Reviewerly/exHarmony huggingface dataset:
ICT Research in Indonesia

Various research related to ICT is growing in the world, including in Indonesia. Unfortunately, there is not much research that discusses the development of ICT research in the country. This article analyzes the development of ICT research in Indonesia, including the field of study that is the study of authors. The research data comes from a collection of studies that are recorded on the Garuda portal as an index site about research in Indonesia.
Majorana decomposition for two-qubit pure states

The Majorana representation (MR), which represents a quantum state and its evolution with the Majorana stars (MSs) on the Bloch sphere, provides us an intuitive way to study symmetric multiqubit pure states physical system with SU(2) symmetry. In this work, we propose a method to extend the MR for generic two-qubit pure states. By adding an additional reference star (RS), which represents the symmetry of the qubits, on the symmetric two qubit state with two base stars, we give a more general MR for both symmetric, anti-symmetric and generic two-qubit states. Using this method, we study the entanglement, Berry phase and their relation by this Majorana decomposition. Furthermore, the symmetries for different type of two-qubit states are also discussed via the RSs and correlation between the two MSs.
Data-Driven Prediction of Judgment. Law’s New Mode of Existence?

This chapter enquires into the upcoming domain of data-driven ‘law’, that is, into the use of big data analytics and predictive technologies as a means to inform the law. I will argue that this may transform the ‘mode of existence’ of law, due to the novel ‘affordances’ of data-driven systems. In the first part I will investigate the promises of legal decision-making based on the mathematical assumptions of machine learning, opening the black box of algorithmic ‘insights’ at the level of the underlying research design. In the second part I will examine the nature of modern positive law as text-driven law, by highlighting the performative nature of legal effect and how this relates to the force of law. Finally, I will identify some of the challenges presented by data-driven ‘law’ in terms of legal protection. This will result in a proposal to integrate ‘by design’ approaches into law and the rule of law, clarifying how and why ‘legal protection by design’ is not equivalent with ‘legal by design’ or ‘techno-regulation’.
Recognition of Automated Hand-written Digits on Document Images Making Use of Machine Learning Techniques

The purpose of this study is to create an automated framework that can recognize similar handwritten digit strings. For starting the experiment, the digits were separated into different numbers. The process of defining handwritten digit strings is then concluded by recognizing each digit recognition module's segmented digit. This research utilizes various machine learning techniques to produce a strong performance on the digit string recognition challenge, including SVM, ANN, and CNN architectures. These approaches use SVM, ANN, and CNN models of HOG feature vectors to train images of digit strings. Deep learning methods organize the pictures by moving a fixed-size monitor over them while categorizing each sub-image as a digit pass or fail. Following complete segmentation, complete recognition of handwritten digits is accomplished. To assess the methods' results, data must be used for machine learning training. Following that, the digit data is evaluated using the desired machine learning methodology. The Experiment findings indicate that SVM and ANN also have disadvantages in precision and efficiency in text picture recognition. Thus, the other process, CNN, performs better and is more accurate. This paper focuses on developing an effective system for automatically recognizing handwritten digits. This research would examine the adaptation of emerging machine learning and deep learning approaches to various datasets, like SVM, ANN, and CNN. The test results undeniably demonstrate that the CNN approach is significantly more effective than the ANN and SVM approaches, ranking 71% higher. The suggested architecture is composed of three major components: image pre-processing, attribute extraction, and classification. The purpose of this study is to enhance the precision of handwritten digit recognition significantly. As will be demonstrated, pre-processing and function extraction are significant elements of this study to obtain maximum consistency.
Face Age Progression with Attribute Manipulation

The human face is one of the predominant means of person recognition. Human faces are affected by many factors i.e. time, attributes, weather, and other subject-specific variations. Although face aging has been studied in the past, the impact of the aforesaid factors, especially, the effect of attributes on the aging process were unexplored. In this paper, we propose a novel holistic “Face Age progression With Attribute Manipulation” (FAWAM) model that generates face images at different ages while simultaneously varying attributes and other subject specific characteristics. We address the task in a bottom-up manner, considering both age and attributes submodules. For face aging, we use an attribute-conscious face aging model with a pyramidal generative adversarial network that can model age-specific facial changes while maintaining intrinsic subject specific characteristics. For facial attribute manipulation, the age processed facial image is manipulated with desired attributes while preserving other details unchanged, leveraging an attribute generative adversarial network architecture. Our proposed model achieves significant qualitative as well as quantitative performance results.
On the Predictability of Pruning Across Scales

We show that the error of iteratively-pruned networks empirically follows a scaling law with interpretable coefficients that depend on the architecture and task. We functionally approximate the error of the pruned networks, showing that it is predictable in terms of an invariant tying width, depth, and pruning level, such that networks of vastly different sparsities are freely interchangeable. We demonstrate the accuracy of this functional approximation over scales spanning orders of magnitude in depth, width, dataset size, and sparsity. We show that the scaling law functional form holds (generalizes) for large scale data (CIFAR-10, ImageNet), architectures (ResNets, VGGs) and iterative pruning algorithms (IMP, SynFlow). As neural networks become ever larger and more expensive to train, our findings suggest a framework for reasoning conceptually and analytically about pruning.
Detection of Leukemia Using Machine Learning Algorithms

Abstract Detection of disorders of Blood usually include visual inspection through a microscope. This may help to classify various conditions related to human blood. The paper details about evolving a system that could help in diagnosis and detection of Leukemia at an early stage. Only then controlling and prevention can be done. The proposed system focuses on White Blood Cells as they are the affected ones in case of Leukemia. The affected cells differ in shape and color features, which are used as classifier inputs.
Optimizing Information Theory Based Bitwise Bottlenecks for Efficient Mixed-Precision Activation Quantization

Recent researches on information theory shed new light on the continuous attempts to open the black box of neural signal encoding. Inspired by the problem of lossy signal compression for wireless communication, this paper presents a Bitwise Bottleneck approach for quantizing and encoding neural network activations. Based on the rate-distortion theory, the Bitwise Bottleneck attempts to determine the most significant bits in activation representation by assigning and approximating the sparse coefficients associated with different bits. Given the constraint of a limited average code rate, the bottleneck minimizes the distortion for optimal activation quantization in a flexible layer-by-layer manner. Experiments over ImageNet and other datasets show that, by minimizing the quantization distortion of each layer, the neural network with bottlenecks achieves the state-of-the-art accuracy with low-precision activation. Meanwhile, by reducing the code rate, the proposed method can improve the memory and computational efficiency by over six times compared with the deep neural network with standard single-precision representation. The source code is available on GitHub: https://github.com/CQUlearningsystemgroup/BitwiseBottleneck.
Penerapan Algoritma Profile Matching pada Aplikasi Computer Based Test (CBT) dalam Proses Seleksi Mahasiswa Baru

Computer Based Test (CBT) is a test that is administered using a computer as tools or the main medium in conducting tests or exams. However, not all universities have utilized this technology. One of them is the Sekolah Tinggi Ilmu Kesehatan (STIKes) Kuningan, which has not yet used a computerized system. Examination implementation and manual processing of exam scores are an obstacle in the selection process for new students. With the existence of a Computer Based Test (CBT) application, it is hoped that it can facilitate the implementation of the test. However, the use of Computer Based Test (CBT) technology is not sufficient in determining which participants get the best score. The Profile Matching algorithm is chosen as an algorithm that can assist in decision making. There are 5 criteria used, that is the value of CBT and parents' income as criteria for the type of Core Factor, Mathematics National Exam scores, English National Exam scores, and Indonesian Language National Exam scores as criteria for the type of Secondary Factor. Testing is done by comparing the results of calculations on the system and Microsoft Excel calculations where there are 3 different values of the Core Factor and Secondary Factor. There is a discrepancy in the test results when multiple profiles have the same final value. One of them, Dea Nur Alifah and Rahma Nurlita with CF values of 60%, SF 40% and CF 70%, SF 30% has a final value of 5.78, is in the order of 3 and 4. Furthermore, the CF values of 80% and SF 20% are both has decreased in value, to 5.77 and still ranks 3 and 4. The application of the Profile Matching algorithm with an output in the form of ranking values in the Computer Based Test (CBT) application is still not optimal.
THE STUDENTS’ PERCEPTION OF EXTENSIVE READING IN ONLINE LEARNING AT FLSP CLASS

The purpose of this research aimed to know the students’ perception on their online Extensive reading learning. Survey design was used to conduct this study, and therefore, the questioner was constructed as the instruments of the study besides the researcher himself. The participants of this research were 44 students of first semester in the Accounting Department at FLSP class of University of Muhammadiyah Malang (Class A). The researcher adopted the questionnaire from a previous study conducted by Yogurtcu (2013), Tseng (2015), and Torres (2017), and the questionnaire showed the students’ perceptions of their online learning. The questionnaire consists of 20 statements and each statement has five alternative responses from 1 to 5: Strongly disagree, disagree, neutral, agree, and strongly agree. Google Form is the media to distribute the questionnaire due to the global pandemic of Covid 19. The results of the study indicate that students show positive response towards the use of Extensive reading in online learning. The students perceived that benefits of the activities had substantial effect on increasing their vocabularies, and students focused on educational evaluation they read during the semester. Based on the data, there are advantages, they are: we can complete to read the whole text, we can determine mind idea and supporting idea of the texts, The online reading activities helped me to get more interested in reading text, we can easily understand the text from online reading activities, and there is disadvantage, they are: distraction (Tools and Signal).
Financial instrument of the new economy or a pyramid scheme: The issues of cryptocurrency security

Subject. This article discusses cryptocurrency as a new financial instrument in the global financial system. Objectives. The article aims to consider the current situation of cryptocurrencies and justify the advantages and risks of their use. Methods. For the study of cryptocurrency, Blockchain technology, and Bitcoin and the US dollar relationship, we used a comprehensive approach based on systems, functional and structural, and correlation analyses. Results. Based on the analysis of the trends in the cryptocurrency market formation, the problematic aspects of their legal regulation, the investment aspect, and the changes in the cryptocurrency exchange rate against the US dollar, the article identifies some shortcomings of this financial instrument from the point of view of supervisory bodies. Conclusions. Cryptocurrency really has significant prospects to become a valuable financial tool in the world economy, despite the rather critical problems of their regulation. Cryptocurrency can be a substitute for the US dollar in international settlements due to its advantages in the form of transparency and decentralization, especially given the fact that in 2019–2020, the governments of a number of countries (EU, Russia) began to actively develop legislation to regulate this area.
The modern outline of the triple helix system: generating ideas in the fight against the pandemic

The article examines the directions of improving the triple helix system in the current conditions of a pandemic. The basis of the process is the generation of knowledge by national research centers, universities, with direct support from the authorities and constructive interaction with business. The modern contour of this spiral ensures the development and implementation of innovative domestic developments, methods and technologies, expands the possibilities for the treatment of various diseases by introducing new equipment and pharmaceuticals. The key element of the system is the introduction to the market of the results of intellectual activity, carried out through the prompt implementation of science-intensive technologies and industries in all spheres, complexes and sectors of the country's economy. The results of effective realization of the potential of the triple helix model in the first city of science - Obninsk and Kaluga region are demonstrated. Examples of developments of scientific organizations that are part of innovative clusters are given, which made it possible to formulate a treatment regimen for diseases in the shortest possible time, create the necessary drugs, and apply them in the realities of the spread of coronavirus infection. The authors substantiate the opinion that an adequate response to emerging dangerous challenges is prepared in advance, thanks to the conduct of active research activities, and an effectively formed triple helix system acts as a guarantor of the successful implementation of intellectual activities that can save the lives of millions of people.
Improving Web Service Recommendation using Clustering with K-NN and SVD Algorithms

In the advent of the twenty-first century, human beings began to closely interact with technology.Today, technology is developing, and as a result, the world wide web (www) has a very important place on the Internet and the significant task is fulfilled by Web services.A lot of Web services are available on the Internet and, therefore, it is difficult to find matching Web services among the available Web services.The recommendation systems can help in fixing this problem.In this paper, our observation was based on the recommended method such as the collaborative filtering (CF) technique which faces some failure from the data sparsity and the cold-start problems.To overcome these problems, we first applied an ontology-based clustering and then the k-nearest neighbor (KNN) algorithm for each separate cluster group that effectively increased the data density using the past user interests.Then, user ratings were predicted based on the model-based approach, such as singular value decomposition (SVD) and the predictions used for the recommendation.The evaluation results showed that our proposed approach has a less prediction error rate with high accuracy after analyzing the existing recommendation methods.
Explainable Inference Over Grounding-Abstract Chains for Science Questions

We propose an explainable inference approach for science questions by reasoning on grounding and abstract inference chains.This paper frames question answering as a natural language abductive reasoning problem, constructing plausible explanations for each candidate answer and then selecting the candidate with the best explanation as the final answer.Our method, ExplanationLP, elicits explanations by constructing a weighted graph of relevant facts for each candidate answer and employs a linear programming formalism designed to select the optimal subgraph of explanatory facts.The graphs' weighting function is composed of a set of parameters targeting relevance, cohesion and diversity, which we finetune for answer selection via Bayesian Optimisation.We carry out our experiments on the WorldTree and ARC-Challenge datasets to empirically demonstrate the following contributions: (1) ExplanationLP obtains strong performance when compared to transformer-based and multi-hop approaches despite having a significantly lower number of parameters; (2) We show that our model is able to generate plausible explanations for answer prediction; (3) Our model demonstrates better robustness towards semantic drift when compared to transformerbased and multi-hop approaches.
Digital teaching skills: comparative study in higher education

Research entitled Comparative study of the teacher's digital competencies since the perception of students from four Senati locations, Lima-2020 was intended to establish the significant differences in digital competences between teachers of the different senati headquarters, located in Lima Metropolitana and Callao; the work followed the quantitative methodology, descriptive, comparative and explanatory level; the population was 4440 students with a random sample stratified of 354; the instrument was subjected to the reliability and validity of both content and construct. The main results established that at least two population means are different, as indicated by the p value (0,000) and F (11,819). Lima Cercado's headquarters were also shown to perform better compared to the other headquarters.
The Use of Social Media in Facilitating the Teaching and Learning Process: A Case Study of Undergraduates at University of Bisha in Saudi Arabia

The study explores the use of social media in accelerating the teaching and learning process of English language. It was conducted in the Girls college of Al Namas, University of Bisha, Saudi Arabia. The electronic questionnaire consisted of multiple closed questions to acquire both qualitative and quantitative results. The participants are undergraduate students of the English Department of the above-mentioned college. The data was analyzed using pie chart and histogram. During the period of teaching English language, we, as lecturers, noticed that the students are very efficient in the use of modern technology, but unfortunately, they do not use them in the target language, instead they use all the social media networks in their mother tongue, i.e, Arabic. This way even being coherent to the modern technology they do not use it to improve their second language skill. Their disposition of using Arabic language everywhere and everything creates a big hindrance for them to avail the benefits of social media networks in learning English language. Through this paper we tried to motivate the students to enhance their capabilities to utilize social media network in boosting their English language skills. At the same time, we have tried to make the English language as an interesting subject to facilitate the teaching and learning process.
Pengaruh algoritma filter bubble dan echo chamber terhadap perilaku penggunaan internet

Introduction. This study aims to explore effects of bubble filter and echo chamber on information searching behaviour on the internet, how it affects the scope of the internet ecosystem and users, and what we can see from the behaviour.&#x0D; Data Collection Method. The paper used a mixed-methods approach with surveys, online discussions, and literature research. Twenty respondents between 19-21 years old participated in this exploratory study.&#x0D; Analysis Data. Data was obtained and calculations have automatically accumulated through Google Form. The user data analysis and discussion were conducted manually by considering aspects of rationality concerning the literature and previous research.&#x0D; Results and Discussions. The analysis results obtained were bubble filter and echo chambers were proven to affect internet users both in positive and negative ways.&#x0D; Conclusions. Bubble filter and echo chamber may have both positive and negative effects simultaneously, however, it depends on the user because the system has provided control features to reduce both features. Using internet wisely is also parts of the important aspect. &#x0D;
Efficient Approaches for Density-Based Spatial Clustering of Applications with Noise

A significant challenge for the growing world of data is to analyze, classify and manipulate spatial data. The challenge starts with the clustering process, which can be defined to characterize the spatial data with their relative properties in different groups or classes. This process can be performed using many different methods like grids, density, hierarchical and others. Among all these methods, the use of density for grouping leads to a lower noise data in result, which is called Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The DBSCAN algorithm defines a data set in a group and separates the group from the other groups based on the density of the data surrounding the selection of data points. These data points and the density of the data are calculated depending on two parameters. One parameter is used as the radius of the data point to find the neighborhood data points. Another parameter is used to identify the noise in the collected data by keeping the minimum number of data points for the data density. Like other popular method k-means, DBSCAN does not require any input of the cluster number. It can sort the data set with the number of clusters according to data density. The purpose of this article is to explain the Efficient Density-based Spatial Clustering of Applications with Noise (DBSCAN) using a sample of data set, compare the results, identify the constraints, and suggest some possible solutions.
A Compression-Compilation Co-Design Framework Towards Real-Time Object Detection on Mobile Devices

The rapid development and wide utilization of object detection techniques have aroused requirements for both accuracy and speed of object detectors. In this work, we propose a compression-compilation co-design framework to achieve real-time YOLOv4 inference on mobile devices. We propose a novel fine-grained structured pruning, which maintain high accuracy while achieving high hardware parallelism. Our pruned YOLOv4 achieves 48.9 mAP and 17 FPS inference speed on an off-the-shelf Samsung Galaxy S20 smartphone, which is 5.5x faster than the original state-of-the-art detector YOLOv4.