Reviewerly/exHarmony huggingface dataset:
Fraud Patterns Classification: A study of Fraud in business Process of Indonesian Online Sales Transaction

Fraud detection has become an important research topic in recent years. In online sales transaction, fraud can occur on a business process. Fraud which occurs on business process is popularly known as process-based fraud (PBF). Previous studies have proposed PBF detection on process business model, however, false decisions are still often made because of new fraud pattern in online sales transactions. False decision mostly occurs since the method cannot identify the attributes of fraud in online sales transaction. This research proposes new fraud attributes and fraud patterns in online transactions. The attributes can be identified by exploring the event logs and Standard Operating Procedure (SOP) of online sales transactions. First, this is conducted by collecting event logs and creating SOP of online sales transaction; then, performing conformance between event logs and SOP; further, discussing with fraud experts about the result of SOP deviations which have been identified; moreover, determining convention value of the SOP deviation to fuzzy value, and classifying the SOP deviation; and at last, establishing fraud attributes and fraud patterns based on classification result. The new fraud attribute and fraud patterns are expected to increase accuracy of fraud detection in online sales transaction. Based on the evaluation, this method resulted a better accuracy 0.03 than the previous one.
Face Semantic Segmentation and Enhanced Attribute Description on Portrait Robot

Traditionally, image drawing robot draws a portrait only by generated image, without concerning the certain order of people's facial features. In this paper, a method of attribute enhanced description based on semantic segmentation of face image is proposed. The semantics of face image is segmented by BiSeNet, and the data sets of different feature organs are constructed. We use the enhanced attribute module to generate the description of the relevant attributes for each feature organ image. Unlike the previous soft biological features of human face, this method can more intelligently realize the description of feature attributes. The experimental results show that our method can obtain better organ localization and enhanced attribute description. Combining our existing drawing robot platform with our drawing strategy based on enhanced attribute description, it has a good promotion effect on humanoid drawing of robots.
Privacy Issues in Big Data from Collection to Use

Big data management and analysis has become an important matter in academic and industry research. In fact, big data can be very powerful and have significant positive impact on most organizations. However, a large portion of big data in service today is personal data. One of the methods currently in use to preserve privacy of personal data that are collected from big data, is to control the process of data collection according to corresponding privacy policies. In this paper we aim to highlight issues need to be resolved in order to achieve a sustainable balance of growth and protection in the use of big data. We have discussed three main issues including: the advanced definition of personal data, the concern of collecting big data: how and why big data collected and which challenges big data faced, the main privacy principles required in preserving privacy of collected big data, and finally we have proposed a case study to analyze the most famous online organization – Google- privacy policy in collecting big data and decide to which extent privacy is preserved.
Monocular adaptive inverse depth filtering algorithm based on Gaussian model

This paper presents an adaptive filtering algorithm for monocular depth estimation. This is a geometric calculation method under the assumption that the inverse depth conforms to the Gaussian distribution. In the inverse depth update, the average value of the historical stable points is introduced to smooth the output, and the depth uncertainty of the unit pixel offset is calculated to eliminate the estimation error. In the similarity search along the epipolar line, the normalized cross-correlation and gradient are used as joint metrics. Finally, the effectiveness of the algorithm is verified through experiments, and it can get a better trade-off between performance and time.
Jointly Learning Multi-view Features for Human Action Recognition

Classical methods of multi-view human action recognition focus on constructing similar feature between each view so that videos under different view can be classified identically. However, the features from a certain action under different views are variable especially when the view changes sharply. Using the similarity from different views solely can't get a desirable result. In this paper we proposed a joint learining model for jointly learning features from different views. Our model explores the shared information of an action under different views and the specific information of an action under a certain view. The combination of the shared and specific information shows a distinguishable feature. The features from different views show are reconstructed by a linear projection matrix so that they can show a same structure. To obtain a optimal solution under a certain convergence, the model consists of a three-step iterative optimization process. The effectiveness of our method has been verified on WVU dataset.
Optimization of Single Image Super-Resolution Reconstruction Algorithm Based on Residual Dense Network

Aiming at the problem of low performance of the Single Image Super-Resolution (SISR) method, a method based on residual dense network is proposed. The shallow convolution features are input to the residual dense block to obtain local and global features, and the reconstruction is performed on the images to obtain a clear high-resolution image. To verify the effectiveness of the method, quantitative experiments were performed on three public datasets including Set5, Set14 and B100. Experimental results show that this method can better recover high-resolution images.
The Path of Higher Education Management in the Era of Big Data

China’s education system is constantly developing and changing, and the education system of colleges and universities (CAU) is also constantly developing and innovating, among which the construction of education management (EM) information has become a hot topic. The arrival of big data (BD) has triggered revolutions in all sectors of society. Information symmetry and shared exchange not only affect the traditional management model, but also stimulate higher education management to release more power. The goals and contents of university education management have undergone tremendous changes. The high efficiency in the era of BD requires the use of data management and more scientific and rational overall planning in university management. The purpose of this article is to analyze in depth the pros and cons of the era of BD and its impact on current university education management, and to study the path of university education management in the era of BD. This article surveyed 40 universities. In the context of the era of BD, in order to realize the construction and development of education management information, we need to combine the current BD technology and apply it to the management of various higher education. It was found that the fragmentation and asymmetry of education management information accounted for 35.4%. Only in this way can we achieve innovation and optimization of the education system.
Optimization and Improvement for the Game 2048 Based on the MCTS Algorithm

The game 2048 is a popular and fashionable mobile telephone game, which is characterized by the strong uncertainty. The Monte Carlo Tree Search is always used to be applied for the game search algorithm in the field of the computer games. The basic algorithm is usually divided into four steps: path selection, node expansion, simulation experiment and node update. In the step of path selection, the paper proposes the optimized and improved strategy for selecting nodes based on the UCT algorithm. In the step of simulation experiment, the two effective and heuristic simulation strategies are designed and applied based on the parallel computer games system coded by own self. In the steps of node expansion and node update, the adaptive search strategy is provided to improve the number of search times according to the complexity of the game situations. Through a plenty of experiments, the experimental results show that the provided optimization and improvement algorithm is able to be applied for the game 2048 effectively and practically to reach the senior level finally.
Deep Receiver Design for Multi-carrier Waveforms Using CNNs

In this paper, a deep learning based receiver is proposed for a collection of multi-carrier wave-forms including both current and next-generation wireless communication systems. In particular, we propose to use a convolutional neural network (CNN) for jointly detection and demodulation of the received signal at the receiver in wireless environments. We compare our proposed architecture to the classical methods and demonstrate that our proposed CNN-based architecture can perform better on different multi-carrier forms including OFDM and GFDM in various simulations. Furthermore, we compare the total number of required parameters for each network for memory requirements.
Classification and Principles of Building Question-Answer Search Systems

The analysis of the ambiguity of the natural language is based on the development of Question Answering Systems that can process a user-entered question in a natural language and give a meaningful answer. Unlike the classical keyword search task, in which the result is a list of documents, in the question-answer search task, the result is a short and concise answer generated by the system as a result of analysis of various data sources. The review article lists and considers the main approaches and principles for constructing question-answer systems: a meta-search system, an annotated text search system, an expert system, a search system in question and answer collections. One of the first approaches to Question Answering Systems can be called the BASEBALL system of the early 60s of the last century, since it became possible to ask questions to the system in a natural language, but the knowledge base was a regular structured database. Thus, it can be considered its natural input system. All early Question Answering Systems were faced with the problem of the lack of BigData — a large amount of digitized facts and rules. Really working expert systems were obtained only in a limited domain of knowledge. Therefore, for a qualitative discussion of Question Answering Systems, it is proposed to classify them in the following dimensions: types of supported questions, types of supported answers, source of information, technique for outputting a question or answer by source of information, limited domain of knowledge, quality assessment methods, direction — who asks the question: user or system. Direction is a dimension proposed for the classification of Question Answering Systems for the first time in this article. It defines the lead question-answer dialogue in a pair — a man-computer. A direct question-answer system implies that a person asks questions and the machine answers. An inverted system assumes that the computer is leading this dialogue. Today, one of the most developed and well-known direct Question Answering Systems is the question and answer system on the IBM Watson supercomputer. In the last decade, there has been an active development of educational technologies on the Internet (EduTech). Using the accumulated amount of data on successful or dead-end paths by the user on digitized course materials, it is possible to form an adaptive training course for each of them, which allows to maximize the student’s readiness coefficient. In practice, training is faced with a high threshold of entry by the author of the course, the teacher. He needs not only to restructure the structure of his classical courses, breaking up into smaller blocks, but also to add original questionnaires and elements of gamification of instruction to the pauses between blocks. Inverted Question Answering Systems with an open dialog domain of knowledge have a great prospect in solving the problem of generating original questionnaires, conducting a simple dialogue on an adaptive question graph and introducing gamification elements to improve the perception and assimilation of lecture material.
First year students’ experience in a Cyber World course – an evaluation

Although cybersecurity is a major present concern, it is not a required subject in University. In response, we developed Cyber World which introduces students to eight highly important cybersecurity topics (primarily taught by none cybersecurity experts). We embedded it into our critical thinking Common Course (core curriculum) which is a team-taught first-year experience required for all students. Cyber World was first taught in Fall 2018 to a cohort of over 150 students from various majors at the University of New Haven. This article presents the evaluation of our Fall taught course. In detail, we compare the performance of Cyber World students to other Common Course sections that ran in parallel and conclude that despite the higher workload students performed equally well. Furthermore, we assess the students' development throughout the course with respect to their cybersecurity knowledge where our results indicate a significant gain of knowledge. Note, this article also presents the idea and topics of Cyber World; however a detailed explanation has been released previously.
A New Approach on Advanced Encryption Standards to Improve the Secrecy and Speed Using Nonlinear Output Feedback Mode

The proposed cipher system is fast and secure stream cipher for software-based applications. The new cipher system is designed using output feedback mode on Advanced Encryption Standard with an extra XOR operation. It contains initial vectors (IV1 and IV2), and these IVs are updated by nonlinear functions. The increased security of the proposed cipher is due to the nonlinear functions. Along with the security, the speed of execution increased by executing Advanced Encryption Standard in only eight rounds. Various security evaluation parameters considered in the security analysis of the proposed cipher system. In addition to it, the speed of execution of the new approach analyzed with other systems with respect to the time complexity.
Factors affecting distribution of precious metals in rocks of the South Sopchinsky massif and the ‘Moroshkovoe Lake’ target (southern framing of the Monchegorsk pluton, Kola Peninsula): experience of applying statistical methods

Abstract Three types of vein bodies are developed within the southern framing of the Monchegorsk pluton, in metapyroxenites of the South Sopchinsky massif: 1) plagioclase-pyroxene; 2) quartz-feldspar, and 3) amphibole-plagioclase composition. PGM mineralization is related to sulfide-oxide dissemination in these vein bodies (they are also found in the neighboring ‘Moroshkovoe Lake’ target) and mainly occurs as tellurides, arsenides, bismuthides, and Pt and Pd sulfides. A correlation analysis of non-ferrous and precious metal contents in the veins and metapyroxenites enclosing them was carried out using the Spearman’s rank correlation nonparametric coefficient. It showed an inheterogenous distribution of Cu, Ni, Pt, and Pd in these bodies. In conjunction with geological observations and petrochemical studies, the implementation of this statistical method allowed drawing a conclusion about a possible mechanism of formation of the low-sulfide PGM mineralization in rocks of the South Sopchinsky massif.
Computations in the Pre-Bloch group

Abstract For compute the five term relations in the pre-Bloch group for specify an infinite-order- element in <?CDATA ${K}_{3}({\mathbb{Q}}(\sqrt{-m}))$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>K</mml:mi> </mml:mrow> <mml:mrow> <mml:mn>3</mml:mn> </mml:mrow> </mml:msub> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>ℚ</mml:mi> <mml:mrow> <mml:mo>(</mml:mo> <mml:msqrt> <mml:mrow> <mml:mo>−</mml:mo> <mml:mi>m</mml:mi> </mml:mrow> </mml:msqrt> <mml:mo>)</mml:mo> </mml:mrow> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> , m ∈ ℕ square- free. For the quadratic imaginary number fields 𝔽 of discriminant (−1,−2,−3,−7,−17,−19). We use the GAP Programming software to implement our method.
Automatic Coil Delivery for Uncoiling Machine Based on Image Processing

In order to automate the coil delivery for the cold rolling process and reduce manual operation error, this paper presents a method to locate and track the steel coil using monocular camera and image processing, and the core circle of coil is used as the tracking target. This proposed method firstly determines the initial target location information by RHT, then configure the qualification conditions of detection for the next time step based on the target parameters from the last time step. Canny edge detection is used to detect initial contour candidates, limited domain method, contour separation method, quadrant method are adopted to separate contour candidates and filter interference, then fit the target, and track the target by dynamic window method. Fitting the theoretical motion trajectory of the target by process a large number of video data, accurately mark the falling point and alignment point of the coil delivery process. It has been verified in the industrial field that this method has high accuracy, good tracking stability and real-time performance in the case of sufficient light. Compare with the traditional key-points detection methods based on sensor array, this system requires only a monocular camera, and realize the real-time location tracking, with higher accuracy and controllability, convenient for later maintenance.
High-Level Synthesize of Backpropagation Artificial Neural Network Algorithm on the FPGA

The studies related to the synthesis of backpropagation artificial neural network algorithms are still based on the direct synthesis, so it requires an effort to modify the algorithm into hardware language so it can be optimized, synthesized and implemented into the FPGA. The High-Level Synthesis (HLS) is a software compiler which able to convert C specifications into Register Transfer Level (RTL) form, which can be synthesized into FPGAs. So the designer can focus on the optimization of the algorithm itself, including speed and resource optimization. This paper discus the results of the synthesis of backpropagation artificial neural network algorithms using HLS (High-Level Synthesis) software. The C-synthesis results based on the Zynq7000 FPGA showed an accuracy of 96.56%, were able to be clocked with a period of around 9.37 ns, with resource usage of 18% for BRAM_18K, 67% for DSP48E, 25% for FF and 71% for LUT. While the utilization difference is not significant compare to the previous studies, the optimization effort using an HLS tools need to be taken into account.
Optimization of Connect6 based on Principal Variation Search and Transposition Tables Algorithms

Connect6 is an intuitive and fair game with no first-hand advantage. Because of the large search tree, the search depth is often lower, to sacrifice strength of chess to improve search efficiency. In this case, this paper proposes an optimization algorithm for pruning Connect6 search tree based on the principal variation search and transposition tables algorithms. The optimization algorithm obtained PV node through searching, narrowed the pruning interval, and stored the key value of the situation into the transposition table through searching, and then directly returned the evaluation value when the same chess game was searched again, which greatly improved the search efficiency. Through a large number of repeated experiments, the effectiveness of the algorithm can be proved.
Optimization of The Gronsfeld Cipher Key Using Okamoto-Uchiyama Public-Key Cryptosystem for Data Security

All aspects of life requires security, including in computer. Cryptography is one of the methods continually developed in computer security. Combining ciphers is an effort to improve security with the expectation to increase the security level. This paper compared the execution time as a parameter between Gronsfeld cipher and optimized Gronsfeld cipher key using Okamoto-Uchiyama cryptosystem. The result of execution time was between 0.0030-0.0040 seconds for Gronsfeld cipher, and up to 0.0050 seconds for Gronsfeld cipher with key optimization using Okamoto-Uchiyama.
Medical Image Enhancement Base on Fractional Differential Approach

This paper introduces the concept of fractional differentiation and medical image briefly. Based on this, it is proposed that a new medical image enhancement method with fractional differential. The primary contribution of this paper is the Adaptive Fractional Differential Algorithm, which uses the improved Ostu algorithm to segement the edges, textures and smooth areas of images. This algorithm allows the optimal function constructed based on the area feature of image. Furthermore, we come to an important conclusion that AFDA has better image enhancement than other methods by making edges clearer and textures richer.
Objective priors in the empirical Bayes framework

Abstract When dealing with Bayesian inference the choice of the prior often remains a debatable question. Empirical Bayes methods offer a data‐driven solution to this problem by estimating the prior itself from an ensemble of data. In the nonparametric case, the maximum likelihood estimate is known to overfit the data, an issue that is commonly tackled by regularization. However, the majority of regularizations are ad hoc choices which lack invariance under reparametrization of the model and result in inconsistent estimates for equivalent models. We introduce a nonparametric, transformation‐invariant estimator for the prior distribution. Being defined in terms of the missing information similar to the reference prior, it can be seen as an extension of the latter to the data‐driven setting. This implies a natural interpretation as a trade‐off between choosing the least informative prior and incorporating the information provided by the data, a symbiosis between the objective and empirical Bayes methodologies.
PET Image Denoising Based on Non-local Low Rank Matrix Approximation

Positron emission tomography (PET) is an effective molecular imaging method that allows non-invasive quantitative observation of physiological and biochemical changes in living organisms. PET image is reconstructed via sinogram, so the quality of the reconstructed PET image is often limited by various physical degradation factors. PET image denoising is vital because it can enhance both visual quality and quantitative metrics for better diagnostic decisions. However, because of the high contrast and low spatial resolution of the PET image, it is difficult to suppress the noise and preserve the fine details at the same time. To address this issue, this paper develops a novel PET image denoising scheme based on low rank matrix approximation (LRMA) that exploits both advantage of self-similarity and implied low rank of the images. Moreover, an enhanced back projection step is proposed for compensating the loss details to improve the denoising result. Experimental results have shown that the proposed approach outperforms related methods includng iNLM, ELME and BM3D in terms of both perceptual quality and quantitative metrics.