Reviewerly/exHarmony huggingface dataset:
Parallel Matrix Sort Using MPI and CUDA

Sorted data is essential. Apart from information presentation or manual retrieval of information, sorted data is beneficial even when using machines’ computational power. In many science and engineering fields, the sorting of extensive dataset is essential in matrix form. Matrix sort is an algorithm, which can sort a large amount of data in matrix form efficiently. In this paper, parallel algorithms are developed for the Matrix Sort algorithm (designed by S. Kavitha et al. Int J Comput Appl 143(9):1–6, 2016) [1]. This algorithm sorts the matrix rows and columns in parallel, subsequently applying the further procedure on resultant data. The implementations of parallel algorithms have been discussed by comparing the execution time results obtained in sequential and parallel form.
RSA Coprocessor Against Power Attack

As an asymmetric encryption algorithm, RSA is currently known as the ripest and most widely used one. However, it is showed in many documents in recent years that RSA algorithm is lack of security, due to its weakness in defending side channel attacks, especially power analysis attacks. This paper proposes an RSA coprocessor resistant to power analysis attack, which adds pseudo operation and exponential randomization masking to defend SPA and DPA. Then, this paper improves the speed of RSA by using 256-base subtracted-free Montgomery multiplier combining with two-layer Karatsuba multiplier and CSA adder. This design is able to be implemented on both FPGA and ASIC. With a technology of 100 MHz clock frequency and SMIC 130 nm process, DC synthesis is implemented as well. The experiment result shows that our 1024-bit RSA design costs an area of 310 K gates and has a throughput of 110 Kbps.
Subtractive Clustering for Categorical Data with a Novel Separation Difference Validity Index

The subtractive clustering method (SCM), proposed by Chiu in 1994, is an effective algorithm that can be used as an approximate clustering for numerical data. Since SCM heavily depends on parameter selections of the density function, Yang and Wu in 2005 proposed a modified SCM with a modified revised density function. However, those subtractive clustering methods are used only for numerical data, not for categorical data. In this paper, we re-construct the subtractive clustering so that it can be used for categorical data. Furthermore, we propose a novel separation difference validity index for the modified SCM to find an optimal number of clusters. Thus, we propose a modified categorical SCM (MC-SCM) for grouping categorical data sets. The proposed MC-SCM algorithm is able to get good performance for clustering categorical data without any initial value that can also find an optimal number of clusters. Some examples are presented to demonstrate the superiority and effectiveness of the proposed MC-SCM clustering algorithm.
Electro-education and distance education in the implementation of higher legal education programs: current definitions and issues of legal regulation

The article is devoted to the study of topical issues of the use of e-learning and remote educational technology in the implementation of higher education programs and, in particular, in the training of specialists with higher legal education. During the study, the author identified and refined the prospects of e-education, presented noteworthy arguments justifying the benefits of e-education in comparison with traditional full-time students. The main sources of legal regulation of e-learning of students in higher law schools have been analyzed.
Efficient Domain-Specific News Push Service Using Deep Learning Based Text Regression without Users’ Information

While there is an ever increasing collection of domain-specific news, it is difficult for people to identify the important ones from massive information. The challenge lies in that users’ information, e.g., comments and CTR (Click-Through-Rate), may not be available in those professional news articles. In this paper, we develop a deep learning model for important news push service, referred to as HMA, which consists of a Hierarchical attention network and a Multi-head Attention mechanism for non-linear text regression. We conduct the experiments with a dataset of marine industry news to evaluate the performance of the proposed deep learning model. The experiment results show that HMA outperforms other alternative deep learning models due to the hierarchical structure and multi-head attention mechanism. Moreover, the execution time of inference by HMA is less than the computation of TF-IDF when adding the news articles into the news repository. Therefore, the proposed method has the potential for efficiently pushing the important domain-specific news articles without users’ information.
Robustness Guarantees for Mode Estimation with an Application to Bandits

Mode estimation is a classical problem in statistics with a wide range of applications in machine learning. Despite this, there is little understanding in its robustness properties under possibly adversarial data contamination. In this paper, we give precise robustness guarantees as well as privacy guarantees under simple randomization. We then introduce a theory for multi-armed bandits where the values are the modes of the reward distributions instead of the mean. We prove regret guarantees for the problems of top arm identification, top m-arms identification, contextual modal bandits, and infinite continuous arms top arm recovery. We show in simulations that our algorithms are robust to perturbation of the arms by adversarial noise sequences, thus rendering modal bandits an attractive choice in situations where the rewards may have outliers or adversarial corruptions.
Marginal posterior distributions for regression parameters in the Cox model using Dirichlet and gamma process priors

A Bayesian treatment of the proportional hazard (PH) model is revisited using Dirichlet process and gamma process priors for the baseline survival and cumulative hazard functions respectively. Such priors, due to their discrete support, conflict with the absolutely continuous nature of survival responses expressed through the hazard function structure that defines the PH model. We resolve this conflict through the use of a proposed ε-grid likelihood approach which we apply to the PH model to consider marginal inference for the regression parameter β using expansions as ε↓0. Using Dirichlet process priors, the ε-grid likelihood approach leads to a new explicit marginal posterior density expansion for β which accommodates the most general setting with arbitrary ties and right censoring. In the previously considered case of gamma process priors, the approach extends the results of Kalbfleisch (1978) to deal with arbitrary arrangements of ties and also provides a rigorous justification for his posterior expressions. As in Kalbfleisch (1978), we show that the leading terms of both expansions approximate Cox’s partial likelihood when there are no ties and the process priors are diffuse. The ε-grid likelihood approach is similar in concept to the grouped data likelihood approach of Sinha et al. (2003) but differs in the likelihood approximation. Whereas our expansions are Poincaré expansions (with relative error O(ε) as ε↓0), those of Sinha et al. (2003) are stochastic expansions and not proper Poincaré expansion (as we show), so that they lack relative error O(ε). Using a flat improper prior for β, the two marginal posterior expressions for β are shown to be integrable for general arrangements of ties and censoring under weak conditions on the design and failures.
UndarkGAN: Low-light Image Enhancement with Cycle-consistent Adversarial Networks

The paper presents a novel method for enhancing low-light images with a cycle-consistent adversarial network. Images are an essential medium for expressing information and capturing memories. However, the environment always influences the quality of images and the results may be less than ideal, with poorly lit images a common occurrence. The network we propose here processes these low-light images and enhances them while keeping true to their structure and preserving their original details. Unlike several other methodologies, our technique does not introduce any odd contrasts, visual artifacts, or noise in the enhanced images. Our proposed network enhances the dark regions without needlessly overexposing or oversaturating the well-lit portions of the input image. While the predominant share of low-light enhancement techniques requires a paired set of well-lit and poorly-lit images to learn from, the technique we present here does not. We perform experiments and compare our proposed methodology against several recent techniques to demonstrate how the network outpaces them, both perceptually and quantitatively.
Difficult concern of consciousness and artificial intelligence: The problem of unconscious

The purpose of this article is to study the difficult problem of consciousness in the context of its relevance and practical significance for the development of artificial intelligence systems; theoretical and bibliographic analysis of various concepts and approaches to solving this problem; as well as the development of an alternative approach to explaining the phenomenon of consciousness and the philosophical and methodological justification for using this approach in the development of systems of strong artificial intelligence.
Perancangan dan Pembuatan Aplikasi Retensi dan Pemusnahan Berkas Rekam Medis di RSUD Dr. H. Moch. Ansari Saleh Banjarmasin

The archives that are owned by the hospital must be managed so that they can serve the community properly, including medical record archives. Data for patients who do not have a visit for 5 years, the medical record documents may be destroyed, except for the summary of discharge and approval of medical treatment which is stored for 10 years. Before being destroyed, it must go through a retention process. One of the hospitals that have carried out retention and destruction activities is Dr. H. Moch. Ansari Saleh Banjarmasin. The retention and destruction of medical records are still done manually. This research’s purpose by design and create a retention and extermination application of medical records file. This type of research uses qualitative research with the development of the waterfall model system. Data collection includes interviews, observation, FGD, and documentation. The results of the study are functional and non-functional system requirements for making retention and destruction applications, based on the results of the FGD to the medical record filling staff and the head of the medical record unit. The results of the requirements that have been obtained are interpreted in the design of systems and software made through flowchart systems, context diagrams, data flow diagrams (DFD), and entity-relationship diagrams (ERD). The implementation process and unit test use the MySQL database and the PHP programming language and the interface design uses the Codeigniter Framework. The application system integration test uses the functional test of the Black Box Test method. The test results show that the retention and destruction applications can be integrated and function properly with innovations in the form of an annual import database feature, a feature for retention and destruction reports, and storage of scanned files in pdf format. The existence of this application can help the implementation of retention and destruction of medical record files at Dr. H. Moch. Ansari Saleh Banjarmasin has become more effective and efficient in terms of time, use value, and data security.
Efficient Forward and Backward Private Searchable Symmetric Encryption for Multiple Data Sources

Searchable symmetric encryption (SSE) has been widely applied in the encrypted database for keyword queries. Although SSE is powerful and feature-rich, it is always plagued by information leaks. Some recent researches have pointed out that forward and backward privacy which disallows leakage from update operations should be the basic requirement for a secure SSE scheme. However, most existing forward and backward private SSE schemes only consider the single data source model which is not practical in the IoT scenario (e.g., data are often separately distributed over multiple devices). Considering the above issues, this paper proposes an efficient forward and backward private SSE scheme for multiple data sources (FBSSE-MDS). As far as we know, FBSSE-MDS is the first efficient SSE scheme which supports both forward privacy and backward privacy BP-II (the second level of backward privacy) in the scene of multiple data sources. Finally, we implement our scheme and compare its performance with two other related schemes. The experiment results show that our scheme is highly efficient.
Multi-Scale Recurrent Network for Single Image Deraining

Rain streaks noise is one of the greatest influence of outdoor scene tasks. In order to improve the effect of the outdoor scene tasks, we need to reduce the impact of the rain streaks noise while ensuring that other important details are preserved. To handle this issue, we proposed a multi-scale recurrent network (MSReNet) for single image rain removal. By divided MSReNet into two stages: (i) removal rain streaks in the first stage and (ii) restoring background-truth details in the second stage, our network can availably remove huge rain streaks and restore some important background details. Extensive experiments on both synthetic and real images demonstrated that the proposed MSReNet prominently exceeds many recent state-of-the-art methods. Taking the proposed method effectiveness, it is also attractive as a preprocessing process for some visual tasks.
Decision Support System for Breast Cancer Risk Scoring

Assessing women's risk for developing breast cancer is significant for recommending additional screening suggestions and formulating constructive preventive strategies. Recent studies also suggest that parenchymal texture indicates the diversity of breast parenchymal pattern, playing an important role in detecting information that may not be reflected by other established risk features. To better characterize patterns of the parenchymal tissues, a novel method that uses a combination of Scale-Invariant Feature Transform (SIFT) and Local Binary Patterns (LBP) is presented in this work to detect cancerous images and classifying them according to the Breast imaging reporting and data system (BI-RADS), a widely used scheme for reporting findings on mammography. A score based on the combined effect of density and textural patterns is calculated to assess the risk of global breast cancer.
Face Feature Extraction and Recognition Based on Lighted Deep Convolutional Neural Network

Face recognition is one of the most challenging research fields in image processing, pattern recognition and artificial intelligence, and its practical application has a bright future. Face is vulnerable to illumination and expression factors in the process of image acquisition. The traditional machine learning method for extracting face features usually requires high clarity of face images to achieve ideal results. Face feature extraction and recognition based on deep learning method can effectively solve this kind of problem, but the recognition rate is greatly improved, at the same time, the size of the network and the difficulty of training are also sharply increased. It does not meet the increasingly lightweight requirements of the network. This paper studies the current mainstream DCNN architecture, especially MobileNet and ShuffleNet at light-weight mobile level. Some high-efficiency and practical structural features of convolution are summarized; A Nonlinear separable deep convolutional neural network X-Net is designed; Focusing on the loss of the sample in the real face dataset, focus loss is introduced to reduce the effect of sample deviation on network fitting. The experimental results show that the feature extraction based on deep convolutional neural network in this paper can use dynamic library DLL to play an effective role in practical commercial projects.
3D-Texture-Segmentation of Prostate Cancer from Multimodal MRI Data

Prostate cancer is the fourth most commonly diagnosed cancer worldwide. The TRUS-guided prostate biopsy e.g. is painful and lack accuracy. A noninvasive, reproducible and precise localization of prostate cancer without causing pain is still required. Multispectral MRI data of various modalities are currently promising technique for prostate cancer localization. However, it is obvious that these 2D images are difficult to read. With the help of a computerized segmentation the time required to read the 3D images could be reduced and the accuracy of eventual biopsies could be increased by focusing on a specific region. For this purpose, three major 3D textural features are exploited, i.e. GLCM, LBP, and three dimensional wavelet transform, together with T1-weighted, T2-weighted and contrast enhanced data will be used to generate a multispectral basis for the features. In this paper, all mentioned enhanced MRI features are investigated, at the end the best promising 3D features are tested to classify the prostate cancer area.
Advancing the Use of Games as a Model for Scientific Practice

Abstract Science is similar to a game, as both involve rules-based participation in search of optimal outcomes. Supported by key texts in history and philosophy of science, the authors propose a game-based model for understanding scientific inquiry and practice, particularly through computational resources. They conclude that this model creates space for more speculative and reflective approaches to scientific practice and can contribute to the design and development of better scientific software, simulation and visualization.
Photon-efficient quantum key distribution using multiqubit time-bin encoding

A method for photon-efficient quantum key distribution (QKD) is proposed and analyzed theoretically. The technique is based on nested encoding of multiple logical qubits into the discretized temporal degree of freedom of a single photon. The states of individual logical qubits are measured using a cascade of interferometric stages followed by time-resolved photon counting. The method may be useful in implementations of entanglement-based QKD protocols whose performance is limited by the brightness of onboard sources of nonclassical light, based e.g. on spontaneous parametric down-conversion. Numerical optimization taking into account the presence of background noise indicates the potential of multiqubit encoding for a nearly tenfold increase of the attainable key rate for entanglement-based LEO satellite QKD systems.
Use of Industrial System Solutions for Image Processing

This work addresses the use and compare of machine vision software for image recognition compared to commercially available systems in automation industry. The aim is to compare computational stability and time-cost efficiency of integration of available algorithms against commercially sold systems. Three types of environments are selected for comparison - code development environment, graphical development environment and industrial machine vision system solution. The evaluation is based on the use of all three systems for the same types of applications and a comparison of their results - processing time, stability, development time and total development cost. Comparison applications are presence/absence, counting, dimensional measurement, search and character recognition.
Mixture Variational Autoencoder of Boltzmann Machines for Text Processing

Variational autoencoders (VAEs) have been successfully used to learn good representations in unsupervised settings, especially for image data. More recently, mixture variational autoencoders (MVAEs) have been proposed to enhance the representation capabilities of VAEs by assuming that data can come from a mixture distribution. In this work, we adapt MVAEs for text processing by modeling each component’s joint distribution of latent variables and document’s bag-of-words as a graphical model known as the Boltzmann Machine, popular in natural language processing for performing well in a number of tasks. The proposed model, MVAE-BM, can learn text representations from unlabeled data without requiring pre-trained word embeddings. We evaluate the representations obtained by MVAE-BM on six corpora w.r.t. the perplexity metric and accuracy on binary and multi-class text classification. Despite its simplicity, our results show that MVAE-BM’s performance is on par with or superior to that of modern deep learning techniques such as BERT and RoBERTa. Last, we show that the mapping to mixture components learned by the model lends itself naturally to document clustering.
Merging Vernam Cipher stream and Rail ?Fence Algorithms and How Effective They are on Internet of Things Devices

Encryption is the effective way to provide the needs of security and privacy. The performance of any encryption algorithm is one of the most important parameters if not the most one in encrypting data. Also, random access memory utilization plays an excellent role in ciphering plain texts. Speaking of standard simple encryption algorithms that most of them are weak and may not be safe to use them to cipher our data communication from any threat such as Caesar algorithm [2].However, they have good results in the parameters. Nevertheless, merging these simple algorithms could generate a more powerful algorithm that takes a very long time and excessive efforts to break it. Both standard algorithms “Vernam Cipher” and “Rail-Fence stream” merged to produce the “Railve” algorithm, which has great performance and consumes a small amount of RAM. The three algorithms are applied on two devices “a mobile phone and a laptop”
Classification Model Simulator: A simulator for different Machine Learning Classification Algorithms

The explosion in the use of Machine Learning increases the need for a more automated approach in order to save time, human resource and tackle the lack of skills and knowledge required in implementing the same. Amongst the various techniques, the widespread use of Machine Learning Classification in today's world has made it an important skill, but building and training models from scratch is a tedious process which requires programming skills along with in-depth knowledge of the domain. The project presents a methodology and design of an open source software to simulate four Machine Learning Classification Models for any given numeric or string based data set along with the option of pre processing the input data set and then observe the final results.
Analysis of Data-Driven Computer Course System

The development of computer technology has brought great convenience to the production and life of the society, and has also led to the rapid growth of the demand for computer talents. Whether we can train more and better computer talents has become an inevitable development of society. In the process of training computer talents, the cultivation of undergraduates is very important. In order to analyze the cultivation process of undergraduates systematically, this paper selects some undergraduate teaching outline documents of computer science and technology major in some universities for analysis. After using TF-IDF to quantify the curriculum information in the syllabus, this paper proposes to use multiple K-means clustering algorithm to calculate the similarity between courses; the similarity matrix between schools is calculated by using the method of sum and average of the maximum similarity without repetition, and the similarity between schools is analyzed by using the calculation results. The work of this paper will provide data reference for computer students, teachers and curriculum.