Reviewerly/exHarmony huggingface dataset:
A Weighted Multi-objective Evolutionary Algorithm Optimization

Weight Based Genetic Algorithms (WBGA) have a computational efficacy and non-cumbersome for multi-objective optimization. The solutions obtained in the converged region do not always produce maximum optimization for all the objective functions simultaneously. But, the combination of the solutions from different iteration may yield optimized values for all the objective functions to a satisfactory level.  The paper attempts to find a method which keeps the simplicity and computational efficiency of WBGA intact, but at the same time counters the problem of inferior pareto-optimal solutions. This is done by finding such a combinational set of solutions which yields strong values for all the objectives. The paper proposes neutrosophic logic (NL) as a postprocessor to the outcome of the WBGA. The NL assigns a percentage of truth, false and indeterminant value to the obtained solutions. The proposed postprocessor operation has been demonstrated with hand calculations on a test problem, and a complex practical example. The results obtained as compared to WBGA show the emergence of a superior solution-set and reaches in close agreement with NSGA-II, while maintaining the computational efficiency.
AKTIVITAS SISWA DALAM PEMBELAJARAN DENGAN MENGGUNAKAN MEDIA FLASHCARD DI SEKOLAH DASAR

The purpose of this study was to determine the activity of class III-A students in learning using flashcard media at Tenggilis Mejoyo 1 Elementary School Surabaya. This research is a descriptive qualitative research. The subject of this study was class III-A students at Tenggilis Mejoyo 1 Elementary School Surabaya Academic Year 2018-2019 which consisted of 29 students. Data collection techniques include observation, interviews, and documentation. Data analysis using data collection, data reduction, data presentation, conclusion drawing. The validity of the data uses Triangulation techniques. The results showed that the activity of class III-A students in learning using flashcard media in Tenggilis Mejoyo 1 Elementary School Surabaya was said to be good, this was seen through visual activity activities in the form of students observing when learning took place, oral activity in the form of students daring to express opinions, writing activity namely by recording from what is heard and seen, listening activity which is listening to the learning delivered.
¡Pensá, para la prevención!

This article has an objective to develop the capacity of&nbsp;critical thinking in dealing with cyberbullying. It addresses&nbsp;a population composed of high school students (2nd grade)&nbsp;from a private school, taking into account the following&nbsp;case studies dimensions: analysis of information, inferences&nbsp;and implications, alternatives of solutions and position of&nbsp;argument. Through the&nbsp; application called “Critical thinking&nbsp;test” relevant information for the study can be collected.&nbsp;Furthermore, the intervention program involves the&nbsp;participation of affected students, aggressors and observers&nbsp;as well as parents, teachers and educational agents.
A Literature Review on Application of Sentiment Analysis Using Machine Learning Techniques

Many businesses are using social media networks to deliver different services and connect with clients and collect information about the thoughts and views of individuals. Sentiment analysis is a technique of machine learning that senses polarities such as positive or negative thoughts within the text, full documents, paragraphs, lines, or subsections. Machine Learning (ML) is a multidisciplinary field, a mixture of statistics and computer science algorithms that are commonly used in predictive and classification analyses. This paper presents the common techniques of analyzing sentiment from a machine learning perspective. In light of this, this literature review explores and discusses the idea of Sentiment analysis by undertaking a systematic review and assessment of corporate and community white papers, scientific research articles, journals, and reports. The goal and primary objectives of this article are to analytically categorize and analyze the prevalent research techniques and implementations of Machine Learning techniques to Sentiment Analysis on various applications. The limitation of this analysis is that by excluding the hardware and the theoretical exposure pertinent to the subject, the main emphasis is on the application side alone. The limitation of this study is that the major focus is on the application side thereby excluding the hardware and theoretical aspects related to the subject. Finally, this paper includes a research proposal for e-commerce environment towards sentiment analysis applying machine learning algorithms.
PENERAPAN TEKNIK WEB SCRAPING UNTUK PENGGALIAN POTENSI PAJAK (STUDI KASUS PADA ONLINE MARKET PLACE TOKOPEDIA, SHOPEE DAN BUKALAPAK)

Currently, millions of transaction data are avaliable on the internet, which can be retrieved and analyzed for excavating potential taxes. This article aims to examine whether the search data through web scraping techniques can be applied in an attempt to excavate the potential tax by the Account Representative. This paper uses an informetric approach, which will be examined quantitative information in the form of transaction data of sellers recorded on the three online marketplace (OMP) namely Tokopedia, Shopee and Bukalapak. The results show that web scraping techniques can be used for extracting potential taxes, and the best web scraping technique that can be done by the Directorate General of Taxation (DJP) is to develop its own integrated web scraping application as a Business Intelligence system. The results of this research are expected to contribute academically in the form of the use of web scraping in data extraction for the excavation of potential taxes and policy implications in terms of data search through the internet by the Directorate General of Taxation
Target Tracking Algorithm Combining Improved GMS and Correlation Filtering

Aiming at the problem that traditional target tracking algorithms cannot detect and track specific targets in the real environment (such as airports) due to the given initial frame target position, this article proposes a target tracking algorithm that combines improved Grid-based Motion Statistics (GMS) matching algorithm and correlation filtering tracking algorithm. First of all, for the problem that GMS cannot adapt to the detection of specific targets in realistic monitoring environment, a template size expansion method is proposed to disperse the centrally gathered feature points and Random Sample Consensus (RANSAC) is introduced to remove outliers in the center of similar areas. Second, the initial template of the tracking algorithm is the target detected by the improved GMS method, and it is used to extract features to train the correlation filter to determine the target position of the video sequence. Finally, two groups of comparative experiments show that not only the improved GMS algorithm has better target detection performance, but also the fusion algorithm has better reliability and robustness for target tracking.
Direct copyright liability as regulation of hosting platforms for the copyright-infringing content uploaded by their users: Quo vadis?

JIPITEC 11 (2020) 3 - The potential direct liability of hosting platforms such as YouTube and Dailymotion, which provide the technical conditions for their users to upload and share copyright-protected content, for the infringement of the right of communication to the public (CTTP) in Article 3(1) Directive 2001/29/EC (and pre-Directive 790/2019) represents one of the most complex and controversial aspects of current European Union (EU) copyright law. The test in Article 3(1) is opaque and may even support opposing conclusions on the matter. Doctrinally, the appropriateness of Article 3(1) to regulate hosting platforms is shaky as it is unclear how the regulation of platforms via Article 3(1) may reflect the balance of interests of rightsholders, of platforms, and internet users. Hosting platforms facilitate both the legal and illegal sharing of copyright content indiscriminately and in an automated fashion. When legal content is shared through their service, hosting platforms play an important role in facilitating the exercise of user’s freedom to send and receive information safeguarded by Article 11 of the EU Charter of Fundamental Rights.The potential application of direct copyright liability to hosting platforms, including the spectre of damages, may chill technical innovation in the area. Some platforms may even close and the opportunities for internet users to share legal content reduces as a result. To address these issues, this article analyses the three alternatives for limiting the responsibility of hosting platforms under Article 3(1). The article first analyses the complex test for CTTP under Article 3(1). To balance the application of liability, Alternative 1 explores the option of integrating a ‘duty of care’ element conditioned by a standard of proportionality within the test for CTTP. Alternative 2 challenges the notion that direct responsibility may be attributed to operators of hosting platforms. It analyses, but ultimately dismisses, the situation where host providers may be considered as mere providers of facilities for enabling communication. Alternative 3 advances a novel application of the test under Article 3(1) which shows that operators of certain hosting platforms do not engage in acts of “communication” of the illegal copyright material uploaded by their users. The purpose of the paper is to bring attention to particular possible constructions of hosting platform liability and their broader implications.
LibGuides: Student Resources: About the Library

Welcome to the University Libraries! We have many services and resources to help you succeed.
Hypothesis Stitcher for End-to-End Speaker-attributed ASR on Long-form Multi-talker Recordings

An end-to-end (E2E) speaker-attributed automatic speech recognition (SA-ASR) model was proposed recently to jointly perform speaker counting, speech recognition and speaker identification. The model achieved a low speaker-attributed word error rate (SA-WER) for monaural overlapped speech comprising an unknown number of speakers. However, the E2E modeling approach is susceptible to the mismatch between the training and testing conditions. It has yet to be investigated whether the E2E SA-ASR model works well for recordings that are much longer than samples seen during training. In this work, we first apply a known decoding technique that was developed to perform single-speaker ASR for long-form audio to our E2E SA-ASR task. Then, we propose a novel method using a sequence-to-sequence model, called hypothesis stitcher. The model takes multiple hypotheses obtained from short audio segments that are extracted from the original long-form input, and it then outputs a fused single hypothesis. We propose several architectural variations of the hypothesis stitcher model and compare them with the conventional decoding methods. Experiments using LibriSpeech and LibriCSS corpora show that the proposed method significantly improves SA-WER especially for long-form multi-talker recordings.
Teknologi Open Source Radio Streaming Berbasis Website dan Android

Music is something that can not be separated from human life. This is what makes technology develop so rapidly that radio emerges. Radio is a very important media in delivering information to the public. Along with the development of the emergence of internet technology that allows information to be accessed by all citizens wherever located. So that radio is now transforming with the internet so that the delivery of information is not limited by geographical areas as is the case with conventional radios that rely on transmitters so that when listeners travel outside the city, favorite radio is impossible to hear. UKM Radio Ekspresi Mahasiswa Universitas Semarang (REMUS) is a radio community that currently does not have facilities such as transmitters and mixers so that the UKM activities are only focused on Lab activities. Radio that is offline and visits to cause the membership of these UKM ups and downs. Website and Android-based open-source radio streaming technology is a solution for this UKM because besides being affordable, UKM REMUS can broadcast online so that it can be listened to by students and the public at large. This research uses the Prototype system development method and system implementation using a combination of open source applications namely are Wordpress, Icecast2, dan Android Studio in the server-side then use Winamp, SAM Broadcaster, dan Shoutcast DSP the radio announcer side. The results of the 20 respondents members of UKM, as many as 85% agreed to this radio streaming system. It is expected that the results of this study are the sustainability and the percentage of the membership of UKM REMUS will run well.
Relationship between Course Evaluations and Learning Achievement for the Software Lecture in General Education

The course evaluations was used to collect the students’ opinion and verify the learning performance for a long time. However, many evaluations’ methods are not consider each characteristics of the course, and many courses were presented to students with the same question for the course evaluations. This research aims to make a learning success model considering the questionnaire of course evaluations and relationship between the factors of learning and learning satisfaction/benefit. In order to make a model, we identify the items of the questionnaire and distinguish the factors considering the items. The factors are Information Quality, Knowledge Quality, System Quality and Service Quality. Consequently, the research shows the connection and relationship between them and learning satisfaction/benefit.
OPTIMALISASI SENTRA HAK KEKAYAAN INTELEKTUAL DALAM MENINGKATKAN POTENSI EKONOMI PERGURUAN TINGGI

With various aspects related to intellectual property owned, universities have great potential to produce high quality intellectual property by implementing Universities Tridharma, namely teaching, research and community devotion loaded with intellectual property, both from input, in process and output. To manage, protect and utilize the Intellectual Property, each university should strive to establish an Intellectual Property Center in its environment in accordance with its capacity. In optimizing IPR centers in increasing the economic potential of tertiary institutions, it can be done by commercializing research results and Intellectual property licenses
OAAE: Adversarial Autoencoders for Novelty Detection in Multi-modal Normality Case via Orthogonalized Latent Space

Novelty detection using deep generative models such as autoencoder, generative adversarial networks mostly takes image reconstruction error as novelty score function. However, image data, high dimensional as it is, contains a lot of different features other than class information which makes models hard to detect novelty data. The problem gets harder in multi-modal normality case. To address this challenge, we propose a new way of measuring novelty score in multi-modal normality cases using orthogonalized latent space. Specifically, we employ orthogonal low-rank embedding in the latent space to disentangle the features in the latent space using mutual class information. With the orthogonalized latent space, novelty score is defined by the change of each latent vector. Proposed algorithm was compared to state-of-the-art novelty detection algorithms using GAN such as RaPP and OCGAN, and experimental results show that ours outperforms those algorithms.
Pengaruh Model Pembelajaran Discovery Learning Terhadap Hasil Belajar IPS Siswa Kelas IV SDI Lengkong Wol Kecamatan Welak Kabupaten Manggarai Barat Tahun Pelajaran 2019/2020.

Abstract: This research is motivated by the importance of the discover learning model that is able to change learning to further improve social studies learning outcomes. The purpose of this study was to determine the effect of the Discovery Learning learning model on the social studies learning outcomes of the fourth-grade students of SDI Lengkong Wol, Welak District, West Manggarai Regency. This study uses a quantitative approach. This type of research is a quasi-experiment. The population and sample used in this study were 20 students of class IV A as the experimental class and 20 students of class IV B as the control class. The results showed that there was an effect of the discovery learning model on social studies learning outcomes for fourth-grade students of SDI Lengkong Wol, Welak District, West Manggarai Regency. This shows that the results of the 2- tailed test are at a significance level of  0.05 (0.00  0.05), which means that Ho is rejected, H1 is accepted. Based on the results of the study, it can be concluded that there is a significant influence between the experimental class using the discovery learning model on the social studies learning outcomes of fourth-grade students of SDI Lengkong Wol, Welak District, West Manggarai Regency, 2019/2020 Academic Year
Psychological Factors Influence EFL Students’ Interest

The subject of this research was the students of SMP Negeri 34 Samarinda, and the object was this research was the factors that influence students’ interest in learning English. The population and sample of this research were all of the students in class VIII at SMP N 34 Samarinda. They consisted of eight D classes. The total were number of the population was 27 students. The technique used in collecting data was questionnaire. To analyze the data, the researcher used descriptive statistic to analyze the data. The researcher used mean to known the score. Based on the data analysis, it can be seen that the factor that influence of students’ interest is categorized into middle (63%). There are some factors that influence students’ interest in learning English. They are: internal factors (come from the students themselves such as attention, intelligence, and readiness) and external factors such us parent and teacher. The dominant factors that influence students interest is external factors from the teacher factor
Blockchain Aided Privacy-Preserving Outsourcing Algorithms of Bilinear Pairings for Internet of Things Devices

Bilinear pairing is a fundamental operation that is widely used in cryptographic algorithms (e.g., identity-based cryptographic algorithms) to secure IoT applications. Nonetheless, the time complexity of bilinear pairing is $O(n^3)$, making it a very time-consuming operation, especially for resource-constrained IoT devices. Secure outsourcing of bilinear pairing has been studied in recent years to enable computationally weak devices to securely outsource the bilinear pairing to untrustworthy cloud servers. However, the state-of-art algorithms often require to pre-compute and store some values, which results in storage burden for devices. In the Internet of Things, devices are generally with very limited storage capacity. Thus, the existing algorithms do not fit the IoT well. In this paper, we propose a secure outsourcing algorithm of bilinear pairings, which does not require pre-computations. In the proposed algorithm, the outsourcer side's efficiency is significantly improved compared with executing the original bilinear pairing operation. At the same time, the privacy of the input and output is ensured. Also, we apply the Ethereum blockchain in our outsourcing algorithm to enable fair payments, which ensures that the cloud server gets paid only when he correctly accomplished the outsourced work. The theoretical analysis and experimental results show that the proposed algorithm is efficient and secure.
PENGARUH SELF EFFICACY DAN SELF REGULATED LEARNING TERHADAP PRESTASI BELAJAR SISWA DENGAN MOTIVASI BELAJAR SEBAGAI VARIABEL MEDIASI PADA MATA PELAJARAN EKONOMI DI SMA NEGERI SE-KOTA BANDUNG(Survey Pada Siswa kelas XI IPS SMA Negeri di Kota Bandung)

Penelitian ini bertujuan mengetahui pengaruh self efficacy dan self regulated learning terhadap prestasi belajar siswa dengan motivasi belajar sevagai variabel mediasi. Penelitian ini menggunakan metode survey. Analisis data menggunakan teknik analisis jalur (path analysis) dengan bantuan aplikasi program IBM SPSS statistic 23. Hasil penelitian ini menunjukkan bahwa: 1) terdapat hubungan self efficacy terhadap self regulated learning secara silmultan dan parsial, 2) terdapat pengaruh self efficacy dan self regulated learning terhadap motivasi belajar secara simutan dan parsial, 3) terdapat pengaruh self efficacy dan self regulated learning terhadap prestasi belajar siswa secara simultan dan parsial, 4) terdapat pengaruh motivasi belajar terhadap prestasi belajar siswa, 5) terdapat pengaruh self efficacy dan self regulated learning terhadap prestasi belajar siswa dengan motivasi belajar sebagai variabel mediasi secara simultan ataupun parsialThis study aims to determine the effect of self efficacy and self regulated learning on student achievement with learning motivation as a mediating variable. This research uses survey method. Data analysis using path analysis techniques with the help of the IBM SPSS statistical application program 23. The results of this study indicate that: 1) there is an effect of self efficacy on silenced and partially regulated learning, 2) there is an effect of self efficacy and self regulated learning on learning motivation simultaneously and partially, 3) there is an effect of self efficacy and self regulated learning on student learning achievement simultaneously and partially, 4) there is an influence of learning motivation on student achievement, 5) there is learning motivation mediating the effect of self efficacy and self regulated learning of student achievement simultaneously or partiallyKeywords: Self Efficacy, Self Regulated Learning, Learning Motivation, Learning Achievement
The Impact of COVID-19 on Education: A New Normal in Teaching and Learning at Universiti Sains Malaysia, Anthropology and Sociology Section

Horizon Research Publishing,USA (HRPUB) is a worldwide open access publisher serving the academic research and scientific communities by launching peer-reviewed journals covering a wide range of academic disciplines. As an international academic organization for researchers & scientists, we aim to provide researchers, writers, academic professors and students the most advanced research achievements in a broad range of areas, and to facilitate the academic exchange between them.
GBe at FinCausal 2020, Task 2: Span-based Causality Extraction for Financial Documents

This document describes a system for causality extraction from financial documents submitted as part of the FinCausal 2020 Workshop. The main contribution of this paper is a description of the robust post-processing used to detect the number of cause and effect clauses in a document and extract them. The proposed system achieved a weighted-average F1 score of more than 95% for the official blind test set during the post-evaluation phase and exact clauses match for 83% of the documents.