in Mordovia

The strategic activities of A. S. Pushkin National Library of the Republic of Mordovia is examined, in particular, the strategic solutions like singly user card, integrated information and library space of the town of Ruzaevka. Integration of Mordovia’s eLibrary and SVET National Electronic Library and partnership with the Russian National Public Library for Science and Technology are discussed.
Privacy for Data: Masking Methods

AbstractThis chapter describes major methods for protecting databases. This includes perturbative and non-perturbative methods, as well as synthetic data generators. This review includes rank swapping, microaggregation, additive and multiplicative noise, PRAM, and generalization. We also describe the use of GANs to generate synthetic data. The chapter also includes a discussion on methods for achieving k-anonymity and methods appropriate for big data.
Hypothesis Tests

AbstractThis chapter describes a typical workflow in the analysis of statistical data. Special attention is paid to visual and quantitative tests of normality for the data. Then the concepts of hypothesis tests and p-values are explained. Type I and type II errors are introduced, and an introduction is given to the power analysis of statistical tests. The common test concepts of sensitivity and specificity are introduced and explained. And it is shown how a ROC curve can be used to find the optimal cut-off value for the distinction between two groups.
Timing analysis of the sanitization process due to the COVID-19 pandemic in an elementary school using simulation

The recent pandemic caused by the COVID-19 virus, forced all educational institutions in the Mexican national territory to close their doors to face-to-face classes during the month of March 2020. It was many months later, when the Ministry of Public Education announced the return to hybrid classes for the 2021-2022 school year. This represented a challenge for schools in this national territory, particularly those from primary level since all institutions must have had biosafety and disinfection protocols in order to receive all their students and safeguard their safety. For this reason, a primary school was chosen for this study and a discrete event simulation was performed; the main objective of the simulation was to analyze the time invested in the different sanitizing stations, as well as the establishment of new stations to choose the best option that can lead to a timely disinfection process that accommodates the total number of students without affecting the planned day-to-day academic activities.
Factors Affecting Student's Interest in Learning in Online Learning in Elementary School

The aim of this study was to find out what factors influence students' interest in learning in online learning. The approach in this research is qualitative and the type of research is a case study. The qualitative method was chosen to describe what factors influence students' interest in learning when learning online at Elementary School. The collection in this study uses humans as an instrument (human instruments). Data collection techniques using interviews and observation. Informants in this study were teachers, students, and parents. Based on the results found in this study, it shows that the factors that influence students' interest in learning consist of two factors, namely internal factors and external factors. Internal factors are the interest of students in learning, the convenience of students in learning, the readiness of students in learning, and the ability to use technology in learning. External factors are family support, student learning facilities and learning atmosphere.
Context Aware Mobile Application Pre-Launching Model using KNN Classifier

Mobile applications are the application software which can be executed in mobile devices. The Performance of the mobile application is major factor to be considered while developing the application software. Usually, the user uses a sequence of applications continuously. So, pre-launching of the mobile application is the best methodology used to increase the launch time of the mobile application. In Android Operating System (OS) they use cache policies to increase the launch time. But whenever a new application enters into the cache it removes the existing application from the cache even it is repeatedly used by the user. So the removed application needs to be re-launched again. To rectify it, we suggest K number of applications for pre-launching by calculating the affinity between the applications. Because, the user may uses the set of applications together for more than one time. We discover those applications from the usage pattern based on Launch Delay (LD), Power Consumption (PC), App Affinity, Spatial and Temporal relations and also, a K-Nearest Neighbour (KNN) classifier machine learning algorithm is used to increase the accuracy of prediction.