Reviewerly/exHarmony huggingface dataset:
An Method of Improved HLDA-Based Multi-document Automatic Summarization of Chinese News

There are a lot of Chïnese news about the same topic on the Internet today. Many of them are similar or repetitive for readers. It is hard to find what are the readers needed exactly. Multi-document news summarization aim at extractioninformationfrommultiple news texts on sametopie to automatically generate summary report for readers. Our paper chooses the news of the Great Wall as an example to illustrate the method of automatic summary generation In ourmethod, combinedwiththe characteristies ofnews corpus, the HLDA topie importance calculation model is improved. Based on the abstractly characteristics of the model, news related features such as news headline words, topie sensitive words and TF-IDF are added. Abstract sentence extraction and sentence fusion, automatic generation of abstracts. Experimental results show that the proposed algorithm is higherin the index thanthe traditional method, indicatingthe accuracy of the corpus combined with news features and the improved HLDA algorithm.
Understanding and Improving Continuous Integration and Delivery Practice using Data from the Wild

Continuous Integration (CI) and Delivery (CD) have been claimed to introduce several benefits in software development, including high software quality reliability, and faster release cycles. However, recent work pointed out challenges, barriers and bad practices characterizing its adoption. This happens especially because many developers are not adequately experiences with the practices, or with the related technology. This talk will outline recent work (by ourselves and by others) aimed at characterizing the CI/CD process from different perspectives. First, the talk will provide an overview of various kinds of anti-patterns related to the CI/CD adoption. Then, it will go more in detail of how certain practices, such as static analysis, testing or code reviews are followed in the context of CI/CD. Last, but not least, the talk will overview approaches to help developers improving their CI/CD practice, also outlining open challenges in this area, and future research directions.
Aspect Based Sentiment Analysis by Pre-Trained Language Representations

Given a paragraph of text, the objective of aspect-level sentiment classification is to identify the sentiment polarity of a specific phrase. Most exisiting work employed LSTM model and attention mechanisms to predict the sentiment polarity of the question targets. Unfortunately, these approaches haven't fully utilize the independent modeling of these target phrases. We propose a model based on TextCNN and Transformer pretrained model. In our model, the representations are generated for the targets and the contexts separately. We use Transformer model to help represent a target and its context via attention learning, which improves the performance of aspect-level sentiment classification. Experiments on COAE2014 and COAE2015 task show the effectiveness of our new model.
RNPLS&amp;T’s bibliographic database “Library Information Systems and Innovations in Digital Environment”: Document analysis

The contents and subject scope of the peer-reviewed bibliographic database Library Information Systems and Innovations in Digital Environment, LIS), based in IRBIS64 software are characterized in brief. The proportion of source document types and bibliographic records is identified. Science and scientific and practical journals prove to be the major source for the bibliographic array, while the share of other source types is relatively small. The major sources of information, or the core journals within the LIS database subject scope are revealed. Based on the LIS databases contents, the parallel is drawn with Bradford scattering law which states that if journals in a field are sorted by number of articles into three groups, each with about one-third of all articles, then the number of publications in specialized journals will make the one-third, the other one third will be published in the related journals and the other will be distributed between in a vast number of science periodicals unpredictable for the said subject scope. The author establishes that the qualitative and typical composition of the LIS database bibliographic array is in full accord withBradford’s law.
Medical Image Denoising Using Spline Based Fuzzy Wavelet Shrink Technique

Denoising is a fundamental requirement in the field of medical image processing. It is the process of reducing the additive noise from a noisy medical image, while preserving the information from the clinical data and the physiological signals. Therefore, it is essential to recover an estimated image that conveys almost the same information present in the original image. Wavelet shrink is a standard method of denoising the medical images due to its spectral decomposition and energy compression mechanism. The orthogonal decomposition and energy based compression of the image preserves its spectral components. However, break points are observed due to the presence of purely harmonic patches in the image. To solve this problem, we suggest a spline based fuzzy wavelet shrink (SFWShrink) model for denoising the medical images. The fuzzy wavelet shrink model assigns the wavelet coefficients using a fuzzy rule based membership value. The fuzzy membership values signify the relative importance of a data point with respect to its neighboring data points. This eliminates the noise points in the data space. Next, the spline estimation removes the break points occurring due to the wavelet transform. The benefits of the proposed method are: (i) it is a simple and effective method of denoising; (ii) it preserves the insignificant image details. The proposed model is evaluated with different modalities of synthetic medical images. It is compared with the standard wavelet shrink models, such as VisuShrink, BayesShrink and NeighShrink. The evaluation parameters show the superiority of the suggested technique in comparison to the other approaches.
A Novel Approach for False Positive Reduction in Breast Cancer Detection

Breast Cancer is the most prevalent cancer among women across the globe. Objective of the Computer Aided Diagnosis (CAD) in breast cancer analysis is to detect the tumorous mass such that patient could get the proper treatment within time. However, existing algorithm undergoes detection of false positives. Thus, reduction of false positives is one of the challenging tasks to improve the performance of the diagnosis systems. In this paper, we propose a convolution neural network based approach for false positive reduction. We propose residual learning (ResNet) for false positive reduction. Masses segmented using a respective segmentation algorithm are given as an input to the proposed network to classify between true positive (tumorous mass) and false positive (non-tumorous mass). Proposed approach is validated on set of mammography scans collected from the Tata Memorial Cancer Hospital (TMCH). The performance of proposed algorithm is measured using precision, recall and F-score and compared with existing deep networks. Performance analysis shows that proposed approach outperforms other existing deep networks for false positives reduction.
Low-Latency and Low-Power Test-Vector Selector for Reed-Solomon’s Low-Complexity Chase

Memory systems increase their demand for error correction capacity moving from single and double error correction decoders to triple and quadruple ones (TEC and QEC). New proposals for TEC and QEC decoders take advantage of some classic algorithms that were discarded when more efficient options appear. However, these algorithms can be applied to softdecoding architectures with a negligible increase in complexity. This brief proposes a new method to select the test vector to be decoded in a soft-decision low-complexity Chase algorithm, without requiring the computation of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">η</sup> polynomials of location or 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">η</sup> searches of roots. The decodable test vector is pre-selected just with the syndrome information and with a very small percentage of hardware resources compared to hard-decision TEC and QEC. Compared to hard-decision, the proposed decoder obtains a coding gain between 0.4 and 0.8dB, depending on the codeword length and the order of the Galois Field, at a cost of 6.5% of extra area in the worst case. This solution introduces a performance loss of 0.1dB compared to the traditional low-complexity Chase algorithm.
Encoding Topology Information for Deep Reinforcement Learning with Continuous Action Space

In the context of reinforcement learning, the training efficiency can decay exponentially with the size of the state space. Therefore, designing easily-optimized state space representation has remained an open problem. In this paper, we focus on a general and challenging scenario, i.e. reinforcement learning with continuous action spaces. We propose a new representation framework by explicitly encoding topology information such as the geometrical and the kinematic relations among different parts of the agent to make the representation more informative, which results in effective optimization. Extensive experiments were conducted on three settings to demonstrate that our method can remarkably stabilize and speed up the training process.
Penerapan Model Pembelajaran Kooperatif Tipe STAD Dalam Meningkatkan Hasil Belajar Matematika Di Kelas IV SDN Girian Indah Bitung

This study aims to obtain a study of the application of the STAD model to improve student learning outcomes in class IV Girian Indah SDN. The design used is the Classroom Action Research design from Kemmis and Mc. Taggart using two cycles consisting of four stages, namely: Planning, Implementation, Observation, and Reflection. This research data was obtained through observation and tests. The results showed that the application of the STAD Type Cooperative Learning Model in Improving Mathematics Learning Outcomes in Class IV. The results achieved by students in the learning activities of the first cycle is 50%, an increase in the second cycle with an average student achievement of 90% with very good qualifications. Starting from the results of this study, it is recommended that mathematics teachers be able to use the STAD Type Cooperative Learning Model in Improving Learning Outcomes in Grade IV Elementary School.
Motion Planning Using MoDs

Maps of dynamics can be beneficial for motion planning. Information about motion patterns in the environment can lead to finding flow-aware paths, allowing robots to align better to the expected motion: either of other agents in the environment or the flow of air or another medium. The key idea of flow-aware motion planning is to include adherence to the flow represented in the MoD into the motion planning algorithm’s sub-units (i.e. cost function, sampling mechanism), thereby biasing the motion planner into obeying local and implicit traffic rules.
Supervised Abnormal Signal Identification Method

Identifying radio anomalies is one of the main purposes of radio monitoring. The current radio anomaly signal identification is mainly finished manually by the Radio monitor, using professional radio knowledge and their work experience. However, because the anomaly signal is hidden in the "massive" data, accompanied by a large amount of noise, and also data imbalance, the anomaly signal is difficult to find. In this paper, we combine the data unevenness processing method SMOTE and the support vector machine (SVM), gradient lifting tree (GDBT), and other classification algorithms to identification the anomaly signal during radio monitoring. Experimental results show that our method can improve the efficiency of existing radio anomaly signal recognization. Moreover, our experiments also show that data imbalance processing plays a key role in anomaly signal recognition.
Camera Model Identification Using Transfer Learning

Wide series of forensic problems can be solved by detecting the camera model from copyright infringement to ownership attribution. There are many proposed methods for detection of the camera model. A method to identify the camera model of any image is proposed in this paper. It involved feature extraction and classification. CNN-based architectures are best suited for the task of image classification.
An on-line path finding algorithm in unknown duct for long redundant robot with laser rangefinder

In this paper, an on-line path finding algorithm in unknown duct for long redundant robot with laser rangefinder is presented. The solution provides the redundant robot with one collision-free path to arrive at the target point inside the duct. Without digital model of the duct in advance, the path planner is designed for online planning, composed by shape detection strategy, collision check strategy and path planning strategy. The approach just applies one laser rangefinder fixed at the end of robot to accomplish automatic entry and guarantees the avoidance with the wall of duct. The feasibility of the proposed solution was demonstrated by testing many different target points.
Study on Mistype Correction Support Using Attention in Japanese Input

How to input characters without mistakes is one of very important points for the interface, when we consider usability of electronic devices such as personal computers and smartphones. Therefore, in this paper, we examine whether we can classify mistypes for Japanese input and inform the users how they make mistakes using "Attention" and "LSTM" of machine learning methods. First, we classified the category of mistypes in Japanese input into seven types (Replacement, Removal, Exchange, Insertion, Involvement, Repetition). Second, we asked subjects to input the displayed words and collected their data. Next, we classified the data and labeled it. Finally, we made the Neural Network (LSTM + Attention) to classify the mistype data and inform how they make mistakes. As a result, the correct answer rate of the Neural Network (LSTM + Attention) was about 70% and we were able to know where point the Neural Network focus on by putting in "Attention". We are planning to use "LSTM" to correct mistype directly and use "Attention" to inform how they frequently make mistakes in the future.
Architectural Implications in Graph Processing of Accelerator with Gardenia Benchmark Suite

Existing generic benchmarks for accelerators (e.g. Parboil and Rodinia) have focused on high performance computing (HPC) applications which have limited control flows and data irregularity. Previous available graph analytics benchmark suites include straightforward implemented workloads which do not employ up-to-date optimization techniques and thus have quite different behaviors from real-world applications. This paper first briefly presents and characterizes the Graph Analytics Repository for Designing Next-generation Accelerators (GARDENIA) 1, which is a benchmark suite for studies of irregular algorithms on various massively parallel accelerators. It includes emerging irregular big-data and machine learning applications, in which mimic massively multithreaded programs deployed on not only datacenters but also hand-on devices. Then we characterize Nvidia GPU with GARDENIA, covering a wide spectrum of metrics such as parallelization, cache locality, off-chip traffic and irregularity. Based on the characterization on Nvidia GPU, we unveil the performance bottlenecks of the current mainstream accelerator and give architectural insights for building high performance and energy-efficient domain-specific accelerators for graph applications.
Automated Industrial Quality Control of Pipe Stacks Using Computer Vision

In this work, we describe an automated quality assurance system for pipes in warehouses and yards using simple handheld and mobile equipment like smartphone cameras. Currently, quality inspection for bent and crooked pipe ends is done manually, which entails additional labour costs and is relatively slower than a mechanised approach. We propose an efficient and robust method of detecting the perfectly circular cross-sections of the pipes in stacks using an adaptive variation of Hough Transform algorithm. As a multistage approach, our proposed method first intensifies the foreground features relative to the background and then applies Canny edge detection to obtain the gradient details. The gradient directions are in turn fed to the modified Hough Transform algorithm using Hough gradient to detect the cross-sections. The novel Hough Transform modification features a region-based processing in a “coarse to fine” manner by dividing the image into smaller grids and detecting circular cross-sections per grid, which has computational advantages of using a smaller accumulator and is less memory intensive. Experiments were performed on real industrial images to validate the efficiency of the proposed algorithm and the results show that the proposed method can successfully and accurately highlight the perfectly circular cross-sections while leaving the faulty pipe-ends undetected.
Enhancement of Incident Solar Energy on Panels and Indoor Load Leveling Due to Seasonal Adjustment of Rooftop Solar PV Arrays

The energy generating modular array network is the costliest unit of solar photovoltaic systems. The amount of energy that is transformed into electricity by the solar photovoltaic panels depends on its tilt angle with the horizontal plane as well as the orientation of the module. The optimum tilt angle of these panels is decided on the basis of annual solar energy incident on the panels, and this angle is kept fixed during the year-round cycle. In this paper, we have investigated the optimum tilt angles determined on the basis of the amount of energy incident on the panels at yearly and seasonal basis. In this investigation, we have also investigated the shading effect of these panels on the rooftop at Delhi (latitude 28.7° N). In accordance with the latitude of Delhi, the solar radiation data has been classified into three seasons, i.e., summer (May, June, July, August), winter (November, December, January, February) and equinoxes (March, April, September, October). It is found that the panels placed facing due south and seasonally adjusted at their optimum tilt angles will produce more electrical energy, 6.52% in summer, 5.94% in winter and 5–7% more electrical energy on annual basis and a larger shadow in summer and smaller shadow in winter in comparison with the panels at annually fixed tilt angle, and this in turn will decrease the indoor solar heat flux in summer and enhance the indoor solar heat flux in winter and hence will favorably affect the indoor solar heat flux as well as load leveling.
Towards Long Duration Self-Sustainable Ocean Sampling System

Ocean sampling is a crucial part in the construction of Smart Ocean Environment development. Previously, unmanned surface vessels and traditional surface drifting buoys have been designed and utilized for ocean sampling, but their high cost, weak stability and poor endurance limit the long time and large-scale applications. This paper presents a novel design of self-sustainable ocean sampling system in the form of ocean surface drifting buoy, which is equipped with solar self-power module, and various sensors for data acquisition. Several interfaces were reserved for the functions extensions as well. In consideration of the overall stability and practical executability, regular polyhedron shape is used for the system structure design. Theoretical analysis and simulation were performed and optimized in terms of energy collection efficiency, and finally dodecahedron shape was chosen. A prototype was built and tested in an outdoor water surface environment. The result of the test shows that the system is capable of self-sustainability for long duration ocean sampling.
Threat Detection in Power Grid Based on Hierarchical Feature Cloud Computing Model

Abstract The extraction of subjective features such as the authority, purpose, and opportunity of power network operators has outstanding subjectivity. It is difficult to accurately quantify subjective features due to the subjective factors of operators, resulting in fuzzy subjective features. Traditional algorithms use subjective features to detect internal threats to the power grid. Once subjective factors interfere, they will cause the defects of fuzzy main features, leading to a decrease in the accuracy of threats to the internal power grid. This paper focuses on the detection of power grid internal threats based on a hierarchical feature cloud computing model. This paper proposes a power network internal threat detection method based on the hierarchical feature cloud mapping model; calculates the hierarchical mapping relationship based on the internal threat features to complete the internal threat detection of the power grid; experiments prove that the algorithm avoids subjective factors being affected by subject characteristics, and The ambiguity of features reduces the missed detection rate to below 15.3%.
Improving Security and Sharing Management in Cloud Computing Using TPA

Cloud computing as a model is capable of providing suitable, ubiquitous and on-demand network access to a mutual pool of configurable computing resources that is able to rapidly provision and release with less effort of management from the side of the user and less interaction of service provider. The aim of this paper is to design and implement a system based on Third Party Auditor (TPA) which is a reliable party works as a key center to generate and distribute the keys between the users in the system, also, it can manage the keys, create secret keys of different lengths and provide a range of services including confidentiality, security, and identity verification Authorized users. Additionally, it encrypts and decrypts and shares data between users and protects the data sent between customers with a high degree of security through the using three types of public, private and symmetric keys using the digital signature. The digital signature is different from user to user for each file uploaded or Modifying or updating it by the user (owner, client) or TPA in the system. Therefore, the chaotic map algorithm is used to enhance the system strength used in TPA for generating different keys.
Rate SSIM Based Preprocessing for Video Coding

An effectively perceptual preprocessing filter method will reduce the sequence's perceptually insignificant details, which can help to alleviate the video coding's pressure and benefit the coding results. In this paper, a perceptual rate distortion optimization based preprocessing filter algorithm is presented to achieve better video compression performance. The proposed algorithm employs an adaptively temporal filter with the rate SSIM (Structure Similarity Index Metric) based optimization constraint to produces optimal filter parameters. The evaluation results confirm that the proposed video filter algorithm has achieved reliable rate-visual quality improvements with -4.35% and -2.96% SSIM based BD-Rate performance gain on JM18.3 and HM16.9, respectively. Additionally, the subjective quality comparison results show that our algorithm has reduced significant bits with negligible visual difference for both the experiments on JM18.3 and HM16.9.