Reviewerly/exHarmony huggingface dataset:
Word and Class Common Space Embedding for Code-switch Language Modelling

Code-switch language modelling is challenging due to limited linguistic resources and less predictable word sequences. Many state-of-the-art systems rely on linguistic information such as Part-of-Speech (POS) or classes to generalize the lexicon. Such systems generally use multi-task learning or conditional network to improve over baseline RNN language model by providing a better word prediction. To overcome the data sparsity through continuous space modelling and back-off mechanism, we propose to constrain the word and class embedding in a common space by means of cross-lingual word embedding, and to make use of the predicted class embedding as a back-off scheme when word prediction model is weak. The proposed word and class Common Space embedding Language Model (CSLM) is able to model word prediction better and is more robust when only sparse training data are available. The CSLM outperforms the state-of-the-art language model by 9.7% on the code-switch SEAME corpus.
Convolutional Neural Networks for Twitter Text Toxicity Analysis

Toxic comment classification is an emerging research field with several studies that have address several tasks in the detection of unwanted messages on communication platforms. Although sentiment analysis is an accurate approach for observing the crowd behavior, it is incapable of discovering other types of information in text, such as toxicity, which can usually reveal hidden information. Towards this direction, a model for temporal tracking of comments toxicity is proposed using tweets related to the hashtag under study. More specifically, a classifier is trained for toxic comments prediction using a Convolutional Neural Network model. Next, given a hashtag all relevant tweets are parsed and used as input in the classifier, hence, the knowledge about toxic texts is transferred to a new dataset for categorization. In the meantime, an adapted change detection approach is applied for monitoring the toxicity trend changes over time within the hashtag tweets. Our experimental results showed that toxic comment classification on twitter conversations can reveal significant knowledge and changes in the toxicity are accurately identified over time.
Understanding the Most In-demand Soft Skills in Requirements Engineering Practice

[Context] Research on the professional occupation of Requirements Engineering (RE) indicated soft skills as very important in employers' search for RE specialists. A very long list of soft skills came out of prior research, with proficiency in English being ranked as the Number One skill. [Question/problem] To what extent, however, are these skills deemed critical in RE in practice, and also in making hiring decisions in RE, and why? Which skills are to be ranked higher in regard to hiring decisions, and which lower? In this study, we compare results based on analyses of job ads with practitioners' perceptions in real-life organizations. [Methodology] We executed two focus groups (FGs) with RE practitioners from industry, as part of the REFSQ 2018 conference. [Principal ideas/results] The soft skills from previous studies were discussed in depth with the FGs participants regarding where and why these skills are needed or sometimes are even unnecessary. Here, we report a ranked list of soft skills, among which communication skills were considered as the most important. Next, some of the skills ranked as the most important in job ads, are not needed everywhere. [Contribution] The results lead to a more differentiated view on those soft skills that requirements engineers need in practice, and the reasons for these skills' importance.
Estimating Viewed Image Categories from Human Brain Activity via Semi-supervised Fuzzy Discriminative Canonical Correlation Analysis

This paper presents a method to estimate viewed image categories from human brain activity via newly derived semi-supervised fuzzy discriminative canonical correlation analysis (Semi-FDCCA). The proposed method can estimate image categories from functional magnetic resonance imaging (fMRI) activity measured while subjects view images by making fMRI activity and visual features obtained from images comparable through Semi-FDCCA. To realize Semi-FDCCA, we first derive a new supervised CCA called FDCCA that can consider fuzzy class information based on image category similarities obtained from WordNet ontology. Second, we adopt SemiCCA that can utilize additional unpaired visual features in addition to pairs of fMRI activity and visual features in order to prevent overfitting to the limited pairs. Furthermore, Semi-FDCCA can be derived by combining FDCCA with SemiCCA. Experimental results show that Semi-FDCCA enables accurate estimation of viewed image categories.
A Denoising Autoencoder for Speaker Recognition. Results on the MCE 2018 Challenge

We propose a Denoising Autoencoder (DAE) for speaker recognition, trained to map each individual ivector to the mean of all ivectors belonging to that particular speaker. The aim of this DAE is to compensate for inter-session variability and increase the discriminative power of the ivectors prior to PLDA scoring. We test the proposed approach on the MCE 2018 1st Multi-target speaker detection and identification Challenge Evaluation. This evaluation presents a call-center fraud detection scenario: given a speech segment, detect if it belongs to any of the speakers in a blacklist. We show that our DAE system consistently outperforms the usual LDA + PLDA pipeline, achieving a Top-S EER of 4.33% and Top-1 EER of 6.11% on the evaluation set, which represents a 45.6% error reduction with respect to the baseline system provided by organizers.
Security maturity model of web applications for cyber attacks

Bearing in mind that the projections made for the area of information security point to an increase in attacks on the health sector, added to the lack or little diffusion of security maturity models that allow organizations to know the status of their website in terms of security and that the existing models lack a post-evaluation monitoring, it is necessary to propose a model of security maturity of web applications against cyber-attacks, oriented to the health sector, which is simple to apply. The proposed model will be based on the International Professional Practice Framework methodology and will include the main vulnerabilities published by the Open Web Application Security Project to propose attacks that identify the weakness of the evaluated web system, so that the client company has the possibility to reinforce its weaknesses. Guides will also be proposed to select strategies to improve critical points from a security perspective. As a result of the validation, it was found that, of the 14 tests applied, 5 were approved, positioning the web at level 3 of maturity, which means that there are validations in the structure of the web; however, they are partial or inefficient.
MOQAS: Multi-objective question answering system

Question Answering Systems (QASs) are search engines that have the ability to provide a brief and accurate answer to each question in natural languages. The question asked in such a system is answered with a set of documents, a paragraph, a sentence, etc. In this paper, a solution is proposed to op timize the performance of web-based QASs for answering definitional and factoid questions in English. As evolutionary algorithms are suitable for issues with large search space and also texts can be examined from a variety of aspects, this approach proposes for the first time employing Multi-Objective Evolutionary algorithms (MOEAs) to optimize the performance of QASs. In the present work, we provided a Multi-Objective QAS (MOQAS) that would be more accurate in choosing the most probable answer from the documents that the standard search engine has retrieved. Through the ranking process, various features can be extracted from the text. Each of these features examines the text from a perspective, but changes in the values of these features are not consistent with each other; thus, we need to use a method that takes all these views into account. For this purpose, three different aspects of the text are considered and NSGA-II as a MOEA is applied. We used two standard datasets and web data for evaluating our system. Comparing the obtained results from the proposed MOQAS with other existing QASs reveals that MOQAS yields promising and effective results.
Anisotropic conjugate stress/strain base pair approach for laminates undergoing large deformations

The anisotropic, conjugate, stress/strain pair approach has been implemented for laminates undergoing large deformations. Together with a previous study [1], our results provide compelling evidence that our model can describe both small and large deformation histories. A conjugate material parameter n describing anisotropy has been formulated and correlated with Young's moduli in the fiber and transverse-fiber directions. Data were obtained from 0° and 90° samples of a compliant silicone/silicone composite material by pulling them uniaxially. Longitudinal and transverse strain measurements were made using digital image correlation (DIC). Conjugate-pair material parameters have been tabulated and compared for both stiff and compliant composites.
Comparative Analysis of Mining Fuzzy Association Rule using Genetic Algorithm

Genetic algorithm is a global optimization search technique that offers a powerful search method. Fuzzy logic is used to find association rules that can overcome the problems of crisp sets. In this paper, comparison between two genetic fuzzy algorithms: Genetic Cooperative-Competitive Learning Algorithm (GFS.GCCL) and Structural learning algorithm on vague environment (SLAVE) are discussed. Genetic algorithm based machine learning approach named GFS.GCCL, which generates fuzzy rules for classifying patterns. Slave algorithm uses iterative approach to learn fuzzy association rules. This experiment has been performed on two real-world renowned datasets: Iris dataset and Wine dataset. It has found SLAVE algorithm provides a better result than GFS.GCCL algorithm for real world datasets.
Jointly Predicting Future Sequence and Steering Angles for Dynamic Driving Scenes

Generative Adversarial Network (GAN) has attracted rising attention for video future sequence prediction in driving scenes. However, the images generated by GAN often miss the target for lack of any constraints for its generated target. In this paper, an encoder-decoder based multi-task video prediction network - SegVAE is proposed by simultaneously accomplishing the predictions (generations) of both future sequence and steering angles for egocentric driving videos at pixel-level. Specifically, the encoder is constructed based on Varitional Auto-Encoder (VAE) to learn the complex latent distribution of real driving scenes. The decoder is exploited with a multi-task manner to jointly predict the future sequence and steering angles of dynamic driving scenes, where an enhanced generation mechanism is also proposed. Varitional Auto-Encoder (VAE) and Long Short Term Memory Networks (LSTM) are introduced to optimize the learning of SegVAE. The experimental results on public KITTI and NVIDIA driving datasets indicate that the proposed Seg-VAE can effectively mimic humans prediction mechanism, and outperform standard VAE and CNN-based generative adversarial network.
Quality of Service for Traffic Monitoring System based on Static Routing using EoIP Tunnel over IPSec

The development of internet networks requires a network administrator to implement various topology schemes on the location of the different network. It is intended to make each other enables to communicate in one area and monitor the use of traffic. This research aim is to create private network connection by ensuring its security and distributing public address through routing static algorithm. Network Development Life Cycle (NDLC) is utilized as the research method. Based on the implementation of EoIP tunnel network over IPSec, the researcher analysed the Quality of Service (QoS) for EoIP tunnel non-IPsec with EoIP tunnel over IPSec to find out the comparison. The Result revealed that EoIP tunnel over IPsec has throughput 93.04%, delay 1.43 ms, jitter 1.41 ms, and packet loss 0.43%. The data is fewer than the data of EoIP non-IPsec with throughput 94.85%, delay 1.39 ms, jitter 1.37 ms, and packet loss 0.52%. The differentiation caused by the encryption negotiation process during the network connected. Hopefully, this research has a contribution to make user is more accessible to communicate in the local area and monitor the traffic use centrally.
End-to-End Acoustic Modeling Using Convolutional Neural Networks

State-of-the-art automatic speech recognition (ASR) systems map speech into its corresponding text. Conventional ASR systems model the speech signal into phones in two steps; feature extraction and classifier training. Traditional ASR systems have been replaced by deep neural network (DNN)-based systems. Today, end-to-end ASRs are gaining in popularity due to simplified model-building processes and the ability to directly map speech into text without predefined alignments. These models are based on data-driven learning methods and competition with complicated ASR models based on DNN and linguistic resources. There are three major types of end-to-end architectures for ASR: Attention-based methods, connectionist temporal classification, and convolutional neural network (CNN)-based direct raw speech model. This chapter discusses end-to-end acoustic modeling using CNN in detail. CNN establishes the relationship between the raw speech signal and phones in a data-driven manner. Relevant features and classifiers are jointly learned from raw speech. The first convolutional layer automatically learns feature representation. That intermediate representation is more discriminative and further processed by rest convolutional layers. This system performs better than traditional cepstral feature–based systems but uses a high number of parameters. The performance of the system is evaluated for TIMIT and claimed better performance than MFCC feature-based GMM/HMM (Gaussian mixture model/hidden Markov model) model.
A hierarchical game approach on real-time navigation scheduling of agricultural harvesters

A navigation scheduling framework for agricultural harvesters (AHs) is proposed, which takes the impacts from both farmer’s demands and dispatch system (DS) into consideration. Farmer’s demands with AHs and the DS are linked in this framework. It benefits farmers and AHs owners by attracting AHs to schedule at rush hours and saving the time of AHs owners with real-time navigation. A hierarchical game approach is proposed based on the formulated framework to effectively navigate AHs to farms that need to harvest (FNHs). And a non-cooperative game is proposed to model the competition between FNHS at the upper level of the hierarchical game. At the lower level, multiple evolutionary games are formulated based on the pricing strategies obtained from the non-cooperative game to evolve AHs’ strategies in choosing FNHs. The simulation results show that the proposed navigation scheduling method is effective in improving both the reliability of the DS and economic profits of AHs owners.
On-the-fly scheduling versus reservation-based scheduling for unpredictable workflows

Scientific insights in the coming decade will clearly depend on the effective processing of large data sets generated by dynamic heterogeneous applications typical of workflows in large data centers or of emerging fields like neuroscience. In this article, we show how these big data workflows have a unique set of characteristics that pose challenges for leveraging HPC methodologies, particularly in scheduling. Our findings indicate that execution times for these workflows are highly unpredictable and are not correlated with the size of the data set involved or the precise functions used in the analysis. We characterize this inherent variability and sketch the need for new scheduling approaches by quantifying significant gaps in achievable performance. Through simulations, we show how on-the-fly scheduling approaches can deliver benefits in both system-level and user-level performance measures. On average, we find improvements of up to 35% in system utilization and up to 45% in average stretch of the applications, illustrating the potential of increasing performance through new scheduling approaches.
Electronic Study Guides for Applied Bachelor’s Degree Programs

The development of electronic study guides (ESG) for Bachelor’s disciplines enables to reduce procurement costs for print library collections. Posting of ESGs on the University Internet resources will provide their accessibility and usability, so the subject is topical. The article analyzes the experience of developing electronic study guides for the disciplines of basic professional bachelor’s degree programs in various universities and reviews the literature on this subject. The main features that distinguish ESG from paper teaching materials are the following: multimedia presentation of information, interactivity, dynamic content. In addition, remote interaction between a teacher and students in forums or video conferences is possible, depending on the format of the ESG. Furthermore, ESG allows one to remotely monitor the completeness and timeliness of the study of certain topics of the discipline, unlike other types of educational and methodological support. The implementation of the ESG makes it possible to maintain control in two forms: internal and external. There are also such criteria for assessing the quality of ESG as: proportion in ensuring the total volume of discipline, proportion in ensuring the self-directed student work, quality of design, the effectiveness of multimedia, adaptability, level of remote access. The result of the analysis was the development of requirements for the ESG design for the discipline «Structure and calculation of engines». This discipline includes the basics of structure and calculation elements and systems of internal combustion engines and the processes occurring in them. In Perm National Research Polytechnic University, according to the curriculum, the discipline is studied for 2 semesters; the labor intensity is 7 credits. Classroom lessons consist of lectures, laboratory and practical classes, additionally, coursework was provided. The difficulty of organizing and maintaining a laboratory in working condition is due to high labor and material costs for fuels and lubricants, electricity, forced-air ventilation, maintenance and repair of internal combustion engines, etc. In this regard, it is relevant to use simulation methods and create virtual laboratory benches to determine the characteristics of the internal combustion engine. These benches may be considered as an alternative to field experiments and stands. As a result of the generalization experience, it was possible to draw up general requirements to the structure and content of the ESG and to provide recommendations on the development of ESGs taking into account the specifics of applied bachelor’s programs.
Perceptual Quality Preserving Image Super-resolution via Channel Attention

Generative Adversarial Network (GAN) has been widely applied on Single Image Super-Resolution (SISR) problems. However, there can be quite a variability in the results from the GAN-based methods. In some cases, the GAN-based methods might cause structure distortion, which can be easily distinguished by human beings, especially for artificial structures, because the methods only focus on the perceptual quality of the whole image. On the other hand, PSNR-oriented methods can prevent structure distortion but with overly smoothed context. To overcome these problems, we propose a deep neural net refiner for SISR methods, not only improving perceptual quality but also preserving context structures. In the experiments, our model qualitatively and quantitatively performs favorably against the state-of-the-art SISR methods.
Large-scale distributed semantic augmented reality services – A performance evaluation

This paper presents an architecture for large-scale distributed AR services and an experimental evaluation of the main component – Semantic Augmented Reality Middleware. The architecture is based on a client-server design, which supports semantic modeling and building contextual AR presentations for a large number of users. The architecture is based on the Service Oriented Architecture (SOA) paradigm that enables building distributed systems that provide application functionality as services to either end-user applications or other services. The proposed architecture consists of two client-side components and multiple server-side distributed service applications – semantic middleware and AR service providers. The presented findings are promising and demonstrate that application of semantic web techniques can be a useful solution to implement large-scale contextual distributed AR environments.
The application of photography in investigating fraud

The impact of fraud on the world economy is significant. As fraud has evolved it has become more organized and increasingly cyber-enabled and committed across borders of law enforcement jurisdictions. The technological demands facing fraud investigators are increasing. Yet, little attention has been given to how forensic photography can help tackle emerging crime types, like fraud. This study reviews literature surrounding forensic photography and provides an up to date and collective narrative. This article focuses on the advancements in photography and how imaging-science techniques can apply to a fraud investigation. Photographic practice plays a key role in many crime investigations but must embrace continual change to ensure that it remains relevant to modern policing. Fresh approaches are essential and practitioners must fully utilize new technologies and adapt to tackle the increasingly demanding scope of fraud types.
A Rotation Invariant HOG Descriptor for Tire Pattern Image Classification

Texture feature is important in describing tire pattern image which provides useful clue in solving crime cases and traffic accidents. In this paper, we propose a novel texture feature extraction method based on HOG (Histogram of Oriented Gradient) and dominant gradient (DG) in tire pattern images, named HOG-DG. The proposed HOG-DG is not only robust to illumination and scale changes but also is rotation-invariant. In the proposed HOG-DG, HOG features are first computed from circular local cells, and HOG features from an image are concatenated and normalized using the DG to construct the HOG-DG feature. HOG-DG is used to train a support-vector-machine (SVM) classifier for tire pattern classification. Experimental results demonstrate its outstanding performance for tire pattern description.
Profiling of knowledge organisation systems for the annotation of Linked Data cultural resources

The importance of cultural heritage for supporting the knowledge economy has promoted its digitisation and online publication. Many cultural heritage repositories have published millions of digitised items using semantic web technologies and Linked Data approaches. These repositories frequently use knowledge organisation systems to classify the resources, but the domain heterogeneity makes it difficult to know if they are the most suitable ones. This paper describes the process used to discover and profile the knowledge organisation systems used in the cultural heritage domain. Additionally, for those knowledge organisation systems with a thesaurus-like structure, a detailed quality analysis is performed. The results of this analysis can be used as a key factor for the selection of knowledge organisation systems in classification tasks.