Reviewerly/exHarmony huggingface dataset:
Regularized Non-Negative Spectral Embedding for Clustering

Spectral Clustering is a popular technique to split data points into groups, especially for complex datasets. The algorithms in the Spectral Clustering family typically consist of multiple separate stages (such as similarity matrix construction, low-dimensional embedding, and K-Means clustering as post-processing), which may lead to sub-optimal results because of the possible mismatch between different stages. In this paper, we propose an end-to-end single-stage learning method to clustering called Regularized Non-negative Spectral Embedding (RNSE) which extends Spectral Clustering with the adaptive learning of similarity matrix and meanwhile utilizes non-negative constraints to facilitate one-step clustering (directly from data points to clustering labels). Two well-founded methods, successive alternating projection and strategic multiplicative update, are employed to work out the quite challenging optimization problems in RNSE. Extensive experiments on both synthetic and real-world datasets demonstrate RNSE's superior clustering performance to some state-of-the-art competitors.
Detecting and Diagnosing Anomalous Behavior in Large Systems with Change Detection Algorithms

Large telecommunications networks are designed to achieve high reliability with hardware and software redundancy that is managed through complex fault-tolerant mechanisms for error detection and recovery. Because of the fault tolerant mechanisms, when errors do occur they do not always cause failures and, hence, it can be difficult to detect anomalous behavior of the system and to determine its root cause. In this paper, using sequential system performance data, we present the application of multivariate change detection algorithms and visual analytics methods for detecting and diagnosing anomalous behavior with low latency in telecommunications systems. Such methods, coupled with domain knowledge, are efficient and effective for detecting and diagnosing anomalies as compared to log analysis. We demonstrate our methods with real data from a large system.
Design of Blockchain Implementation for Cooperative: Indonesia Case

Cooperatives are social business entities which should be formed with the principle of transparency, equality and togetherness among its members. Fraud is one of the top problems that exist in cooperatives movement, especially in developing countries, in terms of recording transactions and managing assets. Blockchain as an emerging technology that can be a solution to prevent the problem and increase member trust to cooperative system, because financial data do not only hold on one party. Hence, there are many types of blockchain and its consensus that should be analyzed to determine which one that fits into cooperatives business operation. This study aims to design an architectural model of blockchain system by taking cooperatives as the case of implementation. This study uses a comparative analytical study to identify the type of blockchain and the consensus system that is most suitable for use in cooperatives, along with a description of the design of applications at different cooperative scales.
Nonlinear algorithms of residual stresses reconstruction for axisymmetric problem

Abstract A non-destructive testing method for three-dimensional stress of a glass cylinder based on photoelasticity is presented. Based on simulated stresses in the glass cylinder, intensity images output from a circular polariscope are calculated by Jones calculus. Therefore, the isoclinic angle and optical retardation can be simulated. Then the residual stresses in the glass cylinder can be reconstructed in cylindrical coordinates. By comparing the reconstructed stresses with numerical simulated stresses, there is good agreement but with some mismatches.
A Study on Self-Regulated Mobile Learning Model with Real-Time Diagnosis to Students' Learning Behaviors

A mobile learning with real-time diagnosis combined with self-regulated learning is developed in this study to compare students' use of the concept map learning with the real-time diagnosis. The experimental result reveals that the mobile learning model with real-time diagnosis could assist in effective connection of new and old knowledge in the field observation to achieve the meaningful learning. Moreover, the learning model could help students constantly precede self-regulated learning in the time learning process.
퍼지이론에 기반한 포병 표적 공격우선순위 선정방안 연구

The purpose of this study is to present a model for assisting the decision of target priority in artillery. The currently available system considers the intention of the commander, the priority of the request and proximity by separate rankings, which does not reflect all the necessary elements. First, based on field manuals and the discussion with artillery experts, target characters, reliability, commander intention and priority of request are reestablished as critical elements in target priority.Then, the elements are modeled using the fuzzy inference technique in order to make an efficient and automatic decision of attack priority. To verify the proposed model, the target priority of the model is compared with that of 30 artillery officers over 20 artificial targets. The comparative study shows that the proposed model can automatically provide valid attack priorities without a human involvement.
An Improved Rotation Invariant CNN-Based Detector with Rotatable Bounding Boxes for Aerial Image Detection

Methods based on Convolutional neural network (CNN) have been effectively applied to object detection. However, the Traditional object detection models face huge obstacles in the field of the aerial scene due to the complexity of the aerial image and the wide degree of freedom of the remote sensing object in density, scale, and orientation. In this article, we applied rotatable bounding boxes (RBox) instead of the traditional bounding box (BBox) and improved the CNN-based network to effectively handle the oriented angle of the detected object so that the rotation invariant property can be achieved. Our model is trained and tested on 3 classes of remote sensing category Airplanes, ships, and Cars. Compared with two of the common benchmarks of the BBox methods Faster R-CNN and SDD, our model outperforms both of them. Where our model efficiently output the orientation angles of the detected objects.
A Taxonomy for Classifying User Group Activity in Online Political Discourse

Twitter has become a popular platform for political conversation. Within the TwitterSphere, multiple users contribute to the discourse through different modalities and with varying degrees of engagement. In this paper, we seek to profile the different groups of users present in online political discourse. We provide a taxonomy that can be used to classify the various users, determine each group's representation and their respective share of the online conversation. Along with core information foraging concepts, the taxonomy aims to reduce the foraging costs associated with exploratory searching. This work provides a practical approach for analyzing and pre-processing social media data and offers valuable recommendation for future expansion into sentiment analysis.
Dense Stereo Matching Optimization Algorithm based on Image Segmentation and ORB Gravitational Field

This paper reference the data field ideology from Cognitive Physics, presents a novel dense stereo matching method, in which the cost metric of robustness to texture-less and occluded regions. In order to correct the mismatching and constrain error propagation in cost aggregation, Oriented FAST and Rotated BRIEF (ORB) gravitational field is introduced to the traditional matching cost metrics on the basis of previous studies. Strong feature points can be regarded as correct depth estimation points, which can be used as the center to establish gravitational field for effective energy transmission of each pixel. Then the combined matching cost metrics are aggregated exploiting image segmentation structure. Finally, the experiments on the stereo images of datasets and ours prove that the proposed algorithm has better performance in matching accuracy and robustness.
Content Based Image Retrieval using Heron Filtering, Fast BEMD and Gray level features

In today's world, large amount of digital image data is generated and stored in the repository. Finding for a particular image/s accurately in the repository poses the challenge. The aim of the proposed work is to search for relevant images from the repository based on user query image. In this work, the low-level features of images present in themselves such as edges and gray levels are used to retrieve the similar images. A novel approach is proposed in this paper by using a new filter applied on images called Heron mean filter, to smoothen the image, then combination of the edge features extracted by Fast and Adaptive Bidimensional Empirical Mode Decomposition (FABEMD) technique and gray level features through histogram. The experiments are conducted using Wang's dataset of 10 categories of 100 images each. The tabulated results are obtained by using different similarity measures like Euclidean distance and Bhattacharya's Co-efficient method. It is observed from the results that there is an improvement in the proposed system in terms of average precision and average recall, against the existing system.
Perspective Cryptocurrency Projects Supported by China

In this article are discussed the most perspective cryptocurrency and blockchain projects that China is investing in. After the regulations regarding cryptocurrencies that is put forth by China, the Chinese government decided to create several financial bodies to regulate and develop the cryptocurrency. Despite the strict regulation of cryptocurrencies, China has been significantly investing in blockchain projects. China has developed the Digital Currency Research Institute (DCRI) of the People’s Bank of China — a research body under the aegis of PBOC that focuses on the research and development of digital currencies and blockchain-related technologies. China actively supports more than 40 platforms, mostly in such fields as AI, Deep Learning and Software. The Chinese government has shown a positive attitude towards blockchain technology. Blockchain and cryptocurrency come hand-in-hand (except a private chain where a token is unnecessary). In the nearest future, China plans to introduce a blockchain to the most different spheres. For this purpose, there will even double the volume of investment to 3 billion dollars, since the second quarter of 2018. “This technology can transform many spheres of our life. As soon as in the country pursue powerful technological policy, it is sure that even more companies will begin to work in the field of the blockchain” —the partner of the international consulting company PwC in Shanghai Chongg Chong Yin commented to journalists.
Scene Categorization by Hierarchical Clustering on Adaptive Spatio-Temporal Features

The complex task of vision based analytics by computer gets more challenging when mutual motion between camera assembly and object of interest creates false motion features in the scene. This adds further complexity when adverse environmental conditions are handled along with scene understanding, scene captioning, object and activity recognition. The current work has proposed a novel hierarchical categorization based methodology to categorize scenes based on selected spatio-temporal features. The procedure of determination of feature hierarchy and the detailed description of step by step methodology have been provided along with comprehensive results.
No-reference Stereoscopic Image Quality Assessment Based on Visual Saliency Region

Stereoscopic image quality assessment (SIQA) plays an important role in the development of 3D image system. In view of the problem for existing SIQA methods cannot effectively extracted features of stereoscopic image and the dimension for the related features data extracted from stereoscopic image is too large. In this paper, we proposed a no-reference stereoscopic image quality assessment method combined with visual saliency regions and wavelet transform. The method firstly combines the distorted image pairs into two separated cyclopean images by using Gabor filtering and the SSIM-based stereoscopic algorithm. Then, detect the visual significant region for the distorted image pair, the two synthetic cyclopean image and the corresponding depth map respectively and segment those images into patches. Make a wavelet decomposition and obtain the phase amplitude and gradient features of the wavelet subband as stereoscopic image features. Finally, we use SVR to establish the mapping relationship between the features of stereo image quality and DMOS. The experimental results show that compared with the existing state-of-the-art no-reference stereoscopic image quality assessment (NR SIQA) methods, the proposed model can maintain good consistency with human subjective perception.
Wound Classifications Of Breast Tissues with Electrical Impedance Spectroscopy (EIS): Comparison of LVQ and GA-LVQ

Deaths of people with breast diseases have reached 40% and are increasing every year. Breast Disease arises from wounds in breast tissue is breast cancer. Wounds of breast can be seen and discriminated the type of wound using Electrical Impedance Spectroscopy(EIS). The results of this EIS data can be used as an initial diagnosis before the doctor carries out treatment. This research tried to classify the types of wounds from breast tissue by using the Learning Vector Quantization (LVQ) method with the Genetic Algorithm (GA) to find out whether the solution provides the expected optimum results. The dataset we used for this study was very small which did not represent the existing class, with 106 data for 6 classes. The results of the comparison between LVQ and GA-LVQ obtained LVQ added by GA has higher accuracy values than LVQ with 73% accuracy and compared to LVQ, GA increased accuracy by ± 50%.
Event-Oriented Wiki Document Generation

We aim to automatically generate event-oriented Wikipedia articles by viewing it as a multi-document summarization problem. In this paper, we propose a new model named WikiGen, which consists of two parts: the first one induces a general topic template from existing Wikipedia articles, and the second one generates a summary for each topic by collecting, filtering, and integrating relevant web news, which will be assembled into the full document. Our evaluation results show that WikiGen is capable of generating fluent and comprehensive Wikipedia documents and outperforms previous work, achieving state-of-the-art ROUGE scores.
Ethereum as a Hedge: The intraday analysis

In this study, we examine on intraday basis Ether - the token or cryptocurrency based on the Ethereum platform. Ether is the second largest crypto-currency, together with Bitcoin they dominate the cryptocurrency universe and account for almost 70% of combined market share. Similar to Bitcoin, Ethereum experienced rapid growth in price from a few cents per Ether after its introduction in 2015 reaching maximum of $1,432.88 on January 13, 2018. In this paper, we study whether Ethereum crypto-currency is a hedge, diversifier or a safe haven asset. We find that Ethereum crypto-currency is a hedge against the US stock and gold markets. Also, Ethereum tends to behave as a safe haven for gold markets. When currency markets are concerned, we document that Ethereum is a diversifier for the US Dollar.
A Correlation Filter Target Tracking Algorithm Based on Continuous Convolution Operator and Spatial-Temporal Regularization Term

In order to accurately estimate the location of a target that is occluded and rotated rapidly by the STRCF algorithm, a correlation filter target tracking algorithm based on continuous convolution operator and spatial-temporal regularization term is proposed. The algorithm uses interpolation operators to transform a response function into a continuous function within a certain period, thus enhancing the accuracy of target location. Spatial-temporal regularization terms are added to the new model of correlation filter to ensure that it is similar to the model of the previous frame of image and that the algorithm is more robust. A fast multi-scale filter is used to update the scale, thus improving the computational efficiency. The experimental results show that the average overlap rate of the proposed algorithm can reach 73% and that the central position error is less than 8.2. The proposed algorithm can achieve a real-time and robust target tracking.
Adaptive Histogram Normalization based Loss Function in Deep Learning Algorithm for Face Recognition

To improve the recognition accuracy and to solve the overfitting problem of traditional face recognition methods, this paper proposed an adaptive histogram normalization algorithm to reduce brightness effect in training data and designing loss function. The proposed algorithm can adaptive adjustment training images and inference parameters based on the real-time captured images data. In experimental results, the proposed algorithm has higher accuracy than other algorithms and has higher testing accuracy to improve overfitting.
An Optimized Crypto Coin Mining System

In this study, we will explain what settings the RX580 4GB AMD graphics card should be operated for in order to be able to perform Ethereum and Monero mining at high speed and with less energy consumption. Core and memory undervolting, core underclocking, memory overclocking and tightening memory timings are the techniques which are applied to the card. With the adjustments made, approximately 30 percent savings were achieved in electrical energy spent in Ethereum and Monero mining. Also, the mining speed increased by approximately 20 percent. It was measured that the temperature of the graphics card decreased by 10 degrees due to the decrease of the power drawn.
Exploring the Generalization of Knowledge Graph Embedding

Knowledge graph embedding aims to represent structured entities and relations as continuous and dense low-dimensional vectors. With more and more embedding models being proposed, it has been widely used in many tasks such as semantic search, knowledge graph completion and intelligent question and answer. Most knowledge graph embedding models focus on how to get information about different entities and relations. However, the generalization of knowledge graph embedding or the link prediction ability is not well-studied empirically and theoretically. The study of generalization ability is conducive to further improving the performance of the model. In this paper, we propose two measures to quantify the generalization ability of knowledge graph embedding and use them to analyze the performance of translation-based models. Extensive experimental results show that our measures can well evaluate the generalization ability of a knowledge graph embedding model.
A low latency sequential model and its user-focused evaluation for automatic punctuation of ASR closed captions

In Automatic Speech Recognition (ASR), inserting the punctuation marks into the word chain hypothesis has long been given low priority, as efforts were concentrated on minimizing word error rates. Punctuation, however, also has a high impact on the transcription quality perceived by the users. Prosody, textual context and their combination have since been used successfully for automatic punctuation of ASR outputs. The recently proposed RNN based solutions show encouraging performance. We believe that current bottlenecks of punctuation technology are on one hand the complex punctuation models, which, having high latency, are not suitable for use-cases with real-time requirements; and on the other hand, punctuation efforts have not been validated against human perception and user impression. The ambition of this paper is to propose a lightweight, yet powerful RNN punctuation model for on-line (real-time including low latency) environment, and also to assess user opinion, in general and also for target users living with hearing loss or impairment. The proposed on-line RNN punctuation model is evaluated against a Maximum Entropy (MaxEnt) baseline, for Hungarian and for English, whereas subjective assessment tests are carried out on real broadcast data subtitled with ASR (closed captioning). As it can be expected, the RNN outperforms the MaxEnt baseline system, but of course not the off-line systems: limiting the future context to minimize latency results only in a slighter performance drop, but ASR errors obviously influence punctuation performance considerably. A genre analysis is also carried out w.r.t. the punctuation performance showing that both recognition and punctuation of more spontaneous speech styles is challenging. Overall, the subjective tests confirmed that users perceive a significant quality improvement when punctuation is added, even in presence of word errors and even if punctuation is automatic and hence itself may contain further errors. For users living with hearing loss or deafness, an even higher, clear preference for the punctuated captions could be confirmed.
Coverage-Based Variational Generative Decoder for Abstractive Text Summarization

Neural abstractive summarization models, based on the attentional sequence-to-sequence model, can generate simple summarization from source text. However, these models inevitably face with repetition problem, and ignore the rich structure information contained in target summarization. To alleviate these problems, we propose a generation model that combines the coverage mechanism and neural variational inference to make full use of all information in sentences. First, we employ Variation Auto-Encoders(VAEs) for generating latent variables, which obtain the latent structure information in target summarization. Second, we introduced coverage mechanism in final decoding process, which is expected greatly improve the weights of those important parts in the encoder and effectively reduce repetition problem. Abstractive summaries are generate by combining the generated latent structure information and the coverage information. Experimental results on LCSTS dataset show that our model outperforms baseline methods.
Source Device Attribution of Thermal Images Captured with Handheld IR Cameras

Source camera attribution of digital images has been a hot research topic in digital forensics literature. However, the thermal cameras and the radiometric data they generate stood as a nascent topic, as such devices are expensive and tailored for specific use-cases - not adapted by the masses. This has changed dramatically, with the low-cost, pluggable thermal-camera add-ons to smartphones and similar low-cost pocket-size thermal cameras introduced to consumers recently, which enabled the use of thermal imaging devices for the masses. In this paper, we are going to investigate the use of an established source device attribution method on radiometric data produced with a consumer-level, low-cost handheld thermal camera. The results we represent in this paper are promising and show that it is quite possible to attribute thermal images with their source camera.