Reviewerly/exHarmony huggingface dataset:
Big Brother Knows that you are infected: wearable devices to track potential COVID-19 infections

Over the course of the COVID-19 pandemic, technology has been widely deployed to deter the spread of the virus. One such technology is wearable devices that can collect health data and inform health authorities about potential infections. Despite the laudable purposes of this technology, we might be on the verge of another digital Big Brother. This is not a case in which our moves are being watched, but our bodies are, in an extreme form of public health surveillance. This paper analyses the use of wearable devices to track potential COVID-19 infections within the framework of public health and related individual and state duties. The paper focuses on the threats that these devices may pose to individual rights and liberties, particularly self-determination (the freedom to not be tested for infectious diseases) and privacy (the protection of private data).
Exploring post-Covid-19 Lockdown Students’ Satisfaction in Ethiopian Higher Education Context in case of some Selected Universities

This study aimed to explore post Covid-19 lockdown students’ satisfaction from some higher education institutions in Ethiopia. The study used descriptive and explanatory research designs. A total of 480 students were taken from twelve public Universities as a sample and 366 valid questionnaires were collected. Convenience and snowball sampling were used to select the employees from each University. Also, cross- sectional survey method was applied to collect data via a Likert scale questionnaire. Correlation and multiple regression modeling were used to predict the relationships. Initially, a pilot test was a sample of 35 students to check data scale reliability. The study found that all the independent variables (crush time, leadership, and students’ initiation, instructors’ commitment) variables had a statistically significant correlation with Students’ satisfaction. Moreover, the study founded that all the studied variables were predictors of students’ satisfaction (R2= 0.672); however, the predictors that had highest influence were instructors’ commitment and crushes time. Significant emphasis and devotion is required particularly on variables such as instructors’ commitment and crush time as they are identified as a significant influencer of students’ satisfaction. Universities shall focus on enhancing instructors’ commitment and they have to allocate adequate time as before COVID -19.
The Effect of Kaneta Media Implementation on the Early Reading Ability of Children

Early reading skills are one of the most important language developments to be developed in early childhood. This can be done through appropriate stimulation of learning, one of which is the accuracy in using learning media. The purpose of this study is to analyze whether there is an effect of the application of Kaneta media (Smart Flannel Bags) on children's early reading ability. This study used a pre-experimental quantitative method type one-group pretest-posttest design. The population of this research is the children of group A TK Dharma Wanita Persatuan Kedungboto, Porong District with saturated sampling technique with a total of 20 students. The data collection technique in this study used the results of the preliminary reading ability test. The data analysis technique used the dependent t test with the research results t count = 10.87 and t table = 2.093 so that tcount &gt; t table, which means H 1 is accepted and H 0 is rejected. The results showed that the application of Kaneta media had an effect on children's pre-reading abilities, so teaching early reading in early childhood could be done using Kaneta media.
Generalisation of Alpha-Beta Search for AND-OR Graphs With Partially Ordered Values

We define a new setting related to the evaluation of AND-OR directed acyclic graphs with partially ordered values. Such graphs arise naturally when solving games with incomplete information (e.g. most card games such as Bridge) or games with multiple criteria. In particular, this setting generalises standard AND-OR graph evaluation and computation of optimal strategies in games with complete information. Under this setting, we propose a new algorithm which uses both alpha-beta pruning and cached values. In this paper, we present our algorithm, prove its correctness, and give experimental results on a card game with incomplete information.
A COMPARATIVE STUDY FOR VIDEO CLASSIFICATION TECHNIQUES USING DIRECT FEATURES MATCHING, MACHINE LEARNING, AND DEEP LEARNING

Videos are considered the new era communication language between internet users due to the explosion of smart-phones usage and increase in internet bandwidth and storage space. This has fueled the need to develop robust video analysis techniques. Specifically, video classification presents a unique task for field researchers, as it has numerous critical applications, such as video indexing, searching, annotation and surveillance. Videos inherently embody static and dynamic information that is encoded in frames. The task is further prioritized due to the gigantic amounts of available videos in the digital world, which requires a robust way to organize these videos. Throughout literature, researchers have generally adopted three main techniques to classify videos, i.e., direct features matching, machine learning-based methods, and deep learning-based methods. Each of these methods is suitable for a specific application type. This paper is designed to assess which of the three common working approaches are better for video classification. Furthermore, the paper aims to examine whether and how these methods affect/improve video classification performance and key factors to constructing a robust video classification system. This novel research paper covers an important research gap by introducing a rigorous comparative analysis of the three methods highlighting their advantages and disadvantages and guiding field researchers. A comprehensive analysis brings the paper findings together using a benchmark group of challenging large-scale video datasets (~29k videos). This would provide field researchers with the necessary information to choose the best method for their video classification research work.
Integration of innovative technologies in the professional training of teachers of literature

The main purpose of the study is to analyze the features of the integration of innovative technologies in the context of the professional training of literature teachers. The role of education in general and higher education in particular is being transformed from the status of a resource for the development of society into the status of a leading public sphere. The boundaries of the growth of modern civilization are determined precisely by education, its quality and availability. In order to adequately respond to the modern challenges of education, a new formation of dynamic teaching staff is needed, they constantly update their professional knowledge, know how to live and work in an innovative environment. As a result, the key aspects of the integration of innovative technologies in the context of the professional training of literature teachers were identified.
Anomaly Detection on ADS-B Flight Data Using Machine Learning Techniques

With the rapid increase in the number of flights all over the world, the management and control of flight operations has become difficult in recent years. Moreover, the expectations for the aviation sector indicate that this increase will continue in the upcoming years. Therefore, safer and systematic monitoring systems by eliminating the requirement of human-dependent tracking during the air travel of an aircraft and automating the detection of abnormal situations has become a major problem in aviation sector. With the recent advances in artificial intelligence, a safer and systematic tracking system for controlling the airspace by eliminating the need for human-dependent tracking during the flight of aircraft in the air has become possible.
ONLINE LEARNING ASSESSMENT USING THE RECORDING ZOOM MEDIA AT TK COR JESU MARSUDIRINI

This research is based on a number of phenomena in the field during the COVID-19 pandemic that hit the world, including our country, Indonesia, which also had an impact on the world of education, all activities were carried out online or online and carried out from home so that the level of service provided by teachers in early childhood was not optimal. due to lack of direct service. This research uses experimental research method. Collecting data by conducting experiments. The research sample was kindergarten children, totaling 10 respondents at Cor Jesu Marsudirini Matraman Kindergarten, located on Jl. Matraman Raya 129, East Jakarta. This study aims to (1) How is the effectiveness of children's online learning by using zoom? (2) Are there differences in the effectiveness of children's online learning by using zoom and before using recording zoom? The results showed; (1) The use of recording zoom meetings during learning is proven to be more effective and facilitates teachers in assessing learning and providing implementation of the learning process for early childhood students. (2) There is a significant difference between the effectiveness of children's online learning by using zoom and before using recording zoom with a moderate relationship
Trainable Summarization to Improve Breast Tomosynthesis Classification

Digital Breast Tomosynthesis (DBT) is an emerging imaging technique for breast cancer screening aiming to overcome certain limitations of traditional mammography, such as the superimposition of tissues. On the downside, DBT increases the radiologists’ workload as it generates stacks of high-resolution images, which are time-consuming to review and annotate. In this work, we propose a deep- multiple-instance-based method for DBT volume classification that relies on the local summarization of DBT slices (referred to as slabbing) and only requires volume-wise labels for training. Slabbing offers several advantages: i) it reduces the classifier’s computational complexity across the depth, letting it focus on the higher transversal resolution. Thanks to this strategy, we are the first to train a method at almost full-resolution (as high as 120 \(\times \) 2500 \(\times \) 2000); ii) it produces slabs that are closer to standard mammography, favoring an efficient transfer from classifiers trained on larger mammography databases; and iii) the slabs combined with a Multiple-Instance Learning (MIL) classifier result in localized information favoring interpretability. The proposed slabbing MIL approach is also novel for the automatic classification of DBTs. Moreover, we propose a trainable alternative to the handcrafted slabbing algorithms based on slice-wise attention that improves performance. We perform an experimental validation on a subset of the public BCS-DBT dataset and achieve an AUC of 0.73 with five-fold cross-validation. On a private multi-vendor dataset we obtain a similar AUC of 0.73, demonstrating an excellent performance consistency.
Bibliothekarische Normdaten und digitale Musikwissenschaft

The paper presents the authority data provided by the German authority file (GND), its hisrory and conceptual founding in FRBR and FRAD. Referring to selected examples, the text scrutinizes carefully the potential and obstacles of using name authority data in digital humanities projects. However, the focus lies on the newly implemented work authority data that promises to be highly relevant for digital musicology. Challenges of theoretical concepts as well as practical realization are shown and different application scenarios are discussed. The author calls for a lively exchange between musicologists and librarians on the design and potential of the GND as basic musicological ontology.
The Effects of Vietnamese High School Teacher’s Utility of Google Forms on Eleventh Graders’ Grammatical Knowledge

In Vietnam, grammar has been taught with conventional teaching methods such as the Grammar Translation Method and Audio Lingual Method, which still focus on paper-based grammar exercises in high schools. These traditional methods demotivate students' learning interest and motivation and fail to increase their independent learning and the effectiveness of grammar instruction. The purpose of this study is to explore the effects of Vietnamese high school teachers’ utilization of Google Forms on eleventh graders’ grammatical knowledge and their perceptions of Google Forms in learning English grammar. The study results emphasized the intervention of eleventh-grade students' knowledge of grammar and their perceptions of this treatment. The study's findings indicated that the integration of Google Forms successfully aided students in enhancing their achievement of grammatical knowledge. Furthermore, students have a positive attitude towards this application into grammar teaching.
A Survey of The Various Techniques for Virtualization in Cloud Computing

Abstract: Cloud computing is used to describe the delivery of software, infrastructure and storage devices over the internet. After evolution of the internet, Cloud computing is the next stage. Cloud Computing can simply the way in which the business operates, particularly in terms of needs of hardware. One is able to access and connect the same information but it can be done from anywhere and a more streamed technology installation is enjoyed by organization. VCloud Computing involves the concepts of parallel processing and distributed computing in order to provide the shared resources by means of Virtual Machines(VMs) hosted by physical servers. It is a service oriented design that reduces the cost of access to gather the information of the clients offer greater flexibility and demand based services. The benefits of Cloud Computing are far reaching. It is not a technology solution or server stored in another location but it is business enhanced computing that affects the business positively. Apart of its popularity it has some concerns which are becoming huddles for its wider adoption. A survey of cloud computing and virtual migration is presented in this paper. Keywords: Cloud Computing, Virtualization, SaaS, PaaS, IaaS
Analysis of Effectiveness of Mutual Muraja’ah on Juz Amma Reading Fluency for Kindegarten Class at TPQ Raudlatul Qur’an Karang Duwak Arosbaya

In this study, the researcher raised a learning method problem namely mutual Muroja’ah to read juz amma fluently for kindergarten students at TPQ Raudlatul Qur’an. In this study, researcher used a qualitative descriptive research approach with case study research. The data collection techniques used observation, test, interview and documentation. The analysis techniques used were a) data reduction, b) data display and c) conclusion making. As the result of the study are: a) the mutual muroja’ah method implementation was conducted by many ways such as guided muroja’ah, continuing verse muroja’ah and reading together, b) the method is very effective in helping students to read Alqur’an fluently, in terms of the results of evaluation test every three months that the percentage assessment result show that many students obtain grade B and even some students achieved grade A in terms of reading fluency
Performance evaluation of firefly algorithm with unconstrained optimization issues

In this paper, we have investigated a new spectral Quasi-Newton (QN) algorithm. New search directions of the proposed algorithm increase its stability and increase the arrival to the optimum solution with a lowest cost value and our numerical applications on the standard Firefly Algorithm (FA)and the new proposed algorithm are powerful as in meta-heuristic field. Our new proposed algorithm has quite common uses in several sciences and engineering problems. Finally, our numerical results show that the proposed technique is the best and its accuracy higher than the accuracy of the standard FA. These numerical results are compared using statistical analysis to evaluate the efficiency and the robustness of new proposed algorithm.
Memory-Sample Lower Bounds for Learning Parity with Noise

In this work, we show, for the well-studied problem of learning parity under noise, where a learner tries to learn x = (x₁,…,x_n) ∈ {0,1}ⁿ from a stream of random linear equations over 𝔽₂ that are correct with probability 1/2+e and flipped with probability 1/2-e (0 < e < 1/2), that any learning algorithm requires either a memory of size Ω(n²/e) or an exponential number of samples. In fact, we study memory-sample lower bounds for a large class of learning problems, as characterized by [Garg et al., 2018], when the samples are noisy. A matrix M: A × X → {-1,1} corresponds to the following learning problem with error parameter e: an unknown element x ∈ X is chosen uniformly at random. A learner tries to learn x from a stream of samples, (a₁, b₁), (a₂, b₂) …, where for every i, a_i ∈ A is chosen uniformly at random and b_i = M(a_i,x) with probability 1/2+e and b_i = -M(a_i,x) with probability 1/2-e (0 < e < 1/2). Assume that k,𝓁, r are such that any submatrix of M of at least 2^{-k} ⋅ |A| rows and at least 2^{-𝓁} ⋅ |X| columns, has a bias of at most 2^{-r}. We show that any learning algorithm for the learning problem corresponding to M, with error parameter e, requires either a memory of size at least Ω((k⋅𝓁)/e), or at least 2^{Ω(r)} samples. The result holds even if the learner has an exponentially small success probability (of 2^{-Ω(r)}). In particular, this shows that for a large class of learning problems, same as those in [Garg et al., 2018], any learning algorithm requires either a memory of size at least Ω(((log|X|)⋅(log|A|))/e) or an exponential number of noisy samples.Our proof is based on adapting the arguments in [Ran Raz, 2017; Garg et al., 2018] to the noisy case.
Problem solving experience in data visualization using ArcGIS software

Most users of geographic information systems (GIS) software, in particular, ArcGIS, which is considered one of the most common GIS programs for implementing various applications and visualizing 3D data, do not pay much attention to input data compatibility.This is also the case of the coordinate systems and projections that are the basis of working in GIS.Therefore, sometimes specialists who work with GIS programs without experience or knowledge in cartography have problems.Thus, the ability of GIS users to have basic knowledge of GIS related sciences such as cartography and geodesy is important.The paper introduces some of the ArcScene program capabilities for visualizing and displaying 3D data.In addition, it discusses most frequent difficulties in this field.For example, when displaying 3D data in ArcScene program or when displaying the slopes of the earth's surface based on the data of the DEM (Digital Elevation Model) layer.The paper also describes in detail the reasons and ways to resolve such difficulties.
Review on Mentoring Chatbot

Mentoring can provide valuable support for students at critical points in their academic as well as personal life. Students need mentors to assist them in the areas where they need to improve, to stimulate their personal and professional growth. In standard forms of educational mentoring, students schedule appointments with their assigned mentor, a professor who very well knows the academic curriculum and potential trajectories students goes through. However it is necessary for the college professor to perform various roles and responsibilities that are as much important as student mentoring for their own performance evaluation. To mitigate the issue of student mentoring, we are proposing an automation called Mentoring Chatbot using Artificial Intelligence framework. This technological solution can be beneficial not only students' mentoring but also for prioritizing effectiveness of professors' time toward other major issues and other imperative duties. According to our research, we found the following Bots that also provide approximate answers to the existing problem. One of them is Chatbot for College Student Programme Advisement. This College Student Programme Advisement chatbot can give appropriate responses to users requesting for course details and studens' views. Another available answer in the market is ‘An Intelligent Career Counseling Bot’. It proposes an intelligent chatbot system for career counseling, which will help the user in selecting the right career by giving a proper response to the user’s query. By analyzing these bots we concluded that there exists no system that can act as a virtual mentor and help students whenever needed. This paper presents a literature review that describes the uniqueness of this Mentoring Chatbot.
Solving software project scheduling problem using grey wolf optimization

In this paper, we will explore the application of grey wolf optimization (GWO) methodology in order to solve the software project scheduling problem (SPSP) to seek an optimum solution via applying different instances from two datasets. We will focus on the effects of the quantity of employees as well as the number of tasks which will be accomplished. We concluded that increasing employee number will decrease the project’s duration, but we could not find any explanation for the cost values for all instances that studied. Also, we concluded that, when increasing the number of the tasks, both the cost and duration will be increased. The results will compare with a max-min ant system hyper cube framework (MMAS-HC), intelligent water drops algorithm (IWD), firefly algorithm (FA), ant colony optimization (ACO), intelligent water drop algorithm standard version (IWDSTD), and intelligent water drop autonomous search (IWDAS). According to these study and comparisons, we would like to say that GWO algorithm is a better optimizing tool for all instances, except one instance that FA is outperform the GWO.
MODEL JIGSAW BERBANTU KARTU SOAL MENINGKATKAN AKTIVITAS DAN HASIL BELAJAR MATEMATIKA SISWA KELAS VI

The purpose of this classroom action research was to determine the increase in activity and learning outcomes of Mathematics learning in grade VI SD 2 Kesambi, Kudus Regency through the Jigsaw model assisted by question cards. The Jigsaw learning model is a variation of the Cooperative Learning model, which is a group learning process where each member contributes information, experiences, ideas, attitudes, to jointly improve the understanding of all members. The research was carried out in 2 cycles, each cycle of four stages, namely planning, implementing, observing and reflecting. It is proven by the existence of a jigsaw model assisted by a question card to improve learning outcomes from the initial conditions, cycle I, and cycle II. Classical completeness in the initial conditions was 17 students (49%), cycle I was 25 students (71%), in cycle II it was 29 students (83%). Likewise, student learning activities in the first cycle with a class average of 73 and in the second cycle it became 79
OPTIMIZED INTELLIGENT TRAFFIC LIGHT CONTROL USING IMAGE PROCESSING

Introduction of advanced technology and equipment is required to improve state-of-the art traffic control. Urban traffic Congestion increasing on a faster pace day by day. Traffic issues are growing today due to the rising number of vehicles and limited services offered by the existing infrastructure. For each step, the best way to control the traffic light is to use a timer. Another approach is to use electronic sensors to track vehicles and to produce signal cycles. We suggest an image processing scheme to control the traffic lights. The system will detect vehicles through images, rather than using various sensors embedded on the road surface.