Reviewerly/exHarmony huggingface dataset:
Perbedaan Hasil Belajar Matematika Menggunakan Model Pembelajaran Make a Match dan Model Pembelajaran Pair Checks

This study aims to determine the significant difference between the learning outcomes of mathematics using the model Make a Match learning and Pair Checks learning model on the integer material in class VII MTs IB Silaping. This type of research is a comparative study with experimental methods. The population in this study were students of class VII MTs IB Silaping totaling 92 students. The sample of this research is class VII2 b as the experimental class 1 and class VII1 a as the experimental class 2 with the same number of 16 students. The samples were determined by non-probability sampling. The data collection instrument is a test. The data analysis technique used is the normality test, homogeneity test, and to test the hypothesis with the t-test. The results of this study indicate that: (1) mathematics learning outcomes using the Make a Match learning model with an average value of 86,88; (2) mathematics learning outcomes using the Pair Checks learning model with an average value of 80,56. So it can be concluded that (3) there is a significant difference between the learning outcomes of mathematics using the Make a Match learning model and the Pair Checks learning model on the integer material in class VII MTs IB Silaping. This is in accordance with the calculation of the t-test which is obtained at the significant level is 0,05 and dk = 30 with tcount = 2,433 and ttable = 2,042 this indicates that tcount &gt; ttable.
USING OF INFORMATION TECHNOLOGIES IN DISTANCE LEARNING// International Journal of Educational and Scientific Research 4(12), 2021/ Chief Editor K. Kumpulainen /OEAPS OU - Tallinn, Estonia (Open European Academy of Public Sciences). Vienna, Austria 30.04.2021: OEAPS OU., 2021. P. 33-37.

Distance learning can expand access to education and training for both general populace and businesses since its flexible scheduling structure lessens the effects of the many time-constraints imposed by personal responsibilities and commitments. The widespread use of computers and the internet have made distance learning easier and faster, and today virtual universities deliver full curricula online. The capacity of Internet to support voice, video, text, and immersion teaching methods made earlier distinct forms of telephone, videoconferencing, radio, television, and text-based education somewhat redundant. However, many of the techniques developed and lessons learned with earlier media are used in Internet delivery.
Integrating culture in teaching ESP via the LanGuide mobile application

The present paper aims at describing the uncharacteristic illustration of working with the cultural element when teaching a foreign language against the background of the classicised approaches in this respect, in the context of the LanGuide project, whose objectives are to provide the users with a mobile application for learning and testing foreign languages. The approach tackled by the authors of the exercises, who are also the authors of the present study, combines two aspects from the traditional methodologies i.e., the inclusion of host-country cultural items in tasks devised in and for the English language, in order to meet the requirements and goals of the LanGuide project. Thus, students, teachers and administrative staff members involved in Erasmus+ mobility who need to learn their English language knowledge in the fields of mobility, administration, IT, and/or academic may find useful cultural elements of the countries they will visit while polishing their skills for English.
Reinforcement Learning Guided by Provable Normative Compliance

Reinforcement learning (RL) has shown promise as a tool for engineering safe, ethical, or legal behaviour in autonomous agents. Its use typically relies on assigning punishments to state-action pairs that constitute unsafe or unethical choices. Despite this assignment being a crucial step in this approach, however, there has been limited discussion on generalizing the process of selecting punishments and deciding where to apply them. In this paper, we adopt an approach that leverages an existing framework -- the normative supervisor of (Neufeld et al., 2021) -- during training. This normative supervisor is used to dynamically translate states and the applicable normative system into defeasible deontic logic theories, feed these theories to a theorem prover, and use the conclusions derived to decide whether or not to assign a punishment to the agent. We use multi-objective RL (MORL) to balance the ethical objective of avoiding violations with a non-ethical objective; we will demonstrate that our approach works for a multiplicity of MORL techniques, and show that it is effective regardless of the magnitude of the punishment we assign.
Teacher and Student Talk in Learning Speaking Skill at SMA Bina Bersaudara Medan

This study aimed (1) to investigate the types of teacher and student talk in English classroom interaction and (2) to investigate the teacher and students’ reasons to use the types of categories in classroom interaction. This study focuses on analyzing the process of classroom interaction through Flanders Interaction Analysis Categories System (FIACS). This study was conducted by using descriptive qualitative research. It was conducted in SMA Bina Bersaudara Medan at the X-1 grade class. The instruments of the research were observation, recording and interview. The results of this study were (1) the types of teacher talk that occurred were praise or encourages, asking question, lecturing and giving direction. Furthermore, the types of student talk that occurred were response and silence or confusion. (2) There are four reasons why the teacher used the types of categories in classroom interaction, namely: the teacher used praise and encourage to make students feel appreciated, the teacher used asking question because the teacher want to improve students’ skill and to make students understood about the topic being discussed before getting into the next topic, the teacher used lecturing to make process of teaching and learning in the classroom to be successful, the teacher used giving direction because teacher wanted to know whether the students have understood about the material or not. There are two reasons why students used the types of categories in classroom interaction, namely: students respond to the teacher because they understood about the topic being discussed, the students wanted to try their ability and to know the extent of their knowledge about the topic, the students silence or confusion when the teacher asked question because the students didn’t understand the question given by the teacher, according to them the lesson is very difficult to understood, and they didn’t understand the topic being discussed.Keywords: Teacher Talk, Student Talk, Flanders Interaction Analysis Categories System (FIACS).
NLP and IR Applications Using Graph Theory

The emerging application like multimodal summarization, summarization using knowledge graph with graph neural networks are explained in this chapter. The advanced graph types used in solving the problem of summarization, question answering system are explored. The need and motivation and applications of graph based neural networks are explained in this chapter. The open source libraries, datasets and tools are provided in the last section of the book.
Network Representation Lightening from Hashing to Quantization

Information network embedding is an important way to enable efficient graph analytics. However, it still faces with computational challenges in problems such as link prediction and node recommendation, particularly with the increasing scale of networks. Both hashing and quantization are promising approaches for accelerating these problems by orders of magnitude. In the preliminary work, we have proposed to learn binary codes for information networks, but graph analytics may suffer from large accuracy degradation. To reduce information loss while achieving memory and search efficiency, we further propose to learn quantized codes for information networks. In particular, each node is represented by compositing multiple latent vectors, each of which is optimally selected from a distinct set. Since (generalized) matrix factorization unifies several well-known embedding methods with high-order proximity preserved, we propose a \underline{N}etwork \underline{R}epresentation \underline{L}ightening framework based on \underline{M}atrix \underline{F}actorization (NRL-MF) to learn binary and quantized codes. We also propose an alternating optimization algorithm for efficient parameter learning, even for the generalized matrix factorization case. We finally evaluate NRL-MF on four real-world information network datasets with respect to the tasks of node classification and node recommendation. The results show that NRL-MF significantly outperforms competing baselines in both tasks, and that quantized representations indeed incur much smaller information loss than binarized codes.
You Can See Manhattan from Your Lab: $3B “Cove” Campus Announced in Jersey City

GEN EdgeVol. 3, No. 1 TopicsYou Can See Manhattan from Your Lab: $3B “Cove” Campus Announced in Jersey CityMixed-use campus envisioned as “glue” between New Jersey and New York CityAlex PhilippidisAlex PhilippidisSearch for more papers by this authorPublished Online:7 Apr 2021https://doi.org/10.1089/genedge.3.1.035AboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetails Volume 3Issue 1Jan 2021 Information© 2021 by GEN PublishingTo cite this article:Alex Philippidis.You Can See Manhattan from Your Lab: $3B “Cove” Campus Announced in Jersey City.GEN Edge.Jan 2021.219-224.http://doi.org/10.1089/genedge.3.1.035Published in Volume: 3 Issue 1: April 7, 2021PDF download
Effect of Video Assisted Teaching Program on Nurses' Performance regarding the Care of Patients with Spinal Cord Injury

used for data collection were 1) Nurses self-administered questionnaire sheet. It included two parts.
[Glasses-free Three Dimensional Endoscopic Display System with Multi-face Detection].

The glasses-free three dimensional(3D) endoscopic display system provides the surgeon with the depth information of the minimally invasive surgery scene obtained from the binocular perspective, which can effectively relieve the surgeon's posture fatigue and visual fatigue during the long-term surgery, and assist in the operation of surgical instruments more accurately to reduce the damage to the surrounding tissues of the operation area. However, the glasses-free 3D display device currently has the problem of a narrow optimal viewing zone and easy crosstalk, especially in the surgical teaching application scenario, which performs poorly. In order to overcome the limitation of the narrower field of view, we introduce deep learning algorithms to detect and locate multiple faces, fine-tune the 3D display grating of the endoscope, rearrange pixels, and change the best view area, so that more people can get the best view. The experimental results show that the face detection accuracy of the method is 97.88%, and the detection time is 135 frames/ms, which achieves high accuracy while maintaining real-time performance.
Reactive and Proactive Anomaly Detection in Crowd Management Using Hierarchical Temporal Memory

An effective crowd management system offers immediate reactive or proactive handling of potential hot spots, including overcrowded situations and suspicious movements, which mitigate or avoids severe incidents and fatalities.The crowd management domain generates spatial and temporal resolution that demands diverse sophisticated mechanisms to measure, extract and process the data to produce a meaningful abstraction.Crowd management includes modelling the movements of a crowd to project effective mechanisms that support quick emersion from a dangerous and fatal situation.Internet of Things (IoT) technologies, machine learning techniques and communication methods can be used to sense the crowd characteristic /density and offer early detection of such events or even better prediction of potential accidents to inform the management authorities.Different machine learning methods have been applied for crowd management; however, the rapid advancement in deep hierarchical models that learns from a continuous stream of data has not been fully investigated in this context.For example, Hierarchical Temporal Memory (HTM) has shown powerful capabilities for application domains that require online learning and modelling temporal information.This paper proposes a new HTM-based framework for anomaly detection in a crowd management system.The proposed framework offers two functions: (1) reactive detection of crowd anomalies and (2) proactive detection of anomalies by predicting potential anomalies before taking place.The empirical evaluation proves that HTM achieved 94.22%, which outperforms k-Nearest Neighbor Global Anomaly Score (kNN-GAS) by 18.12%, Independent Component Analysis-Local Outlier Probability (ICA-LoOP) by 18.17%, and Singular Value Decomposition Influence Outlier (SVD-IO) by 18.12%, in crowd multiple anomaly detection.Moreover, it demonstrates the ability of the proposed alerting framework in predicting potential crowd anomalies.For this purpose, a simulated crowd dataset was created using MassMotion crowd simulation tool.
Containers and Kubernetes: Security is not an Afterthought

Abstract Container adoption is increasing across industries, but as Matt Colman, Senior Cloud Security Architect for IBM Security explains, developing, deploying and managing secure applications has its challenges.
THE EFFECT OF COOPERATIVE LEARNING MODEL UPON READING COMPREHENSION OF ELEVEN GRADE STUDENTS OF SMA NEGERI 1 SELONG DIFFERING IN READING INTEREST LANGUAGE EDUCATION

This study aimed at finding out The Effect Of Cooperative Learning Model Upon Reading Comprehension Of Eleven Grade Students Of SMA NEGERI 1 SELONG Differing In Reading Interest. This study was experiment research used post-test only control group design. This study used random sampling technique involved 84 students consisted of eight classes that had the same competence. And then all classes were given random classes to determine the experimental group and the control group. consisted of 42 students . After that groups were given the reading interest questionnaire . From the analysis of the questionnaire , 33% (21 students ) of the high reading interest and 33 % (21 students) of the low reading interest were determined from of each group as samples. Data were collected using reading test and questionnaire reading interest. The data were analyzing two- way ANOVA. The data of the students’ achievement were obtained reading comprehension. Reading comprehension data were collected through a questionnaire of reading interest. Both of reading comprehension and reading interest had been validated before being applied to the research sample. Next, the data that were obtained were analyzed by using Two-way ANAVA analysis and Tukey Test. The result of this research is as follows: (1) There was a significant difference between the achievement of the students who were taught by cooperative learning model and those who were taught by using conventional learning model . (2) There was an interaction between the usage of cooperative learning model and reading comprehension upon students’ reading interest. From the result of this research , it can be concluded that the learning model had significant effect towards the students reading comprehension. The difference of reading comprehension mentioned shows that cooperative learning model is better or effective than conventional learning model. (3)There was a significant difference between the achievement of the students who had high reading interest being taught using cooperative learning model and those who were taught being conventional learning model. (4) there was a significant difference between the achievement of the students who had low reading interest being taught using cooperative learning model and those who were being taught using conventional learning model
Motion-Sequence Authentication System: Guard for Smart Phones

In recent years, the mobile privacy protection is becoming increasingly critical due to the popularity of smartphones. The owner needs a “second line of guard” (user behavior authentication) when unlocking methods are attacked. The traditional user authentication approaches either face smudge attacks or can only work on dedicated devices. In this paper, we present a Motion-sequence Authentication System (MAS), an accurate and robust security authentication system that is not limited to expensive phones. MAS (Motion-sequence Authentication System) distinguishes user categories according to the unique characteristics of different user motion sequences. It is a rapid, non-contact and unobtrusive method of user authentication without predefined motions. MAS exploits Markov model to track the behavior of smartphone users, it can achieve real-time user authentication by utilizing the user’s short-term interaction with the smartphone. Our experiments in multiple real environments show that MAS can achieve higher than 94% accuracy for authenticating user motion sequences, which fills the gap with motion sequence recognition and provides a way of thinking for the development of human-computer interaction and information security.
Airborne infrared high dynamic range signal compression display method based on CLAHE

Infrared imaging has been widely used in military and civil applications. For the characteristics of high dynamic range and concentrated area of interest of airborne infrared signals, this paper proposes a high dynamic signal compression display method based on CLAHE (contrast-constrained adaptive histogram equalization). The method first classifies sub-images by dynamic range, linearly compresses sub-images with small dynamic range to prevent them from increasing noise by over-amplification, and uses histogram statistical information to segment background and target for sub-images with large dynamic range to retain as much target display information as possible. Through multi-scene imaging display and PSNR calculation, it is verified that the algorithm in this paper can effectively suppress the background, enhance the contrast of the image in the region of interest, and improve the airborne infrared imaging display.
DWT based Person Re-Identification using GAN

The recent development in person re-identification has challenging task for variations in pose, illumination, expression, and also similar appearance between two different persons. In this paper, we propose Discrete Wavelet Transform (DWT) based person re-identification using Generative Adversarial Network (GAN). The CMU multi-PIE face database with multiple viewpoints and illuminations is considered to test the model. The profile side view face images to be tested are converted into frontal face images using Two-pathway generator adversarial network (TP-GAN). The frontal face images are loaded into the server to create server database. The synthesized TP-GAN images and server database images are pre-processed to convert RGB into grayscale images and also to convert into uniform face image dimensions. The person re-identification is based on feature extraction through DWT, which generates one low frequency LL band and three high frequency bands LH, HL and HH. The LL band coefficients are considered as final features, which are noise-free and compressed number of features. The features of profile side view images and server database images are compared using Normalized Euclidean Distance (NED) and threshold values for person re-identification.
An Hybrid Approach for Suitable Content Navigation Based on Weighted Clustering for online users

Due to exponential growth of several contents whichis created on the Web, Recommendation procedures need to bedeveloped is becoming critical. Inestimable diverse classes ofrecommendations are done on the Web each day, which includespictures, song, imageries, files recommendations, suggestion ofqueries, recommendation of tags, etc. No restriction that whatcategories of data sources are handled for the recommendations,basically these sources of data can be demonstrated in thecustom of numerous categories of graphs. This paper providedthe Common structure on mining the Web graphs forReferences using a hybrid method for appropriate contentnavigation built on weighted clustering for online users. First werecommend a collaborative filtering method to acquire theappropriate content from web records and creating theweighted clusters of related items for the user created queries.Secondly centered on the click through data study we achievethe interests and hidden semantic associations betweencustomers and queries also queries and clicked Web data. Andbased on this clickthrough information the bipartite graph willbe produced, which represents the relationship amongst thequeries and URLs. Lastly we recommend a new querysuggestion standard, personalized query recommendationcreated on weighted clustering for online users.
Almost APAL

Abstract Arbitrary public announcement logic (APAL) is a logic of change of knowledge with modalities representing quantification over announcements. We present two rather different versions of APAL wherein this quantification is restricted to formulas only containing a subset of all propositional variables: SAPAL and SCAPAL. Such restrictions are relevant in principle for the specification of multi-agent system dynamics. We also present another version of APAL, quantifying over all announcements implied by or implying a given formula: IPAL. We then determine the relative expressivity of all these logics and APAL. We also present complete axiomatizations of SAPAL and SCAPAL and show undecidability of satisfiability for all logics involved, by arguments nearly identical to those for APAL. We show that the IPAL quantifier, motivated by the satisfaction clause for substructural implication, yields a new substructural dynamic consequence relation.
Analysis on the Study Failure of Students in China Today

Selection is one function of education, when some students can not endure the failure. The students fail because of their poor learning methods, learning attitudes and learning behaviors. Teachers do not clarify the functions and purposes of exams and homework, so the students do not like the exams and the homework. The criticisms from parents and teachers are not serious. It is understandable that some students fail in study. If the failure of some students is unavoidable, people should do more to lessen the failure and give the students more success.
A Comparison Study Between English and Chinese Education Resources Distribution

With the progress towards the sustainable development goals, 'quality education,' as one of the goals, has generated great efforts from each country all over the world.This study attempts to compare the function of education resources distribution among developing and developed countries, in particular between China and the UK.It has been found that the UK has not only focused on basic education but also commit great efforts to higher education.The legitimated Chinese education system and the Compulsory Education Law of the People's Republic of China show greater effectiveness in enhancing the proportion of students participating in primary school education than in the UK.Besides, primary school education resources are distributed more equally among Chinese students than those in the UK.