Reviewerly/exHarmony huggingface dataset:
CYBER CRIME AND PRIVACY RIGHT VIOLATION CASES OF ONLINE LOANS IN INDONESIA

This article examines the phenomenon of illegal online loans in Indonesia and the government’s preventive policies to deal with it. This article will be studied using a qualitative descriptive approach and using content analysis methods and literature review. In this article, the author argues that cybercrime does notonly discuss online data theft or acts of harassment, but also relates to online loan crimes . The author also argues that the development of online loan crime is due to weak government regulations and policies. The government has tried to formulate legal regulations regarding the establishment of online loans legal and also legal rules that will protect victims of online loan crimes. However, in practice it still needs to be studied seriously and in depth. Many online loan crime cases are found in Indonesia, even during this pandemic it was reported that many online loan crimes were felt by the public. Therefore, through this online loan crime, the author wants to explore and examine more deeply about why online loan crimes are increasingly occurring among the public. How is the government trying to protect victims of online loan crimes by implementing the Financial Services Authority (POJK) Regulations, the Electronic Information and Transaction Law and other regulations? Also, how does the discussion of online loan crimes relate to human rights and privacy rights?
Pendampingan Penyusunan Rencana Anggaran Belanja (RAB) dan Laporan Keuangan Pada Amal Usaha Muhammadiyah Lamongan

Based on Law no. 12 of 2012 concerning Higher Education, the Internal Quality Assurance System (SPMI) process should be carried out at least once a year. Currently, the quality and managerial system at one of the charities of Muhamamdiyah Lamongan is still not very good. Organizational management is already running but not in accordance with the desired national university standards. Many things are still done manually and have not been synergized properly. This service aims to help partners become quality universities and can compete with other universities. This service activity is also expected to bring changes to the management system, especially in the financial sector so that it is arranged according to university standard rules. The service team will provide explanations regarding national university standards and ways to achieve perfect quality so that they can get excellent or even superior accreditation scores. The method used in this service is to provide assistance and workshops related to the management of the financial system. The results of the training provide benefits, namely the creation of a good financial system in accordance with National Higher Education Standards and improving the quality of higher education as a whole.
IMPROVING STUDENTS READING ABILITY BY USING WEBTOON AT SEVENTH GRADE OF MTS DDI KULO

The objective of the article are to find out whether or not the use of Webtoon can improve students’ reading ability at seventh grade of MTS DDI Kulo in academic year 2017/2018. This article employed pre-experimental method with one group pre-test and post-test design. The population was the seventh year student (VII) of MTS DDI Kulo in academic years 2018-2017, it consisted of one class. The total number of population was 40 students’ and whole class was taken as sample by using random cluster sampling technique, class VII (29) Was a chosen as sample. The article instrument were multiple choice. The result of this article showed that the pre-test got the mean score (60) while the post-test got mean score (80). This showed that there was significant difference between pre-test and post –test. The result of the P-value (0,000) was lower α ( 0.05). This means that H1 was accepted. Based on the result, the researcher concluded that teaching Reading by using webtoon improves students reading ability at seventh year students of MTS DDI Kulo.
Destructive Controlled-Phase Gate Using Linear Optics

Knill, Laflamme, and Milburn [Nature 409, 46 (2001)] showed that linear optics techniques could be used to implement a nonlinear sign gate. They also showed that two of their nonlinear sign gates could be combined to implement a controlled-phase gate, which has a number of practical applications. Here we describe an alternative implementation of a controlled-phase gate that only requires the use of a single nonlinear sign gate. This gives a much higher average probability of success when the required ancilla photons are generated using heralding techniques. This implementation of a controlled-phase gate destroys the control qubit, which is acceptable in a number of applications where the control qubit would have been destroyed in any event, such as in a postselection process.
A Matrix-Valued Inner Product for Matrix-Valued Signals and Matrix-Valued Lattices

A matrix-valued inner product was proposed before to construct orthonormal matrix-valued wavelets for matrix-valued signals. It introduces a weaker orthogonality for matrix-valued signals than the orthogonality of all components in a matrix that is commonly used in orthogonal multiwavelet constructions. With the weaker orthogonality, it is easier to construct orthonormal matrix-valued wavelets. In this paper, we re-study the matrix-valued inner product more from the inner product viewpoint that is more fundamental and propose a new but equivalent norm for matrix-valued signals. We show that although it is not scalar-valued, it maintains most of the scalar-valued inner product properties. We introduce a new linear independence concept for matrix-valued signals and present some related properties. We then present the Gram-Schmidt orthonormalization procedure for a set of linearly independent matrix-valued signals. Finally we define matrix-valued lattices.
Medical Image Encryption using Latin Image Cipher Algorithm

Abstract Image processing has significant applications in the health care sector, medical data processing, analysis, storage, and transfer. The Latin Image Cipher algorithm was proposed in this work for the encryption of medical images. The encryption algorithm proposed in this research work comprises Latin square whitening, substitution, and permutation. The efficiency of the algorithm was also validated by inducing noise in the input images. The performance validation of the proposed algorithm was validated by the histogram analysis and correlation analysis. The information entropy measure also validates the proficiency of the encryption/decryption algorithm. The algorithm was developed on MATLAB 20021 and tested on COVID 19 CT images.
Training of future officers of communication units to managerial activity as a pedagogical problem

Nelin Ye. V. A study of psychoanalytic pedagogy in the ideas of post-JungiansThe article reveals the key ideas of C. G. Jung's followers on the development of the concept of psychoanalytic pedagogy.The classification of currents of analytical psychology is investigated.The contribution of well-known representatives of the Centrist School of Analytical Psychology and Post-Jungians is substantiated.It has been shown that centrists recognized the importance of C. G. Jung's creative ideas for psychoanalysis, but adhered to neutrality in Jung's debates with Freud.Representatives of the Centrist School were S. Spielrein and G. Green.S. Spielrein's research was devoted to the development of preschool children and the spread of ideas of psychoanalytic education.G. Green studied the psychology of school-age children, as well as the importance of the role of the teacher for the formation of the child's personality.Among the post-Jungians, the key ideas of E. Neumann and M. Fordham were also revealed.It has been established that the scientific and practical field of post-Jungian intelligence included studies of fairy tales, myths, the formation of identity and the collective unconscious people.It is proved that due to E. Neumann's activity an independent group of child psychoanalysts was formed among analytical psychologists.E. Neumann noted that every child takes on behavioral patterns from parents and implements them throughout life.English psychoanalyst M. Fordham was the first to determine the existence of identity in newborns and revealed the archetypal nature of children's tales.It has been shown that all post-Jungians held the view that a child is not born «tabula rasa», but appears with a certain handicap of the collective unconscious.Given the importance of the collective unconscious, the post-Jungians argued that any education system should take into account the achievements of folk pedagogy and ancient traditions of education.Prospects for further research are planned in the disclosure of the ideas of individual psychology in the context of the theory of psychoanalytic pedagogy.
Handcrafted Feature Selection Techniques for Pattern Recognition: A Survey

The accuracy of a classifier, when performing Pattern recognition, is mostly tied to the quality and representativeness of the input feature vector. Feature Selection is a process that allows for representing information properly and may increase the accuracy of a classifier. This process is responsible for finding the best possible features, thus allowing us to identify to which class a pattern belongs. Feature selection methods can be categorized as Filters, Wrappers, and Embed. This paper presents a survey on some Filters and Wrapper methods for handcrafted feature selection. Some discussions, with regard to the data structure, processing time, and ability to well represent a feature vector, are also provided in order to explicitly show how appropriate some methods are in order to perform feature selection. Therefore, the presented feature selection methods can be accurate and efficient if applied considering their positives and negatives, finding which one fits best the problem's domain may be the hardest task.
EVALUASI KESESUAIAN LAHAN KERING UNTUK TANAMAN PANGAN DI KECAMATAN KUBUTAMBAHAN KABUPATEN BULELENG

Dry land is land whose use for the development of agricultural commodities is limited, due to constraints on the availability of nutrients and water. The research objectives are: to identify the suitability of dry land for upland rice, corn and peanuts; identify limiting factors and improvement efforts to increase land productivity. The method used is a survey to determine the characteristics of the land in the field and to take soil samples that are analyzed in the laboratory. Determination of sample points based on the results of overlapping maps of soil types, land use maps, slope maps using QGIS 3.4, so that a homogeneous land unit map (SLH) is obtained, and 6 SLH is obtained as observation points. The results of field and laboratory data analysis were matched with the growing requirements for upland rice, corn and peanuts. The actual land suitability for upland rice, corn, and peanuts is classified as S2 (fairly suitable) to N (not suitable). The limiting factors are slope, erosion hazard, temperature, texture, rainfall, pH, N-total, P2O5, Cation Exchange Capacity, Base Saturation, availability of oxygen, and C-organic. Improvement efforts that must be done are making terraces, fertilizing urea, fertilizers containing phosphate, mulching using plant residues, giving organic matter, and improving drainage. After improving the limiting factors that can be improved, the potential land suitability classes for upland rice, corn and peanuts are S1 (very suitable), S2 (fairly suitable) and S3 (marginally appropriate).
Annealing Optimization for Progressive Learning with Stochastic Approximation

In this work, we introduce a learning model designed to meet the needs of applications in which computational resources are limited, and robustness and interpretability are prioritized. Learning problems can be formulated as constrained stochastic optimization problems, with the constraints originating mainly from model assumptions that define a trade-off between complexity and performance. This trade-off is closely related to over-fitting, generalization capacity, and robustness to noise and adversarial attacks, and depends on both the structure and complexity of the model, as well as the properties of the optimization methods used. We develop an online prototype-based learning algorithm based on annealing optimization that is formulated as an online gradient-free stochastic approximation algorithm. The learning model can be viewed as an interpretable and progressively growing competitive-learning neural network model to be used for supervised, unsupervised, and reinforcement learning. The annealing nature of the algorithm contributes to minimal hyper-parameter tuning requirements, poor local minima prevention, and robustness with respect to the initial conditions. At the same time, it provides online control over the performance-complexity trade-off by progressively increasing the complexity of the learning model as needed, through an intuitive bifurcation phenomenon. Finally, the use of stochastic approximation enables the study of the convergence of the learning algorithm through mathematical tools from dynamical systems and control, and allows for its integration with reinforcement learning algorithms, constructing an adaptive state-action aggregation scheme.
Studying Bias in GANs through the Lens of Race

In this work, we study how the performance and evaluation of generative image models are impacted by the racial composition of their training datasets. By examining and controlling the racial distributions in various training datasets, we are able to observe the impacts of different training distributions on generated image quality and the racial distributions of the generated images. Our results show that the racial compositions of generated images successfully preserve that of the training data. However, we observe that truncation, a technique used to generate higher quality images during inference, exacerbates racial imbalances in the data. Lastly, when examining the relationship between image quality and race, we find that the highest perceived visual quality images of a given race come from a distribution where that race is well-represented, and that annotators consistently prefer generated images of white people over those of Black people.
DC-Art-GAN: Stable Procedural Content Generation using DC-GANs for Digital Art

Art is an artistic method of using digital technologies as a part of the generative or creative process. With the advent of digital currency and NFTs (Non-Fungible Token), the demand for digital art is growing aggressively. In this manuscript, we advocate the concept of using deep generative networks with adversarial training for a stable and variant art generation. The work mainly focuses on using the Deep Convolutional Generative Adversarial Network (DC-GAN) and explores the techniques to address the common pitfalls in GAN training. We compare various architectures and designs of DC-GANs to arrive at a recommendable design choice for a stable and realistic generation. The main focus of the work is to generate realistic images that do not exist in reality but are synthesised from random noise by the proposed model. We provide visual results of generated animal face images (some pieces of evidence showing a blend of species) along with recommendations for training, architecture and design choices. We also show how training image preprocessing plays a massive role in GAN training.
Fast Generation of Exchangeable Sequence of Clusters Data

Recent advances in Bayesian models for random partitions have led to the formulation and exploration of Exchangeable Sequences of Clusters (ESC) models. Under ESC models, it is the cluster sizes that are exchangeable, rather than the observations themselves. This property is particularly useful for obtaining microclustering behavior, whereby cluster sizes grow sublinearly in the number of observations, as is common in applications such as record linkage, sparse networks and genomics. Unfortunately, the exchangeable clusters property comes at the cost of projectivity. As a consequence, in contrast to more traditional Dirichlet Process or Pitman-Yor process mixture models, samples a priori from ESC models cannot be easily obtained in a sequential fashion and instead require the use of rejection or importance sampling. In this work, drawing on connections between ESC models and discrete renewal theory, we obtain closed-form expressions for certain ESC models and develop faster methods for generating samples a priori from these models compared with the existing state of the art. In the process, we establish analytical expressions for the distribution of the number of clusters under ESC models, which was unknown prior to this work.
Interpretations Steered Network Pruning via Amortized Inferred Saliency Maps

Convolutional Neural Networks (CNNs) compression is crucial to deploying these models in edge devices with limited resources. Existing channel pruning algorithms for CNNs have achieved plenty of success on complex models. They approach the pruning problem from various perspectives and use different metrics to guide the pruning process. However, these metrics mainly focus on the model's `outputs' or `weights' and neglect its `interpretations' information. To fill in this gap, we propose to address the channel pruning problem from a novel perspective by leveraging the interpretations of a model to steer the pruning process, thereby utilizing information from both inputs and outputs of the model. However, existing interpretation methods cannot get deployed to achieve our goal as either they are inefficient for pruning or may predict non-coherent explanations. We tackle this challenge by introducing a selector model that predicts real-time smooth saliency masks for pruned models. We parameterize the distribution of explanatory masks by Radial Basis Function (RBF)-like functions to incorporate geometric prior of natural images in our selector model's inductive bias. Thus, we can obtain compact representations of explanations to reduce the computational costs of our pruning method. We leverage our selector model to steer the network pruning by maximizing the similarity of explanatory representations for the pruned and original models. Extensive experiments on CIFAR-10 and ImageNet benchmark datasets demonstrate the efficacy of our proposed method. Our implementations are available at \url{https://github.com/Alii-Ganjj/InterpretationsSteeredPruning}
Revealing Topology in Metals using Experimental Protocols Inspired by $K$-Theory

Topological metals are special conducting materials with gapless band structures and nontrivial edge-localized resonances, whose discovery has proved elusive because the traditional topological classification methods do not apply in this context. Inspired by recent theoretical developments that leveraged techniques from the field of $C^*$-algebras to identify topological metals \cite{cerjan_local_2021}, here, we directly observe topological phenomena in gapless acoustic crystals and provide a general experimental technique to demonstrate their topology. Specifically, we not only observe robust boundary-localized states in a topological acoustic metal, but also re-interpret a composite operator, mathematically derived from the K-theory of the problem, as a new Hamiltonian, whose physical implementation allows us to directly observe a topological spectral flow and measure the topological invariants. Our observations and experimental protocols may offer insights for discovering topological behavior across a wide array of artificial and natural materials that lack bulk band gaps.
Non-Standard Vietnamese Word Detection and Normalization for Text-to-Speech

Converting written texts into their spoken forms is an essential problem in any text-to-speech (TTS) systems. However, building an effective text normalization solution for a real-world TTS system face two main challenges: (1) the semantic ambiguity of non-standard words (NSWs), e.g., numbers, dates, ranges, scores, abbreviations, and (2) transforming NSWs into pronounceable syllables, such as URL, email address, hashtag, and contact name. In this paper, we propose a new two-phase normalization approach to deal with these challenges. First, a model-based tagger is designed to detect NSWs. Then, depending on NSW types, a rule-based normalizer expands those NSWs into their final verbal forms. We conducted three empirical experiments for NSW detection using Conditional Random Fields (CRFs), BiLSTM-CNN-CRF, and BERT-BiGRU-CRF models on a manually annotated dataset including 5819 sentences extracted from Vietnamese news articles. In the second phase, we propose a forward lexicon-based maximum matching algorithm to split down the hashtag, email, URL, and contact name. The experimental results of the tagging phase show that the average F1 scores of the BiLSTM-CNN-CRF and CRF models are above 90.00%, reaching the highest F1 of 95.00% with the BERT-BiGRU-CRF model. Overall, our approach has low sentence error rates, at 8.15% with CRF and 7.11% with BiLSTM-CNN-CRF taggers, and only 6.67% with BERT-BiGRU-CRF tagger.
A multi-chain synchronization protocol that leverage zero knowledge proof to minimize communication trust base

Delphinus cross-chain aggregator is a universal firmware which synchronise states between different smart contracts on different block-chains. In the world of block-chains, synchronization challenges are two-folded. Firstly, contracts from different main block-chain can not communicate with each other which makes it hard to establish a trustworthy communication channel for them to share and maintain a universal state between each other. Secondly, transactions on different block-chains can hardly be ordered thus conflicts are common and we need a novel way to avoid and handle these conflicts. Delphinus cross-chain aggregator is a ZKSNARK based multi-block-chain layer on top of which rich cross chain applications can run safely and efficiently.
A Deep Reinforcement Learning Strategy for UAV Autonomous Landing on a Platform

With the development of industry, drones are appearing in various field. In recent years, deep reinforcement learning has made impressive gains in games, and we are committed to applying deep reinforcement learning algorithms to the field of robotics, moving reinforcement learning algorithms from game scenarios to real-world application scenarios. We are inspired by the LunarLander of OpenAI Gym, we decided to make a bold attempt in the field of reinforcement learning to control drones. At present, there is still a lack of work applying reinforcement learning algorithms to robot control, the physical simulation platform related to robot control is only suitable for the verification of classical algorithms, and is not suitable for accessing reinforcement learning algorithms for the training. In this paper, we will face this problem, bridging the gap between physical simulation platforms and intelligent agent, connecting intelligent agents to a physical simulation platform, allowing agents to learn and complete drone flight tasks in a simulator that approximates the real world. We proposed a reinforcement learning framework based on Gazebo that is a kind of physical simulation platform (ROS-RL), and used three continuous action space reinforcement learning algorithms in the framework to dealing with the problem of autonomous landing of drones. Experiments show the effectiveness of the algorithm, the task of autonomous landing of drones based on reinforcement learning achieved full success.