Reviewerly/exHarmony huggingface dataset:
PELATIHAN PENGGUNAAN APLIKASI EDMODO DALAM MENUNJANG PROSES PEMBELAJARAN DI SMAN 1 CIOMAS

Mastery of technology is one aspect that teachers and students must have. Learning that is not accompanied by using of technology, will make learning difficult to develop. Edmodo is a technology-based application that is accessed online and can help teachers carry out learning. Edmodo has various features such as creating online classes, arranging agendas, doing digital storage, even teachers can create quizzes, assignments or exams that teachers can design, such as national exams. The purpose of this community service program is to introduce students to what edmodo is and how to use it. The training was carried out by providing materials and tutorials on how to use edmodo. After that, students are welcome to practice using Edmodo and the presenters monitor the practice of the students. Based on interviews conducted with students related to training in the use of edmodo, it was concluded that students felt tremendous benefits from this training and hoped that technology-based learning, especially using edmodo, could be applied by teachers in their schools.
Distributing bipartite quantum systems under timing constraints

Abstract In many quantum information processing protocols, entangled states shared among parties are an important resource. In this article, we study how bipartite states may be distributed in the context of a quantum network limited by timing constraints. We explore various tasks that plausibly arise in this context, and characterize the achievability of several of these in settings where only one-way communication is allowed. We provide partial results in the case where two-way communication is allowed. This builds on earlier work on summoning single and bipartite systems.
gMSR: A Multi-GPU Algorithm to Accelerate a Massive Validation of Biclusters

Nowadays, Biclustering is one of the most widely used machine learning techniques to discover local patterns in datasets from different areas such as energy consumption, marketing, social networks or bioinformatics, among them. Particularly in bioinformatics, Biclustering techniques have become extremely time-consuming, also being huge the number of results generated, due to the continuous increase in the size of the databases over the last few years. For this reason, validation techniques must be adapted to this new environment in order to help researchers focus their efforts on a specific subset of results in an efficient, fast and reliable way. The aforementioned situation may well be considered as Big Data context. In this sense, multiple machine learning techniques have been implemented by the application of Graphic Processing Units (GPU) technology and CUDA architecture to accelerate the processing of large databases. However, as far as we know, this technology has not yet been applied to any bicluster validation technique. In this work, a multi-GPU version of one of the most used bicluster validation measure, Mean Squared Residue (MSR), is presented. It takes advantage of all the hardware and memory resources offered by GPU devices. Because of to this, gMSR is able to validate a massive number of biclusters in any Biclustering-based study within a Big Data context.
Quantum Speed Limits for Time Evolution of a System Subspace

One of the fundamental physical limits on the speed of time evolution of a quantum state is known in the form of the celebrated Mandelstam-Tamm inequality. This inequality gives an answer to the question on how fast an isolated quantum system can evolve from its initial state to an orthogonal one. In its turn, the Fleming bound is an extension of the Mandelstam-Tamm inequality that gives an optimal speed bound for the evolution between non-orthogonal initial and final states. In the present work, we are concerned not with a single state but with a whole (possibly infinite-dimensional) subspace of the system states that are subject to the Schroedinger evolution. By using the concept of maximal angle between subspaces we derive an optimal estimate on the speed of such a subspace evolution that may be viewed as a natural generalization of the Fleming bound.
Terminological Base and Topics of the Direction of Interdisciplinary Research “Informatization of Scientific Activity”

The development of the information society has led to the Informatization of all areas of human activity, including scientific.This is expressed in the widespread use of information and communication technologies (ICTs) in the organization, information support, research, and publication of their results.There are well-established trends in the use of ICTs in scientific activities.However, "Informatization of scientific activity" as an integral direction of scientific research has not yet been formed.Therefore, the research is aimed at identifying the main thematic areas that make up the direction of interdisciplinary research "Informatization of scientific activity", determining the core of its terminological base and further research the formation of a thesaurus and the development of this direction.For the solution of the research problems used developed by the authors' approach (synthetic method) to research the development of the thematic and terminological apparatus interdisciplinary scientific fields for the application of methods of search, extraction, clarification, explication, analysis, and presentation of contextual knowledge through the use of information and communication technologies.
School management: The optimization of learning facilities to improve the quality of vocational schools

The creation of qualified graduates is a parameter of the implementation of quality education. Vocational schools become one of the learning places to produce quality human resources who are ready to enter the world of business and industry. Effective and efficient management of learning facilities and infrastructure is a leadership strategy of the related parties in the school to improve the quality of graduates. This research uses a qualitative approach with a case-study design. A total of three vocational schools in Sumedang Regency were selected as participants. This research aims to understand, describe, and analyze the school principals' leadership strategies in managing and developing learning facilities and infrastructure to improve the quality of their graduates. The research data were collected through observation, interviews, documentation studies, and field notes. The results showed that despite all the problems due to the limited availability of practical equipment and the minimal cost allocated, vocational schools in Sumedang Regency are able to produce graduates with the certified expertise competencies by the industrial world. The use of learning facilities and infrastructure to improve the quality of graduates in the three vocational schools participating in the research has been running optimally. However, the strategy for developing learning facilities and infrastructure has not been implemented as expected. Planning, implementation, and supervision are stages that must be highly considered by school principals in managing learning facilities and infrastructure.
One-Shot Only Real-Time Video Classification: A Case Study in Facial Emotion Recognition

Video classification is an important research field due to its applications ranging from human action recognition for video surveillance to emotion recognition for human-computer interaction. This paper proposes a new method called One-Shot Only (OSO) for real-time video classification with a case study in facial emotion recognition. Instead of using 3D convolutional neural networks (CNN) or multiple 2D CNNs with decision fusion as in the previous studies, the OSO method tackles video classification as a single image classification problem by spatially rearranging video frames using frame selection or clustering strategies to form a simple representative storyboard for spatio-temporal video information fusion. It uses a single 2D CNN for video classification and thus can be optimised end-to-end directly in terms of the classification accuracy. Experimental results show that the OSO method proposed in this paper outperformed multiple 2D CNNs with decision fusion by a large margin in terms of classification accuracy (by up to 13%) on the AFEW 7.0 dataset for video classification. It is also very fast, up to ten times faster than the commonly used 2D CNN architectures for video classification.
Shape Adaptor: A Learnable Resizing Module

We present a novel resizing module for neural networks: shape adaptor, a drop-in enhancement built on top of traditional resizing layers, such as pooling, bilinear sampling, and strided convolution. Whilst traditional resizing layers have fixed and deterministic reshaping factors, our module allows for a learnable reshaping factor. Our implementation enables shape adaptors to be trained end-to-end without any additional supervision, through which network architectures can be optimised for each individual task, in a fully automated way. We performed experiments across seven image classification datasets, and results show that by simply using a set of our shape adaptors instead of the original resizing layers, performance increases consistently over human-designed networks, across all datasets. Additionally, we show the effectiveness of shape adaptors on two other applications: network compression and transfer learning.
Penerapan Strategi Pembelajaran Berbasis Masalah dalam Upaya Meningkatan Keterampilan Menulis Teks Negosiasi bagi Siswa SMA Negeri 2 Labuapi

This study aims to improve the writing skills of negotiation texts through the application of problem-based learning strategies. This research is a classroom action research with the research subjects of class X MIPA 2 SMA Negeri 2 Labuapi consisting of 25 students. The research instrument used tests, observations, document analysis, interviews, and questionnaires. The data analysis technique used in this research was qualitative and quantitative descriptive analysis. The results of this study indicate that in the process there is an increase in student enthusiasm in participating in learning, student interaction in groups, and student attention. This can be shown from the results of observations during the learning process. The product improvement can be seen with the increase in the students' writing value of negotiating text. In the pre-cycle there were 16 students who had not yet completed it and in the first cycle there were still 6 students who had not finished, and in the second cycle it increased again, that all 25 students had all completed. These results indicate that the application of problem-based learning strategies can increase student activeness in teaching and learning activities and can improve writing negotiation text skills for class X MIPA 2 SMA Negeri 2 Labuapi.
Query Focused Multi-document Summarisation of Biomedical Texts

This paper presents the participation of Macquarie University and the Australian National University for Task B Phase B of the 2020 BioASQ Challenge (BioASQ8b). Our overall framework implements Query focused multi-document extractive summarisation by applying either a classification or a regression layer to the candidate sentence embeddings and to the comparison between the question and sentence embeddings. We experiment with variants using BERT and BioBERT, Siamese architectures, and reinforcement learning. We observe the best results when BERT is used to obtain the word embeddings, followed by an LSTM layer to obtain sentence embeddings. Variants using Siamese architectures or BioBERT did not improve the results.
A quantum key distribution on qudits using quantum operators

Cryptography is processing for securing communication between two people. The opponent wants to know the message that is encrypted using a secret key. Although the opponent can eavesdrop the message sent between the sender and the receiver, the opponent is unable to decrypt to read the message. Therefore, the secret key is very important. The sender and the receiver agree with the secret key in an insecure channel by using key distribution protocol such as the Diffie–Hellman protocol. Since quantum computer is coming soon, Diffie–Hellman protocol is not secure. We will develop a quantum key distribution protocol. The benefit of the quantum system is the quantum state that cannot copy by no‐cloning theorem. Thus, the opponent does not copy and keeps the message that is quantum. In this paper, a novel quantum key distribution protocol between two people (Alice and Bob) based on quantum operators is developed. The opponent (Eve) wants to know the secret key. Although Eve knows this quantum key distribution protocol, Eve does not behave similarly to Alice and Bob. For example, Eve eavesdrops Alice's quantum state that was sent to Bob, and Eve sends another quantum state. Therefore, we cannot control Eve's behavior. So we give the upper bound of mutual information between the user and opponent by using Holevo's bound. We verify the usual security definition for quantum key distribution that is equality‐and‐uniformity and privacy in the mutual information sense.
Hand Gesture Recognition and Voice Conversion for Hearing and Speech Aided Community

Sign language recognition (SLR) aims to interpret sign languages automatically by a computer in order to help the deaf communicate with hearing society conveniently. Our aim is to design a system to help the person who trained the hearing impaired to communicate with the rest of the world using sign language or hand gesture recognition techniques. In this system, feature detection and feature extraction of hand gesture is done with the help of Support Vector Machine (SVM), K-Neighbors-Classifier, Logistic-Regression, MLP-Classifier, Naive Bayes, Random-Forest-Classifier algorithms are using image processing.
An Empirical Study of Contextual Data Augmentation for Japanese Zero Anaphora Resolution

One critical issue of zero anaphora resolution (ZAR) is the scarcity of labeled data. This study explores how effectively this problem can be alleviated by data augmentation. We adopt a state-of-the-art data augmentation method, called the contextual data augmentation (CDA), that generates labeled training instances using a pretrained language model. The CDA has been reported to work well for several other natural language processing tasks, including text classification and machine translation. This study addresses two underexplored issues on CDA, that is, how to reduce the computational cost of data augmentation and how to ensure the quality of the generated data. We also propose two methods to adapt CDA to ZAR: [MASK]-based augmentation and linguistically-controlled masking. Consequently, the experimental results on Japanese ZAR show that our methods contribute to both the accuracy gain and the computation cost reduction. Our closer analysis reveals that the proposed method can improve the quality of the augmented training data when compared to the conventional CDA.
Acceleration analysis of population growth using winter’s method: case study of lombok island

Abstract The research aims to analyze the acceleration of the population growth in Lombok which consists of 5 districts/cities using forecasting system by constructing the winter’s method in the form of a GUI Multiple Forecasting System (G-MFS) based on Matlab by calculating the indicator level of accuracy to find predictive data for the next 10 years. At the data simulation stage, researchers used population data over the last 11 years. The evaluation of forecasting results is calculated by calculating the value of Mean Absolute Percentage Error (MAPE) with 27 attempts through modified Winter parameter values. From the simulation data obtained the most optimal parameter value is α, β, and γ sequential values of 0.9, 0.5, and 0.9. Then with the value of the parameter obtained MAPE value of 1.25%. Furthermore, it can be noted that for the next 10 years the increase in the population of Lombok Island on average of 1.49%, with the average East Lombok district details of 1.15%, the average West Lombok district by 1.17%, the average central Lombok district of 0.98%, the North Lombok district averaged 0.98%, and the average Mataram city of 3.05%. These results suggest that the simulated and numeric techniques using the GUI of MATLAB provide quite accurate results. In subsequent research, it is necessary to do predictions based on the number of births, the number of deaths, the number of transmigration, the amount of productive and non-productive, and other data series that is more complex so that it will be seen how the development of the population productivity on the island of Lombok.
A framework for scalable tracking of physical objects to enhance immersive prototyping for design thinking

Design thinking is regarded as one of the important problem-solving skills that include a prototyping stage for early testing the feasibility of an idea. With the advancement in technologies, virtual reality is being applied to create immersive experiences in design prototyping. While tracking physical objects and visualizing the tracking in the virtual world could enhance the immersive experiences, existing tracking devices are not easy to set up and might not scale cost-effectively in multi-user scenarios. The aim of this study is, therefore, to propose a framework for using simple and low-cost hardware for tracking physical objects in a multi-user environment. A sample implementation along with demonstrations on the tracking are also presented.
Generative Adversarial Network Using Weighted Loss Map and Regional Fusion Training for LDR-to-HDR Image Conversion

High dynamic range (HDR) imaging refers to digital image processing that modifies the range of color and contrast to enhance image visibility. To create an HDR image, two or more images that include various information are needed. In order to convert low dynamic range (LDR) images to HDR images, we consider the possibility of using a generative adversarial network (GAN) as an appropriate deep neural network. Deep learning requires a great deal of data in order to build a module, but once the module is created, it is convenient to use. In this paper, we propose a weight map for local luminance based on learning to reconstruct locally tone-mapped images.
Developing and Implementing a Barcode Based Student Attendance System

In view of the importance of students' attendance at lectures and their impact on their academic achievement, universities take the necessary measures to reduce excessive absenteeism. This is a highly important problem. The administration requires careful follow-up, taking care of it and not being lenient. At present, attendance and absence at the universities are recorded by calling the names of the students or by signing the student's attendance paper. In the process of admitting students into an examination hall in most KSA universities, 85% of attendance must be met and also considered for grade computation, therefore there is a huge need for monitoring and recording students’ attendance. The aim of this work is to design and implement a barcode based student attendance system that can be easily accessed by the lecturers, to help them to avoid maintaining the registry book, providing valuable information about the students and the reports can be generated using real-time processing. The proposed work was designed and implemented using the Unified Modeling Language (UML), Microsoft Access 2007 and ASP.NET programming language.
Changes in Gaze Behavior during the Learning of the Epidural Technique with a Simulator in Anesthesia Novices

Background: Current literature demonstrates the ability of eye tracking to provide reliable quantitative data as an objective assessment tool, with potential applications to medical and surgical training to improve performance.Objective: The aim of this study was to evaluate the changes in gaze behavior in anesthesia novice trainees when performing a simulated epidural technique before and after a hands-on training on the epidural simulator.Methods: We enrolled 48 novice trainees who had never previously performed an epidural block.After a standardized learning module, each trainee practiced the epidural procedure on the epidural simulator while wearing a pair of eye tracking glasses (Tobii Pro Glasses 50 Hz wearable wireless eye tracker).After this baseline recording, each trainee spent two hours practicing with the epidural simulator and afterwards once again performed the eye tracking epidural procedure.Eye tracking metrics and epidural learning (duration of the procedure and number of attempts) before and after the simulated practice were recorded.Results: The duration of the epidural procedure and of the epidural needle advancement phase (P < 0.05) and the number of epidural attempts (P < 0.001) were reduced after the tutorial.When considering the eye tracking metrics, after the tutorial, the number of visit counts decreased and their duration increased (P < 0.05).The number of epidural needle insertions (additional attempts) showed a significant positive correlation with the visits number (aOR = 2.02 (95% CI = 1.26 -3.55; P = 0.008)) and a significant negative correlation with the visit duration (aOR = 0.65 (95% CI = 0.39 -0.99;
A Prototype solar tracking system design and implementation

In this work comparison between the results of the first systems is a fixed solar and the second is the sun tracking in an attempt to increase the proportion of electricity production. Here a microcontroller (Arduino) and the light-dependent resistor (LDR) photo detector is used in this tracker. And then compare the results in different weather conditions and on different days to test the efficiency of the two systems. The efficiency of the tracking system is better than the fixed system by 12.3% on a sunny day and 4.9% on a partly cloudy day. However, it failed by 3.3% on a cloudy day. With a sunny day preference in the tracking system at 6.9% of partially cloudy days, and 12.1% with partially cloudy to a cloudy day. And verified from The efficiency of the work of the microcontroller (Arduino) system and the optical detector (LDR).
PENGGUNAAN USER PERSONA UNTUK EVALUASI DAN MENINGKATKAN EKSPEKTASI PENGGUNA DALAM KEBUTUHAN SISTEM INFORMASI AKADEMIK

Human-Computer Interaction (HCI) is a scientific field to determine user characteristics or so-called persona. HCI describes a system that must be easy to use, provides security to users, easy to learn and has usability. The purpose of the study was conducted to obtain various perspectives on the use of software in the InfoKHS University of Muhammadiyah Malang (UMM) academic system according to user characteristics so that the design of software requirements is expected to be representative of various types of users. The HCI assessment is carried out in software development (SD) focusing on the elicitation of needs. Focus on using user methods. As an analysis of user needs. Qualitative data were analyzed based on country hypotheses obtained for the first time in this research phase. In this study, a User Persona was done with an iterative method to ensure each phase was validated. The results show that iterations are needed several times to get the use of detailed cases diagram of each user's needs.