Computing and Informatics (E-Journal - Institute of Informatics, SAS, Bratislava)
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Determining the Relative Importance of Personality Traits in Influencing Software Quality and Team Productivity
Get PDFSoftware projects are almost always team efforts and successful projects involve well-formed and well-composed teams. Past studies have revealed that personality contributes to effective team composition and, therefore, project success. Yet despite its importance, only a couple of empirical studies have quantitatively evaluated the impact of personality on software quality and team productivity. Our previous study was an effort in this direction. In that study, we proposed a metric called Team Homogeneity Index and evaluated its impact on software quality and team productivity for two phases (implementation and testing) of the software development life cycle. This study is a continuation of our previous work. In this study, we replicate our experiment on three different phases of software development life cycle (i.e. analysis and design, implementation, and testing). We also determine the weights for all five personality traits using input from the industry and propose an improved version of Team Homogeneity Index called Weighted Team Homogeneity Index. Finally, we conduct a comparative analysis of Team Homogeneity Index and Weighted Team Homogeneity Index to determine whether weights assigned to personality traits make any difference. Our findings reveal that weights do make a difference and Weighted Team Homogeneity Index is more strongly correlated than Team Homogeneity Index for almost all of the teams, especially those composed of practitioners, in the three different phases of Software Development Life Cycle
Concept Similarity in Formal Concept Analysis with Many-Valued Contexts
Get PDFFormal Concept Analysis (FCA) is a mathematical framework which can also support critical activities for the development of the Semantic Web. One of them is represented by Similarity Reasoning, i.e., the identification of different concepts that are semantically close, that allows users to retrieve information on the Web more efficiently. In order to model uncertainty information, in this paper FCA with many-valued contexts is addressed, where attribute values are intervals, which is referred to as FCA with Interordinal scaling (IFCA). In particular, a method for evaluating concept similarity in IFCA is proposed, which is a problem that has not been adequately investigated, although the increasing interest in the literature in this topic
Event Detection in Twitter Using Multi Timing Chained Windows
Get PDFTwitter is a popular microblogging and social networking service. Twitter posts are continuously generated and well suited for knowledge discovery using different data mining techniques. We present a novel near real-time approach for processing tweets and detecting events. The proposed method, Multi Timing Chained Windows (MTCW), is independent of the language of the tweets. The MTCW defines several Timing Windows and links them to each other like a chain. Indeed, in this chain, the input of the larger window will be the output of the smaller previous one. Using MTCW, the events can be detected over a few minutes. To evaluate this idea, the required dataset has been collected using the Twitter API. The results of evaluations show the accuracy and the effectiveness of our approach compared with other state-of-the-art methods in the event detection in Twitter
Deep LSTM with Guided Filter for Hyperspectral Image Classification
Get PDFHyperspectral image (HSI) classification has been a hot topic in the remote sensing community. A large number of methods have been proposed for HSI classification. However, most of them are based on the extraction of spectral feature, which leads to information loss. Moreover, they rarely consider the correlation among the spectrums. In this paper, we see spectral information as a sequential data which should be relevant to each other. We introduce long short-term memory (LSTM) model, which is a typical recurrent neural network (RNN), to deal with HSI classification. To tackle the problem of overfitting caused by limited labeled samples, regularization strategy is introduced. For unbalance in different classes, we improve LSTM by weighted cost function. Also, we employ guided filter to smooth the HSI that can greatly improve the classification accuracy. And we proposed a method for modeling hyperspectral sequential data, which is very useful for future research work. Finally, the experimental results show that our proposed method can improve the classification performance as compared to other methods in three popular hyperspectral datasets
Multi-Platform Intelligent System for Multimodal Human-Computer Interaction
Get PDFWe present a flexible human--robot interaction architecture that incorporates emotions and moods to provide a natural experience for humans. To determine the emotional state of the user, information representing eye gaze and facial expression is combined with other contextual information such as whether the user is asking questions or has been quiet for some time. Subsequently, an appropriate robot behaviour is selected from a multi-path scenario. This architecture can be easily adapted to interactions with non-embodied robots such as avatars on a mobile device or a PC. We present the outcome of evaluating an implementation of our proposed architecture as a whole, and also of its modules for detecting emotions and questions. Results are promising and provide a basis for further development
European HPC Landscape
Get PDFThis paper provides an overview on the European HPC landscape supported by a survey, designed by the PRACE-5IP project, accessing more than 50 of the most influential stakeholders of HPC in Europe. It focuses at Tier-0 systems on the European level providing high-end computing and data analysis resources. The different actors are presented and their provided services are analyzed in order to identify overlaps and gaps, complementarity and opportunities for collaborations. A new pan-European HPC portal is proposed in order to get all information on one place and facilitate access to the portfolio of services offered by the European HPC communities
Unsupervised Adaptation for High-Dimensional with Limited-Sample Data Classification Using Variational Autoencoder
Get PDFHigh-dimensional with limited-sample size (HDLSS) datasets exhibit two critical problems: (1) Due to the insufficiently small-sample size, there is a lack of enough samples to build classification models. Classification models with a limited-sample may lead to overfitting and produce erroneous or meaningless results. (2) The 'curse of dimensionality' phenomena is often an obstacle to the use of many methods for solving the high-dimensional with limited-sample size problem and reduces classification accuracy. This study proposes an unsupervised framework for high-dimensional limited-sample size data classification using dimension reduction based on variational autoencoder (VAE). First, the deep learning method variational autoencoder is applied to project high-dimensional data onto lower-dimensional space. Then, clustering is applied to the obtained latent-space of VAE to find the data groups and classify input data. The method is validated by comparing the clustering results with actual labels using purity, rand index, and normalized mutual information. Moreover, to evaluate the proposed model strength, we analyzed 14 datasets from the Arizona State University Digital Repository. Also, an empirical comparison of dimensionality reduction techniques shown to conclude their applicability in the high-dimensional with limited-sample size data settings. Experimental results demonstrate that variational autoencoder can achieve more accuracy than traditional dimensionality reduction techniques in high-dimensional with limited-sample-size data analysis
Credit Risk Assessment of Banks' Loan Enterprise Customer Based on State-Constraint
Get PDFCommercial banks are facing increasingly complex enterprise loan customers and businesses. It is important for banks' enterprise loan business to efficiently assess credit risks. Our study builds an enterprise credit risk assessment model based on the state and constraint of bank and customer, and get empirical researches with RF, SVM and DT algorithms. The results show that our model has excellent performance with accuracy 99 % and great characteristic importance in the evaluation of enterprise credit risk. The study can provide important decision-making reference for bank loan business and enrich the theoretical system of bank credit risk research
REDUCER: Elimination of Repetitive Codes for Accelerated Iterative Compilation
Get PDFLow Level Virtual Machine (LLVM) is a widely adopted open source compiler providing numerous optimization opportunities. The discovery of the best optimization sequence in this large space is done via iterative compilation, which incurs substantial overheads, especially for big data applications operating on high volume and variety datasets. The large search space is mostly comprised of identical codes generated via different optimizations. However, no mechanism is implemented inside the LLVM compiler to suppress the redundant testings. In this regard, this paper proposes REDUCER for eliminating the identical code executions by performing Intermediate Representation (IR) level comparisons. REDUCER has been tested using the well-accepted MiCOMP technique in LLVM 3.8 and 9.0 compiler, with embedded (cBench) and big data workloads. In comparison to MiCOMP 19.5 k experiments, REDUCER lowers the experiment count up to 327, i.e. 98 %, and on average to 4 375, i.e. 77 %, for cBench (LLVM-3.8). Similarly, for LLVM-9.0 the reductions are up to 1 931, i.e. 90 %, and on average 5 863, i.e. 69.9 %. Due to the significant experiment reduction, for embedded workloads, the iterative compilation is up to 58.6× and on average 4.1× faster with REDUCER (LLVM-3.8) than MiCOMP, whereas, with REDUCER (LLVM-9.0) the compilation is up to 8.5× and on average 2.9× faster. Moreover, REDUCER is found to be scalable and efficient for big data workloads where the iterative compilation is reduced to few days, as code is compared one time only for a single application tested on multiple datasets
Method for Repairing Process Models with Selection Structures Based on Token Replay
Get PDFEnterprise information systems (EIS) play an important role in business process management. Process mining techniques that can mine a large number of event logs generated in EIS become a very hot topic. There always exist some deviations between a process model of EIS and event logs. Therefore, a process model needs to be repaired. For the process model with selection structures, the mining accuracy of the existing methods is reduced because of the additional self-loops and invisible transitions. In this paper, a method for repairing Logical-Petri-nets-based process models with selection structures is proposed. According to the relationship between the input and output places of a sub-model, the deviation position is determined by a token replay method. Then, some algorithms are designed to repair the process models based on logical Petri nets. Finally, the effectiveness of the proposed method is illustrated by some experiments, and the proposed method has relatively high fitness and precision compared with its peers