Reviewerly/exHarmony huggingface dataset:
Structure-based low-rank Retinex model for low-light image enhancement

In this paper, we propose a structure-based low-rank Retinex model for simultaneous low-light image enhancement and noise removal. Based on the traditional variational-based Retinex framework, in the proposed model, a smooth prior is forced on the illumination, and a gradient fidelity term and the weighted nuclear norm are used to suppress noise and enhance structural details in the reflectance. By considering that the manifold structure similarity is more effective than intensity similarity in describing the structural features of image patches, we further propose to use the manifold structure similarity in image patch grouping. Then, an alternating direction minimization algorithm is used to solve the reflectance estimating model. The entire process for solving the proposed model uses a sequential optimization. The final enhancement results is obtained by combining the reflectance and the Gamma corrected illumination. Experiment show that, the proposed method can simultaneously enhance and denoise the low-light image, and produce better or comparable results compared with the state-of-the-art methods
End-to-end single-shot composite fringe projection profilometry based on deep learning

Using a single fringe image to complete the dynamic absolute 3D reconstruction has become a tremendous challenge and an eternal pursuit for researchers. In fringe projection profilometry (FPP), although many methods can achieve high-precision 3D reconstruction from simple system architecture via appropriate encoding ways, they usually cannot retrieve the absolute 3D information of objects with complex surfaces through only a single fringe pattern. In this work, we develop a single-frame composite fringe encoding approach and use a deep convolutional neural network to retrieve the absolute phase of the object from this composite pattern end to- end. The proposed method can directly obtain spectrum-aliasing-free phase information and robust phase unwrapping from single-frame compound input through extensive data learning. Experiments have demonstrated that the proposed deep-learning-based approach can achieve absolute phase retrieval using a single image.
Modifying NOVA-annotated Myanmar Data to Universal Part-of-Speech Tagset

We offer grateful thanks to the anonymous reviewersfor their valuable comments and writers who publishedthe “Myanmar Grammar” and “Myanmar-EnglishDictionary.
Clustering analysis of senior high school in West Java based on educational facilities

Abstract The problem of education in senior high school remains. One of the roots of the problem is the lack of educational facilities. The government has responded to this case by giving the state budget (APBN) for education. Nevertheless, the large number of senior high schools in Indonesia is one of the barriers to efficient distribution of APBN funds. Thus, clustering analysis of senior high school facilities in Indonesia especially in West Java is expected to be a solution. The number of observations is 13,486 with nine categorical variables recorded on a website of the Ministry of Education and Culture in August 2019. The method used is Robust Clustering Using Link (ROCK). To get a clearer profile of the clusters, we used ROCK method modified with Nested Clustering. The results of this study indicate that 14 clusters were formed and have their profiles. Cluster 3 is a relatively good cluster while cluster 1a is a relatively poor cluster. Clusters representing poor facilities were found to be dominant in West Java Province.
Facial Age Estimation using Transfer Learning and Bayesian Optimization based on Gender Information

Age estimation of unrestricted imaging circumstances has attracted an augmented recognition as it is appropriate in several real-world applications such as surveillance, face recognition, age synthesis, access control, and electronic customer relationship management. Current deep learning-based methods have displayed encouraging performance in age estimation field. Males and Females have a variable type of appearance aging pattern; this results in age differently. This fact leads to assuming that using gender information may improve the age estimator performance. We have proposed a novel model based on Gender Classification. A Convolutional Neural Network (CNN) is used to get Gender Information, then Bayesian Optimization is applied to this pre-trained CNN when fine-tuned for age estimation task. Bayesian Optimization reduces the classification error on the validation set for the pre-trained model. Extensive experiments are done to assess our proposed model on two data sets: FERET and FG-NET. The experiments’ result indicates that using a pre-trained CNN containing Gender Information with Bayesian Optimization outperforms the state of the arts on FERET and FG-NET data sets with a Mean Absolute Error (MAE) of 1.2 and 2.67 respectively.
Sistem Pakar Diagnosa Kifosis Menerapkan Metode Fuzzy Mamdani

Kyphosis (kyphosis) is a disorder in the curvature of the spine that makes the upper back appear abnormally rounded or bent. Everyone has a curved spine, ranging from 25 to 45 degrees. However, in people with kyphosis, the curvature of the spine can reach 50 degrees or more. These conditions make people hunchbacked. Kyphosis is still difficult or rare to find so it is difficult for sufferers to consult their experts (Kofosis diagnosis specialists) to obtain information related to the diagnosis of the disease they are experiencing. The expert system built in this study to diagnose Kofosis was designed and built using the Visual Basic Net 2008 programming language, as with the development of this expert system, it is expected to have a positive impact as an alternative tool for diagnosing Kofosis in sufferers of this disease. In the expert system for diagnosing Kofosis in this study, the mamdani fuzzy logic method is applied with the aim of increasing the progress of the system's work in producing accurate diagnostic output and in accordance with the expert knowledge obtained by the author when conducting research on this Kofosis disease. Keywords : Expert System, Diagnosis, Cofosis, Fuzzy Mamdani
Multi-Directional Total Variation and Wavelet Transform Based Methods: Application for Correlation Fringe Patterns Denoising and Demodulation

In this work, we present a multi-directional total variation method to reduce the high frequency speckle noise in order to prepare the digital speckle pattern interferometry fringes for demodulation. In this method, the local fringe directional information is involved to increase the sensitivity of the variational regularization to the spatially varying directional intensity changes. Once the speckle noise is removed from the fringe pattern, the fringes are demodulated using directional wavelet-based phase retrieval algorithm. The extracted phase can be used for the three-dimensional defect inspection. Through simulation works, we demonstrate the ability of the denoising method to reduce the speckle noise, in terms of accuracy and computational efficiency. A comparison with the Stationary Wavelet Transform based denoising results is made.
A Matrix-based Mathematical Model for Redundant Data Reduction from DICOM Images

In the era of the 21st century, there are many Nations who are not able to provide proper medical facilities for their citizens, as proper medical facilities require a large amount of investment and support. Here we have proposed a hybrid model of the digital image in communication and medicine (DICOM) image compression for the hospitals of rural India in which important information from a diagnostic point of view and non-important information can be kept together in the same image. As we are aware of the fact that DICOM images require large storage space so it is important to reduce the property of the DICOM images so that they can be easily stored, transmitted and operated wherever it is required. In this research work, we have obtained 60.83 % compression by using the proposed technique.
Sentiment analysis of movie reviews in Chinese

Sentiment analysis aims at figuring out the opinions of the users towards a certain service or product. In this research, the aim is at classifying the sentiments of users based on the comments the ...
Online Dispute Resolution and Computational Law a Study on Digital Justice

Access to justice in can be improved significantly through adoption of disruptive legal technologies within a broader online dispute resolution (ODR) framework. This article explores the potential for technology to increase access to justice. In the future, much legal work will be discharged by systems; the first part of this study reflects on disruptive legal technologies and development of online dispute resolution systems and the second part explores the evolution of computational law and deploys the evolutionary computational model as a tool for decomposing legal service which would steadily increase online dispute resolution. Third part assesses the implications of such mechanisms for the future of dispute resolution and technology. The developing legal technology has the potential to bring legal understanding and legal tools to everyone in society, not just legal professionals. Thus enhancing access to justice and improving the legal system as a whole.
Media Literacy Levels of Archivists in Institut Teknologi Sepuluh Nopember (ITS)

Media literacy is a concept that describes a person's ability to utilize media, analyze information, and communicate it. This concept is used by researchers to see a person's ability to relate to information media. This study discusses the level of media literacy of archivists in the Institut Teknologi Sepuluh Nopember (ITS). The archivist has a strategic position in information management of the university. In this respect, it needs to be reviewed related to the competence of the media literacy of archivists. In this era of information, archivists are faced with various job challenges due to the development of information technology, including the fast in disseminating information and the emergence of false or misleading information (hoaxes). This condition causes archivists not only to be required to have competence in utilizing information technology but also to be able to analyze and communicate information correctly to provide a fast, accurate, and reliable service. This competency will be reflected in the level of media literacy of archivists. This research uses the descriptive quantitative method by focusing on the aspects of a phenomenon and the reality of media literacy by archivists in ITS. The level of media literacy can be seen from the Individual competence framework presented by the European Commission (2009). The result of the study showed that the media literacy of archivists in ITS is at a medium level, which indicated the competence is more focused on the ability of technical skill and critical understanding. While in the social competence aspect are classified as passive users.
Input Excitation Analysis for Black-Box Quadrotor Model System Identification

Abstract System identification provides a process to develop different dynamic models of varying structures based on user-defined requirements. For a quadrotor, system identification has been primarily in the field of off-white and grey-box models, but black-box models have the advantage of incorporating nonlinear aero-dynamic effects while also maintaining performance. For the identification, both a chirp and Hebert-Mackin parameter identification method waveform are used as inputs to maximize excitation while minimizing nonlinear responses. The quadrotor structure is defined by the an autoregressive with exogenous input (ARX) model, an autoregressive-moving-average (ARMAX) model, and a Box-Jenkins (BJ) models and then identified with the prediction error method. The black-box method shows that it maintains identification performance while improving upon the flexibility of different cases and ease of implementation.
A Socio Responding Implementation Using Big Data Analytics

In recent days, social network plays a major task in the student community, most probably children under 18 sharing their personal emotions and information through chat options in social media like WhatsApp, etc. monitoring the children is a big challenge for parents, nowadays the interaction with children is one of a fantasy for parents at this modern era and this leads the children to try unwanted things that spoil their valuable lives sometimes (e.g. Blue whale). This research may give the solution to solve the trending problem and parents can easily monitor their children’s chat history without reading the messages one by one, this data analytics technique can result in capturing the emotions of the children or anyone in a single word by analyzing the words mostly used by the victim in chats and also produce the word count in the data which feed to this tool and the same technique may be used in feedback analysis by the customer for a particular product. Now the question is what if the data, i.e. chat message be deleted and how can we analyze such deleted information, more retrieving techniques and tools will be used to read the deleted messages and chat history in WhatsApp one of such tool is to be proposed to retrieve the data. Feedback analysis with a merge of emotions and sentiments should be evaluated using the positive, negative approach by applying the NLP (Natural Language Processing) technique. If the evaluation is based on a person’s sentiments then the procedure of dependency processing should be performed where the ‘amod’ (adjective modifier) should be found to achieve the task. This Research paper is a novice idea to startup this concept with NLP based applications, to help the society mainly parents to save their children from viewing and using unwanted websites.
Application of Shallow Geophysical Methods for Imaging of the Active Fault

Summary As framework of 60 year of development of Astronomical and Geophysical science in modern Mongolia, various geophysical methods (electrical tomography, ground penetrating radar and high-resolution reflection seismic profiles) were used to image an active fault in depth range between few decimeters to few tens meters. An active fault was fractured by an earthquake magnitude 7.6 during 1967. After geophysical investigations, trench excavations were done at the sites to expose the fault surfaces. The complex geophysical survey in the Mogod fault, Bulgan region of central Mongolia shows interpretable reflection arrivals range of &lt; 5 m to 60 m with the potential for increased resolution. Reflection profiles were used to help interpret the significance of neotectonic surface deformation at earthquake active fault. The interpreted profiles show a range of shallow fault structures and provide subsurface evidence with support of paleoseismologic trenching photos, electrical survey.
A Novel Intelligent Learning Control Scheme for Discrete-Time Nonlinear Uncertain Systems in Multiple Environments

Abstract In this paper, we propose a novel intelligent control scheme for a class of discrete-time nonlinear uncertain systems operating under multiple environments/control situations. First, based on the deterministic learning theory, artificial neural networks (NNs) are employed to accurately learn/identify the uncertain system dynamics under each individual environment. The learned knowledge is then utilized to: (i) achieve improved control performance by developing a family of experience-based controllers (EBCs), each of which is tailored to an individual environment; and (ii) determine real-time activation of the EBCs by developing a pattern recognition mechanism for online identifying the active control situation. In addition, a robust quasi-sliding mode controller is further designed and embedded in the overall control scheme to guarantee system stability during the transition process among multiple environments. The novelty of the proposed control scheme lies in its intelligent capabilities of knowledge acquisition and re-utilization in real-time control, enabling self-adaption to uncertain changing control environments. A simulation example is included to verify the effectiveness of the proposed results.
Certification of Radome Lightning Direct Effects Protection

Abstract Composite material is used more and more in airplanes year after year. The electrical conductivity of composite material structure is poorer than metal structure, so appropriate lightning protection measures should be adopted to shunt lightning current when composite structure suffering lightning strike, and the harmful consequence will be minimized. The paper takes radome as an example, which is located at nose area liable to suffer lightning strike, introduces radome lightning protection measures, and according to lightning direct effect verification process, discusses on means of compliance, plans high voltage strike attachment test and high current physical damage test of radome. It provides reference for airworthiness verification for radome.
Symbolic Rule Extraction From Neural Networks: An Application to Identifying Organizations Adopting IT

Interest in the application of neural networks as tools for decision support has been growing in recent years. A major drawback often associated with neural networks is the difficulty in understanding the knowledge represented by a trained network. This paper describes an approach that can extract symbolic rules from neural networks. We illustrate how the approach successfully extracted rules from a data set collected from a survey of the service sectors in the United Kingdom. The extracted rules were then used to distinguish between organizations using computers from those that do not. The classification scheme based on these rules was used to identify specific segments of a market for promoting adoption of information technology. The extracted rules are not only concise but also outperform discriminant analysis in terms of predictive accuracy.
Generalizing Graph Convolutional Networks via Heat Kernel

Graph convolutional networks (GCNs) have emerged as a powerful framework for mining and learning with graphs. A recent study shows that GCNs can be simplified as a linear model by removing nonlinearities and weight matrices across all consecutive layers, resulting the simple graph convolution (SGC) model. In this paper, we aim to understand GCNs and generalize SGC as a linear model via heat kernel (HKGCN), which acts as a low-pass filter on graphs and enables the aggregation of information from extremely large receptive fields. We theoretically show that HKGCN is in nature a continuous propagation model and GCNs without nonlinearities (i.e., SGC) are the discrete versions of it. Its low-pass filter and continuity properties facilitate the fast and smooth convergence of feature propagation. Experiments on million-scale networks show that the linear HKGCN model not only achieves consistently better results than SGC but also can match or even beat advanced GCN models, while maintaining SGC’s superiority in efficiency.
Comparative Study of Access Control Methods in Enterprise Information Systems, Based on RBAC, ABAC, and TBAC policies

Controlling access to a company’s IT systems is a way to ensure that users are the ones who say they are and have proper access to company data and documents. At a high level, controlling access to a company’s data and applications is a selective restriction on access to data. It consists of two main components: authentication and authorization. Authentication is used to confirm that someone is the claimant, and this is not enough for themselves to ensure data protection. Authorization is additional levels which determines which user should be allowed access to data or perform an action (operation / transaction). For their implementation, several authentication and authorization methods have been created, of which, within this study, we approach, Role Based Access Control (RBAC), Attribute-based access control (ABAC) and Trust-based access control (TBAC). This study makes a comparative analysis on the principles underlying RBAC (Role Based Access Control), ABAC (Attribute-based access control) and TBAC (Trust-based access control) and the ways of application and collaboration between them.
Educación superior en México, COVID-19 y la respuesta emergente: el caso de la Universidad Autónoma Metropolitana Xochimilco

Adapting all activities of one of Mexico’s largest universities to fit a virtual format is not an easy task, since it requires deep knowledge of the entire organizational structure, its dynamics and its legislation. This paper analyzes the response of the Universidad Autonoma Metropolitana (UAM) Unidad Xochimilco, where the Emergency Program for Remote Education (PEER) was used (in combination to other measures) to warrantee continuity in the academic tasks during the COVID-19 outbreak. Basing on a graduate student’s life story, we reconstruct the implementation of PEER to analyze it from three perspectives: program implementation, organizational communication, and decision-making associated vulnerability. Our analysis reveals a series of tensions between the contents of the university legislation and the way in which window actors, such as students and workers, experienced the process of bringing the activities into an online and distance format. Our results suggest that certain deficiencies in organizational communication are affecting the capacity of the UAM to respond efficiently to emerging situations while maintaining its structure as a participative and decentered university. Likewise, we point out the relevance of generating protocols that help guarantee the protection of vulnerable groups in the university community as part of the decision-making processes. Finally, we discuss further research lines to deepen this educational-organizational phenomenon.
Phase-sensitive Landau-Zener-Stückelberg interference in superconducting quantum circuit*

Superconducting circuit quantum electrodynamics (QED) architecture composed of superconducting qubit and resonator is a powerful platform for exploring quantum physics and quantum information processing. By employing techniques developed for superconducting quantum computing, we experimentally investigate phase-sensitive Landau-Zener-St\"uckelberg (LZS) interference phenomena in a circuit QED. Our experiments cover a large range of LZS transition parameters, and demonstrate the LZS induced Rabi-like oscillation as well as phase-dependent steady-state population.
Local Clustering Graph Neural Networks

Graph Neural Networks (GNNs), which benefit various real-world problems and applications, have emerged as a powerful technique for learning graph representations. The depth of a GNN model, denoted by K, restricts the receptive field of a node to its K-hop neighbors and plays a subtle role in the performance of GNNs. Recent works demonstrate how different choices of K produce a trade-off between increasing representation capacity and avoiding over-smoothing. We establish a theoretical connection between GNNs and local clustering, showing that short random-walks in GNNs have a high probability to be stuck at a local cluster. Based on the theoretical analysis, we propose Local Clustering Graph Neural Networks (LCGNN), a GNN learning paradigm that utilizes local clustering to efficiently search for small but compact subgraphs for GNN training and inference. Compared to full-batch GNNs, sampling-based GNNs and graph partition-based GNNs, LCGNN performs comparably or even better, achieving state-of-the-art results on four Open Graph Benchmark (OGB) datasets. The locality of LCGNN allows it to scale to graphs with 100M nodes and 1B edges on a single GPU.