Reviewerly/exHarmony huggingface dataset:
On the Importance of Temporal Features in Domain Adaptation Methods for Action Recognition

One of the most common vision problems is Video based Action Recognition. Many public datasets, public contests, and so on, boosted the development of new methods to face the challenges posed by this problem. Deep Learning is by far the most used technique to address Video-based Action Recognition problem. The common issue for these methods is the well-known dependency from training data. Methods are effective when training and test data are extracted from the same distribution. However, in real situations, this is not always the case. When test data has a different distribution than training one, methods result in considerable drop in performances. A solution to this issue is the so-called Domain Adaptation technique, whose goal is to construct methods that adapt test data to the original distribution used in training phase in order to perform well on a different but related target domain. Inspired by some existing approaches in the scientific literature, we proposed a modification of a Domain Adaptation architecture, that is more efficient than existing ones, because it improves the temporal dynamics alignment between source and target data. Experiments show this performance improvement on public standard benchmarks for Action Recognition.
Compact N-gram Language Models for Armenian

Applications such as speech recognition and machine translation use language models to select the most likely translation among many hypotheses. For on-device applications, inference time and model size are just as important as performance. In this work, we explored the fastest family of language models: the N-gram models for the Armenian language. In addition, we researched the impact of pruning and quantization methods on model size reduction. Finally, we used Bye Pair Encoding to build a subword language model. As a result, we obtained a compact (100 MB) subword language model trained on massive Armenian corpora.
Runge Kutta Optimization for Fixed Size Multimodal Test Functions

In this study, it is aimed to increase the success of the Runge Kutta (RUN) algorithm, which is used in the solution of many optimization problems in the literature, on fixed-size test functions by changing the parameter values. Optimization can be defined as making a system most efficient at the least possible cost under certain constraints. For this process, many optimization algorithms have been designed in the literature and used to obtain the best solutions for certain problems. The most important parts in solving these problems are modeling the problem correctly, determining the parameters and constraints of the problem, and finally choosing a suitable meta-heuristic algorithm for the solution of the objective function. Not every algorithm is suitable for every problem structure. Therefore, in this study, the suitability of the RUN algorithm for the solution of fixed-size functions will be evaluated. Theoretically, Runge-Kutta methods used in numerical analysis are an important type of the family of closed and open iterative methods for solution approximations of ordinary differential equations. The RUN algorithm is also designed with inspiration from these methods. In order to evaluate the performance of the RUN algorithm on fixed-size functions in the study, 10 fixed-size multimodal test functions (Shekel's Foxholes, Kowalik, Six-Hump Camel-Back, Branin, Goldstein-Price, Hartman3, Hartman6, Shekel5, Shekel7, Shekel10) have been found in the literature before was selected. Solutions for each of the selected functions are obtained by changing the parameter values of the RUN algorithm. The obtained solution values were evaluated by comparing the solutions obtained with Slime Mold Algorithm (SMA) and Hunger Games Search (HGS) algorithms.
A Conceptual Model of Health Monitoring Systems Centered on ADLs Performance in Older Adults

Older adults usually present physical and mental problems such as anxiety, stress, depression, and mood disorders. In addition, there is a strong correlation between emotions/socialization and health. Negative emotions affect mental and physical health and can be caused by other diseases. Social isolation is a health risk factor comparable to smoking or physical inactivity. The diagnosis process is usually time-consuming and requires resources. The performance of the Activities of Daily Living (ADLs) could be used as an index of the decay of the elders, which can be delayed. ICTs can provide valuable and automatic support to health professionals facilitating routine tasks. Health monitoring systems, especially multi-sensing and intelligent, should be designed to fulfil requirements from each specific health domain. This paper reviews state-of-the-art and proposes a conceptual model centered on the ADLs concept, considering different health dimensions (social, emotional, physical and cognitive). Our proposal allows the evaluation of the elders’ health holistically, and transparently. The conceptual model provides comprehensibility for this domain and provides a basis for developing multi-sensing and intelligent health monitoring systems.
Development of performance indicators and a measurement of a child welfare teacher support project from the perspective of children's right

연구목적: 본 연구에서는 사회적 일자리 창출보다 아동권리 관점에서 아동복지교사 지원사업의 성과를 평가하기 위해 논리모델을 사용하여 객관적이며 지속 가능한 성과지표 및성과측정 방안을 개발하고자 한다. 방법: 기존에 만들어진 아동복지교사 지원사업 및 유사 사업들을 문헌분석하여 성과지표초안을 만들고, 관련 공무원과 지역사회복지사 등 현장실무자를 대상으로 한 표적집단면담, 전문가 자문회의, 그리고 관련 공무원, 현장실무자, 대학교수, 연구자 등을 대상으로 한델파이 조사 등을 실시했다. 결과: 논리모델에 따라 투입 단계(지원체계, 인적 자원, 물적 자원 영역)의 10개 지표, 과정단계(서비스 내용, 서비스의 질, 서비스 운영 영역)의 6개 지표, 산출 단계(양적 성장, 교사배치, 효율성 영역)의 6개 지표, 성과 단계(아동, 교사, 센터의 만족도와 효과성)의 6개 지표로최종안(28개 지표)이 구성되었다. 결론: 본 연구에서 개발한 아동복지교사 지원사업의 성과지표 및 측정방안은 논리모델에따라 사업 자체를 분석단위로 하고 있지만, 일부 지표 및 측정방안을 수정하면 지자체나센터 평가 도구로 활용할 수 있을 것이다.Objectives: This study intends to develop performance indicators and a performance measurement method to evaluate the performance of a child welfare teacher support project using a logical model as a measurand. Methods: After analyzing the performance indicators of the existing child welfare teacher support project and other projects similar to the child welfare teacher support project (literature analysis), a focus group interview, an expert advisory meeting, and a Delphi survey were conducted. Results: According to the logical model, the final draft was composed of a total of 28 indicators: ten indicators(areas of support system, human resources, and physical resources) in the input stage, six indicators(areas of service content, quality, operation) in the process stage, six indicators(areas of quantitative growth, teacher placement, and efficiency) in the output stage and six indicators(areas of satisfaction and effectiveness of children, teachers, and centers) in the performance stage. Conclusions: The performance indicators and measurement methods developed in this study use the project itself as the unit of analysis for the purpose of revealing the performance of the child welfare teacher support project from the point of view of child welfar. If some indicators and measurement methods are revised and supplemented, it could serve as a tool for local government evaluation or center evaluation.
Design of IOT Based Health Monitoring System

With advancement of technology at present health monitoring is one of the most emerging fields. Recently Internet of things is used in various applications like remote monitoring of parameters; control the process far away from the plant etc. In our work we used internet of things to design a health monitoring system using Arduino and several sensor network. This system will monitor the body temperature, environmental temperature, humidity, heart rate and fall detection of the patients. The measured value will be communicated to the thingspeak cloud platform. The thingspeak will read the values and send an alert message to the responsible person in the family and also to the respective doctors to do the immediate aid for the patients under abnormal conditions.
Sistem Pencatatan Keuangan Aplikasi Android Dengan Menggunakan Design Pattern Model-View-View-Model (MVVM), Room Database, SharedPreference dan API.

Most of people in the world are familiar with smart phones or smartphones, and because of their very complete and practical uses, smartphone users are always increasing every year. Smartphones are used for various types of activities such as for social media, for entertainment such as playing games, doing office work, even personal matters such as financial management applications. Therefore, an application was created that can support personal financial recording so that it can make it easier for users.
When gullibility becomes us: exploring the cultural roots of Indonesians’ susceptibility to investment fraud

Purpose This paper aims to explore various cultural and behavioral issues associated with the problem of investment fraud in Indonesia. Design/methodology/approach By examining multiple cases of investment fraud in Indonesia as well as reviewing publicly available government reports, this study highlights several important cultural and behavioral issues associated with the susceptibility of Indonesian financial services consumers to investment fraud to understand better the dynamics of the victimization process. By using multiple cultural and behavioral theories, this study demonstrates how such issues shape the interactions between investment fraudsters and investment fraud victims. Findings This study demonstrates that multiple cultural and behavioral factors have created and shaped an environment where fraudsters can exploit people’s behavioral loopholes for their fraudulent schemes. In particular, the high power distance and high collectivism have been identified by this study as contributing to the high level of materialism in the country, which in turn makes people more susceptible to the temptation of get-rich-quick schemes. Investment fraudsters, being students of human behavior, use their behavioral knowledge to devise various means to deceive their victims. They use multiple psychological principles to stimulate target victims “gullibility to make them more vulnerable to fraudulent persuasion. In many cases, even financially literate people are not immune to fraudsters” deceitful messages. This study highlights gullibility production as a foundation for investment fraudsters to devise their means by which victims are manipulated to accept certain beliefs that depart from facts and evidence. Practical implications This paper contributes to the innovation in anti-fraud practice by building a better understanding of multiple cultural and behavioral issues associated with investment fraud victimization. Originality/value This paper brings a new perspective into the field of anti-fraud to stimulate innovation, in particular in investment fraud prevention.
Igniting Students’ Language Study-Skill: An Overview of the Practice of English Literary Material

Detail English literary materials were selected and processed to establish a course-book to be implemented in Junior-High Schools. The materials were designed to ignite students’ language study skills namely inspiration, interactivity and creativity as important aspects in English education. The study was a developmental research and experimental study. The data were collected through (a) an English essay test involving different difficulty levels of questions (cognitive, analytic, evaluative and creative), and (b) classroom observation and interview. Results from this study identified that the learning processes and learning results clearly reflected the three language study skills. By providing and facilitating proper interactive reading materials and a lively atmosphere of classroom discussion, students got inspired to get their own ideas, got free to write their own sentences and this circumstance assigned students to become more creative. Such content of an inspirational, interactive and creative course book must be relevant to the students’ preferences like interest and need to induce students think critically by relating things into facts.
비교과 활동으로서의 영어연극 대회 참여 경험이 학생들의 언어 능력, 정의적 태도, 협업 역량에 미치는 영향

This study explored if and to what extent foreign language high school students would benefit from participating in a school English drama contest in terms of English proficiency, affective attitudes, and collaborative competence. For this purpose, questionnaires were constructed to measure students’ perception of their growth in those areas after participating in the contest. Pre- and post-questionnaires were administered to 85 students, and in-depth interviews were conducted with 13 of the survey respondents. In a comparison of the pre- and post-test measures, the study found that the drama contest contributed to reducing students’ anxiety and enhancing their intrinsic motivation. Although pre- and post-test differences in language proficiency and collaborative competence were found to be non-significant, the participants addressed positive aspects of their experience in the contest. For example, they reported that they improved their pronunciation, speaking, and writing skills and that they collaborated and learned from one another by sharing ideas, providing feedback, and exchanging opinions.
Optimizing Deep Learning Application for Edge Computing

eep learning (DL) models such as convolutional neural networks (CNN) are being deployed to solve various computer vision and natural language processing tasks at the edge. It is a challenge to find the right DL architecture that simultaneously meets the accuracy, power and performance budgets of such resource-constrained devices. Hardware-aware Neural Architecture Search (HW-NAS) has recently gained steam by automating the design of efficient DL models for a variety of target hardware platform.However, such algorithms require excessive computational resources. Thousands of GPU days are required to evaluate and explore modern DL architecture search space. In this talk I will present state-of-the-art approaches that are based on two components: a) Surrogate models to predict quickly architecture accuracy and hardware performances to speed up HW-NAS, b) Efficient search algorithm that explores only promising hardware and software regions of the search space.
David Hume’s ‘Causation’ in Database Semantics

In physics, causation is an agent-external phenomenon, for example gravity causing the movement of the planets (Newton 1687). According to Hume’s (1739) philosophical reception (Slavov 2013) of Newton’s law of gravitation, the cause must precede the effect (called temporal priority), and cause and effect must be spatiotemporally conjoined (called contiguity).
Graphic Composites with PROC SGPANEL

PROC SGPANEL (and PROC SGSCATTER) makes it possible to display different views of a data set in a composite of images in one column, one row, or an array of M rows and N columns.
Creating Composites of Graphs, Tables, and Text with ODS LAYOUT

The advantages of composites include the ability to present
Accurate Real-time Polyp Detection in Videos from Concatenation of Latent Features Extracted from Consecutive Frames

An efficient deep learning model that can be implemented in real-time for polyp detection is crucial to reducing polyp miss-rate during screening procedures. Convolutional neural networks (CNNs) are vulnerable to small changes in the input image. A CNN-based model may miss the same polyp appearing in a series of consecutive frames and produce unsubtle detection output due to changes in camera pose, lighting condition, light reflection, etc. In this study, we attempt to tackle this problem by integrating temporal information among neighboring frames. We propose an efficient feature concatenation method for a CNN-based encoder-decoder model without adding complexity to the model. The proposed method incorporates extracted feature maps of previous frames to detect polyps in the current frame. The experimental results demonstrate that the proposed method of feature concatenation improves the overall performance of automatic polyp detection in videos. The following results are obtained on a public video dataset: sensitivity 90.94%, precision 90.53%, and specificity 92.46%.
Prediction Of Software Defects Using Ensemble Machine Learning Techniques

During software development and maintenance, predicting software bugs becomes critical. Defect prediction early in the software development life cycle is an important aspect of the quality assurance process that has received a lot of attention in the previous two decades. Early detection of defective modules in software development can support the development team in efficiently and effectively utilizing available resources to provide high-quality software products in a short amount of time. The machine learning approach, which works by detecting hidden patterns among software features, is an excellent way to identify problematic modules. The software flaws in NASA datasets MC1, MW1, KC3, and PC4 are predicted using multiple machine learning classification algorithms in this work. A new model was developed based on altering the parameters of the previous XGBoost model, including n_estimator, learning rate, max depth, and subsample. The results were compared to those obtained by state-of-the-art models, and our model outperformed them across all datasets.
Importance of prefrontal meta control in human-like reinforcement learning

Recent investigation on reinforcement learning (RL) has demonstrated considerable flexibility in dealing with various problems. However, such models often experience difficulty learning seemingly easy tasks for humans. To reconcile the discrepancy, our paper is focused on the computational benefits of the brain's RL. We examine the brain's ability to combine complementary learning strategies to resolve the trade-off between prediction performance, computational costs, and time constraints. The complex need for task performance created by a volatile and/or multi-agent environment motivates the brain to continually explore an ideal combination of multiple strategies, called meta-control. Understanding these functions would allow us to build human-aligned RL models.
Lyrics Training: An E-learning Approach to Improve Listening Skills

Due to the COVID-19 pandemic, educational agents have been forced to apply strategies and methodologies and use new technological tools that allow them to continue with the teaching–learning processes. Because of this, the present research implements an innovative methodology to analyze the contribution of the Lyrics Training method in the development of listening skills for English language improvement. The experiment was carried out with high school seniors from an academic unit in the city of Ambato. A sample of 62 students was divided into two groups, an experimental group and a control group, each of 31 learners. When comparing both groups through a t-student test for independent samples, the results showed a p-value lower than 0.05 and a Cohen’s d coefficient higher than 0.8, establishing that the teaching process through the Lyrics Training methodology is efficient. Finally, to verify if the Lyrics Training method is suitable for the selected experimental group, the usability scale (SUS) was applied, in which an average acceptance of more than 70 points was obtained, indicating that, in general terms, the Lyrics Training method through a web page is efficient for teaching the English language.
Technological Shift of the Healthcare System Using IoT

The Internet of things (IoT) is a system of interconnected devices and linked electronic devices which can collect, transmit, and manage data without human or computer intervention. The IoT is also potential technology that transforms everyday objects into dependable and effective intelligent things. It has impacted every element of life; however, its influence on healthcare has become particularly profound because of its slicing facet shift. It is now feasible to link innovative items over the internet and provide more digitally connected world ways for special applications. In published research on IoT in health care and the COVID-19 epidemic, the potential of this platform is examined. This literature-based research might also help specialists imagine related problems and combat the COVID-19 outbreak. This study report examines the human healthcare IoT (H2IoT) state, including existing services, architecture, topology, tools, services, and healthcare applications. In modern hospitals, “IoT” technology and internet-connected digital gadgets are commonplace. Clinicians have now abandoned paper and have become entirely or partly reliant on devices linked to wireless connections and tablets.
Model of learning in the age of digital society: instructional design of blended learning

This article analyzes the results of the first phase of the experiment in studying the theory and practice of blended learning implementation in higher education institutions, determining the content and structure of the instructional design of blended learning, including the concept, didactic and technological integrity. Having studied numerous studies on the possibilities of blended learning, forms of combining traditional and distance learning formats, effective use of digital technologies and innovative methods, the authors suggest a deeper consideration of the pedagogical aspects of blended learning, ensuring student's conscious activity in education, self-regulation, self-control. In this regard, it is of great importance to identify the pedagogical potential of digital resources, to study the issues of changing the structure of educational programmes and the learning process, the technologization of blended learning. The methodological bases are humanitarian, systemic, interdisciplinary, synergetic, competence, reflexive approaches, theoretical analysis of the potential of didactics of blended learning. The methods of theory and practice study at the intersection of pedagogy and information technology, comparative analysis, modelling, synthesis have been applied in the implementation of research tasks. At the stage of empirical data collection there were carried out questionnaires, interviews with students and teachers, analysis of educational programmes and normative documents, generalization, comparison of the results of the experiment, analysis of digital resources in education. The identification of the components of methodological, didactic, technological and structural-content model of instructional design of blended learning based on the analysis of the theory and practice of learning in higher education constitutes the scientific significance and novelty of the study. The practical significance of the study is the definition of the necessary level of institutional, pedagogical and technological training, evaluation objects, criteria and indicators of their readiness.&nbsp; The results of the first stage of the study, the obtained results and conclusions serve as a basis for the transition to the next stage of instructional design of blended learning. The survey was conducted at the West Kazakhstan Innovation and Technical University, 280 students of 2-3 courses and 56 magistrates took part in it. Keywords: blended learning, educational transformation, instructional design, blended learning didactics, integration of educational resources, institutional, pedagogical, technological training.