Reviewerly/exHarmony huggingface dataset:
Analisis Pelaksanaan Ujian Sekolah Berbasis Android (USBA)

This study aims to determine the implementation of the Android-Based School Examination (USBA) at SMAN 3 Sungai Penuh. This type of research is descriptive with a qualitative approach. Data collection techniques by interview and documentation. The data sources used were primary data and secondary data, which were analyzed by using triangulation techniques. The number of informants in this study were 6 people, namely USBA operators and class XII students of SMAN 3 Sungai Penuh. The results showed that the USBA implementation process at SMAN 3 Sungai Penuh was the same as the conventional exam implementation process, but at USBA students were more independent. The advantages of USBA include exams that can be done anywhere, USBA costs are more efficient than paper exams, test results can be seen after students have finished taking exams, with this USBA minimizing the occurrence of cheating by students. Constraints in implementing USBA are the low capacity of the internet network. Comparing conventional exams with USBA, there are many differences between the two, this can be seen from the equipment used, the form of the questions, the time for taking the questions, the place and the operational costs of the exam.
EF21: A New, Simpler, Theoretically Better, and Practically Faster Error Feedback

Error feedback (EF), also known as error compensation, is an immensely popular convergence stabilization mechanism in the context of distributed training of supervised machine learning models enhanced by the use of contractive communication compression mechanisms, such as Top-$k$. First proposed by Seide et al (2014) as a heuristic, EF resisted any theoretical understanding until recently [Stich et al., 2018, Alistarh et al., 2018]. However, all existing analyses either i) apply to the single node setting only, ii) rely on very strong and often unreasonable assumptions, such global boundedness of the gradients, or iterate-dependent assumptions that cannot be checked a-priori and may not hold in practice, or iii) circumvent these issues via the introduction of additional unbiased compressors, which increase the communication cost. In this work we fix all these deficiencies by proposing and analyzing a new EF mechanism, which we call EF21, which consistently and substantially outperforms EF in practice. Our theoretical analysis relies on standard assumptions only, works in the distributed heterogeneous data setting, and leads to better and more meaningful rates. In particular, we prove that EF21 enjoys a fast $O(1/T)$ convergence rate for smooth nonconvex problems, beating the previous bound of $O(1/T^{2/3})$, which was shown a bounded gradients assumption. We further improve this to a fast linear rate for PL functions, which is the first linear convergence result for an EF-type method not relying on unbiased compressors. Since EF has a large number of applications where it reigns supreme, we believe that our 2021 variant, EF21, can a large impact on the practice of communication efficient distributed learning.
Task-oriented Analysis on Debugging Process Based on Eye Movements and IDE Interactions

Understanding debugging behaviors is a classical topic in various research areas. Most of the existing work focused on the comparison between different groups (e.g. experts and novices). As to programming education, the details of debugging process are vital for teachers to conduct individual diagnosis. In this paper, we focus on this requirement, and perform a measurement based study on student&#x2019;s debugging process. We take C programming in Eclispe IDE as the scenario, and measure the student&#x2019;s eye movements and IDE interactions. A analysis method is proposed to interpret the obtained multi-modal data. The event of running program in IDE is adopted as the division indicator for debugging tasks. With this task organization model, our propose a task-oriented analysis method, which divides the data analysis work into two levels, the tasks focusing the relevant visual AoI and the actions in the same task. We further investigate the temporal evolution in multiple aspects, including the migration of task focus, the distribution of cognitive load, the time consumption of various actions, and the visual attention on relevant AoIs. Two students, who had the similar overall debugging performance, are selected as our study cases. The results show that, our method can not only recover the details of their temporal progress, but also infer their main difficulty in the debugging task.
Identification of Book Needs Level Using the K-Means Method at STMIK Pelita Nusantara Medan Library

Library Sekolah Tinggi Manajemen Ilmu Komputer Pelita Nusantara Medan is one of the facilities that provide book loan services to students. To improve services to the needs of books for Pelita Nusantara STMIK students. This test aims to determine the amount of books needed to borrow, and determine how many students borrow books based on books that are often borrowed simultaneously. The method used is the K-Means method, the K-Means algorithm is a non-hierarchical data clustering method with an effort to partition the available data into one or even more clusters. The data to be used is the amount of book data that is often borrowed, and the number of students who borrow books in 2015-2019. From the results obtained in the overall data process above that cluster 0 is a low-potential book borrower totaling 52 titles, Cluster 1 is a potential borrower of 30 books, and Cluster 2 is a high-potential book borrower totaling 1 book title, therefore knowledge is obtained that borrowing books at STMIK Pelita Nusantara books that are often borrowed will be reviewed for books. K-Means algorithm method has been able to be applied to identify the level of book requirements. Data is processed to obtain the number of books that are often borrowed will be multiplied. The data is processed using Rapid Miner software.
Identifying Metacognitive Online Reading Strategies Of English Department Student

The purpose of this study is to determine the metacognitive reading strategy EFL students of the English Department use during their academic period during this internet era. The participants were 76 English Education Study Program students from the academic year 2020/2021. Data came from the OSORS (Online Survey of Reading Strategy) questionnaire that provided accurate data. This study revealed that the students who participated in this study reported a wide range of metacognitive strategies when reading online academic texts. The Global Reading Strategies were the most popular metacognitive reading approach students use when reading online. The students participate in live chat with native speakers of English, where interacting with native speakers, the students can better grasp the native English language, phrases, and intonation by conversing with a native speaker. In keeping the reported use of strategies that include looking at sites covering both sides of an issue. The student searches for other areas that would analyze the topic, then combined his previous knowledge with information on the website. And the learners must have learned the first language. The role of L1 or its use is relevant in issues such as transfer or interference from L1 or even as a learning strategy in learning a second language.
Artificial Intelligence for University Marketing

Artificial intelligence (AI) is embedded in digital marketing technologies and so facilitates the development of university marketing management. The article discusses digital globalization trends and factors influential for AI deployment in higher education.
The Effect of Students Team Achievement Divisions (STAD) Teaching Method and Small Group Discussion Method on Students’ Writing Descriptive Text Achievement

This research presents a research report on the using of Students Team Achievement Divisions (STAD) Method and Small Group Discussion Method for students’ writing skill at SMK Kesehatan Tridarma. The objectives are to find out the effect of using Students Team Achievement Divisions (STAD) Method and Small Group Discussion (SGD) Method for students’ writing skill and which method is the most effective one. The subject of this study was grade X students of Senior High School SMK Kesehatan Tridarma. In this study the writer took 100 students of SMK Kesehatan tridarma a as the sample. They were divided into three groups. The first and the second groups are (Experimental Group). They were taught by using Students Team Achievement Divisions (STAD) Method and Small Group Discussion (SGD) method in writing text, while the third group (Control Group) was taught without using Students Team Achievement Divisions (STAD) Method and Small Group Discussion method. The technique for data analysis is quantitative research. In analyzing the data the mean of the students score for pre-test in experimental group one (STAD Method) is 69.27 and the mean of the students score for post-test in experimental group one is 81.30. The mean of the students score for pre-test in experimental group two (SGD Method) is 68, 83 and the mean of the students score for post-test in experimental group two is 80, 29.The mean for pre-test in control group is 66, 8 and the mean for post-test in control group is 76.77 The conclusion is that the using of Students Team Achievement Divisions (STAD) Method and Small Group Discussion are significantly affect the students’ writing skill. STAD Method is the most effective one in teaching news item text than SGD Method. It is suggested that teachers should apply this method as one of methods to improve students’ ability in writing descriptive text.
Analysis of the Differences of Emoticons in Intercultural Online Communication

With the popularization of the online social media, the communication of various cultures in the world is becoming closer, and the use of emoticons is becoming much more frequent.Among them, there are not only emoji, a traditional emoticon, but also Animoji and "Biaoqingbao", which have sprung up in recent years.All kinds of emoticons are transmitted in the online world, but the differences then come forth obviously.This paper uses text analysis, comparing emoticons used in various countries, proposes to conclude how emoticons differ on the social media including the production, usage and comprehension.Because of the differences of way of thinking, language mechanism, culture and social media preference, Easterners and Westerners prefer using different forms of emoticons in different ways, and they also understand the meanings differently.Hence, for the sake of better intercultural online communication, people have to respect others from different cultures of both nations and social media, as try to use acknowledged and simple emoticons.
Developing an Integrated Model to Enhance the Efficiency in the Detection and Erasing of Duplicate Files from Cloud

Identifying and disposing of the copied document is one of the serious issues in the wide space of information cleaning and information quality in the framework. Ordinarily, a similar sensible true element might have numerous portrayals in the information distribution centre. Copy disposal is hard because it is brought about by a few blunders like typographical mistakes and various pictures of similar consistent worth. Our primary aim of this study is to recognise specific and inaccurate representations by utilising copy description and end rules. This methodology is used to work on the proficiency of the information. The significance of information precision and quality has expanded with the blast of information size. In the copy disposal step, just one duplicate of accurate copied records or documents is held and dispensed with other copy records or documents. The end cycle is vital to delivering cleaning information. Before the end sequence, the similitude limit esteems are determined for every one of the records available in the informational collection. The closeness limit admires significant for the end communication.
SMILE - Smart eMaIl Link domain Extractor

Phishing over email continues to be a significant threat, as these messages still end-up in users' inboxes.Several studies showed that users rarely check the URL in the statusbar before clicking a link and that they have difficulties reading URLs.To support users, we propose SMILE (Smart eMaIl Link domain Extractor), a novel approach that provides the relevant information to distinguish between legitimate and phishing emails.In most cases, the SMILE-string is the domain and top-level-domain of the URL behind a link (e.g., ''click here'' in an authentic Amazon email is modified to ''click here [ amazon.com ]'').
Bureaucratic routines and error management in algorithmic systems

This article discusses how an analogy between algorithms and bureaucratic decision-making could help conceptualize error management in algorithmic systems. It argues that a view of algorithms as irreflexive bureaucratic processes is insufficient as an account of errors in complex public sector contexts, where algorithms operate jointly with other organizational work practices. To conceptualize such contexts, the article proposes that algorithms could be viewed as analogous to more traditional work routines in bureaucratic organizations. Doing so helps clarify that algorithmic irreflexivity becomes problematic when the coordination of routine work around automation fails. Thus, also the challenges of error management come to concern the wider context of organized work. This argument is illustrated using known examples from the critical literature on algorithms. Finally, drawing on recent studies in routine dynamics, the article formulates empirical research directions on error management in algorithmic systems.
A technical review of no-reference image quality assessment algorithms for contrast distorted images

Automatic image quality assessment similar to human vision perception is an essential process for real-time image processing applications to perform perceptual image assessments for effectively achieving their goals. As no-reference image quality assessment (NR-IQA) schemes perform perceptual assessments of images without any information about their original version, these algorithms suit real-time computer vision techniques because of the non-availability of reference images. Contrast and colorfulness play important roles in determining the quality of color images. By combining many IQA metrics, a number of combined metrics had been devised. This study provides an insight into major NR-IQA methods and their effectiveness in assessing contrast, colorfulness, and overall quality of contrast-degraded images with technical analysis. The effectiveness of top-ranking NR-IQA methods is experimentally assessed with benchmark assessment methods on images from benchmarked databases. The study provides insight into open research challenges in the area of NR-IQA for developing new promising methods by clearly demarcating the difficulties of top-ranking NR-IQA methods.
Neural Path Hunter: Reducing Hallucination in Dialogue Systems via Path Grounding

Dialogue systems powered by large pre-trained language models exhibit an innate ability to deliver fluent and natural-sounding responses. Despite their impressive performance, these models are fitful and can often generate factually incorrect statements impeding their widespread adoption. In this paper, we focus on the task of improving faithfulness and reducing hallucination of neural dialogue systems to known facts supplied by a Knowledge Graph (KG). We propose Neural Path Hunter which follows a generate-then-refine strategy whereby a generated response is amended using the KG. Neural Path Hunter leverages a separate token-level fact critic to identify plausible sources of hallucination followed by a refinement stage that retrieves correct entities by crafting a query signal that is propagated over a k-hop subgraph. We empirically validate our proposed approach on the OpenDialKG dataset (Moon et al., 2019) against a suite of metrics and report a relative improvement of faithfulness over dialogue responses by 20.35% based on FeQA (Durmus et al., 2020). The code is available at https://github.com/nouhadziri/Neural-Path-Hunter.
Design of solar light tracking system Research on energy autonomy system design of outdoor patrol car based on solar energy

Abstract The Inner Mongolia has abundant solar energy and electricity resources. Because of the long distance between cities, transmission lines are too long, making it difficult to check lines. In order to solve the problems existing in the inspection work, this paper studies a kind of outdoor inspection vehicle using solar energy, the energy system of the inspection vehicle can independently complete the charge and discharge, so as to realize the inspection task. This paper focuses on the energy autonomy of the on-site inspection vehicle for solar energy. According to the design requirements of the inspection vehicle, appropriate parts are selected to build an energy autonomy inspection system for the inspection vehicle. Then the solar tracking algorithm and maximum power tracking control algorithm are used to improve the conversion rate of solar panels and achieve fast charging. Finally, the hardware and software of the solar controller are designed, and the corresponding functions are debugged.
Gender and School Type as Correlates of Secondary School Students’ Extensive Reading Habit and Writing Performance in Ilorin, Nigeria

Reading is a critical skill, both within and beyond the classroom. The nature of writing too cannot be described without reference to literacy, because the latter subsumes the former. This study investigated the relationship between extensive reading habit and writing performance of secondary school students in Ilorin, Nigeria, based on gender and school type. The research is descriptive in nature. Senior Secondary 2 students were purposively sampled based on the variables of the study. Two schools from each of Ilorin East and South and four schools from Ilorin West were proportionately sampled for the study. Fifty students were selected through the simple random sampling technique from each of the eight schools sampled for the study. A total of four hundred respondents were therefore involved in the study. The data collected through the Students’ Reading Habit Questionnaire (SRHQ) and a Students’ Writing Performance Test (SWPT) were analyzed using mean ratings, percentages and the Pearson’s Product Moment Correlation Co-efficient at 0.05 significance level. The findings of the study revealed that there was a significant relationship between students’ extensive reading habit and their writing performance. Also, no significant relationship between students’ extensive reading habit and their writing performance in relation to gender and school type even though a greater percentage of the students (88.7%) recorded low writing performance and ambivalent reading habit. The study recommended among others that teachers should make reading and writing activities at the secondary school extensively functional irrespective of gender and school type.
Predicting Polarity of Tourists Reviews using LSTM Deep Learning Model over Machine Learning Classical Approach to increase the accuracy in Text Classification

The objective of sentiment analysis is to find positive and negative reviews. Hotel reviews have been analyzed in this researchpaper using machine learning algorithms and these are compared with most efficient deep learning method based on LSTM.The idea is to use the concept of text classification in the form of customer reviews. Although there are many machine learning techniques available for text classification but here two most important methods of machine learning have been used which are Naive Bayes algorithm and another is Random Forest algorithm. These techniques are further compared with LSTM based deep learning technique. The goal is to develop a deep learning model that uses the LSTM technique to work on hotel reviewsin the context of online tourism and outperforms earlier machine learning techniques. This methodology will aid the tourismindustry in growing its business by studying consumer hotel reviews. Sentiment analysis is widely used in business domains to improve products and services by understanding customer opinions about these services and this is the case with hotel reviews analysis using LSTM to obtain efficient and clear results that will undoubtedly help the tourism industry in better understanding the customer opinions so that in the future, the tourism industry can deliver better services to customers.Keywords: Machine learning, Deep Learning, Hotel Reviews, Online Tourism, LSTM
Context-aware Entity Typing in Knowledge Graphs

Knowledge graph entity typing aims to infer entities’ missing types in knowledge graphs which is an important but under-explored issue. This paper proposes a novel method for this task by utilizing entities’ contextual information. Specifically, we design two inference mechanisms: i) N2T: independently use each neighbor of an entity to infer its type; ii) Agg2T: aggregate the neighbors of an entity to infer its type. Those mechanisms will produce multiple inference results, and an exponentially weighted pooling method is used to generate the final inference result. Furthermore, we propose a novel loss function to alleviate the false-negative problem during training. Experiments on two real-world KGs demonstrate the effectiveness of our method. The source code and data of this paper can be obtained from https://github.com/CCIIPLab/CET.
Practical Implementation of 3D Smart Blind Walking Stick for Blind Peoples

Vision is a valuable gift from God that you may able to see and appreciate this attractive globe. But masses of people at some stage in the world are robbed of this. According to October 2017 file of World Wellness Company an estimated 253 million people deal with vision impairment: 36 million are blind and also 217 million have mild to extreme vision impairment. Un-operated cataract is the main purpose for lack of sight in low earnings and also organizing international locations. Also in China by means of the stop of 2017, the populace over 60 will attain 241 million, accounting for 17.3 percent of the United States of America’s complete populace as well as genuinely forty million are handicapped and semi impaired, according to information released via the Committee for the aged in 2018. So, in this case a whole lot of the aesthetically challenged people can't come up with the money for a pricey tool to make use of as their fan. So, on this task we have genuinely counseled a cheap 3-D clever Strolling device. This is usually is based on the sensors due to the fact Sensing units can enhance the globe through diagnostics in several packages and it aids to boost performance. This tool is implemented using ARDUINO Controller, IR Sensors (For 3D), Vibration Sensor (Piezoelectric sensing unit is for Stress and Acceleration) along with GSM in addition to GPS for area Sharing. Additionally we're presenting Voice module with this to offer the instructions with audio format. This Entered device is ready by using clean machine finding out algorithms to maximize the gadget.