Reviewerly/exHarmony huggingface dataset:
Research on the Countermeasures of College Students’ innovation and entrepreneurship development under the perspective of “Internet +”

Abstract “Internet +” innovation and entrepreneurship education is a new educational concept, and it is the inherent requirement of university education reform. In this paper, we take the cultivation of College Students’ “Internet +” innovation and entrepreneurship capabilities as the core, in-depth analysis of the major problems encountered by college students in “Internet +” innovation and entrepreneurship, and realize the profound educational reform from four levels of government, universities, enterprises and university students. Based on the evaluation index of graduate innovation and entrepreneurship education, this paper constructs an evaluation model of graduate innovation and entrepreneurship education based on analytic hierarchy process (AHP), which provides basis and reference criteria for the training and evaluation of graduate innovation and entrepreneurship education.
Analysis and specification of consistency rules for UML diagrams

This paper presents a survey of consistency rules identified between UML diagrams. It bases on a representative collection of publications and author’s experience of developing original method of software architecture construction (e-CMDA). A new notation for consistency rules is introduced. It uses regular expressions combined with symbols of UML diagram elements and the introduced "projected" (custom) UML diagram symbols. This notation facilitates analysis and selection of appropriate consistency rules in software development at the architectural level.
Robust Nonlocal Low-Rank Sar Stack Despeckling With Application To Change Detection

We present a nonlocal low-rank denoising algorithm for synthetic aperture radar (SAR) image stacks. The method extends the widely known DespecKS algorithm by integrating low-rank approximation, outlier removal, and total variation (TV) regularization into the estimation process. Preliminary experiments shows increased robustness against outliers and comparable performance to state-of-the-art stack despeckling algorithms.
Hybrid Load Identification Model Based on Grey Wolf Optimization Algorithm

Non-intrusive load monitoring is important for the development of smart grids. In order to get the load status and power consumption information of each device, single classifiers such as the support vector machines, the MLP neural networks and the extreme learning machines are widely used to identify the appliances. But the single classifiers are faced with the risk of local optimum and overfitting. In order to improve the recognition accuracy of the single classic classifiers, a hybrid identification model based on grey wolf optimization algorithm is proposed in this paper. The experimental results based on the actual measured data verified that the recognition accuracy of the proposed method is significantly higher than that of the single classical classification models.
Automatic Extraction of Potential Debris Flow Based on GF-2 Satellite Data

Debris flow is the deadliest geological disaster in Beijing, China, which directly threatening the safety of local people and economic development. Meanwhile, with the continuous progress of remote sensing technology and machine learning algorithm, the remote sensing monitoring methods of potential geological disasters are developing towards automation and quantification. On that basis, the paper took Fangshan and Mentougou districts, Beijing, China as the research area, used Gaofen-2 (GF-2) satellite data, applied Support Vector Machine (SVM) algorithm to automatically extract the provenances of potential debris flows, and combined Digital Elevation Model (DEM) data to delineate the catchment, so as to build a potential debris flows survey system. Through field verification, the accuracy was about 81.31%, indicating that the survey system is scientific and feasible.
A Summary of Studies on Bilingual Comparable Corpus

How to construct bilingual comparable corpus and further mine fine-grained bilingual resources, which is an important way to solve the lack of bilingual resources in cross-language information processing. Firstly, this paper expounds the important value of bilingual comparable corpus construction. Secondly, the methods of quality evaluation are discussed on bilingual comparable corpus. Thirdly, the research status of comparable corpus construction is analyzed, including lexicon-based feature, document-oriented representation and non-content-based approach. Furthermore, the progress in the application of bilingual comparable corpus has been expounded, which mainly involves bilingual lexicons extraction, bilingual multiword expressions extraction, parallel sentences extraction and cross-language information processing. Finally, the existing problems and future research directions are proposed.
Feel the Water

This paper describes an explorative design study conducted in the scope of a collaborative research project in the district heating domain. In the scope of the project, we have arranged extensive field studies at two power plants to understand the workflows, problems, and needs of industrial operators. We relied on the gained knowledge to design and develop novel visual interfaces that would communicate the overall status of the district heating system at-a-glance. We aimed at exploring potential directions and alternatives beyond conventional industrial interfaces. One particular aspect of our research was related to how the physicality of the underlying industrial processes can be expressed by purely visual means. The paper introduces three high-fidelity prototypes demonstrating the novel visualizations developed. The paper explains the design choices made, namely the relation between the selected visual encodings to the requirements of the industrial operators' tasks. Preliminary evaluation indicates industrial operators' interest in the designed solutions. Future work will incorporate an extensive qualitative evaluation on site.
THE USE OF FORMING ASSESSMENT FOR THE STEP-UP DEVELOPMENT OF METASUBJECT SKILLS DURING THE IMPLEMENTATION OF THE ROBOTIC PROJECT

The article describes the use of forming assessment (assessment for learning) for the step-up development of students' skills to plan and evaluate their own project and research activity during the implementation of a robotic project. Criteria for self-assessment during project activity and a point-rating system for assessment of the results of the project in connection with descriptors indicating the formation of competencies that are part of the project culture are proposed.
RESEARCH OF MOTIVATION OF FUTURE ENGINEERS OF COMPUTER PROFESSIONAL PEDAGOGES

The article analyzes the scientific literature of domestic and foreign scientists, on the basis of which the approaches to the concept of motivation (motivation as a combination of factors and motives, motivation as a process and mechanism of activity, motivation as a set of all determinants of behavior) and a motive (an internal position of the student; driving force of the student). On the basis of the analysis of leading editions and own understanding of the concept of motivation, in the context of the scientific article, the concept of motivation will be understood as a set of factors and motives.The analysis of the educational activities of future engineers-teachers of the computer profile has shown that low motivation is one of the reasons for the lack of interest of those who are taught in the educational process while studying pedagogical disciplines. On the basis of the questionnaire on the methodology «The motivation of students studying at higher educational institution», the prevailing external and pragmatic motives for future engineers-teachers of the computer profile were established.In the course of student interviewing, it has been established that the regress of motives from the domestic ones in the process of entering the institution of higher education and the actual ones arose as a result of the fact that 61% of students disappointed in choosing their future profession. According to the results of interviews, the respondents found a low level of motivation in the process of studying humanitarian disciplines, including the pedagogical cycle. The survey shows that 64% of students do not see themselves as teachers, 73% study pedagogical disciplines because they are included in the curriculum and are mandatory. On the other hand, the information society creates demand for skilled workers in the IT industry, whose training can only be provided by highly skilled engineers-teachers of the computer profile provided that they have the appropriate pedagogical training. Recommendations on formation of positive motivation of students are developed.
POSTER: Pairing Up CNNs for High Throughput Deep Learning

To facilitate the efficient execution of convolutional neural networks (CNNs) on cloud servers, this paper proposes Yin Yang (YY), an input-driven synergistic deep learning system, which dynamically distributes CNN computation between a complex (Yang) and a simple (Yin) CNN. YY runs most of the inferences on Yin, while Yang is invoked only when Yin has low confidence. On average, compared to the traditional CNN as a service approach, YY improves datacenter throughput by 1.8× and reduces inference latency by 31% on an NVIDIA TITAN X GPU without any accuracy loss across 21 CNNs.
Multiple Context Aggregation Network for Saliency Prediction

With the rapid development of deep learning techniques, saliency prediction has been widely studied with deep convolutional neural networks (DCNNs). In this paper, we delve into the feature characteristics that heavily affect saliency prediction, feature scale and feature level, to be specific. It is shown that in addition to high-level features, large-scale and/or low-level features are also important to saliency prediction. Therefore, a dilated asymmetric convolution with large kernel (DACLK) is designed to obtain large-scale features. Then, a global multi-level feature aggregation method (GMLFA) is proposed to incorporate features from shallow layers. GMLFA can increase the scale of the features by down-sampling operations and improve saliency prediction with more large-scale (global) multilevel features. Based on the two modules, a multiple context aggregation network (MCA-Net) is developed for saliency prediction. Experiments demonstrate that MCA-Net outperforms other models on SALICON test dataset and have very competitive results on MIT300 Benchmark.
Assessment of the quality of well water in the spatial terraces of river code in Yogyakarta Indonesia

Abstract Many residents live on the banks of the Yogyakarta Code River. Some of them are still there who dispose of liquid waste directly into the river so that it has the potential to pollute the environment. Pollution also occurs in population wells. In general the condition of the riverbank is terraced, so that liquid waste originating from terrace 1 (the top) will flow to terrace 2 (the middle part), then flow to terrace 3 (bottom), and finally enter the river. The purpose of this study was to assess the quality of well water on the banks of the River Code, based on DO parameters, pH, DHL, Temperature, TDS, and E-Coli bacteria and to ascertain whether there was a direct correlation of pollution accumulation from high elevation to low elevation. The methods used include: identification of wells, measurement of the position of the well with GPS, plotting the location of wells to maps, sampling of well water, analyzing the quality of well water in the laboratory, and assessing the spatial water pollution of the well. Based on the results of the study, it can be concluded that there is a decrease in the quality of well water from high elevation to low elevation so that the quality of well water at low elevations is worse than the quality of well water at higher elevations. So that there is a direct correlation of accumulated pollution from well water at high elevations to lower elevations in the terraces of the Code River banks.
A Performance Analysis of Novel Classification Algorithm (C18) for Credit Card Fraud Detection

Payment by credit card is becoming very common today. Credit card is the simplest way to use your bank account. Despite the hype, it also has some security challenges. Fraudsters are the primary intruder in the event of loan card. Some unauthorized transactions can be accessed by these intruders. In this paper an efficient classification algorithm is developed for different datasets, to handle noisy data and produce results with more accuracy. The result of these proposed algorithm compared with Naïve Bayes and AdaBoost. We observe that the proposed C18 algorithm gives better result accuracy for the classification of credit card fraud detection.
WellComm2019

Most health tech for daily health and wellbeing is focused on individuals. We know that, with even the best will in the world, trying to practice what we learn in our workplaces, if we are the lone person in our group with that practice, it can be very challenging to sustain that practice. WellComm2019's goal is to explore how we can design interactive technology assets to enable diverse workplace groups to explore, test, build, and create evidence to support practices that will both improve their wellbeing for themselves.
Classification of Hyperfunctions of Rank 2 with Respect to Membership in the Maximal Partial Ultraclones

In this paper, we consider the set of hyperfunctions, which is a subset of the full partial ultraclone of rank 2. For hyperfunctions, the problem of their classification with respect to membership in the the maximal partial ultraclones is solved. The relation of membership in the maximal partial ultraclones is an equivalence relation and generates the corresponding partition into equivalence classes. A complete description of all equivalence classes, the total number of which is 28, is obtained
Service-Microservice Architecture for Context-Aware Content Delivery in National Geoinformation Center of Bulgaria

National Geoinformation Center of Bulgaria is a national scientific infrastructure – a consortium with a mission to produce and provide value-added products of Earth observation data gathered by various agencies governed by the state or received through international cooperation. The architecture of the information system of the center is presented – a layered structure that implements concepts of service and microservice, building an organization that reflects the federated nature of the consortium. The paper reviews various implemented design decisions within the perspective of context-aware systems. Four sources of data exploited for the determination of the context of the user are described. The paper concludes with three scenarios prepared as patterns for building context-aware services within the center.
Robust Detection of Defective Parts Using Pattern Matching for Online Machine Vision System

Reliable detection of defective parts is an essential step for ensuring high-quality assurance standards. This requirement is of primary importance for online vision-based automated pellet stacking system. During nuclear fuel pin manufacturing, the image of a single row (consecutively placed components with no gap) is processed and analyzed to extract meaningful edges. Generally, these edges follow a regular pattern; however, the presence of surface cracks and chips can alter this pattern. In this paper, we formalize the detection of defective parts as a pattern matching problem. Three different patterns are proposed and evaluated for sensitivity, specificity, and accuracy. An experiment performed with the proposed pattern matching techniques show that multi-pattern matching is the most effective method for identifying defective parts.
Word Embedding-Based Biomedical Text Summarization

In this paper, we have proposed a novel word embedding-based biomedical text summarizer. Biomedical words are represented by real dense vectors. Sentences are represented by summing-up the word vectors that contain. The PageRank algorithm is applied to rank sentences using the cosine similarity as a distance measure between sentences vectors. The top N highly ranked sentences are selected to build the summary. For the evaluation, we created a corpus of 200 biomedical papers downloaded from the Biomed Central full-text database. We used a pre-trained Word2vec model of word vectors generated from a combination of PubMed, PMC, and recent English Wikipedia dump texts. We compared our method with four other summarizers using: ROUGE-1, ROUGE-2, ROUGE-3, and ROUGE-SU4 metrics by evaluating the generated summaries with the abstracts of papers. Our summarizer achieved an improvement of 3.48%, 7.68%, 9.76%, and 3.47% respectively against the second-ranked summarizer.
Research on Object-Oriented Design Defect Detection Method Based on Machine Learning

Design defects are one of the main reasons for the decline of software design quality. Effective detection of design defects plays an important role in improving software maintainability and scalability. On the basis of defining software design defects, according to C&K design metrics and heuristics, this paper extracts the relevant features of design defects. Based on classical machine learning methods, classifiers are trained for design defect, and candidate designs are classified by classifiers, so as to identify whether there is a design defect in the design. Experiments show that the method has high accuracy and recall rate in identifying design defects.
Augmenting Datalog$$^{\frac{+}{}}$$ with Customizable Metalogic Features for Powerful Ontological Reasoning

In recent work by Gottlob et al., the usefulness and adequacy of Datalog, and precisely augmented with rules with existential heads to implement ontological reasoning has been shown. Also, it has been shown how to express forms of reasoning that go beyond the expressive capabilities of Description Logics. In our recent work [25] we present a methodology for introducing customizable metalogic features in logic-based knowledge representation and reasoning languages. We made the specific case of Answer Set Programming (ASP), where such features may be part of software engineering toolkits for this programming paradigm. In this paper we show how such metalogic features can further enrich with minor changes to its operational semantics (provided in terms of “chase”) and no additional complexity burden.
Optimally adjusted last cluster for prediction based on balancing the bias and variance by bootstrapping

Estimating a predictive model from a dataset is best initiated with an unbiased estimator. However, since the unbiased estimator is unknown in general, the problem of the bias-variance tradeoff is raised. Aside from searching for an unbiased estimator, the convenient approach to the problem of the bias-variance tradeoff may be to use the clustering method. Within a cluster whose size is smaller than the whole sample, we would expect the simple form of the estimator for prediction to avoid the overfitting problem. In this paper, we propose a new method to find the optimal cluster for prediction. Based on the previous literature, this cluster is considered to exist somewhere between the whole dataset and the typical cluster determined by partitioning data. To obtain a reliable cluster size, we use the bootstrap method in this paper. Additionally, through experiments with simulated and real-world data, we show that the prediction error can be reduced by applying this new method. We believe that our proposed method will be useful in many applications using a clustering algorithm for a stable prediction performance.
K-Means Clustering for Answer Categorization on Latent Semantic Analysis Automatic Japanese Short Essay Grading System

This paper discusses about the development of an automatic essay grading system for Japanese short essay answer by applying the K-Means Clustering to group each question's topic and Latent Semantic Analysis to make the assessment. The system is developed to help facilitate the examination of essay answers that are currently still being done manually. The development of the system itself is done by using Python programming language. The test scenarios were carried out by varying the types of hiragana and romaji input also the process of stopwords elimination. From the results obtained and the analysis carried out, the form or type of text input used and the use of parameter such as stopwords affect the accuracy of the assessment. The developed automatic essay grading system was able to obtain the highest accuracy rate of 89% by using input in the form of romaji letters and without the stopwords elimination process.
Time Series Modelling with Neural Networks

The main objective of time series analysis is to provide mathematical models that offer a plausible description for a sample of data indexed by time. Time series modelling may be applied in many different fields. In finance, it is used for explaining the evolution of asset returns. In actuarial sciences, it may be used for forecasting the number of claims caused by natural phenomenons or for claims reserving.
A Novel Method of Radar Antenna Scan Pattern Recognition Using Visibility Graph

Abstract Radar antenna scan has a great influence on reconnaissance targets, interference decision-making, and electronic support measures. In this paper, we propose a recognition methods of antenna scan pattern based on visibility graph, including circular scan, sector scan, helical scan, raster scan, conical scan, phased array scan, phased array azimuth scan and circular elevation scan. We have mapped the radar scans pattern, the azimuth and elevation curves of various scan pattern and the signal variation of the EW receiver. Moreover, the related features are extracted from the visibility graph and the seven radar scan patterns are classified by five kinds of classifiers. Simulation experiments verify the efficiency of the algorithm.
Application on weak information classification using Bayesian wavelet packet denoising

It is always a hot research issue to get weak fault information from severely contaminated data before further analysis. Wavelet denoising methods have been widely applied by researchers in many different applications. This paper presents the application of the Bayesian wavelet packet method on mec hanical equipment monitoring with an approach for selecting the wavelet decomposition levels based on the statistical autocorrelation function (ACF). Firstly, a reasonable decomposition level is selected; then, the degree of noise pollution of each wavelet coefficient can be got based on the Bayesian rule; finally, the estimated value of each wavelet coefficient can be obtained using the Bayesian hypothesis test. The denoising approach is compared with the conventional soft and hard threshold methods using signals simulated and obtained from track vibration test. The comparison result reveals the proposed method shows better denoising performance for weak information recognition.
Information Systems in Corporate Personnel Training

The use of information technologies in personnel management is an important part of modern labour relations in any organization. The purpose of the article is to present the algorithm, which makes it possible to develop and implement corporate information systems of personnel training. The article views modern tendencies of formation of personnel training information systems; the specifics of information systems of training, presented in the market, is shown; the factors increasing and decreasing the efficiency of their use as well as the risks of information training systems implementation, are described; the efficiency of corporate information systems of personnel training is justified.