Reviewerly/exHarmony huggingface dataset:
Formative learning and its characteristics applied in the classroom in university students

Formative learning is considered as part of the improvement of the education process in students at the university and thus develop their skills in their profession, the present research topic aims to identify how the formative learning process is performed and what are the characteristics applied in the classroom, sampling was performed by selecting 369 students, with the application of a structured survey with 10 closed questions, 86% like to update their knowledge and this has allowed 71% of students to learn management of reliable sources and the application of APA standards.
Penerapan Manajemen Peserta Didik Melalui Pemberian Reward Di Sekolah Dasar Kota Makassar

The purpose of this study was to determine the application of student management in giving rewards at MakassarCityElementarySchool. Thisstudyusedaqualitativeapproachwithadescriptivetype,datacollectionthroughinterviews,observation,anddocumentation.Thesubjectsofthestudywerefour teachers and eight students. The results showed that student management in giving rewards was: 1) The results showed that elementary school students in Makassar City had rewards, interest in learning, readiness to learn, learning attention, learning achievement, learning perseverance; and tenacious in the face of difficulties, namely experiencing improvements in the learning process, 2) Supporting factors for rewarding are supported from teachers, the responses shown by students during and after receiving rewards, and students' desire to achieve other achievements. 3) The inhibiting factors in the implementation of rewarding are a large number of students, the preparation of equipment in giving rewards such as achievement stars, achievement boards, and also award charters), and the limitations of teachers in making observations so that they are not observant. There are also no guidelines used in giving rewards in the form of award certificates to students.
NEED ANALYSIS AND INSTRUCTIONAL STRATEGY IN TEACHING ENGLISH FOR SPECIFIC PURPOSES (ESP) AT NURSING UNIVERSITY IN SERANG, BANTEN (A Qualitative Research at STIKes Faletehan Serang, Banten)

The objectives of this research are to know the validity in teaching English for specific purposes (ESP) at nursing university, to know the reliability in teaching English for specific purposes (ESP) at nursing university. This research uses qualitative approach with descriptive statistic method. Then, technique of collecting data is with analyzing syllabus and questionnaire. The researcher uses the means, percentage and frequencies to reliability and validity. The research findings summarize that the validity in ESP has important role to teaching ESP at STIKes Faletehan Serang that 64 percent of student nurses agree or strongly agree that ESP course should focus on medical English, 66 percent of student nurses view very appropriate or quite appropriate about the ESP course textbooks, 60 percent of student nurses agree or strongly agree about the appropriateness assessment commonly used in the ESP course, and 80 percent of student nurses view quite important or very important about the importance of ESP course to students’ academic studies and target career. Then, the reliability in teaching ESP that the instructional strategy has to be considered which shows 68 percent of student nurses agree or strongly agree about teaching methodologies were appropriate and useful, 72 percent of student nurses agree or strongly agree about ESP lecturers motivated them, 68 percent of student nurses agree or strongly agree about the student’s opportunity to work in groups or pairs, and 72 percent of student nurses agree or strongly agree about the supplementary materials used in the ESP class. Based on the explanation, there are any appropriateness, effective and helpful the need analysis and the instructional strategy of ESP in teaching English at STIKes Faletehan Serang.
IMPROVING READING SKILLS USING THE QUICK READING METHOD IN LEARNING INDONESIAN LANGUAGE SHORT STORY READING MATERIALS CLASS III at SDN Bandung II

This study aims to determine the improvement of students' reading skills by using the Quick Reading in learning Indonesian short story reading material for class III at SDN Bandung II, Banjar District, Pandeglang Regency. This type of research is a class action research with the John Eliot model using two cycles. The subjects of this study were third grade students of SDN Bandung II for the 2021/2022 academic year, totaling 16 students. Based on the problem formulation regarding students' low understanding of the Indonesian language, it encourages the author to conduct research on how the teacher's efforts to improve understanding in fast reading short story material for elementary school students in this research use the Quick Reading method. This research method refers to the classroom action research design according to John Elliot which includes (1) planning, (2) action, (3) observation, and (4) reflection.The results showed that the use of the quick reading could improve learning activities and students' ability to read short stories. The increase in reading comprehension ability using the quick reading in the first cycle was the initial condition was 67.75 increased to 74.62, the second cycle increased by in the initial condition 74.62 increased to 85.
Training In Making It-Based Learning Media In Successful Independent Learning For Vocational School Teachers In Kabupaten Pesisir Selatan

IT-based media creation training at this time is very necessary for teachers to optimize the learning process. The phenomenon in the field of teachers tends to only upload teaching materials, so that sometimes they do not achieve the expected competency targets. This service aims to provide teachers with skills in developing IT-based learning media. The method used in this training is face-to-face and also online and also online via zoom. At the initial stage, face-to-face material is given about the design of IT-based learning media. Introduction of applications for creating IT-based learning media by media lecturers from the Education Technology Study Program. The respondents involved in the training were high school teachers in Pesisir Selatan Regency. The teachers are selected by representatives from high schools in the sub-district with the hope that they can pass on their abilities to other teachers in their schools. The number of respondents in this activity were 35 high school/vocational teachers in Pessel Regency. The implementation of offline and online training has been carried out according to the planned procedure. The offline training stage is carried out at the location, namely SMA N 1 Painan. At this stage the training is carried out by giving an initial presentation to shape students' perceptions of the importance of knowledge about media and digital evaluation. Furthermore, the provision of applications for practice in the form of IT-based media development software. In the final stage, namely practice. Furthermore, at the next stage, training was carried out through zoom meetings for 2 times, namely to strengthen knowledge and also present activities that have been carried out by students. The outputs in this PKM program are teachers who have skills in making digitalization-based teaching materials with a satisfaction level of activity implementation which is 4.54 (scale 1-5). Keywords: Training, learning media, IT-Based, Teacher
THE CONTRIBUTION OF STUDENTS SCHEMATA TOWARD STUDENTS READING COMPREHENSION ACHIEVEMENT AT FOURTH SEMESTER OF LAKIDENDE UNIVERSITY LEARNERS

The contribution of Schemata toward Reading Comprehension: A Case of their contribution at fourth semester of Lakidende University learners. Schemata have an important role in the process of reading. With regard to reading in a foreign language, one crucial determinant of reading comprehension is the amount of background knowledge readers have. Although readers can turn printed symbols into sounds and word accurately, it cannot guarantee their comprehension. Inadequate background knowledge especially cultural one can have an effect on reading comprehension. The objective of this study are to find out the contribution of students schemata toward students reading comprehension achievement at fourth semester of Lakidende University learners. The population of this research are all the fourth semester of lakidende university learners who enrolled in academic year 2020/2021 with 17 students. The data will collect through questionnaire and tests. After collecting the data, the data will analyze by statistically. The first step in analyzing data are checked the normality, homogeneity and linearity of the data as requirement to apply the inferential statistic on non inferential statistic parameters. In testing the hypothesis, T-Test analysis will apply by using SPSS 16 version
THE EFFECT OF FLIPPED CLASSROOM ON STUDENTS‟ READING COMPREHENSION AT SMA NEGERI 1 WAWOTOBI

The purpose of study is to investigate the effect of flipped classroom on students reading comprehension at SMA Negeri 1 Wawotobi. This study employed a pre-experimental design with one group pre-test and post-test design without control group for quantitative approach. The population of this research was the entire XI IPS 2 students’ of SMAN 1 Wawotobi who enrolled in academic year 2021/2022. The data were obtained from students’ pre-test and posttest scores. The pre-test was given before the treatment applied and the posttest is given after the treatment finished. Results of the study found that there was a significant positive effect of flipped classroom on students’ reading comprehension, p (.000) 0.05 with the effect size was 3.57. In light of all these findings, flipped classroom significantly affected students’ reading comprehension.
Special Issue “1st Online Conference on Algorithms (IOCA2021)”

This Special Issue of Algorithms is dedicated to the 1st Online Conference on Algorithms (IOCA 2021), which was held completely online from 27 September to 10 October 2021 [...]
Advanced HRM practices and digital personnel for digital energetics based on the technologies of Industry 4.0

OPINION article Front. Energy Res., 07 November 2022Sec. Sustainable Energy Systems and Policies https://doi.org/10.3389/fenrg.2022.1021886
Layanan informasi dalam mencegah negative peer pressure peserta didik

the aim of the study was to determine the effect of information services in preventing negative peer pressure on students of SMK Bunda Kandung Jakarta. The research approach used by the author is quantitative with a survey method. The population of this study amounted to 226 students with an affordable population of 63 students and the sample in this study using a total sampling of 63 students. This study uses an instrument in the form of a questionnaire. The scale used is the Likert scale. Data were analyzed using simple linear regression test. The results showed that information services were able to prevent the occurrence of negative peer pressure on the students of SMK Bunda Kandung Jakarta
A novel approach for new architecture for green data centre

The massive energy usage of data centers may be traced in part to the growing number of data centers and workstations because of economies of scale for cloud computing. It does, though, indicate wasteful power usage. Consequently, researching ways to improve the energy efficiency of datacenter equipment is now a crucial aspect in reducing datacenter power consumption. In this study, we describe methodologies and algorithms for flexible, energy-efficient, and effective load balancing in data centers, resulting in lower energy consumption by systems. Because of difficulties related with energy consumption, such as capital expenditure, operational costs, and environmental effect, renewable energy is becoming an increasing major consideration in data centers. With the rise in environmental consequences around the world, data centers must consider energy efficiency as one of the most critical factors. Cloud computing techniques have made a huge impact all over the world. Green DC is a concept that has been tested. We suggested an algorithm in this paper based on certain current algorithm limitations that will help to reduce environmental effect. We investigated the efficiency of our program further and used a load balancing method to improve its performance.
Signal multiple encodings by using autoencoder deep learning

Encryption is a substantial phase in information security. It permits only approved persons to get private information. This study suggests a signal multi-encryptions system (SMES) technique for coding and decoding signals created by a deep autoencoder network (DAN). The DAN of four layers is employed for a coding package of signals multiple times before decoding or restructuring the original signals again. The suggested SMES offers a high level of security as it can produce and exploit multiple encryptions for signals. Many statistical calculations are applied to measure the reliability of the system. The outcomes are promising where noteworthy encryptions-decryptions are obtained.
CON_PCA method to solve the human monochromacy color blindness

Color blindness is an inherited genetic ocular disorder. Acquired or congenital color blindness can become a major cause of problems in everyday life tasks. Contributing to solving the trouble of color blindness is important. Because the problem of color blindness leads to the non-discrimination of data seen by the infected person. Data visualization can contribute to solving the hassle of color blindness. This paper proposed a new technique using data visualization ideas that may contribute to easy data recognition. Our focus has been on monochromacy color blindness. The results are based on the famous Ishihara data sets measuring color blindness. The efficiency of the results was mathematically proven using equations and images via a website to simulate color blindness.
The development and effects of the logotherapy-based ego-identity improvement program with the application of group art activities in late school-aged children

The purpose of this study was to develop the logotherapy-based ego-identity improvement program through group art activities and to examine its effects. This program used concept synthesis. This program was exploited in accordance with the consultation and psychological education program development stages. Out of seven local children’s centers in 3 Cities, 37 children in the experimental group and 33 children in the control group agreed to participate. The data collection and the program adaptation were conducted between April 1, 2019, and September 30, 2019. The effect of the program was evaluated by t-test and repeated measures ANOVA. The researches show that the ego-identity of the experimental group (F=23.42, p&lt;.001), the meaning of life (F=27.82, p&lt;.001), the academic stress (F=14.14, p&lt;.001), the stress symptoms (F=15.52, p&lt;.001) had a significant difference between the measurement period and the group interaction. Resultingly, the logotherapy-based ego-identity improvement program was indicated to be effective in improving academic stress and stress symptoms and for enhancing ego-identity formation and the meaning of life among late school-aged children. Therefore, it is expected that this program will be applied as an efficient program for the betterment of ego-identity among late school-aged children in the community.
Arabic vowels characterization and classification using the normalized energy in frequency bands

The main objective of this work is to conduct an acoustic study of Arabic vowels (/a/, /a:/, /u/, /u:/, /i/ and /i:/) in order to determine the most relevant characteristics that allow recognizing these vowels. The analysis of vowel spectrograms reveals that the energy distribution as a function of time and frequency clearly differs according to the considered vowel. Thus, we used the normalized energy in frequency bands to classify these vowels. Thereafter, we have exploited the obtained results to develop algorithms that allow the classification of vowels and the distinction of the long vowels from the short ones. The efficiency of these algorithms was evaluated by testing their performances on our Arabic corpus.
A Survey of Transformer-Based Stance Detection

Stance detection systems are built in order to determine the position of text authors using the text that they produce and other contextual information. As the result of the stance detection procedure, the position of the text producer is determined as favor, against, or none. On the other hand, transformer-based technologies are reported to perform well for various natural language processing tasks. These are deep learning-based models that also incorporate attention mechanism. BERT and its variants are among the most popular transformer-based models proposed so far. In this chapter, the authors provide a plausible literature review on stance detection studies that are based on transformer models. Also included in the current chapter are important further research directions. Stance detection and transformer-based models are significant and recent problems in natural language processing and deep learning, respectively. Hence, they believe that this chapter will be an important guide for related researchers and practitioners working on these topics of high impact.
iSPY: Issue–Solution Pairing From Developer Live Chats

Live chat streams are often used in open source software (OSS) development as a fast way to share information around the problem. But when live chat gets used a lot, important project information is lost in the middle of so many streams. AI to rescue! Here we describe “iSPY”, a scalable tool for helping developers understand busy live chat streams. In a study with 173,278 live chats, we show that iSPY can automatically extract issue-solution pairs from intensive developer live-chats (using neural networks technology). In this article we introduce iSPY as well as discussing future directions for learning knowledge on issue resolution from crowd developers.
Editorial Editorial of Special Issue on Self-Supervised Learning for Speech and Audio Processing

The papers in this special section focus on self-supervised learning for speech and audio processing. A current trend in the machine learning community is the adoption of self-supervised approaches to pretrain deep networks. Self-supervised learning utilizes proxy-supervised learning tasks (or pretext tasks)—for example, distinguishing parts of the input signal from distractors or reconstructing masked input segments conditioned on unmasked segments—to obtain training data from unlabeled corpora. These approaches make it possible to use the tremendous amount of unlabeled data available on the web to train large neural models. Recent self-supervised approaches for speech and audio processing are also gaining attention.
Software Engineering’s Adolescent Growing Pains

Software engineering has a long way to go to reach the stature of other mature scientific or engineering disciplines. Based on the history of these professions, software engineering may not even be halfway from “infancy” to a mature profession.
Blockchain and Artificial Intelligence Are Also Hot Topics in the IEEE Systems, Man, and Cybernetics Society [Editorial]

This issue presents three regular feature articles and a special section including three articles. I appreciate the effective efforts of the guest editors of this special section on “Explainable Artificial Intelligence for the Social Internet of Things: Analysis and Modeling Using Collaborative Technologies”: Prof. Gaurav Dhiman from Chandigarh University, India; Prof. Atulya Nagar from Liverpool Hope University, United Kingdom; and Prof. Seifedine Kadry from Noroff University College, Norway. Their special issue attracted many high-quality submissions. We decided to publish the accepted papers as a special section in this issue. We believe that this special section is beneficial for all of the related parties, the editorial board of the magazine, the guest editors of the special issue, and the authors of the accepted papers. A similar special issue will be published in 2023.
Optimal linear optical discrimination of Bell-like states

Quantum information processing using linear optics is challenging due to the limited set of deterministic operations achievable without using complicated resource-intensive methods. While techniques such as the use of ancillary photons can enhance the information processing capabilities of linear optical systems they are technologically demanding. Therefore, determining the constraints posed by linear optics and optimizing linear optical operations for specific tasks under those constraints, without the use of ancillas, can facilitate their potential implementation. Here, we consider the task of unambiguously discriminating between Bell-like states without the use of ancillary photons. This is a basic problem relevant in diverse settings, for example, in the measurement of the output of an entangling quantum circuit or for entanglement swapping at a quantum repeater station. While it is known that exact Bell states of two qubits can be discriminated with an optimal success probability of 50% we find, surprisingly, that for Bell-like states the optimal probability can be only 25%. We analyze a set of Bell-like states in terms of their distinguishability, entanglement as measured by concurrence, and parameters of the beam-splitter network used for unambiguous discrimination. Further, we provide the linear optical configuration comprised of single photon detectors and beam splitters with input state-dependent parameters that achieves optimal discrimination in the Bell-like case.
Bi-Discriminator Class-Conditional Tabular GAN

This paper introduces a bi-discriminator GAN for synthesizing tabular datasets containing continuous, binary, and discrete columns. Our proposed approach employs an adapted preprocessing scheme and a novel conditional term for the generator network to more effectively capture the input sample distributions. Additionally, we implement straightforward yet effective architectures for discriminator networks aiming at providing more discriminative gradient information to the generator. Our experimental results on four benchmarking public datasets corroborates the superior performance of our GAN both in terms of likelihood fitness metric and machine learning efficacy.
NeSF: Neural Semantic Fields for Generalizable Semantic Segmentation of 3D Scenes

We present NeSF, a method for producing 3D semantic fields from posed RGB images alone. In place of classical 3D representations, our method builds on recent work in implicit neural scene representations wherein 3D structure is captured by point-wise functions. We leverage this methodology to recover 3D density fields upon which we then train a 3D semantic segmentation model supervised by posed 2D semantic maps. Despite being trained on 2D signals alone, our method is able to generate 3D-consistent semantic maps from novel camera poses and can be queried at arbitrary 3D points. Notably, NeSF is compatible with any method producing a density field, and its accuracy improves as the quality of the density field improves. Our empirical analysis demonstrates comparable quality to competitive 2D and 3D semantic segmentation baselines on complex, realistically rendered synthetic scenes. Our method is the first to offer truly dense 3D scene segmentations requiring only 2D supervision for training, and does not require any semantic input for inference on novel scenes. We encourage the readers to visit the project website.