Reviewerly/exHarmony huggingface dataset:
Deep Neural Networks Applied to User Recognition Based on Keystroke Dynamics

Several studies have investigated how to use Machine Learning algorithms to recognize users based on keystroke dynamic. All those studies required Feature Engineering (FE), i.e., a process in which specialists choose what attributes should be considered for learning. However, this process is susceptible to problems such as original information loss or inappropriate attribute choices. Thus, the objective of this work is to demonstrate the hypothesis that user recognition algorithms applied to keystroke dynamics raw (original) data can perform better than the ones that depend on FE. Therefore, this work proposes a deep neural network named DRK. The proposed network contains layers that learn adequate data representations to perform user recognition based on keystroke dynamics raw data, avoiding FE. Experiments compared DRK with four other deep neural networks that use FE in four datasets with 280 users. The proposed network achieved better results in all datasets, showing strong evidence that the stated hypothesis is, in fact, valid.
An Integrated Requirements Engineering Framework for Agile Software Product Lines

Requirements engineering (RE) techniques play a determinant role within Agile Product Lines development methods; these notably allow to establish the relevance to adopt or not the product line approach for software-intensive systems production. This paper proposes an integrated goal and feature-based meta-model for agile software product lines development. The main objective is to permit the sepecification of the requirements that precisely capture stakeholder's needs and intentions as well as the management of product line variabilities. Adopting practices from requirements engineering, especially goal and feature models, helps designing the domain and application engineering tiers of an agile product line. Such an approach allows a holistic perspective integrating human, organizational and agile aspects to better understand product lines dynamic business environments. It helps bridging the gap be-tween product lines structures and requirements models, and proposes an integrated framework to all actors involved in the product line architecture. In this paper we show how our proposed metamodel can be applied to the requirements engineering stage of an agile product line development mainly for feature-oriented agile product lines such as our own methodology called AgiFPL.
Research on an improved anisotropic diffusion filtering method

PreviousNext No AccessSEG Technical Program Expanded Abstracts 2019Research on an improved anisotropic diffusion filtering methodAuthors: Feng ChaoZhang HuquanPan shuxinYao QingzhouWang HongbinFeng ChaoResearch Institute of Petroleum Exploration & Development-Northwest, CNPCSearch for more papers by this author, Zhang HuquanResearch Institute of Petroleum Exploration & Development-Northwest, CNPCSearch for more papers by this author, Pan shuxinResearch Institute of Petroleum Exploration & Development-Northwest, CNPCSearch for more papers by this author, Yao QingzhouResearch Institute of Petroleum Exploration & Development-Northwest, CNPCSearch for more papers by this author, and Wang HongbinResearch Institute of Petroleum Exploration & Development-Northwest, CNPCSearch for more papers by this authorhttps://doi.org/10.1190/segam2019-3215650.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractFracture is an important channel for hydrocarbon migration and is also conducive to reservoir development. Fault identification is of great significance in oil and gas exploration. However, noise in seismic data severely interferes with fault identification. How to reduce noise in seismic data while preserving features of faults is the key problem. Based on the work of previous researchers and detailed comparison of physical diffusion in nature and diffusion filtering algorithms, an improved anisotropic diffusion filtering method has been proposed by us in this paper. The uniqueness of the method is that it takes the variation of diffusion media into consideration, making it easier to reduce noise while preserving the edge of seismic image. The formula was established here and our idea was realized by repeated estimation of parameters of the changing media .Finally, an example was offered to show the effect of this method by comparing it with the dipguided filtering method and with the traditional anisotropic diffusion filtering method.Presentation Date: Wednesday, September 18, 2019Session Start Time: 9:20 AMPresentation Start Time: 11:25 AMLocation: Poster Station 12Presentation Type: PosterKeywords: filtering, anisotropy, faults, noise, attributesPermalink: https://doi.org/10.1190/segam2019-3215650.1FiguresReferencesRelatedDetailsCited byAnalysis of Histopathological images: An Overview SEG Technical Program Expanded Abstracts 2019ISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2019 Pages: 5407 publication data© 2019 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 01 Aug 2019 CITATION INFORMATION Feng Chao, Zhang Huquan, Pan shuxin, Yao Qingzhou, and Wang Hongbin, (2019), "Research on an improved anisotropic diffusion filtering method," SEG Technical Program Expanded Abstracts : 4705-4709. https://doi.org/10.1190/segam2019-3215650.1 Plain-Language Summary KeywordsfilteringanisotropyfaultsnoiseattributesPDF DownloadLoading ...
A bootstrap‐augmented alternating expectation‐conditional maximization algorithm for mixtures of factor analyzers

Finite mixture models are a popular approach for unsupervised machine learning tasks. Mixtures of factor analyzers assume a latent variable structure, thereby modelling the data in a lower dimensional space. Herein, we augment the traditional alternating expectation‐conditional maximization algorithm by incorporating the nonparametric bootstrap during the parameter estimation process. This augmentation is shown to improve discovery of both the true number of groups and the true latent dimensionality through simulations, while also showing superior clustering performance on benchmark data sets.
Effective Quaternion and Octonion Cryptosystems and Their FPGA Implementation

An approach for effective hardware implementation of the proposed quaternion encryption algorithm (HW-R4) as well as modifications for known quaternion HW-QES and octonion HW-OES schemes are discussed. Instead of 3-D rotations as usually, 4-D transformations for encryption with quaternions and 8-D with octonions are suggested. Such size of transformation matrices increases the size of plaintext/ciphertext blocks and eliminates the need to calculate elements of rotation matrices. To speed up an encryption process the HW-R4, HW-QES, and HW-OES include mainly addition and shift operations with modular arithmetic. In our experiments, we used a product Intel (former Altera) OpenCL SDK (AOCL), which allows compiling OpenCL programs for FPGAs. The launch of the developed algorithms was carried out on two devices: the Intel Core i7 920 CPU and the Terasic DE5-Net FPGA (Stratix V). Experimental results show that the proposed algorithms and modifications are about 30–50% more effective in the encryption speed of signals than the original HW-QES/HW-OES. Additionally, HW-R4 is shown to be more effective in the encryption quality of images than the original QES. Our approach can also be used for robustness increasing when the Feistel network is added to the system.
LIT: Rule Based Italian Lemmatizer

In natural language processing applications, such as those related to question answering systems, and more specifically, to semantic role labelling, an important task to perform during the text normalization phase is lemmatization which consists in determining those two words which have the same root, despite their surface differences. Due to the lack of a practical lemmatizing tool suitable for the Italian language (which is a highly inflectional one), in this paper we aim to present LIT, a Rule based Italian lemmatizer consisting of a full rule-base lemmatization of all dictionary-words and a discovery algorithm which attempts to predict the grammar of neologisms. This is followed by a practical application of LIT on Europarl v7, a well-known open corpus.
A Sentiment Measurement Model for Online Reviews of Complex Products

In order to analyze the polarity and intensity of the fine-grained emotion based on online reviews of complex products, and to verify the difference between the rating and the sentiment of reviews, a sentiment measurement model based on a domain ontology, named DO-SSM(Sentiment Score Measurement model based on the Domain Ontology, DO-SSM) is proposed. Firstly, build a fine-grained domain ontology based on features of the complex product, then leverage the fine-grained emotional intensity quantification method to obtain the emotional intensity taxonomy of online reviews. Empirical study indicates that the resulting emotional intensity taxonomy of the complex product online reviews of the model can achieve high accuracy and interpretability. The proposed model can provide decision support for users and enterprises, offering corrective information for online review sites.
Using Local Objects to Improve Estimation of Mobile Object Coordinates and Smoothing Trajectory of Movement by Autoregression with Multiple Roots

The paper deals with the task of positioning an autonomous mobile object. At the beginning, attention was paid to popular modern methods used in robotics. Then equations for estimating coordinates in the case of the SLAM algorithm are described briefly. At the same time, models of movement, observations and filtering in the case of movement without specified landmarks are described in detail. These models are based on autoregressive equations, and Kalman vector filter is implemented for filtering. In addition, the models are modified when the first landmark is highlighted in the visibility zone, as well as during the subsequent change (appearance and disappearance) of new landmarks. The task of building a path on a plane along selected landmarks is considered. On the basis of autoregression with multiple roots of characteristic equations, the SLAM algorithm was modified, which allowed smoothing the trajectory of the mobile object and get a better characteristics when estimating the trajectory using this model.
Colour Texture Features Based Approach for White Blood Cells Segmentation

Blood cell segmentation is an important research topic in Hematology and other related fields. In this article, a technique for microscopic images segmentation is proposed in order to extract the white blood cells (WBC) and its components (nucleus, cytoplasm) from the red blood cells and plasma. The image is represented in different color spaces, Haralick features extracted from the chromatic co-occurrence matrices (CCM) are used to characterize the textures present in these color images. A pre-treatment is carried out to extract the background (plasma) in order to reduce the execution time and noise. Segmentation has been done by supervised pixel-based classification using support vector machines (SVM). The proposed method was tested on twenty-seven real microscopic color images with promising results and nucleus recognition accuracy reaching 95%.
Mapping and visualization of complex relational structures in the graph form using the Neo4j graph database

Various mechanisms of mapping relational structures in graph database Neo4j presented in previous publications describing both detailed and more universal solutions, did not take into account the whole complexity of relational databases. The main aim of the aforementioned actions was to utilize the possibilities of graph database of programming languages paying special attention to searching for connections between tables when both their number and relations is very high and documentation is incomplete or unavailable. The effects of queries realization were presented with graph form, which resulted from the potential of the client program of the platform called Neo4j used for the needs of this research. The problem that the authors have failed to take into consideration so far, and that is presented now, is the case of intermediate tables occurrence, which are used in great measure to eliminate many to many relations on the level of relational bases as well as to eliminate relations based on complex keys. Another improvement of the presented application is new user interface, which eliminates the necessity for user of having Cypher language skill in order to query graph database. At the same time the application ensures results visualization in richer form than client program with the engine Neo4j that is available and offered.
Bi-directional Features Reuse Network for Salient Object Detection

Recently, unidirectional convolutional neural networks have been widely used for salient object detection. However, most methods cannot solve common problems (i.e., the loss of valid information, tiny predicted feature, and isolated features in one block), which lead to inefficient feature reuse and blurred salient object edges. To address these problems, we propose a novel bi-directional features reuse network (BDFRN) for salient object detection, which consists of two subnets: forward-skip subnet and reverse-connect subnet. The forward-skip subnet employs an encoder-decoder structure to remedy the loss of salient details, and progressively refine the size of the predicted feature; meanwhile, the reverse-connect subnet can transmit the location features from top blocks to bottom blocks, such that these features can be reused and communicated between different blocks. Extensive experiments are conducted to demonstrate the performance of the proposed method, as compared with baseline methods.
Interfaces Used for Smartglass Devices into the Augmented Reality Projects

This paper addresses the themes of the interfaces for the devices usable in the virtual reality projects, respectively augmented reality. Smart goggles are typically used to implement these. Today’s device interfaces are designed for use on flat screens, such as monitors, TVs, or smart phone screens. These screens are located some distance away from the eyes of the user, while the lenses of the smart glasses will be just a few millimeters away. Intelligent eyeglass interfaces will have several patterns, some framed in the user’s field of view, others will require sliding on their surface. Everyone’s field of view is unique, and this will require a calibration of the device so that the graphics can be readable.
Meta-design of Processes Based on Visualization Tools

Interactive visualization, used to represent and interpret input data, makes it possible to employ visual perception potential to search for and resolve internal contradictions in the studied data, the source of which in many cases is errors made during the development of the program. The purpose of visual analytics in this study is to identify contradictions in the design of an educational process, provided by the curriculum, and to form students’ meaningful variable and individual educational trajectories.
Hierarchical Convolutional Attention Networks Using Joint Chinese Word Embedding for Text Classification

We propose a hierarchical convolutional attention network using joint Chinese word embedding for text classification. Compared with previous methods, our model has three notable improvements: (i) it considers not only words but also their characters and fine-grained sub-character components; (ii) it employs self-attention mechanisms with the benefits of convolution feature extraction, enable it to attend differentially to more and less important content; (iii) it has a hierarchical structure that can get the document vector. We demonstrate the effectiveness of our architecture by surpassing the accuracy of the current state-of-the-art on four classification datasets. Visualization of our hierarchical structure illustrates that our model is able to select informative sentences and words in a document.
Research of Improved Particle Swarm Optimization Algorithm Based on Big Data

With the era of big data, the problems of data size and data optimization have become more diversified and complicated, thus the optimization method has become the focus of people's attention. However, the group intelligence algorithm is used to simulate the social biological groups in nature by using efficient optimization algorithms to solve practical problems in various fields. In this paper, an improved hybrid algorithm is proposed to overcome the shortcomings of complex optimization problems. Through the test of standard function, the convergence rate and convergence precision of the hybrid algorithm are both superior to the particle swarm optimization algorithm and Artificial Bee Colony algorithm.
Belief Change Properties of Forgetting Operations over Ranking Functions

Intentional forgetting means to deliberately give up information and is a crucial part of change or consolidation processes, or to make knowledge more compact. Two well-known forgetting operations are contraction in the AGM theory of belief change, and various types of variable elimination in logic programming. While previous work dealt with postulates being inspired from logic programming, in this paper we focus on evaluating forgetting in epistemic states according to postulates coming from AGM belief change theory. We consider different forms of contraction, marginalization, and conditionalization as major representatives of forgetting operators to be evaluated. We use Spohn's ranking functions as a common semantic base to show that all operations can be realized in one logical framework, thereby exploring the richness of forgetting operations in a comparable way.
Mixed Reality for Industry? An Empirical User Experience Study

Due to the development of both computational power of mobile devices and display technology mixed reality solutions have become possible. As the entertainment industry has taken the technology eagerly in use, it has also opened several possibilities to develop solutions to enhance and support work processes in industrial and maritime use. In this paper user-testing of a few of these prototypes of mixed reality solutions is presented.
Understanding Shopping Behaviors With Category- and Brand-Level Market Basket Analysis

In a digital transformation environment, most businesses shift towards e-business and encounter businesses and customer interaction on digital channels. Information Technology renders data access and processing more efficient, and use of customer data in decision making has become a focal interest area that attracts researchers. Customer data is a relevant subject for numerous studies in Data Mining. In this chapter, Association Rule Mining has been utilized to extract purchase behavior patterns with a multilevel approach. Basket data obtained from an online retailer was analyzed to discover purchase behaviors with a focus on category and brand attributes of products. Brands and categories purchased together frequently were discovered. Brand and category-wise association rules were also presented in the results. The analysis differs from the majority of prior analyses, by referring to the category and brand attributes in basket data. It could be noted that generalized rules obtained with this approach might prove useful in recommending new items of existing brands or categories.
A Predictive Modeling for Detecting Fraudulent Automobile Insurance Claims

Fraudulent automobile insurance claims are not only a loss for insurance companies, but also for their policyholders.The goal of this research is to develop, first, a decision-making algorithm to classify whether a claim is classified as fraudulent or not; and, second, what types of variables should be focused to detect fraudulent claims.To achieve this goal, highly accurate prediction models are built by discovering important sets of features via variable selection algorithms, which can in turn help prevent future loss.In this research, parametric and nonparametric statistical learning algorithms are considered to reduce uncertainty and increase the chances of detecting the appropriate claims.An important set of features for a model is determined by measuring variable importance based on the observed characteristics of a claim via a cross-validation and by testing improvement of the performance at which automobile fraudulent claims are accurately classified using Akaike Information Criterion.We could achieve accuracy above 95% with a set of features selected via a cross-validation.This research would offer some benefit to the insurance industry for their fraud detection research in order to prevent insurance abuse from escalating any further.
Estimation of Average Information Content: Comparison of Impact of Contexts

In this paper, we compare Linear Mixed Effect Models (LMM) which utilise the predictors Average Information Content (IC) and frequency for the prediction of lengths of aspect-marked verbs. IC is the information which target elements convey to their context. Focusing on typologically diverse languages, we took as contexts dependency frames and n-grams, and found that IC estimated from n-grams outperforms IC estimated from dependency frames: the models which utilise IC from n-grams achieve high correlations between predicted and actual verbs’ lengths, while models which utilise IC form dependency frames perform poorly. Only in few languages we found prediction effects of IC.
Recommending Unnecessary Source Code Based on Static Analysis

Grown software systems often contain code that is not necessary anymore. Unnecessary code wastes resources during development and maintenance, for example, when preparing code for migration or certification. Running a profiler may reveal code that is not used in production, but it is often time-consuming to obtain representative data this way. We investigate to what extent a static analysis approach which is based on code stability and code centrality, is able to identify unnecessary code and whether its recommendations are relevant in practice. To study the feasibility and usefulness of our static approach, we conducted a study involving 14 open-source and closed-source software systems. As there is no perfect oracle for unnecessary code, we compared recommendations of our approach with historical cleanup actions, runtime usage data, and feedback from 25 developers of 5 software projects. Our study shows that recommendations generated from stability and centrality information point to unnecessary code. Our results suggest that static analysis can provide quick feedback on unnecessary code that is useful in practice.
A Lost Croatian Cybernetic Machine Translation Program

We are exploring the historical significance of research in the field of machine translationMachine translation conducted by Bulcsú László, Croatian linguist, who was a pioneer in machine translationMachine translation in Yugoslavia during the 1950s. We are focused on two important seminal papers written by members of his research group between 1959 and 1962, as well as their legacy in establishing a Croatian machine translationMachine translation program based around the Faculty of Humanities and Social Sciences of the University of Zagreb in the late 1950s and early 1960s. We are exploring their work in connection with the beginnings of machine translationMachine translation in the USA and USSR, motivated by the Cold War and the intelligence needs of the period. We also present the approach to machine translationMachine translation advocated by the Croatian group in Yugoslavia, which is different from the usual logical approaches of the period, and his advocacy of cyberneticCybernetics methods, which would be adopted as a canon by the mainstream AI community only decades later.