Reviewerly/exHarmony huggingface dataset:
Öğretmen Eğitiminde Mobil Öğrenmenin Kullanımındaki Eğilimlerin Gözden Geçirilmesi

The present study purposed to examine recent trends in mobile learning in teacher education. In this context, articles were reviewed until November 11, 2020 that met the research criteria. As a result, systematic review and bibliometric analysis were conducted for 58 articles indexed in SSCI. According to the results of the study, 2016 was the year in which the most studies were published. The results also showed that the continent with the most studies were Asia and the USA was the country where the most studies were carried out. In addition, quantitative articles were the most used research methods and questionnaire was the most preferred data collection tool. The most commonly used sampling method, sampling group, and sampling size are purposive sampling, teachers, and 1-50, respectively. Other results of the study were presented and discussed and some suggestions were provided. The study results can gain insights for teachers, scholars, and policymakers into tendencies in the use of mobile learning in teacher education.
The sensors of mobile devices: a innovative tool in the teaching of physical sciences

We show how builtin sensors in mobile devices can be used as portable laboratories at the service of teaching experimental sciences, especially physics, in the last years of high school and first years of university. We describe experiments that previously required expensive apparatus or were not feasible in teaching laboratories. Finally, we discuss some perspectives about the use of sensors in the physics teaching.
Logistic regression with more than two categories

When the outcome variable has only two levels or categories, standard binary logistic regression can be used. When it has three or more, we can use other variants of logistic regression.
New Classes of Binary Sequences with High Merit Factor

The Merit Factor (MF) measure was first introduced by Golay in 1972. Sequences possessing large values of MF are of great interest to a rich list of disciplines - from physics and chemistry to digital communications, signal processing, and cryptography. Throughout the last half-century, manifold approaches and strategies were proposed for finding such sequences. Referenced as one of the most difficult optimization problems, Golay wrote that it is a "challenging and charming problem". His publications on this problem spanned more than 20 years. Golay himself introduced one beneficial class of sequences, called skew-symmetric sequences, or finite binary sequences with odd lengths with alternate autocorrelation values equal to 0. Their sieving construction greatly reduces the computational efforts of finding binary sequences with odd lengths and high MF. Having this in mind, the majority of papers to be found in the literature are focused solely on this class, preferring them over binary sequences with even lengths. In this work, a new class of finite binary sequences with even lengths with alternate autocorrelation absolute values equal to 1 is presented. We show that the MF values of the new class are closely related to the MF values of adjacent classes of skew-symmetric sequences. We further introduce new sub-classes of sequences using the partition number problem and the notion of potentials, measured by helper ternary sequences. Throughout our experiments, MF records for binary sequences with many lengths less than 225, and all lengths greater than 225, are discovered. Binary sequences of all lengths, odd or even, less than $2^8$ and with MF $>8$, and all lengths, odd or even, less than $2^9$ and with MF $>7$, are now revealed.
Towards Data Storage Security in Cloud Computing Using Hybridized Advanced Encryption Standard &amp; Authentication Scheme

Cloud computing is at the heart of modern day computing and thus the rise in malicious attacks are increasing every day towards it. In recent times hackers have been engaging in different string concept on login fields and phishing in order to intrude cloud accounts. This research is proposed to carry out effective security mechanisms on loopholes in cloud data storage. We intend to engage the use of the Advanced Encryption Standard (AES), security Algorithms like Secure Hash Algorithm 1 &amp; 3 (SHA1, SHA 3) and Message-Digest Algorithm (MDS) and the Standard Authentication Scheme (AS) as sure guide against phishing which when implemented can provide various login stages and codes like OTP (One Time Password) to mitigate attacks. With the application of these methods towards securing cloud computing data there will be less intrusion in accounts and loss of files. Keywords: Brute-force, Cracking, Hacking, Advanced Encryption Standard, Authentication
Indecision Trees: Learning Argument-Based Reasoning under Quantified Uncertainty

Using Machine Learning systems in the real world can often be problematic, with inexplicable black-box models, the assumed certainty of imperfect measurements, or providing a single classification instead of a probability distribution. This paper introduces Indecision Trees, a modification to Decision Trees which learn under uncertainty, can perform inference under uncertainty, provide a robust distribution over the possible labels, and can be disassembled into a set of logical arguments for use in other reasoning systems.
TEACHING READING COMPREHENSION IN NARRATIVE TEXT THROUGH SKIMMING AND SCANNING

The purpose of the study was to see how successful skimming and scanning techniques are at improving students' reading comprehension. This study employed a pre-test and post-test control group design. The research subjects were first-year students from SMP Unggulan Negeri 1 Pulau Morotai. There were 66 students in all, divided into two groups of equal size: an experimental group and a control group. The results show that students or (33) got deficient levels in the control class. After the students were taught by using the conventional method, there was no improvement. While in the experimental group pre-test results showed that 100 percent of students (33) scored 0-54. After skimming and scanning techniques was applied the students’ score in reading comprehension were higher, this can be seen in the figure, from 100 percent of students (33) 64 percent students who got poor level (0-54), 24 percent students got moderate level, while in the range of scores from 70 to 84, there were 12 students. From these findings, skimming and scanning techniques were clearly beneficial in improving students' reading comprehension.
Konsep E-System Menggunakan Teknologi Android di Smartphone untuk Memudahkan Pengelolaan Administrasi di Sekolah

The existence of internet in modern era makes society easy to do their activities. All aspect of human life is based on online or connected to internet. The development of e-administration in school administration is one of them. This study was descriptive qualitative which aims to describe and to explain school administration concept based online to ease school management in digital era. Source of data divided into two, including document and informants referring to education expert, education implementer unit and society as education user. Technique of collecting the data were content analysis and in-depth interview. Triangulation of source of data technique were used to validate the data. The result shows that there are some school administration’s system based online to ease school administration process. They are school registration based online (e-registration), school payment based online integrated with certain bank account (e-school payment), teaching journal based online (e-journal), and student achievement report based online (e-raport). Besides, school is also facilitated by school administrator which can be contacted via whatsapp and telegram applications to ease education user on asking for information about school and education
Problematika Pembelajaran Tatap Muka Terbatas (PTMT) dalam Menulis Resensi Novel pada Siswa Kelas XII SMAN Ploso Jombang

This study aims to describe the problems of limited face-to-face learning (PTMT) in writing novel reviews and their solutions to class XII students of SMAN Ploso. This type of research is a qualitative research with descriptive method. The results showed that there were problems which included 1) problems with limited face-to-face learning tools, 2) problems in the delivery of limited face-to-face learning materials, 3) problems in classroom management, and 4) problems with techniques and assignments. While the solutions that can be given are 1) students will be given a more effective learning flow, 2) students will be given learning methods and media that are in accordance with the material and needs of students, 3) students will be given more attention with personal guidance and change learning techniques and tactics , and 4) students will be directed in more detail about the structure, spelling, and steps of writing a novel review text.
CTMQ: Cyclic Training of Convolutional Neural Networks with Multiple Quantization Steps

This paper proposes a training method having multiple cyclic training for achieving enhanced performance in low-bit quantized convolutional neural networks (CNNs). Quantization is a popular method for obtaining lightweight CNNs, where the initialization with a pretrained model is widely used to overcome degraded performance in low-resolution quantization. However, large quantization errors between real values and their low-bit quantized ones cause difficulties in achieving acceptable performance for complex networks and large datasets. The proposed training method softly delivers the knowledge of pretrained models to low-bit quantized models in multiple quantization steps. In each quantization step, the trained weights of a model are used to initialize the weights of the next model with the quantization bit depth reduced by one. With small change of the quantization bit depth, the performance gap can be bridged, thus providing better weight initialization. In cyclic training, after training a low-bit quantized model, its trained weights are used in the initialization of its accurate model to be trained. By using better training ability of the accurate model in an iterative manner, the proposed method can produce enhanced trained weights for the low-bit quantized model in each cycle. Notably, the training method can advance Top-1 and Top-5 accuracies of the binarized ResNet-18 on the ImageNet dataset by 5.80% and 6.85%, respectively.
Faster Exponential-Time Approximation Algorithms Using Approximate Monotone Local Search

We generalize the monotone local search approach of Fomin, Gaspers, Lokshtanov and Saurabh [J.ACM 2019], by establishing a connection between parameterized approximation and exponential-time approximation algorithms for monotone subset minimization problems. In a monotone subset minimization problem the input implicitly describes a non-empty set family over a universe of size $n$ which is closed under taking supersets. The task is to find a minimum cardinality set in this family. Broadly speaking, we use approximate monotone local search to show that a parameterized $\alpha$-approximation algorithm that runs in $c^k \cdot n^{O(1)}$ time, where $k$ is the solution size, can be used to derive an $\alpha$-approximation randomized algorithm that runs in $d^n \cdot n^{O(1)}$ time, where $d$ is the unique value in $d \in (1,1+\frac{c-1}{\alpha})$ such that $\mathcal{D}(\frac{1}{\alpha}\|\frac{d-1}{c-1})=\frac{\ln c}{\alpha}$ and $\mathcal{D}(a \|b)$ is the Kullback-Leibler divergence. This running time matches that of Fomin et al. for $\alpha=1$, and is strictly better when $\alpha >1$, for any $c > 1$. Furthermore, we also show that this result can be derandomized at the expense of a sub-exponential multiplicative factor in the running time. We demonstrate the potential of approximate monotone local search by deriving new and faster exponential approximation algorithms for Vertex Cover, $3$-Hitting Set, Directed Feedback Vertex Set, Directed Subset Feedback Vertex Set, Directed Odd Cycle Transversal and Undirected Multicut. For instance, we get a $1.1$-approximation algorithm for Vertex Cover with running time $1.114^n \cdot n^{O(1)}$, improving upon the previously best known $1.1$-approximation running in time $1.127^n \cdot n^{O(1)}$ by Bourgeois et al. [DAM 2011].
Principal coefficient encoding for subject-independent human activity analysis

Tracking human physical activity using smartphones is an emerging trend in healthcare monitoring and healthy lifestyle management. Neural networks are broadly used to analyze the inertial data of activity recognition. Inspired by the autoencoder neural networks, we propose a layer-wise network, namely principal coefficient encoder model (PCEM). Unlike the vanilla neural networks which apply random weight initialization andback-propagation for parameter updating, an optimized weight initialization is implemented in PCEM via principal coefficient learning. This principal coefficient encoding allows rapid data learning with no back-propagation intervention and no gigantic hyperparameter tuning. In PCEM, the most principal coefficients of the training data are determined to be the network weights. Two hidden layers with principal coefficient encoding are stacked in PCEM for the sake of deep architecture design. The performance of PCEM is evaluated based on a subject-independent protocol where training and testing samples are from different users, with no overlapping subjects in between the training and testing sets. This subject-independent protocol can better assess the generalization of the model to new data. Experimental results exhibit that PCEM outperforms certain state-of-the-art machine learning and deep learning models, including convolutional neural network, and deep belief network. PCEM can achieve ~97% accuracy in subject-independent human activity analysis.
Sentiment Analysis of Stakeholder Satisfaction Measurement

Measuring the satisfaction of stakeholders is very impoirtant in order to get feedback and input for the purposes of developing and implementing the improvement strategies. ITB STIKOM Bali routinely measures student stakeholder satisfaction every semester. This study aims to analyze stakeholder comments to generate sentiment analysis on stakeholder satisfaction. The data used are comments on the results of the measurement of stakeholder satisfaction (students) for the Odd Semester of 2020/2021 which are filled out through questionnaire. The algorithm used in this research is the Naïve Bayes Classifier (NBC). The research method in this study consisted of several stages, namely problem identification and literature study, data collection on stakeholder satisfaction (students), data preprocessing, feature extraction in order to facilitate classification using the Naïve Bayes Classifier (NBC) algorithm. The training data used is 200 data while the training data is 2133 data. The results of this study can provide recommendations to ITB STIKOM Bali for the results of student comments as a whole where the percentage of sentiment generated is 58% positive sentiment and 42% negative sentiment.
A survey of deepfakes in terms of deep learning and multimedia forensics

Artificial intelligence techniques are reaching us in several forms, some of which are useful but can be exploited in a way that harms us. One of these forms is called deepfakes. Deepfakes is used to completely modify video (or image) content to display something that was not in it originally. The danger of deepfake technology impact on society through the loss of confidence in everything is published. Therefore, in this paper, we focus on deepfakedetection technology from the view of two concepts which are deep learning and forensic tools. The purpose of this survey is to give the reader a deeper overview of i) the environment of deepfake creation and detection, ii) how deep learning and forensic tools contributed to the detection of deepfakes, and iii) finally how in the future incorporating both deep learning technology and tools for forensics can increase the efficiency of deepfakes detection.
Annotated Speech Corpus for Low Resource Indian Languages: Awadhi, Bhojpuri, Braj and Magahi

In this paper we discuss an in-progress work on the development of a speech corpus for four low-resource Indo-Aryan languages -- Awadhi, Bhojpuri, Braj and Magahi using the field methods of linguistic data collection. The total size of the corpus currently stands at approximately 18 hours (approx. 4-5 hours each language) and it is transcribed and annotated with grammatical information such as part-of-speech tags, morphological features and Universal dependency relationships. We discuss our methodology for data collection in these languages, most of which was done in the middle of the COVID-19 pandemic, with one of the aims being to generate some additional income for low-income groups speaking these languages. In the paper, we also discuss the results of the baseline experiments for automatic speech recognition system in these languages.
Evaluation of ULISYS Acceptance in UNTAG Surabaya Library

Background of the study: The functionality of the library information system needs to be improved for users be able to receive the system. Evaluation of user acceptance is one of the benchmarks for the successful implementation of library information systems.&#x0D; Purpose: This study aims to describe the response of acceptance from users to the UNTAG Library Information System (ULISYS).&#x0D; Method: This research used qualitative methods using interviews with 6 main informants according to criteria, observations at the UNTAG Surabaya Library, and documentation. Data validity and accuracy tests using source triangulation techniques. Data analysis techniques use the Miles and Huberman concept.&#x0D; Findings: The use of ULISYS which is considered easy is proven to be able to increase labor productivity from the Education Staff of UNTAG Surabaya Library. The positive attitude of users to use ULISYS is said to be the acceptance phase towards ULISYS. The desire of the Education Staff of UNTAG Surabaya Library to keep using existing information systems can affect the intensity of ULISYS use.&#x0D; Conclusion: ULISYS can be accepted by the Education Staff of UNTAG Surabaya Library with 4 factors that support the acceptance including ease of use factors, usefulness factors, attitudes towards using of information systems, and behavioral intention to use information systems.
OPTIMASI ALOKASI AIR WADUK MENINTING UNTUK PENINGKATAN SUPLESI AIR IRIGASI SALURAN HLD JANGKOK-BABAK-JURANG SATE

To increase the allocation of water in the river basin (WS) of southern Lombok, the potential for irrigation areas (AI) has spread widely, since the 1980s, efforts have been made to transfer water from wet river basins to dry watersheds. The transfer is through the Babak-Renggung-Rutus High Level Diversion (HLD) channel (18 km) and the Jangkok-Babak-Jurang Sate channel (43 km), as well as 22 other channeling channels in / across the watershed (87 km). Until now, 12 watersheds are connected, namely: Meninting, Jangkok, Remening, Babak, Dodokan, Renggung, Pare, Rere, Palung, Moyot, Aikampat and Starfruit. The number of main structures for taking water (headwork or HW) in the watersheds include 3 dams (BD), 77 reservoirs (E), 355 dams (B), and 55 diversions in the Jangkok-Babak-Jurang Sate canal. A total of 487 HWs were intended to irrigate 98,878 ha of irrigated land in 432 Irrigation Areas (DI) and a little to supply raw water of 404 l / sec. In addition, there are 2442 l / sec raw water withdrawals directly from 42 points of springs which are spread in the watershed: Jangkok, Babak, Aikampat, Meninting Midang, Kelongkong Remening, Renggung, Rung and Starfruit. Analysis of reservoir operational performance using the Hashimoto method yields a coefficient of Reliability (R) = 100%, Resiliency (R) = 100%, and Vulnerability (V) = 0%. So based on this RRV value the reservoir operational performance is categorized as maximum sustainable
The Democratic Role of the National Library in Improving the Culture of Information Literacy During the Covid-19 Pandemic

This study was conducted to describe the role of the National Library (Perpustakaan Nasional) in improving literacy culture during the Covid-19 pandemic through the analysis of the SWOT+PEST communication strategy. With the desk research analysis method, data and information from observational information sources from online media and books combined with a description of the SWOT+PEST analysis, it was found that libraries can be said to play an important role in increasing public information literacy. The achievement of a strong literacy democracy can lead to a stronger development of Indonesia.
Measure Counter-Suggestion at University Students

This research aims to measure counter-suggestion students at University and identify its levels according to variable (gender ) ,The study sample consisted of )302( students at Salahaddin University-Erbil who were selected in a random manner in Society different colleges at Salahaddin University namber of (21105) students for the academic year )2020-2021( at morning study , the researchers construction the scale counter-suggestion as composite (62( items, after derivatio the honesty and stability of the construction as well as measuring instrnment, the researchers applied the scale counter-suggestion as composite(62( items at sampling final form of the research, for statistical means of data analysis of usage (Spss) program, showed the following search results:&#x0D; 1- The members of the study sample in general have a good level of counter-suggestion.&#x0D; &#x0D; There are statistical differences between students according to the gender variable and it is more in favor of the male sample.&#x0D; &#x0D; In the light of the results of the study, a number of conclusions and recommendations were reached and the researchers made a number of recommendations and proposals at the end of the study.
Role of academic library in creating students’ mental health literacy

Background of the study: Mental health literacy is a very important skill because mental health is as important as physical health. University students must have mental health literacy because they face heavy pressure in the learning process and in social interactions. Academic libraries can play an active role in creating students' mental health literacy.Purpose: to identify and explain how the role of academic libraries in creating students' mental health literacy. Method: It is a literature review, namely reviewing the books, journal articles, news, reports on mental health, mental health among students, the function and role of the academic library. Next, synergize the results of the study with the results of the researchers' thoughts. Findings: Academic libraries can carry out various activities as an effort to create students’ mental health literacy, namely providing easy access to mental health information sources through library collections development and providing mental health corners; promoting mental health collections in the library; organize mental health literacy campaigns through events such as seminars; providing health consultation facilities in collaboration with mental health professionals such as psychologists and psychiatrists. Conclusions: Academic libraries can play an active role in creating students' mental health literacy by utilizing their resources and by collaborating with professionals.