Miracle-dz/UniFAR-FTUs huggingface dataset:
Res2s2aM: Deep residual network-based model for identifying functional noncoding SNPs in trait-associated regions

Noncoding single nucleotide polymorphisms (SNPs) and their target genes are important components of the heritability of diseases and other polygenic traits. Identifying these SNPs and target genes could potentially reveal new molecular mechanisms and advance precision medicine. For polygenic traits, genome-wide association studies (GWAS) are preferred tools for identifying trait-associated regions. However, identifying causal noncoding SNPs within such regions is a difficult problem in computational biology. The DNA sequence context of a noncoding SNP is well-established as an important source of information that is beneficial for discriminating functional from nonfunctional noncoding SNPs. We describe the use of a deep residual network (ResNet)-based model-entitled Res2s2aM-that fuses anking DNA sequence information with additional SNP annotation information to discriminate functional from nonfunctional noncoding SNPs. On a ground-truth set of disease-associated SNPs compiled from the Genome-wide Repository of Associations between SNPs and Phenotypes (GRASP) database, Res2s2aM improves the prediction accuracy of functional SNPs significantly in comparison to models based only on sequence information as well as a leading tool for post-GWAS noncoding SNP prioritization (RegulomeDB).
A Plagiarism Detection Engine for Images in Docode

Plagiarism is turning in someone else's work as your own. Though tools exist for checking the originality of texts, those do not work with images, which can be plagiarized as well. In previous works, we have developed the Docode plagiarism detection system, which works with text, but, as commercial requirements have evolved, it is necessary for it to be able to work with images. In this work we present a plagiarism detection engine for images, which works by fusing texture and color features in a weighted combination, so that it can work as a general-purpose engine. We ran experiments with the system, analyzing the improvement made by fusing color and texture features, and the impact of downsizing images on the performance of the system. We see that we reach a recall at 10 elements of 80% and the system allows an image compression of one half without considerable impact on its performance, and with this we can conclude we can build a plagiarism detection engine for images, able to handle general collections for its integration in the Docode system.
Comparative study of visual saliency maps in the problem of classification of architectural images with Deep CNNs

Incorporating Human Visual System (HVS) models into building of classifiers has become an intensively researched field in visual content mining. In the variety of models of HVS we are interested in so-called visual saliency maps. Contrarily to scan-paths they model instantaneous attention assigning the degree of interestingness/saliency for humans to each pixel in the image plane. In various tasks of visual content understanding, these maps proved to be efficient stressing contribution of the areas of interest in image plane to classifiers models. In previous works saliency layers have been introduced in Deep CNNs, showing that they allow reducing training time getting similar accuracy and loss values in optimal models. In case of large image collections efficient building of saliency maps is based on predictive models of visual attention. They are generally bottom-up and are not adapted to specific visual tasks. Unless they are built for specific content, such as "urban images"-targeted saliency maps we also compare in this paper. In present research we propose a "bootstrap" strategy of building visual saliency maps for particular tasks of visual data mining. A small collection of images relevant to the visual understanding problem is annotated with gaze fixations. Then the propagation to a large training dataset is ensured and compared with the classical GBVS model and a recent method of saliency for urban image content. The classification results within Deep CNN framework are promising compared to the purely automatic visual saliency prediction.
Detection proposal method based on shallow feature constraints

Rapid detection of small or non-salient attacking objects constitutes the dominant technical concern for prevention of airport bird strike. According to changes of the object observed from far to near, a novel detection proposal method based on shallow feature constraints (ShallowF) is thus proposed. Specifically, the object is located approximately by virtue of feature points, narrowing search spaces, reducing the number of sampling frames, and improving the efficiency of detection proposals. Then sampling rules are specified by connected domains and feature points, further narrowing search spaces and reducing the number of sampling frames. Finally, based on the difference between the target contour and the background, the structured edge group in the bounding boxes is extracted as the scoring basis for target detection before test and validation on the COCO Bird Dataset [1] and the VOC2007 Dataset [2]. Compared with the most advanced detection proposal methods, this method can improve the accuracy of candidate bounding boxes while reducing their quantity.
A Look At Non-Cooperative Presentation Attacks in Fingerprint Systems

Scientific literature lacks of countermeasures specifically for fingerprint presentation attacks (PAs) realized with non-cooperative methods; even though, in realistic scenarios, it is unlikely that individuals would agree to duplicate their fingerprints. For example, replicas can be created from finger marks left on a surface without the person’s knowledge. Existing anti-spoofing mechanisms are trained to detect presentation attacks realized with cooperation of the user and are assumed to be able to identify non-cooperative spoofs as well. In this regard, latent prints are perceived to be of low quality and less likely to succeed in gaining unauthorized access. Thus, they are expected to be blocked without the need of a particular presentation attack detection system. Currently, the lowest Presentation Attack Detection (PAD) error rates on spoofs from latent prints are achieved using frameworks involving Convolutional Neural Networks (CNNs) trained on cooperative PAs; however, the computational requirement of these networks does not make them easily portable for mobile applications. Therefore, the focus of this paper is to investigate the degree of success of spoofs made from latent fingerprints to improve the understanding of their vitality features. Furthermore, we experimentally show the performance drop of existing liveness detectors when dealing with non-cooperative attacks and analyze the quality estimates pertaining to such spoofs, which are commonly believed to be of lower quality compared to the molds fabricated with user’s consensus.
Comparison of Two Distributed Fault Diagnosis Approaches based on Binary Integer Linear Programming (BILP) Optimization

Two distributed fault diagnosis approaches were compared, by analogy, to determine which is more efficient regarding computational complexity. The first approach considered all “locally computed” global and compound sets with minimal cardinality using a heuristic optimization method while minimizing subsystems interactions (communication). The second approach aimed at obtaining minimal coupled MSOs for minimizing the number of common links between MSOs by adding constraints in already existing optimal sensor placement algorithm, which uses BILP, but not in a distributed context. As a result of comparison, complexity of both approaches is characterized.
Multiscale parallel feature extraction convolution neural network for image denoising

Abstract. Image denoising based on a convolution neural network (CNN) can be described as the problem of learning a mapping function from a noisy image to a clean image through an end-to-end training. We propose a multiscale parallel feature extraction module (MPFE) for CNN denoising, which integrates residual learning and dense connection. The MPFE uses convolution kernels of different sizes to adaptively extract multiple features in different scales from the input image. We introduce dense connection to connect each MPFE, which can make different features interact with each other and concatenate together, so as to fully exploit the image features. The dense connection can pass the features that carry many image details, which help reduce image distortion. Meanwhile, it can also reduce gradient disappearance and improve convergence speed. The MPFE uses residual learning to resolve the gradient loss caused by high network depth while still ensuring that the network learns the details of the noisy image. Simulation experiments show that our denoising method has the ability of suppressing Gaussian noises with different noise levels, it performs superior performance over the state-of-the-art denoising methods.
Personality Exploration System for Online Social Networks: Facebook Brands As a Use Case

User-generated content on social media platforms is a rich source of latent information about individual variables. Crawling and analyzing this content provides a new approach for enterprises to personalize services and put forward product recommendations. In the past few years, brands made a gradual appearance on social media platforms for advertisement, customers support and public relation purposes and by now it became a necessity throughout all branches. This online identity can be represented as a brand personality that reflects how a brand is perceived by its customers. We exploited recent research in text analysis and personality detection to build an automatic brand personality prediction model on top of the (Five-Factor Model) and (Linguistic Inquiry and Word Count) features extracted from publicly available benchmarks. The proposed model reported significant accuracy in predicting specific personality traits form brands. For evaluating our prediction results on actual brands, we crawled the Facebook API for 100k posts from the most valuable brands' pages in the USA and we visualize exemplars of comparison results and present suggestions for future directions.
Web Page Recommendation from Sparse Big Web Data

In many real-life web applications, web surfers would like to get recommendation on which collections of web pages that would be interested to them or that they should follow. In order to discover this information and make recommendation, data analytics—and specially, association rule mining or web data mining—is in demand. Since its introduction, association rule mining has drawn attention of many researchers. Consequently, many association rule mining algorithms have been proposed for finding interesting relationships—in the form of association rules—among frequently occurring patterns. For instance, in IEEE/WIC/ACM WI 2016 and 2017, serial and parallel algorithms were proposed to find interesting web pages. However, like most of the existing association rule mining algorithms, these two algorithms also were not designed for mining big data. Moreover, the search space of web pages can sparse in the sense that web pages are connected to a small subset of all web pages in the search space. In this paper, we present a compact bitwise representation for web pages in the search space. Such a representation can then be used with a bitwise serial or parallel association rule mining system for web mining and recommendation. Evaluation results show the effectiveness of our compression and the practicality of our algorithm—which discovers popular pages on the web, which in turn gives the web surfers recommendation of web pages that might be interested to them—in real-life web applications.
Knowledge-based Identication of Emotional Status on Social Networks

A knowledge based methodology is proposed for the content understanding and sentiment identi cation of the shared comments in social networks. The goal of this work is to retrieve the sentiment information associated to an opinion and classify it by its polarity and sentiment by means of a semantic analysis. Our approach implements knowledge graphs, similarity measures, graph theory algorithms and disambiguation processes. The results obtained were compared with data retrieved from Twitter and users' reviews in Amazon. We measured the e ciency of our contribution with precision, recall and F-measure comparing it with the traditional method of just looking up concepts in sentiment dictionaries which usually assigns averages. Moreover an analysis was carried out in order to nd the best performance for the classi cation by using polarity, sentiment and a polarity-sentiment hybrid . A study is presented for remarking the advantage of using a disambiguation process in knowledge processing.
Extraction of Functional Structure Graph from System Design Documents

In system development, reuse of modules from existing systems is an important issue. However, such reuse becomes difficult in similar but customized applications because the relations among module functions are complicated. To address this problem, we aim to establish a technology that can extract, from design documents, a knowledge graph that represents the functional structure of a system and use it in development of other systems. In this paper, we introduce our knowledge-graph extraction framework and describe an experiment show a partial extraction.
Measuring Architectural Misfit: A Preliminary Real Life Study

Context: A well suited physical structure and architecture is vital for large-scale software development as they impact success of development and the whole project. During development however, a well-designed architecture is vulnerable to subtle decay. Objective: This preliminary study aims for initial indication of this phenomenon for a large-scale software project consisting of hundreds of developers spread all over the world. Information about architectural fit especially over time could be useful for management and software architects. In fact, moves of reengineering could then be anticipated and calibrated to happen regularly. While development would be deferred, the architecture of the software product would be manageable and agility would be preserved. Method: A sliding window approach over the time has been applied. Then for each time window, the prediction error of the duration of an incremental build job estimated by Random Forest models has been measured. Results: As measurements indicate, prediction error increases with advancement in time. Conclusion: Results indicate an increase in misfit.
Joint Deep Learning and Clustering Algorithm for Liquid Particle Detection of Pharmaceutical Injection

At present, the detection of pharmaceutical injection products is a quite important step in the pharmaceutical manufacturing, as it has the direct related to the quality of medical product quality. Aiming at the difficulty that liquid particle has a smaller pixel point in the high resolution image of detection of pharmaceutical liquid particle, hence consider combined with deep neural network and clustering algorithm for detection and localization of little particle, and a processing method combining single frame images with multi-frame images was proposed to identifying liquid particle. Firstly, the single-frame image is detected by using Faster-RCNN deep neural network, and it can obtain the detection result of the 8-frame sequence image. Then hierarchical clustering and K-means clustering algorithm are used for clustering to obtain the same target motion area. In this way, liquid particle can be more accurately identified and the accuracy of detection can be greatly improved. The experimental results show that the accuracy of detection and recognition of foreign substances in liquid medicine is improved by more than 10% on average.
PubTag: Generating Research Tag-Clouds with Keyphrase Extraction and Learning-to-Rank

We investigate automated methods to generate tag-clouds for Computer Science researchers based on keyphrase extraction methods and learning-to-rank models. Given as input the identifier of an author in a bibliographical database (currently DBLP), the method extracts links to the PDFs containing the full-text of the paper. Keyphrase extraction methods are then applied to extract multi-term tags from the text. In order to select the most important tags for the researcher, we propose a set of features that serve as input for a variety of learning-to-rank models. Evaluation is conducted with respect to 12 Computer Science professors, who score a selection of keyphrases extracted from their papers indicating their relevance as a description of research topics. These scores are used to train and compare various learning-to-rank models for reordering the most important keyphrases, which in turn are used to generate final tag clouds for the professors. We further validate the proposed approaches by asking professors to evaluate the final tag-clouds.
Using LNT Formal Descriptions for Model-Based Diagnosis

Providing models for model-based diagnosis has always been a challenging task. There has never been an agreement on an underlying modeling language, making it almost impossible to share models within our community. In addition, there are other domains like formal methods or model-based testing relying on system models for formal verification and automated test case generation. Although, there we face the situation of different modeling languages as well, the question remains whether it is possible to re-use these models in the context of model-based diagnosis. In this paper , we elaborate on this question and show how models written in LNT can be used for fault local-ization only requiring simple modification. This allows re-using formal method's models for diagnosis directly. Besides discussing the underlying principles, we also present a use case showing the applicability of the methods.
Analytics for the Measurement of Process Dimensions of Self-Regulated Learning and Feedback Impact

Blended learning environments have the potential to provide educators with valuable insights into learner behaviours and strategies. Capturing and analysing learner data, using traditional frequency-based statistical methods, is a challenge if the objective is to understand self-regulated learning (SRL) as a dynamic process. Current research on SRL has recognised the potential of data science methods for analysis of temporal processes. The current studies, however, rely on think-aloud data and other self-report measures, which are costly and in-trusive. The use of trace data is a promising alternative, though raw trace data, as captured by contemporary learning environments, is insufficient to represent self-regulation processes (aka micro-level SRL processes) as theorised in well-known models. To address these challenges, this research aims to 1) improve the measurement of SRL by deriving micro-level processes from trace data; 2) analyse these micro-level processes for temporal associations; 3) explore how such temporal associates between micro-level processes are correlated with learning strategies; and 4) assess the impact of formative data-driven feedback on these SRL processes. We undertook a preliminary study and found that certain temporal activity traits relate to performance in the summative assessments attached to the course, mediated by strategy type. In addition, more strategically minded activity, embodying learner self-regulation, generally proves to be more successful than less disciplined reactive behaviours.
Analysis of User Dwell Time by Category in News Application

Dwell time indicates how long a user looked at a page, and this is used especially in fields where ratings from users such as search engines, recommender systems, and advertisements are important. Despite the importance of this index, however, its characteristics are not well known. In this paper, we analyze the dwell time of news pages according to category in smartphone application. Our aim is to clarify the characteristics of dwell time and the relation between length of news page and dwell time, for each category. The results indicated different dwell time trends for each category. For example, the social category had fewer news pages with shorter dwell time than peaks, compared to other categories, and there were a few news pages with remarkably short dwell time. We also found a large difference by category in the correlation value between dwell time and length of news page. Specifically, political news had the highest correlation value and technology news had the lowest. In addition, we found that a user tends to get sufficient information about the news content from the news title in short dwell times.
Extracting Sentiment Attitudes from Analytical Texts via Piecewise Convolutional Neural Network

For deep text understanding, it is necessary to explore the connections between text units mentioning events, entities, etc. Depending on the further goals, it allows to consider the text as a graph of task-specific relations. In this paper, we focused on analysis of sentiment attitudes, where the attitude represents a sentiment relation from subject towards object. Given a mass media article and list of mentioned named entities, the task is to extract sentiment attitudes between them. We propose a specific model based on convolutional neural networks (CNN), independent of handcrafted NLP features. For model evaluation, we use RuSentRel 1.0 corpora, consisted of mass media articles written in Russian.
Location-Based Test Case Prioritization for Software Embedded in Mobile Devices Using the Law of Gravitation

Considering that some intelligent software in mobile devices is related to location of sensors and devices, regression testing for it faces a major challenge. Test case prioritization (TCP), as a kind of regression test optimization technique, is beneficial to improve test efficiency. However, traditional TCP techniques may have limitations on testing intelligent software embedded in mobile devices because they do not take into account characteristics of mobile devices. This paper uses a smart mall as a scenario to design a novel location-based TCP technique for software embedded in mobile devices using the law of gravitation. First, test gravitation is proposed by applying the idea of universal gravitation. Second, a specific calculation model of test gravitation is designed for a smart mall scenario. Third, how to create a faulted test case set is designed by the pseudocode. Fourth, a location-based TCP using the law of gravitation algorithm is proposed, which utilizes test case information, fault information, and location information to prioritize test cases. Finally, an empirical evaluation is presented by using one industrial project. The observation, underlying the experimental results, is that our proposed TCP approach performs better than traditional TCP techniques. In addition, besides location information, the level of devices is also an important factor which affects the prioritization efficiency.
Toward a Voice Querying on Smart Speaker via SPARQL

In this paper, we present a mechanism and its prototype design to realize speak to query interface for querying SPARQL queries via smart speakers. Our prototype system allows us to retrieve results from an endpoint as a voice command. Additionally, our approach tries to create the answers for the given SPARQL queries by voice commands via smart speakers.