Reviewerly/exHarmony huggingface dataset:
On the Role of Online Courses in Higher Education during COVID-19 Pandemic

During the COVID-19 epidemic, online teaching began to flourish. This paper introduces the status quo of online courses in higher education China, the teaching methods under the epidemic, the advantages and disadvantages of online teaching, and how to make better use of online teaching in the future. Keywords: Online courses, teaching, COVID-19 epidemic.
Spatial Location Constraint Prototype Loss for Open Set Recognition

One of the challenges in pattern recognition is open set recognition. Compared with closed set recognition, open set recognition needs to reduce not only the empirical risk, but also the open space risk, and the reduction of these two risks corresponds to classifying the known classes and identifying the unknown classes respectively. How to reduce the open space risk is the key of open set recognition. This paper explores the origin of the open space risk by analyzing the distribution of known and unknown classes features. On this basis, the spatial location constraint prototype loss function is proposed to reduce the two risks simultaneously. Extensive experiments on multiple benchmark datasets and many visualization results indicate that our methods is superior to most existing approaches.
Conditions for the development of psychological and pedagogical competence of teachers of vocational (professional and technical) education

The urgency of the research problem is due to the need to improve approaches to the development of professional competence of teachers and insufficient development of theoretical, practical and scientific-methodological aspects of creating conditions for psychological and pedagogical competence of teachers of vocational education in the system of continuing education. The conditions of the development are characterized as a set of motivation of professional activity and readiness for innovative activity. The program of the School of Pedagogical Coaching was developed on the basis of the diagnostic study of the peculiarities of the conditions for the development of psychological and pedagogical competence. The purpose of the article is to determine and study the conditions of development of the psychological and pedagogical competence of teachers of professional (vocational) education in the system of continuing education. The leading method of empirical research of this problem is a questionnaire, which allows to cover a significant number of respondents in different regions of Ukraine and analyze in detail the conditions for the development of psychological and pedagogical competence of teachers such as motivation and readiness for innovation activity.
Practical Exploration of Using 'Cloud Classroom' to Organize Online Learning: A Case Study of Jianye District, Nanjing during the COVID-19 Pneumonia

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A Real-Time and Long-Term Face Tracking Method Using Convolutional Neural Network and Optical Flow in IoT-Based Multimedia Communication Systems

The development of the Internet of Things (IoT) stimulates many research works related to Multimedia Communication Systems (MCS), such as human face detection and tracking. This trend drives numerous progressive methods. Among these methods, the deep learning-based methods can spot face patch in an image effectively and accurately. Many people consider face tracking as face detection, but they are two different techniques. Face detection focuses on a single image, whose shortcoming is obvious, such as unstable and unsmooth face position when adopted on a sequence of continuous images; computing is expensive due to its heavy reliance on Convolutional Neural Networks (CNNs) and limited detection performance on the edge device. To overcome these defects, this paper proposes a novel face tracking strategy by combining CNN and optical flow, namely, C-OF, which achieves an extremely fast, stable, and long-term face tracking system. Two key things for commercial applications are the stability and smoothness of face positions in a sequence of image frames, which can provide more probability for face biological signal extraction, silent face antispoofing, and facial expression analysis in the fields of IoT-based MCS. Our method captures face patterns in every two consequent frames via optical flow to get rid of the unstable and unsmooth problems. Moreover, an innovative metric for measuring the stability and smoothness of face motion is designed and adopted in our experiments. The experimental results illustrate that our proposed C-OF outperforms both face detection and object tracking methods.
LiDAR-based 3D Video Object Detection with Foreground Context Modeling and Spatiotemporal Graph Reasoning

The strong demand for autonomous driving in the industry has promoted researches on 3D object detection algorithms. However, the vast majority of algorithms use a single-frame detection diagram, ignoring the spatiotemporal correlations across the point cloud frames. In this work, a novel Foreground Context Modeling Block (FCMB) is proposed to model the foreground spatial context and channel-wise dependency of point cloud features which maintains the original inference speed. Besides, to explore the information of multiple frames, we design a two-stage Spatial-Temporal Graph Neural Network (STGNN). In STGNN, the first stage consumes the coarse proposals of each point cloud frame and conducts intra-frame proposals refinement by massage update functions. And the second stage performs multiple graph convolutions based on the similarity graph to aggregate the semantically similar objects across the input frames. Experimental results show that our 3D video object detector outperforms the LiDAR-based state-of-the-art (SOTA) models on the nuScenes benchmark.
Learning Text-image Joint Embedding for Efficient Cross-modal Retrieval with Deep Feature Engineering

This article introduces a two-phase deep feature engineering framework for efficient learning of semantics enhanced joint embedding, which clearly separates the deep feature engineering in data preprocessing from training the text-image joint embedding model. We use the Recipe1M dataset for the technical description and empirical validation. In preprocessing, we perform deep feature engineering by combining deep feature engineering with semantic context features derived from raw text-image input data. We leverage LSTM to identify key terms, deep NLP models from the BERT family, TextRank, or TF-IDF to produce ranking scores for key terms before generating the vector representation for each key term by using Word2vec. We leverage Wide ResNet50 and Word2vec to extract and encode the image category semantics of food images to help semantic alignment of the learned recipe and image embeddings in the joint latent space. In joint embedding learning, we perform deep feature engineering by optimizing the batch-hard triplet loss function with soft-margin and double negative sampling, taking into account also the category-based alignment loss and discriminator-based alignment loss. Extensive experiments demonstrate that our SEJE approach with deep feature engineering significantly outperforms the state-of-the-art approaches.
Pelatihan Komputer Dasar Berbasis Aplikasi Microsoft Office bagi Siswa SMK Negeri 3 Merauke Provinsi Papua

The purpose of this community service is to improve basic computer skills based on the Microsoft Office application for students of SMK Negeri 3 Merauke. This community service method used training with material presentation techniques and questions and answers. The training participants were 34 students of class X Multimedia Department. There were 2 (two) activities carried out, namely: the preparation of a Microsoft office training manual and a questionnaire response to training participants as well as the implementation of basic computer training activities online. The instruments used are response questionnaires and activity documentation. The results of this community service activity indicate that overall each indicator of the response of basic computer training participants is in the 'good' and 'very good' categories. The average percentage of responses is 98.23 which is included in the 'very good' category.
Mind the gap: Tech-based dispute resolutions in global supply blockchains

Blockchain, a type of distributed ledger technology (DLT), can be used for supply chain and is commonly discussed among distribution giants and small players. Blockchain’s actual and perceived benefits, in addition to track-and-trace mandates from regulatory bodies, have driven these discussions. However, there is no mention of how existing dispute resolution processes have been overhauled in favor of blockchain smart contracts, which can be used to manage and resolve disputes. This conversation gap is critical as trust will erode if supply chain partners cannot settle disputes adequately. We highlight the importance of supply chain DLT management and suggest tactics for resolving the inevitable disputes that arise with disparate information. These guidelines, including adopting portable and enforceable contractual terms and a standardized dispute resolution process, can have practical applications beyond blockchain.
Approximation from Noisy Data

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 30 December 2020Accepted: 15 July 2021Published online: 25 October 2021Keywordsapproximation analysis, wavelet frame, random sampling, noisy dataAMS Subject Headings42C40, 65D05, 65D10, 65D15Publication DataISSN (print): 0036-1429ISSN (online): 1095-7170Publisher: Society for Industrial and Applied MathematicsCODEN: sjnaam
Using E-Learning Platforms in Online Classes: A Survey on Tertiary English Teachers' Perceptions

E-learning platforms have been widely applied in many tertiary institutions during the crisis of the Covid-19 pandemic for their favorable advantages. This study employed a survey design to (1)  explore certain e-learning platforms used by teachers in online classes and (2) discover their perceptions towards those tools. The open-ended questionnaire and semi-structured interviews, both conducted online, were the two primary research instruments in the study. The questionnaires discovered that English teachers from different tertiary settings were applying various information communication technology tools to their online classrooms for three distinctive purposes: providing information or delivering lessons, generating communication between teachers and students, and boosting collaboration among students. The data set gathered from the interviews revealed that teachers had positive perceptions towards the tools they were using and that their choices were reasonable based on the regulated environment, the functionality of the tools, teachers’ information technology literacy, the appropriateness of the lesson contents and the utmost convenience the tools offered.
From ML models to intelligent applications

The last 5+ years in ML have focused on building the best models, hyperparameter optimization, parallel training, massive neural networks, etc. Now that the building of models has become easy, models are being integrated into every piece of software and device - from smart kitchens to radiology to detecting performance of turbines. This shift from training ML models to building intelligent, ML-driven applications has highlighted a variety of problems going from "a model" to a whole application or business process running on ML. These challenges range from operational challenges (how to package and deploy different types of models using existing SDLC tools and practices), rethinking what existing abstractions mean for ML (e.g., testing, monitoring, warehouses for ML), and collaboration challenges arising from disparate skill sets involved in ML product development (DS vs. SWE), and brand-new problems unique to ML (e.g., explainability, fairness, retraining, etc.) In this talk, I will discuss the slew of challenges that still exist in operationalizing ML to build intelligent applications, some solutions that the community has adopted, and highlight various open problems that would benefit from the research community's contributions.
Explicitly Encouraging Low Fractional Dimensional Trajectories Via Reinforcement Learning.

A key limitation in using various modern methods of machine learning in developing feedback control policies is the lack of appropriate methodologies to analyze their long-term dynamics, in terms of making any sort of guarantees (even statistically) about robustness. The central reasons for this are largely due to the so-called curse of dimensionality, combined with the black-box nature of the resulting control policies themselves. This paper aims at the first of these issues. Although the full state space of a system may be quite large in dimensionality, it is a common feature of most model-based control methods that the resulting closed-loop systems demonstrate dominant dynamics that are rapidly driven to some lower-dimensional sub-space within. In this work we argue that the dimensionality of this subspace is captured by tools from fractal geometry, namely various notions of a fractional dimension. We then show that the dimensionality of trajectories induced by model free reinforcement learning agents can be influenced adding a post processing function to the agents reward signal. We verify that the dimensionality reduction is robust to noise being added to the system and show that that the modified agents are more actually more robust to noise and push disturbances in general for the systems we examined.
Virtual Implementation of Mobile-Game Based Learning: Enhancing Students’ Vocabulary Mastery and Self-Motivation

This paper is based on a research on students’ vocabulary mastery at the first grade students of Junior High School at Loeitadnonputtana School, Loei Thailand using Mobile-Game Based Learning strategy. This research is conducted based on the preliminary study which indicated that the students faced some difficulties in mastering English vocabulary. The purpose of this research is to enhance students’ vocabulary mastery through Mobile Game Based Learning. The data were collected by quiz, teacher made-tests, observation, and questionnaire. The data were analyzed by using triangulation. The research design used on this study is Classroom Action Research (CAR). The result of this study shows that Mobile Game Based Learning can significantly enhancing students’ vocabulary mastery from 72% into 86%. Mobile Game Based Learning also makes the students enjoy in learning vocabulary so that they feel motivated and interested more in learning and mastering vocabulary.
Morpheme Embedding for Bahasa Indonesia Using Modified Byte Pair Encoding

Word embedding is an efficient feature representation that carries semantic and syntactic information. Word embedding works as a word level that treats words as minor independent entity units and cannot handle words that are not in the training corpus. One solution is to generate embedding from more minor parts of words such as morphemes. Morphemes are the smallest part of a word linguistic that has meaning in the grammatical unit of languages. This study aims to build a morpheme embedding model for Bahasa Indonesia (in English: Indonesian Language) in sort: Bahasa. However, there were many morphological rules in Bahasa, such as inflectional and derivational affixes. This implies that all rules in word segmentation will increase the computational complexity. Moreover, the rules were not regular and similar for all words in Bahasa. Therefore, this study modified a Byte Pair Embedding (BPE) algorithm to generate morpheme embedding appropriate to the morphology of Bahasa. The study implemented a simple method by filtering the BPE segmentation results with the list of Bahasa’s morphemes. This process has proven to anticipate the limitation of a conventional BPE algorithm that produces intermediate junk tokens that are not meaningful. Based on three evaluation scenarios, the model in the study can handle OOV and carry semantic and syntactic information in the embedding value of the words.
Human Falling Recognition Based on Movement Energy Expenditure Feature

Falls in the elderly are a common phenomenon in daily life, which causes serious injuries and even death. Human activity recognition methods with wearable sensor signals as input have been proposed to improve the accuracy and automation of daily falling recognition. In order not to affect the normal life behavior of the elderly, to make full use of the functions provided by the smartphone, to reduce the inconvenience caused by wearing sensor devices, and to reduce the cost of monitoring systems, the accelerometer and gyroscope integrated inside the smartphone are employed to collect the behavioral data of the elderly in their daily lives, and the threshold analysis method is used to study the human falling behavior recognition. Based on this, a three-level threshold detection algorithm for human fall behavior recognition is proposed by introducing human movement energy expenditure as a new feature. The algorithm integrates the changes of human movement energy expenditure, combined acceleration, and body tilt angle in the process of falling, which alleviates the problem of misjudgment caused by using only the threshold information of acceleration or (and) angle change to discriminate falls and improves the recognition accuracy. The recognition accuracy of this algorithm is verified by experiments to reach 95.42%. The APP is also devised to realize the timely detection of fall behavior and send alarms automatically.
INTERNAL MOTIVATION OF STUDENTS AS A PRIORITY COMPONENT OF LEARNING TO PLAY THE PIANO

The article deals with the need for the formation of internal motivation as one of the most important components of pedagogical activity in teaching piano, which is based on the development of creative individuality of the student and provides a constant need to share emotional experience. Establishing such communication, mediated by a work of art, the "sensory fabric" of which is unfolded in time, concerns the immediate environment and circulation of the experience of previous generations with the constantly updated impressions of a student-musician who is faced with the world of classical piano music. On the basis of a deep inner interest, which can develop into a passion for the process of mastering the instrument, a special language of auto-communication is built, a sensitive attitude to one's own feelings, which is later reflected with the help of the musical language. This contributes to an increase in the general level of reflexivity and awareness, and therefore there is a natural shift in focus on the development of the student's personality in the context of solving urgent problems of mastering playing the instrument. The focus on getting pleasure from playing music arises as a guarantee of high-quality mastery of the skills necessary for creative self-expression. The work analyzes the research of domestic and foreign scientists. The authors give recommendations for increasing the level of intrinsic motivation in the process of learning to play the piano. The paper concludes that the main factors that affect the increase in the level of intrinsic motivation are the building of harmonious relationships in the learning process. To do this, the authors propose to expand the range of psychological and pedagogical training of teachers of musical art on the basis of studying the current state of psychology and behavior of student, introducing digital teaching tools in the process of mastering the piano as a condition for rapprochement between different generations
Traffic Congession Detection through Modified Resnet50 and Prediction of Traffic using Clustering

Today metro cities in India are facing more problems due the traffic congestion in the roads that connects different neighboring cities. Due to overwilling of population and covid - 19 pandemic that increased the usage private vehicles by the mankind. Many of the existing methods use the sensor to detect the number of vehicles in that location and implied on to the google maps. The model has been proposed to overcome the issue by considering the existing video cameras been installed in different location in the signals and crowed places. The proposed model incorporated in the edge computing through Nvidia Jetson nano for the object detection using Common Objects in Context (COCO) to detect the boundary boxes for the ten categories of the object. The detection Score is obtained by considering probability of the detection boxes identified in the particular location with respect to time. The accuracy level of each object in the frame is detected and filtered based on the threshold value such that the false positive and true negative can be avoided in the Contributory matrix. Based on the results obtained from the model the location wise table that classify the images with respect low medium and High. The proposed model proves that accuracy of predicting the traffic based on three categories as Low, Medium and High are estimated correctly. The results are apprised based on the accuracy of the particular object identified in the image or frame with respect to number of images tested for the accuracy. The mean square errors are assessed based on the number of objects identified on each category in a particular image.
Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks

Deep neural networks (DNNs) are known to be vulnerable to adversarial attacks. A range of defense methods have been proposed to train adversarially robust DNNs, among which adversarial training has demonstrated promising results. However, despite preliminary understandings developed for adversarial training, it is still not clear, from the architectural perspective, what configurations can lead to more robust DNNs. In this paper, we address this gap via a comprehensive investigation on the impact of network width and depth on the robustness of adversarially trained DNNs. Specifically, we make the following key observations: 1) more parameters (higher model capacity) does not necessarily help adversarial robustness; 2) reducing capacity at the last stage (the last group of blocks) of the network can actually improve adversarial robustness; and 3) under the same parameter budget, there exists an optimal architectural configuration for adversarial robustness. We also provide a theoretical analysis explaning why such network configuration can help robustness. These architectural insights can help design adversarially robust DNNs. Code is available at \url{this https URL}.
EnMAP: THE GERMAN SPACEBORNE IMAGING SPECTROSCOPY MISSION

The Environmental Mapping and Analysis Program (EnMAP) is a spaceborne German hyperspectral satellite mission that aims at monitoring and characterizing the Earth’s environment on a global scale. This paper presents an update of the mission status with developments from the space and the ground segment.