Reviewerly/exHarmony huggingface dataset:
Spatially adaptive sparse representation prior for blind image restoration

Variation-based methods with different priors have been proven their ability in preserving edges for image restoration. Blind image decomposition is an inverse problem that is much harder to be solved than non-blind image decomposition from noisy images, commonly producing staircase effects in flat regions and smoothing fine structures. In this paper, we have tried to use spatially adaptive sparse representation (SASR) prior to restore a clean result from a blurred and noised image. In order to fastly and efficiently solve the SASR model, the alternating direction method of multipliers (ADMM) is firstly exploited to separate it into two subproblems. Then the final solution is alternatively optimized with the employment of fast Fourier transformation (FFT) and generalized soft-threshold formula. The experiments on both synthesized images and practical polluted images show that the proposed algorithm is effectiveness in quantitation and qualification, and is even better than state-of-the-arts.
Security Software System Design and Implementation for Microcontrollers Based on TrustZone

With the rapid growth of microcontroller applications, especially in the IoT field, more and more devices have the requirements of inter connection, the most important thing which should be took into account is the security. The TrustZone feature developed by ARM based on ARMv8-M architecture provides the hardware basis for security among devices. Based on this, software is needed for building a secure platform to satisfy the application security requirements. Device secure boot scheme can resist software malicious modification, firmware update scheme can apply security patches in time, secure storage scheme can manage the application confidential assets and can resist malicious modification, secure crypto scheme is used to ensure the secure data transmission. This paper gives some software security features when design and implement secure system microcontroller software.
Students� Perceptions of Teachers� Use of Bahasa Indonesia in the English Classroom

The use of the first language in the teaching and learning process is often found in English as a foreign language (EFL) classes. In Indonesia, Bahasa Indonesia is also employed in many English classes. This might bring both advantages and disadvantages to the students. This study aimed to find out the students� perceptions toward teachers� use of Bahasa Indonesia in the English classroom. The research employed an explanatory sequential mixed methods design and administered a questionnaire to collect the quantitative data and an open-ended interview to gain the collective data. The participants were 60 students of SMA Fransiskus 2 Jakarta. The findings revealed that most participants perceived the use of Bahasa Indonesia in their English classes positive.
Population-Guided Parallel Policy Search for Reinforcement Learning

In this paper, a new population-guided parallel learning scheme is proposed to enhance the performance of off-policy reinforcement learning (RL). In the proposed scheme, multiple identical learners with their own value-functions and policies share a common experience replay buffer, and search a good policy in collaboration with the guidance of the best policy information. The key point is that the information of the best policy is fused in a soft manner by constructing an augmented loss function for policy update to enlarge the overall search region by the multiple learners. The guidance by the previous best policy and the enlarged range enable faster and better policy search, and monotone improvement of the expected cumulative return by the proposed scheme is proved theoretically. Working algorithms are constructed by applying the proposed scheme to the twin delayed deep deterministic (TD3) policy gradient algorithm, and numerical results show that the constructed P3S-TD3 outperforms most of the current state-of-the-art RL algorithms, and the gain is significant in the case of sparse reward environment.
Semantic Text Segmentation from Synthetic Images of Full-Text Documents

An algorithm (divided into multiple modules) for generating images of full-text documents is presented. These images can be used to train, test, and evaluate models for Optical Character Recognition (OCR).&#x0D; The algorithm is modular, individual parts can be changed and tweaked to generate desired images. A method for obtaining background images of paper from already digitized documents is described. For this, a novel approach based on Variational AutoEncoder (VAE) to train a generative model was used.&#x0D; These backgrounds enable the generation of similar background images as the training ones on the fly.The module for printing the text uses large text corpora, a font, and suitable positional and brightness character noise to obtain believable results (for natural-looking aged documents).&#x0D; A few types of layouts of the page are supported. The system generates a detailed, structured annotation of the synthesized image. Tesseract OCR to compare the real-world images to generated images is used. The recognition rate is very similar, indicating the proper appearance of the synthetic images. Moreover, the errors which were made by the OCR system in both cases are very similar. From the generated images, fully-convolutional encoder-decoder neural network architecture for semantic segmentation of individual characters was trained. With this architecture, the recognition accuracy of 99.28% on a test set of synthetic documents is reached.
Blockchain Technology and its Impact on the Global Economy

The world’s most valuable resource is no longer oil, but data. Smartphones and the internet have made data abundant, ubiquitous and far more valuable. Modern algorithms can predict when a customer tends to buy, a car needs servicing or a person is at risk of a disease. Meanwhile, artificial intelligence techniques extract more value from data. As individuals accumulate information which transforms into knowledge, entrepreneurs will want to use and/or share that knowledge. It is the sharing of knowledge that needs a decentralized, autonomous mechanism so that knowledge can be shared fairly amongst all peoples of the world, not just within corporations. Blockchain technology gives us that mechanism. Blockchain is one of a kind decentralized technology and it is distributed as well as decentralized ledger. Blockchain is the answer to a lot of obstacles the world has to go through today. Before today, nobody could think of transferring money from one account to another safely without any financial institution in the middle, like a bank. Blockchain technology presents a radical and disruptive new way of conducting all manner of transactions over the Internet. The advent of Bitcoin and the blockchain has brought a lot of change to the world of finance even the world economy was formerly run using fiat currencies. Introducing the blockchain environment will actually enhance the economics because in blockchain, all transactions are recorded right from the manufacturer to the buyer. This paper explores the emerging landscape for blockchain technology focusing on the economics.
The Effect of Corporate Social Responsibility on Customer Loyalty in Mobile Telephone Companies

Purpose: The present study aims at developing and empirically testing a research model that presents the influence of Corporate Social Responsibility on corporate image, customer satisfaction and customer loyalty, demonstrating the direct and indirect effects among these structures.Design/Methodology/Approach: The examination of the proposed research model was carried out using a structured questionnaire completed by 358 mobile users in the city of Kavala.The validity and the reliability of the questionnaire were examined, while for the data analysis the Structural Equation Modeling Technique was used with LISREL 8.80.Findings: The findings of this study indicate that Corporate Social Responsibility have not a significant direct effect on customer loyalty, while corporate image and customer satisfaction have a significant positive effect on customer loyalty.Moreover, the findings provide practical new insights in understanding how a mobile company's CSR policy could be developed and implemented to help enhance customer loyalty through the mediating effects of customer satisfaction.Practical Implications: The companies would like to understand the implications of its CSR policy implementation, especially in enhancing its corporate image and customer satisfaction in terms of reputation and impression.Originality/Value: This study is a pioneer research in addressing the mediating role played by customer satisfaction for strengthening the relationship between CSR and customer loyalty in mobile phone companies.
Transformasi Peran Akuntan dalam Era Revolusi Industri 4.0 dan Tantangan Era Society 5.0

This research is titled transformation of the role of accountants in the era of the industrial revolution 4.0 and the challenges of the era of society 5.0. This study aims to determine the transformation of the role of accountants in the era of the industrial revolution 4.0 and the challenges that accountants will face in the era of society 5.0. This research is descriptive research. The conclusion is drawn based on a collection of opinions of experts, both experts in accounting and experts in the field of information technology. Based on the results of research and discussion that has been done it can be concluded that accountants must have a strategy to face the challenges of the accounting revolution. strategies that can be done, such as making sure you have certification, making goal orientation, making time management, always up to date, and using software to maximize performance. To face the challenges of the era of Society 5.0, accountants need to do the following five things: invest in developing digital skills, apply prototypes of new technologies, while learning by doing, education based on international certifycation, responsive to changes in industry, business and technological development, and curriculum and human-digital skills based learning (for educational institutions).
Distributed hybrid consensus–based square-root cubature quadrature information filter and its application to maneuvering target tracking

To handle nonlinear filtering problems with networked sensors in a distributed manner, a novel distributed hybrid consensus–based square-root cubature quadrature information filter is proposed. The proposed hybrid consensus–based square-root cubature quadrature information filter exploits fifth-order spherical simplex-radial quadrature rule to tackle system nonlinearities and incorporates a novel measurement update strategy into the hybrid consensus filtering framework, which takes the predicted measurement error into account and hence produces more accurate estimates. In addition, the proposed hybrid consensus–based square-root cubature quadrature information filter inherits the complementary positive features of both consensus on information and consensus on measurements methods and avoids sensitive matrix operations such as square-root decompositions and inversion of covariances, which is beneficial for numerical stability. Stability analysis with respect to consensus, convergence, and consistency for the proposed hybrid consensus–based square-root cubature quadrature information filter is also developed. The effectiveness of the proposed hybrid consensus–based square-root cubature quadrature information filter is validated through a maneuvering target tracking scenario. The simulation results show that the proposed hybrid consensus–based square-root cubature quadrature information filter outperforms the existing algorithms at the expense of a slight increase in computational cost.
An optimal and secure watermarking system using SWT-SVD and PSO

&lt;p&gt;Digital Watermarking is the efficient way introduced to authenticate ownership of digital information. In a watermarking system, a logo is inserted to identify the ownership to digital information. The proposed system uses the SWT with SVD to embed the watermark image. To enhance robustness of the proposed system PSO is adapted for the selection of optimal coefficients. PSNR and CC are the two performance parameters considered to evaluate the system.&lt;/p&gt;
Semantic Constraint GAN for Person Re-Identification in Camera Sensor Networks

In this paper, we propose a novel data augmentation method named Semantic Constraint Generative Adversarial Network (SCGAN) for person re-identification (Re-ID) in camera sensor networks. The proposed SCGAN can generate multiple style pedestrian images with high-level semantic information. To this end, we design two types of semantic constraints, i.e., attention constraint and identity constraint. The attention constraint aims to restrict the significant areas in the attention map to be consistent before and after image transformation. The identity constraint focuses on keeping the identity of the generated pedestrian image to be the same as that of the real one. After generating pedestrian images using SCGAN, we combine them with the real pedestrian images to train the person Re-ID model. Since the proposed SCGAN increases the diversity of training samples, the generalization of Re-ID model is enhanced. We evaluate the proposed SCGAN on three large-scale person Re-ID databases, i.e., Market1501, CUHK03 and DukeMTMC-reID, and experimental results reveal that the proposed SCGAN yields consistent improvements over other methods.
Applying Aging Effect on Facial Image with Multi-domain Generative Adversarial Network

Face Aging is an important and challenging application in computer vision.This is an application of conditional image generation.Until recently generative model was not good enough to generate considerable good resolution images.A generative model called generative adversarial network has introduced impressive capabilities in generating realistic images in both unconditional and conditional settings.Still, the task of generating images of different age conditioning on a given image is a very challenging task.Because there are two constraints to satisfy here in the generated images.The generated image must preserve the identity of the person in the source image and the image must have the features of the target age.In this work, we have applied the generative adversarial network in conditional settings along with custom loss function to satisfy the two mentioned constraints.The experiment has shown improved performance both in preserving the person's identity and classification accuracy of generated images in the target class compared to previous known approach to this problem.
Weighted inverse document frequency and vector space model for hadith search engine

Hadith is the second source of Islamic law after Qur’an which make many types and references of hadith need to be studied. However, there are not many Muslims know about it and many even have difficulties in studying hadiths. This study aims to build a hadith search engine from reliable source by utilizing Information Retrieval techniques. The structured representation of the text that used is Bag of Word (1-term) with the Weighted Inverse Document Frequency (WIDF) method to calculate the frequency of occurrence of each term before being converted in vector form with the Vector Space Model (VSM). Based on the experiment results using 380 texts of hadith, the recall value of WIDF and VSM is 96%, while precision value is just around 35.46%. This is because the structured representation for text that used is bag of words (1-gram) that can not maintain the meaning of text well).
Pengenalan Open Journal System (OJS) untuk Publikasi Ilmiah Mahasiswa

INTRODUCTION OF OPEN JOURNAL SYSTEM FOR SCIENTIFIC PUBLICATION OF STUDENTS. This activity is intended to provide understanding for students that according to the latest regulations, students are required to conduct scientific publications. Scientific publications are intended to communicate the results of academic research carried out to contribute to the development of science. However, many students do not understand how to publish their work properly. This training provides material on the ethics of scientific publications and how to submit articles in scientific journals. Students are introduced to ways to find accredited journals in accordance with the field of study. This is done to avoid student publications in predatory journals. As a result of this activity, students understand the business processes of journals for the scientific publication of their work. They also learned that each journal had its own unique style of environment so they had to pay close attention to the author’s guidelines.
Augmented example-based synthesis using relational perturbation properties

Example-based specifications for program synthesis are inherently ambiguous and may cause synthesizers to generate programs that do not exhibit intended behavior on unseen inputs. Existing synthesis techniques attempt to address this problem by either placing a domain-specific syntactic bias on the hypothesis space or heavily relying on user feedback to help resolve ambiguity. We present a new framework to address the ambiguity/generalizability problem in example-based synthesis. The key feature of our framework is that it places a semantic bias on the hypothesis space using "relational perturbation properties" that relate the perturbation/change in a program output to the perturbation/change in a program input. An example of such a property is permutation invariance: the program output does not change when the elements of the program input (array) are permuted. The framework is portable across multiple domains and synthesizers and is based on two core steps: (1) automatically augment the set of user-provided examples by "applying" relational perturbation properties and (2) use a generic example-based synthesizer to generate a program consistent with the augmented set of examples. Our framework can be instantiated with three different user interfaces, with varying degrees of user engagement to help infer relevant relational perturbation properties. This includes an interface in which the user only provides examples and our framework automatically infers relevant properties. We implement our framework in a tool SKETCHAX specialized to the SKETCH synthesizer and demonstrate that SKETCHAX is effective in significantly boosting the performance of SKETCH for all three user interfaces.
Research on the Crowd Abnormal Behavior Recognition in Surveillance Video Based on Modified Social Force Model

A surveillance monitoring public places is an effective means to prevent security problems, but traditional surveillance can't make rapid respond to existing security problems and cannot satisfy social requirements. For that matter, this paper proposes a crowd abnormal detection algorithm based on modified social force model (MSF)to recognize the abnormal behavior of panics and flees caused by emergencies in public. Based on the conventional social force model (CSF), this paper combines the research results of predecessors to improve the expected speed, in the consideration of the influence of relative speed between pedestrians at different locations in the social force calculation, it adopts the positional state factor and the relative speed influence coefficient to improve the social force model. This paper uses Lucas-Kanande algorithm to extract image optical flow, clusters optical flow information, and then uses modified social force model to extract the crowd motion features, and finally applies support vector machine (SVM) to recognize abnormities. In the UMN dataset test, the area under ROC curve is 0.9963.
Effective and Generalizable Graph-Based Clustering for Faces in the Wild

Face clustering is the task of grouping unlabeled face images according to individual identities. Several applications require this type of clustering, for instance, social media, law enforcement, and surveillance applications. In this paper, we propose an effective graph-based method for clustering faces in the wild. The proposed algorithm does not require prior knowledge of the data. This fact increases the pertinence of the proposed method near to market solutions. The experiments conducted on four well-known datasets showed that our proposal achieves state-of-the-art results, regarding the clustering performance, also showing stability for different values of the input parameter. Moreover, in these experiments, it is shown that our proposal discovers a number of identities closer to the real number existing in the data.
Some optimal entanglement-assisted quantum codes constructed from quaternary Hermitian linear complementary dual codes

We establish the existence of optimal entanglement-assisted quantum [Formula: see text] codes for [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text]. These codes are obtained from quaternary Hermitian linear complementary dual codes. We also give some observations on the largest minimum weights.
Full-field analysis of wavefront errors in point diffraction interferometer with misaligned Gaussian incidence

The precision and accuracy of profile measurement achieved by a point diffraction interferometer (PDI) is determined by a spherical diffraction reference wavefront whose quality is mainly controlled by the pinhole’s alignment. In consideration of a Gaussian beam incidence, different diffraction wavefront errors stemming from misalignment of pinhole including lateral shift, defocus, and tilt are analyzed with the help of a proposed systematic model and a new evaluation criterion established under spherical coordinates. The full-field distributions of various diffraction wavefront errors are obtained through simulation. The predicted accuracy of an actual PDI makes a good agreement with the experiment results. The achieved results will be beneficial to the accuracy evaluation of a PDI before and after its design.
Robot Path Planning in Dynamic Environments Based on Deep Reinforcement Learning

Path planning in dynamic environment has been the hot research direction. This paper considers a new dynamic environment—the obstacles are randomly distributed in the environment, and all of the obstacles will be distributed randomly again after robot's movements. In the new dynamic environment, traditional path planning methods have some shortcomings when facing the dynamic environments. The traditional path planning algorithms need to re-calculate the path once the environments change, which is a very time consuming process. The deep reinforcement learning (DRL) model is a single-step algorithm, so the dynamic environments will not affect its running time consumption, which is superior to the traditional path planning algorithms in terms of running time consumption. However, the DRL model will face the problem of sparse rewards in the path planning problem due to the large state space of the environments. This paper uses DRL to solve the shortcomings of traditional path planning algorithms in dynamic environments and we propose a new framework to solve the problem of sparse reward in robot path planning. The framework uses a new strategy searching algorithm and a new shaped reward function. The improved framework can effectively solve the convergence problem in path planning. According to the simulation results, in the stochastic dynamic environments, the running time consumption of the new framework is less than the traditional path planning algorithm, and the new framework is better the classic DRL model in training results and planning results.