Reviewerly/exHarmony huggingface dataset:
Cost-sensitive Feature Selection for Support Vector Machines

Feature Selection is a crucial procedure in Data Science tasks such as Classification, since it identifies the relevant variables, making thus the classification procedures more interpretable, cheaper in terms of measurement and more effective by reducing noise and data overfit. The relevance of features in a classification procedure is linked to the fact that misclassifications costs are frequently asymmetric, since false positive and false negative cases may have very different consequences. However, off-the-shelf Feature Selection procedures seldom take into account such cost-sensitivity of errors. In this paper we propose a mathematical-optimization-based Feature Selection procedure embedded in one of the most popular classification procedures, namely, Support Vector Machines, accommodating asymmetric misclassification costs. The key idea is to replace the traditional margin maximization by minimizing the number of features selected, but imposing upper bounds on the false positive and negative rates. The problem is written as an integer linear problem plus a quadratic convex problem for Support Vector Machines with both linear and radial kernels. The reported numerical experience demonstrates the usefulness of the proposed Feature Selection procedure. Indeed, our results on benchmark data sets show that a substantial decrease of the number of features is obtained, whilst the desired trade-off between false positive and false negative rates is achieved.
Personalized Affective Feedback to Address Students’ Frustration in ITS

The importance of affective states in learning has led many Intelligent Tutoring Systems (ITS) to include students' affective states in their learner models. The adaptation and hence the benefits of an ITS can be improved by detecting and responding to students' affective states. In prior work, we have created and validated a theory-driven model for detecting students' frustration, as well as identifying its causes as students interact with the ITS. In this paper, we present a strategy to respond to students' frustration by offering motivational messages that address different causes of frustration. Based on attribution theory, these messages are created to praise the student's effort, attribute the results to the identified cause, show sympathy for failure or obtain feedback from the students. We implemented our approach in three schools where students interacted with the ITS. Data from 188 students from the three schools collected across two weeks was used for our analysis. The results suggest that the frustration instances reduced significantly statistically (p <; 0:05), due to the motivational messages. This study suggests that motivational messages that use attribution theory and address the reason for frustration reduce the number of frustration instances per session.
A social recommendation method based on the integration of social relationship and product popularity

Web 2.0 technology fosters the flourishing growth and development of social networks. More and more people are participating in the activities on social networks to interact and share information with each other. Thus, consumers are often making their purchasing decisions based on information from the Internet such as reviews, ratings, and comments on products, especially from their trusted friends. However, a great amount of available information may cause the problem of information overload for consumers. In seeking to attain a good recommendation performance by taking the high-potential factors into account as far as possible, this paper proposes a novel social recommendation method on the basis of the integration of interactions, trust relationships and product popularity to predict user preferences, and recommend relevant products in social networks. In addition, the proposed method mainly focuses on analyzing user interactions to infer their latent interactions in accordance with the user ratings and corresponding reviews. Additionally, users may be affected by the popularity of products, so this factor has also been taken into consideration in this work. The experimental results show that the proposed recommendation method has a better recommendation performance in comparisons to other methods because the proposed method can accurately analyze user preferences and further recommend high-potential products to target users in social networks to support their purchase decision making. Furthermore, the proposed method can not only reduce the time and effort users spend on querying information, but also positively relieve the problem of information overload.
Ambient Assisted living system’s models and architectures: A survey of the state of the art

The field of Ambient Assisted living (AAL) is evolving rapidly. Standardization, uniformities and facilitation in the system design, have become an urge to satisfy the growing needs for integrated solutions. In this context, Reference Models (RM) and Reference Architectures (RA) impose as suitable solutions for organizing the relationship between the different elements of a software. Furthermore, they achieve a well-recognized understanding of the AAL domain by raising the congruency between the context, the goals and the design. In this paper, we investigate the AAL system requirements and implementation challenges. Moreover, we examined RM and RA definitions, demands and specifications with the perspective of, describing and evaluating AAL related ones, using quality attributes in a number of dimensions. Our main determination is giving an overview of the current state of the art of AAL oriented layouts, which we found generally research oriented ones, and most of them have been established with one specific concern. Our perspective is to improve and consolidate the future designs of the AAL models and architectures to implement the applications over a survival infrastructure.
A multi-feature selection approach for gender identification of handwriting based on kernel mutual information

This paper presents a new flexible approach to predict the gender of the writers from their handwriting samples. Handwriting features like slant, curvature, line separation, chain code, character shapes, and more, can be extracted from different methods. Therefore, the multi-feature sets are irrelevant and redundant. The conflict of the features exists in the sets, which affects the accuracy of classification and the computing cost. This paper proposes an approach, named kernel mutual information (KMI), that focuses on feature selection. The KMI approach can decrease redundancies and conflicts. In addition, it extracts an optimal subset of features from the writing samples produced by male and female writers. To ensure that KMI can apply the various features, this paper describes the handwriting segmentation and handwritten text recognition technology used. The classification is carried out using a Support Vector Machine (SVM) on two databases. The first database comes from the ICDAR 2013 competition on gender prediction, which provides the samples in both Arabic and English. The other database contains the Registration-Document-Form (RDF) database in Chinese. The proposed and compared methods were evaluated on both databases. Results from the methods highlight the importance of feature selection for gender prediction from handwriting.
A performance comparison between shallow and deeper neural networks supervised classification of tomosynthesis breast lesions images

Computer Aided Decision (CAD) systems, based on 3D tomosynthesis imaging, could support radiologists in classifying different kinds of breast lesions and then improve the diagnosis of breast cancer (BC) with a lower X-ray dose than in Computer Tomography (CT) systems. In previous work, several Convolutional Neural Network (CNN) architectures were evaluated to discriminate four different classes of lesions considering high-resolution images automatically segmented: (a) irregular opacity lesions, (b) regular opacity lesions, (c) stellar opacity lesions and (d) no-lesions. In this paper, instead, we use the same previously extracted relevant Regions of Interest (ROIs) containing the lesions, but we propose and evaluate two different approaches to better discriminate among the four classes. In this work, we evaluate and compare the performance of two different frameworks both considering supervised classifiers topologies. The first framework is feature-based, and consider morphological and textural hand-crafted features, extracted from each ROI, as input to optimised Artificial Neural Network (ANN) classifiers. The second framework, instead, considers non-neural classifiers based on automatically computed features evaluating the classification performance extracting several sets of features using different Convolutional Neural Network models. Final results show that the second framework, based on features computed automatically by CNN architectures performs better than the first approach, in terms of accuracy, specificity, and sensitivity.
Two robust approaches to multicomponent signal reconstruction from STFT ridges

The problem of how to accurately reconstruct the multicomponent signal from the ridges of the short-time Fourier transform (STFT) is considered. Especially when time-frequency representations contain strong noise corruption and crossed components, exact reconstruction becomes more difficult. In this paper, we propose two robust ridge reconstruction approaches, i.e., weighted ridge reconstruction (WRR) and self-paced ridge reconstruction (SPRR). The former is aim to select a more robust loss function to eliminate the influence of the strong noise and outliers. A half-quadratic optimization algorithm is developed to solve the proposed problem efficiently. The latter incorporates self-paced learning (SPL) method into ridge reconstruction model to sequentially include ridge points into signal reconstruction from easy to complex, which not only can suppress noise and outliers, but also can avoid a bad result in the presence of missing observations. Simulation and real-life signals are employed to show the effectiveness and practicability of the proposed approaches.
Luopan: Sampling-Based Load Balancing in Data Center Networks

Data center networks demand high-performance, robust, and practical data plane load balancing protocols. Despite progress, existing work falls short of meeting these requirements. We design, analyze, and evaluate Luopan, a novel sampling based load balancing protocol that overcomes these challenges. Luopan operates at flowcell granularity similar to Presto. It periodically samples a few paths for each destination switch and directs flowcells to the least congested one. By being congestion-aware, Luopan improves flow completion time (FCT), and is more robust to topological asymmetries compared to Presto. The sampling approach simplifies the protocol and makes it much more scalable for implementation in large-scale networks compared to existing congestion-aware schemes. We provide analysis to show that Luopan's periodic sampling has the same asymptotic behavior as instantaneous sampling: taking 2 random samples provides exponential improvements over 1 sample. We conduct comprehensive packet-level simulations with production workloads. The results show that Luopan consistently outperforms state-of-the-art schemes in large-scale topologies. Compared to Presto, Luopan with 2 samples improves the 99.9%ile FCT of mice flows by up to 35 percent, and average FCT of medium and elephant flows by up to 30 percent. Luopan also performs significantly better than Local Sampling with large asymmetry.
A Video Forensic Framework for the Unsupervised Analysis of MP4-Like File Container

Video forensics keeps developing new technologies to verify the authenticity and the integrity of digital videos. While most of the existing methods rely on the analysis of the video data stream, recently, a new line of research was introduced to investigate video life cycle based on the analysis of the video container. Anyway, existing contributions in this field are based on manual comparison of video container structure and content, which is time demanding and error-prone. In this paper, we introduce a method for unsupervised analysis of video file containers, and present two main forensic applications of such method: the first one deals with video integrity verification, based on the dissimilarity between a reference and a query file container; the second one focuses on the identification and classification of the source device brand, based on the analysis of containers structure and content. Noticeably, the latter application relies on the likelihood-ratio framework, which is more and more approved by the forensic community as the appropriate way to exhibit findings in court. We tested and proved the effectiveness of both applications on a dataset composed by 578 videos taken with modern smartphones from major brands and models. The proposed approaches are proved to be valuable also for requiring an extremely small computational cost as opposed to all available techniques based on the video stream analysis or manual inspection of file containers.
Distributed fusion filter for multi-sensor systems with finite-step correlated noises

This paper addresses the distributed fusion filtering problem for multi-sensor systems with finite-step correlated noises. The process noise and observation noises of different sensors are finite-step auto- and cross-correlated, respectively. Based on the optimal local filtering algorithms that we presented before, the filtering error cross-covariance matrices between any two local filters are derived based on an innovation analysis approach. A distributed fusion filter is put forward by using matrix-weighted fusion estimation algorithm in the linear unbiased minimum variance sense. Finally, the proposed algorithms are extended to systems with random parameter matrices. Two simulation examples are given to show the effectiveness of the proposed algorithms.
On the Structural Sensitivity of Deep Convolutional Networks to the Directions of Fourier Basis Functions

Data-agnostic quasi-imperceptible perturbations on inputs are known to degrade recognition accuracy of deep convolutional networks severely. This phenomenon is considered to be a potential security issue. Moreover, some results on statistical generalization guarantees indicate that the phenomena can be a key to improve the networks' generalization. However, the characteristics of the shared directions of such harmful perturbations remain unknown. Our primal finding is that convolutional networks are sensitive to the directions of Fourier basis functions. We derived the property by specializing a hypothesis of the cause of the sensitivity, known as the linearity of neural networks, to convolutional networks and empirically validated it. As a byproduct of the analysis, we propose an algorithm to create shift-invariant universal adversarial perturbations available in black-box settings.
Robust stochastic configuration networks with maximum correntropy criterion for uncertain data regression

This paper develops a robust stochastic configuration network (RSCN) framework to cope with data modelling problems when the given samples contain noises or outliers. Technically, RSCNs are built by generalizing the objective function used in our original stochastic configuration networks with maximum correntropy criterion (MCC) induced losses (the proposed algorithm is termed as RSC-MCC). The half-quadratic (HQ) technique is employed to optimize the penalty weights for each training sample, aiming to weaken the impacts caused by the noisy data or outliers throughout the training session. Alternating optimization (AO) methodology is used to renew the RSCN model in company with updated penalty weights determined by HQ methods. The performance of RSC-MCC algorithm is compared with some existing methods, such as the probabilistic robust learning algorithm for neural networks with random weights (PRNNRW), RVFL networks, improved RVFL networks (Imp-RVFL), and our recent work RSCNs with kernel density estimation (RSC-KDE), on two synthetic function approximation examples, four benchmark datasets and one educational data modelling case study (for student learning performance prediction). The experimental results show that RSC-MCC performs more favourably in robust data analytics, and further indicate that our proposed RSCN framework (both RSC-KDE and RSC-MCC) has a good potential for real-world applications.
A Reference Model for Driver Attention in Automation: Glance Behavior Changes During Lateral and Longitudinal Assistance

This paper introduces a reference model of glance behavior for driving safety assessment. This model can improve the design of automated and assistive systems. Technological limitations have previously hindered the use of unobtrusive eye trackers to measure glance behavior in naturalistic conditions. This paper presents a comprehensive analysis of eye-tracking data collected in a naturalistic field operation test, using an eye tracker that proved to be robust in real-world driving scenarios. We describe a post-processing technique to enhance the quality of naturalistic eye-tracker data, propose a data-analysis procedure that captures the important features of glance behavior, and develop a model of glance behavior (based on distribution fitting), which was lacking in the literature. The model and its metrics capture key defining characteristics of, and differences between, on- and off-path glance distributions, and during manual driving and driving with adaptive cruise control and lane keeping aid active. The results show that drivers' visual response is tightly coupled to the driving context (vehicle automation, car-following, and illumination).
Asymmetric mean reversion of Bitcoin price returns

Non-linearity is characterised by an asymmetric mean-reverting property, which has been found to be inherent in the short-term return dynamics of stocks. In this paper, we explore as to whether cryptocurrency returns, as represented by Bitcoin, exhibit similar asymmetric reverting patterns for minutely, hourly, daily and weekly returns between June 2010 and February 2018. We identify several differences in the behaviour of Bitcoin price returns in the pre- and post-$1000 sub-periods and evidence of asymmetric reverting patterns in the Bitcoin price returns under all the ANAR models employed, regardless of the data frequency considered. We also present evidence indicating stronger reverting behaviour of negative price returns in terms of both reverting speed and magnitude compared to positive returns and evidence of positive serial correlation with prior positive price returns. Finally, we also investigated asymmetries in Bitcoin price return series' persistence by employing higher order ANAR models, finding evidence of a higher persistence of positive returns than negative returns, a result that further supports the existence of asymmetric reverting behaviour in the Bitcoin price returns.
The design and evaluation of a theory-based intervention to promote security behaviour against phishing

This study examined the impact of fear appeal messages on user cognitions, attitudes, behavioural attentions and precautionary behaviour regarding online information-sharing to protect against the threat of phishing attacks. A pre-test post-test design was used in which 768 Internet users filled out an online questionnaire. Participants were grouped in one of three fear appeal conditions: strong-fear appeal, weak-fear appeal and control condition. Claims regarding vulnerability of phishing attacks and claims concerning response efficacy of protective online information-sharing behaviour were manipulated in the fear appeal messages. The study demonstrates positive effects of fear appeals on heightening end-users' cognitions, attitudes and behavioural intentions. However, future studies are needed to determine how subsequent security behaviour can be promoted, as the effects on this crucial aspect were not directly observed. Nonetheless, we conclude that fear appeals have great potential to promote security behaviour by making end users aware of threats and simultaneously providing behavioural advice on how to mitigate these threats.
Analyzing Perception-Distortion Tradeoff Using Enhanced Perceptual Super-Resolution Network

Convolutional neural network (CNN) based methods have recently achieved great success for image super-resolution (SR). However, most deep CNN based SR models attempt to improve distortion measures (e.g. PSNR, SSIM, IFC, VIF) while resulting in poor quantified perceptual quality (e.g. human opinion score, no-reference quality measures such as NIQE). Few works have attempted to improve the perceptual quality at the cost of performance reduction in distortion measures. A very recent study has revealed that distortion and perceptual quality are at odds with each other and there is always a trade-off between the two. Often the restoration algorithms that are superior in terms of perceptual quality, are inferior in terms of distortion measures. Our work attempts to analyze the trade-off between distortion and perceptual quality for the problem of single image SR. To this end, we use the well-known SR architecture- enhanced deep super-resolution (EDSR) network and show that it can be adapted to achieve better perceptual quality for a specific range of the distortion measure. While the original network of EDSR was trained to minimize the error defined based on per-pixel accuracy alone, we train our network using a generative adversarial network framework with EDSR as the generator module. Our proposed network, called enhanced perceptual super-resolution network (EPSR), is trained with a combination of mean squared error loss, perceptual loss, and adversarial loss. Our experiments reveal that EPSR achieves the state-of-the-art trade-off between distortion and perceptual quality while the existing methods perform well in either of these measures alone.
A Systems Overview of Commercial Data Centers

Data center facilities play a vital role in present and forthcoming information and communication technologies. Internet giants, such as IBM, Microsoft, Google, Yahoo, and Amazon hold large data centers to provide cloud computing services and web hosting applications. Due to rapid growth in data center size and complexity, it is essential to highlight important design aspects and challenges of data centers. This article presents market segmentation of the leading data center operators and discusses the infrastructural considerations, namely energy consumption, power usage effectiveness, cost structure, and system reliability constraints. Moreover, it presents data center network design, classification of the data center servers, recent developments, and future trends of the data center industry. Furthermore, the emerging paradigm of mobile cloud computing is debated with respect to the research issues. Preliminary results for the energy consumption of task scheduling techniques are also provided.
Does the fault reside in a stack trace? Assisting crash localization by predicting crashing fault residence

Given a stack trace reported at the time of software crash, crash localization aims to pinpoint the root cause of the crash. Crash localization is known as a time-consuming and labor-intensive task. Without tool support, developers have to spend tedious manual effort examining a large amount of source code based on their experience. In this paper, we propose an automatic approach, namely CraTer, which predicts whether a crashing fault resides in stack traces or not (referred to as predicting crashing fault residence). We extract 89 features from stack traces and source code to train a predictive model based on known crashes. We then use the model to predict the residence of newly-submitted crashes. CraTer can reduce the search space for crashing faults and help prioritize crash localization efforts. Experimental results on crashes of seven real-world projects demonstrate that CraTer can achieve an average accuracy of over 92%.