Reviewerly/exHarmony huggingface dataset:
Implementation of human-centered design methods in designing application interfaces for nursing home service

In a nursing home, the elderly gets health services to support their daily life. These services are documented and reported periodically to elderly families. In addition to managing information about nursing services, there is other information that must also be managed properly to help the smooth operation of the nursing home. To support the information management process, reliable software is needed to have the functions and benefits that are in accordance with the needs. One of the supporting components that contribute to building good software is interface design. This study will discuss the design of nursing home service application interfaces that have fields, characteristics, and functions that are different from other fields, especially the field of special health services for the elderly. The purpose of this study is to provide an overview and insight to desktop, mobile, and web application developers regarding aspects of designing an interface that is acceptable to system users in nursing homes. This will certainly make it easier for developers to produce good and acceptable software. The design of the interface is carried out using the Human-Centered Design (HCD) method which applies a user-focused design approach so that it makes it easy to understand what their needs are. There are several stages in HCD, namely the stage of identifying and determining the context of the user (Understand and specifying the context of use), the stage of identifying user requirements (Specifying the user requirements), the stage of producing design solutions (Producing design solutions) and the stage of evaluating the design (Evaluating the design). This research produces an interactive and easy-to-use interface design because the testing process uses a prototype as a demonstration tool. The test results using the End User Computing Satisfaction (EUCS) method show that 88.25% of users are satisfied with the design made and in line with the needs of users in a nursing home.
Noise-Aware Video Saliency Prediction

We tackle the problem of predicting saliency maps for videos of dynamic scenes. We note that the accuracy of the maps reconstructed from the gaze data of a fixed number of observers varies with the frame, as it depends on the content of the scene. This issue is particularly pressing when a limited number of observers are available. In such cases, directly minimizing the discrepancy between the predicted and measured saliency maps, as traditional deep-learning methods do, results in overfitting to the noisy data. We propose a noise-aware training (NAT) paradigm that quantifies and accounts for the uncertainty arising from frame-specific gaze data inaccuracy. We show that NAT is especially advantageous when limited training data is available, with experiments across different models, loss functions, and datasets. We also introduce a video game-based saliency dataset, with rich temporal semantics, and multiple gaze attractors per frame. The dataset and source code are available at https://github.com/NVlabs/NAT-saliency.
STATISTICAL LEARNING DIFFICULTIES ANALYSIS IN THE ERA OF PANDEMIC COVID-19 IN GUIDANCE AND COUNSELING COLLEGE STUDENTS

COVID-19 is a virus that spreads around the world. Indonesia is one of the affected countries. Everyone has only experienced the current conditions and situations, so that good adaptations and adjustments are needed in all aspects, including learning. Teachers implement online learning methods to students to minimize the risk of spreading the COVID-19. This research was done with a quantitative description method. The results showed that students' learning difficulties when learning at home online were inadequate facilities and internet networks, insufficient understanding of certain subjects, a learning atmosphere at home that was less conducive to the analysis of students' learning difficulties carrying out learning online COVID-19 pandemic period. This study aims to obtain information about the problems experienced by students in carrying out online statistics learning during the COVID-19 pandemic.
EXPERIENCE IN DEVELOPING TRAINING MODULES ON DIGITAL COMPETENCE

М. Tolmach, O. Chaikovska, K. Kotsiubivska, Y. TrachKyiv National University of Culture and Arts (UKRAINE)
Teaching Robotics to Students in Pandemic Conditions

The introduction of quarantine measures seriously changes the approach to teaching technical disciplines, which include robotics.If in the traditional learning mode, students in the classroom have experience in collecting robotic designers, then in the quarantine mode they are deprived of this opportunity.This problem is aggravated by the fact that upon completion of the course, students of the pedagogical specialty come to school with no experience of working with real robotic constructors.Given that this discipline is included in the curriculum of students most often in senior years, it is not always possible to postpone it to a later date.In this article, the authors propose a methodology for organizing the educational activities of students of pedagogical specialties in the field of computer science, taking into account distance learning during the quarantine period.The methodology proposed by the authors was tested under the conditions of the introduction of quarantine measures and showed high results on the exam.
Syntax Customized Video Captioning by Imitating Exemplar Sentences

Enhancing the diversity of sentences to describe video contents is an important problem arising in recent video captioning research. In this paper, we explore this problem from a novel perspective of customizing video captions by imitating exemplar sentence syntaxes. Specifically, given a video and any syntax-valid exemplar sentence, we introduce a new task of Syntax Customized Video Captioning (SCVC) aiming to generate one caption which not only semantically describes the video contents but also syntactically imitates the given exemplar sentence. To tackle the SCVC task, we propose a novel video captioning model, where a hierarchical sentence syntax encoder is firstly designed to extract the syntactic structure of the exemplar sentence, then a syntax conditioned caption decoder is devised to generate the syntactically structured caption expressing video semantics. As there is no available syntax customized groundtruth video captions, we tackle such a challenge by proposing a new training strategy, which leverages the traditional pairwise video captioning data and our collected exemplar sentences to accomplish the model learning. Extensive experiments, in terms of semantic, syntactic, fluency, and diversity evaluations, clearly demonstrate our model capability to generate syntax-varied and semantics-coherent video captions that well imitate different exemplar sentences with enriched diversities.
OPINIONS OF COMPUTER SCIENCE TEACHERS IN GYMNASIUMS IN THE CZECH REPUBLIC ABOUT THE CONTENT OF THE COMPUTER SCIENCE CURRICULUM AND THEIR WEAK AND STRONG PROFESSIONAL COMPETENCES

Information about the paper titled "OPINIONS OF COMPUTER SCIENCE TEACHERS IN GYMNASIUMS IN THE CZECH REPUBLIC ABOUT THE CONTENT OF THE COMPUTER SCIENCE CURRICULUM AND THEIR WEAK AND STRONG PROFESSIONAL COMPETENCES" at IATED Digital Library
Enhancing Identification of Structure Function of Academic Articles Using Contextual Information

With the enrichment of literature resources, researchers are facing the growing problem of information explosion and knowledge overload. To help scholars retrieve literature and acquire knowledge successfully, clarifying the semantic structure of the content in academic literature has become the essential research question. In the research on identifying the structure function of chapters in academic articles, only a few studies used the deep learning model and explored the optimization for feature input. This limits the application, optimization potential of deep learning models for the research task. This paper took articles of the ACL conference as the corpus. We employ the traditional machine learning models and deep learning models to construct the classifiers based on various feature input. Experimental results show that (1) Compared with the chapter content, the chapter title is more conducive to identifying the structure function of academic articles. (2) Relative position is a valuable feature for building traditional models. (3) Inspired by (2), this paper further introduces contextual information into the deep learning models and achieved significant results. Meanwhile, our models show good migration ability in the open test containing 200 sampled non-training samples. We also annotated the ACL main conference papers in recent five years based on the best practice performing models and performed a time series analysis of the overall corpus. This work explores and summarizes the practical features and models for this task through multiple comparative experiments and provides a reference for related text classification tasks. Finally, we indicate the limitations and shortcomings of the current model and the direction of further optimization.
Self-supervised Pretraining with Classification Labels for Temporal Activity Detection

Temporal Activity Detection aims to predict activity classes per frame, in contrast to video-level predictions as done in Activity Classification (i.e., Activity Recognition). Due to the expensive frame-level annotations required for detection, the scale of detection datasets is limited. Thus, commonly, previous work on temporal activity detection resorts to fine-tuning a classification model pretrained on large-scale classification datasets (e.g., Kinetics-400). However, such pretrained models are not ideal for downstream detection performance due to the disparity between the pretraining and the downstream fine-tuning tasks. This work proposes a novel self-supervised pretraining method for detection leveraging classification labels to mitigate such disparity by introducing frame-level pseudo labels, multi-action frames, and action segments. We show that the models pretrained with the proposed self-supervised detection task outperform prior work on multiple challenging activity detection benchmarks, including Charades and MultiTHUMOS. Our extensive ablations further provide insights on when and how to use the proposed models for activity detection. Code and models will be released online.
PEMODELAN DATA SAHAM MENGGUNAKAN ANALISIS TIME SERIES DENGAN PENDEKATAN COPULA GAUSSIAN

One method of predicting stock prices is to use the time series analysis method. In this method, a linear prediction model is made to see patterns from historical stock price data to assess future prices. The stock data used in this study is the daily stock data of PT. Telkom and PT. Indosat in 2020-2021. Autoregressive (AR) model is a time series model that is often used with the assumption that its volatility does not change with time (Homoscedastic). After analyzing the AR Model(1) data for the stock data of PT. Telkom and PT. Indosat has a non-independent error, therefore the AR(1)-N.GARCH(1,1) time series model construction was carried out to model the error (ϵ_(i,t)). Furthermore, the error of the AR(1)-N.GARCH(1,1) model is independent of t, so it can be modeled using Copula. After the Copula model was applied to the data and obtained the value of the fit of the Gaussian Copula distribution error model. From the values generated from the Gaussian Copula C({ϵ_(i,t) }_(t=1)^T ),T=1,2,…, and approximates a uniform distribution. So the stock data of PT. Telkom and PT. It can be said that Indosat is not suitable to be modeled with the Gaussian Copula.
Smart Bracelet: With Handshake Recognition For Information Exchange

Digital bracelets or wrist watches, which include functionalities like calculators and unit converters, were commercially available for decades. Due to the technological advancements, they are equipped with data transferring facilities and becoming multifunctional. This paper presents design of a smart bracelet, which can identify the pairs of people during handshakes and exchange their contact details with each other. An advanced system was implemented to detect pairs of handshakes when happening concurrently as multiple pairs of people may do hand shaking at the same time when the crowd size is large in a gathering. A peak detection algorithm and a top-k algorithm were used to identify the matching handshakes by processing data. Communication links were also established using the Bluetooth Low Energy (BLE) technology to exchange contact information of people who are handshaking with each other. Finally, a smart bracelet with multiple functionalities was manufactured and its performance was evaluated.
A Connection Access Mechanism of Distributed Network based on Block Chain

Cross-border payment optimization technology based on block chain has become a hot spot in the industry. The traditional method mainly includes the block feature detection method, the fuzzy access method, the adaptive scheduling method, which perform related feature extraction and quantitative regression analysis on the collected distributed network connection access data, and combine the fuzzy clustering method to optimize the data access design, and realize the group detection and identification of data in the block chain. However, the traditional method has a large computational overhead for distributed network connection access, and the packet detection capability is not good. This paper constructs a statistical sequence model of adaptive connection access data to extract the descriptive statistical features of the distributed network block chain adaptive connection access data similarity. The performance of the strategy retrieval efficiency in the experiment is tested based on the strategy management method. The experiment performs matching query tests on the test sets of different query sizes. The different parameters for error rate and search delay test are set to evaluate the impact of different parameters on retrieval performance. The calculation method of single delay is the total delay or the total number of matches. The optimization effect is mainly measured by the retrieval delay of the strategy in the strategy management contract; the smaller the delay, the higher the execution efficiency, and the better the retrieval optimization effect.
Applying the Erasmus+ project CultureShake to primary school education - through the example of a geocache

How does one bring together different cultures? In terms of teaching, which didactical and methodological resources are required? How can one convey the opportunities and risks of digital media to an emerging adolescent generation? In what way can one achieve inclusion of all pupils? These research questions serve as examples for the goals of CultureShake, an Erasmus+ project that involves participants from four different countries in regular project meetings. Participants from secondary school education experience a new form of exchange through the many different learning activities. However, cultural learning as well as language learning is not triggered by entering into secondary schools. It originates long before, which is why it is immensely important that the content and goals of the project be applied in primary schools. For this reason, this paper specialises in applying central principles of CultureShake, examplified through geocaching, to a primary school setting, using primary school didactics as a guideline.
Economic Growth and Financial Stability in MENA Countries: Does Exporting Oil Matters?

Using system panel GMM dynamic panel on a sample of nineteen MENA countries over the period 1990 – 2014, the study estimates the effect of financial stability on economic growth. Using the principal component analysis to create a composite index of financial stability consisting of a banking crisis dummy variable, the ratio of credit to government and state-owned enterprises to GDP, and the ratio of domestic credit to private sector as a percent of GDP, the estimation results show financial stability in the MENA region is important for boosting economic growth in the region. Furthermore, when dividing the sample between oil and non-oil exporters, the results suggests no statistically significant difference between the two groups in terms of the impact of financial stability on economic growth. Our results are robust to the use of different fixed effects and random effects estimation methodologies.
Subspace Adversarial Training

Single-step adversarial training (AT) has received wide attention as it proved to be both efficient and robust. However, a serious problem of catastrophic overfitting exists, i.e., the robust accuracy against projected gradient descent (PGD) attack suddenly drops to 0% during the training. In this paper, we approach this problem from a novel perspective of optimization and firstly reveal the close link between the fast-growing gradient of each sample and overfitting, which can also be applied to understand robust overfitting in multi-step AT. To control the growth of the gradient, we propose a new AT method, Subspace Adversarial Training (Sub-AT), which constrains AT in a carefully extracted subspace. It successfully resolves both kinds of overfitting and significantly boosts the robustness. In subspace, we also allow single-step AT with larger steps and larger radius, further improving the robustness performance. As a result, we achieve state-of-the-art single-step AT performance. Without any regularization term, our single-step AT can reach over 51% robust accuracy against strong PGD-50 attack of radius 8/255 on CIFAR-10, reaching a competitive performance against standard multi-step PGD-10 AT with huge computational advantages. The code is released at https://github.com/nblt/Sub-AT.
HIGHER EDUCATION INSTITUTIONS AS LEARNING ORGANISATIONS IN COVID-19 TIMES

Appears in: ICERI2021 Proceedings Publication year: 2021Pages: 4155-4161ISBN: 978-84-09-34549-6ISSN: 2340-1095doi: 10.21125/iceri.2021.0971Conference name: 14th annual International Conference of Education, Research and InnovationDates: 8-9 November, 2021Location: Online Conference
VOCATIONAL SCHOOLS – BETWEEN GOOD PRACTICES AND INNOVATION

In the 2019/2020 academic year, a team from the Faculty of Pedagogy at Sofia University „St. Kliment Ohridski “set itself the goal of researching innovative schools in Bulgaria. We researched the legal framework, interviewed principals, and held meetings with innovative teachers. One of the things that impressed us was that there were too few vocational schools. A study followed, which proved our assumption – out of 357 registered innovative schools, only 57 are vocational high schools. Why so little and what was the innovation in them? The report will be presented explore innovative practices in vocational schools. Some of them are focused on the organisation of the learning process other – the pursuit of closer ties with business, third – in realisation of joint ideas and projects.
HeterPS: Distributed Deep Learning With Reinforcement Learning Based Scheduling in Heterogeneous Environments

Deep neural networks (DNNs) exploit many layers and a large number of parameters to achieve excellent performance. The training process of DNN models generally handles large-scale input data with many sparse features, which incurs high Input/Output (IO) cost, while some layers are compute-intensive. The training process generally exploits distributed computing resources to reduce training time. In addition, heterogeneous computing resources, e.g., CPUs, GPUs of multiple types, are available for the distributed training process. Thus, the scheduling of multiple layers to diverse computing resources is critical for the training process. To efficiently train a DNN model using the heterogeneous computing resources, we propose a distributed framework, i.e., Paddle-Heterogeneous Parameter Server (Paddle-HeterPS), composed of a distributed architecture and a Reinforcement Learning (RL)-based scheduling method. The advantages of Paddle-HeterPS are three-fold compared with existing frameworks. First, Paddle-HeterPS enables efficient training process of diverse workloads with heterogeneous computing resources. Second, Paddle-HeterPS exploits an RL-based method to efficiently schedule the workload of each layer to appropriate computing resources to minimize the cost while satisfying throughput constraints. Third, Paddle-HeterPS manages data storage and data communication among distributed computing resources. We carry out extensive experiments to show that Paddle-HeterPS significantly outperforms state-of-the-art approaches in terms of throughput (14.5 times higher) and monetary cost (312.3% smaller). The codes of the framework are publicly available at: https://github.com/PaddlePaddle/Paddle.
Immortal Tracker: Tracklet Never Dies

Previous online 3D Multi-Object Tracking(3DMOT) methods terminate a tracklet when it is not associated with new detections for a few frames. But if an object just goes dark, like being temporarily occluded by other objects or simply getting out of FOV, terminating a tracklet prematurely will result in an identity switch.We reveal that \textbf{premature tracklet termination} is the main cause of identity switches in modern 3DMOT systems. To address this, we propose \tracker, a simple tracking system that utilizes trajectory prediction to maintain tracklets for objects gone dark. We employ a simple Kalman filter for trajectory prediction and preserve the tracklet by prediction when the target is not visible.With this method, we can avoid 96\% vehicle identity switches resulting from premature tracklet termination.Without any learned parameters, our method achieves a mismatch ratio at the 0.0001 level and competitive MOTA for the vehicle class on the Waymo Open Dataset test set. Our mismatch ratio is tens of times lower than any previously published method. Similar results are reported on nuScenes. We believe the proposed \trackerspace can offer a simple yet powerful solution for pushing the limit of 3DMOT. Our code is available at https://github.com/ImmortalTracker/ImmortalTracker.
A New Compiler Code Conversion at Assembly Level

Ever switched programming languages? If yes, you know how difficult it is to learn the syntax and get familiar with new language. But what if we write the code in our preferred language and it can run as any other language’s code. The thing is, whatever we write ultimately gets converted to 0’s and 1’s, the only difference is how these 0’s and 1’s is shown to our machine. We may need different languages, but what if the code with the syntax of one language, runs reasonably well as if it was written with syntax of some other language. This is where a compiler comes in[1]. The aim of this paper is to develop a compiler which could create a new code for another language, based on the machine code developed by other languages. This compiler solves two problems Syntax issue and Universal Compiler.
THE EDUCATORS' OPINION ON MONITORING AND DOCUMENTING THE EDUCATIONAL PROCESS IN PRESCHOOL INSTITUTIONS

THE EDUCATORS' OPINION ON MONITORING AND DOCUMENTING THE EDUCATIONAL PROCESS IN PRESCHOOL INSTITUTIONS
Fast and Optimal Planner for the Discrete Grid-Based Coverage Path-Planning Problem

This paper introduces a new algorithm for solving the discrete grid-based coverage path-planning (CPP) problem. This problem consists in finding a path that covers a given region completely. Our algorithm is based on an iterative deepening depth-first search. We introduce two branch-and-bound improvements (Loop detection and Admissible heuristic) to this algorithm. We evaluate the performance of our planner using four types of generated grids. The obtained results show that the proposed branch-and-bound algorithm solves the problem optimally and orders of magnitude faster than traditional optimal CPP planners.
ARE WE READY FOR THE DIGITAL TRANSFORMATION OF EDUCATION?

M. Banek Zorica1, N. Lazić2, J. Klindzic2 1University of Zagreb, Faculty of Humanities and Social Sciences, Department of Information and Communication Technologies (CROATIA) 2University of Zagreb (CROATIA)