Reviewerly/exHarmony huggingface dataset:
A Framework for Quantitative Modeling and Analysis of Highly (Re)configurable Systems

This paper presents our approach to the quantitative modeling and analysis of highly (re)configurable systems, such as software product lines. Different combinations of the optional features of such a system give rise to combinatorially many individual system variants. We use a formal modeling language that allows us to model systems with probabilistic behavior, possibly subject to quantitative feature constraints, and able to dynamically install, remove or replace features. More precisely, our models are defined in the probabilistic feature-oriented language QFLan, a rich domain specific language (DSL) for systems with variability defined in terms of features. QFLan specifications are automatically encoded in terms of a process algebra whose operational behavior interacts with a store of constraints, and hence allows to separate system configuration from system behavior. The resulting probabilistic configurations and behavior converge seamlessly in a semantics based on discrete-time Markov chains, thus enabling quantitative analysis. Our analysis is based on statistical model checking techniques, which allow us to scale to larger models with respect to precise probabilistic analysis techniques. The analyses we can conduct range from the likelihood of specific behavior to the expected average cost, in terms of feature attributes, of specific system variants. Our approach is supported by a novel Eclipse-based tool which includes state-of-the-art DSL utilities for QFLan based on the Xtext framework as well as analysis plug-ins to seamlessly run statistical model checking analyses. We provide a number of case studies that have driven and validated the development of our framework.
Enhancing the security of FinTech applications with map-based graphical password authentication

With the rapid development of information technology (IT) in financial industry, financial technology (FinTech) has become an emerging area for enterprises and organizations. Due to the wide adoption of IT, various FinTech applications are used by financial industry to help process information and offer financial services. Traditionally, textual passwords are the most widely deployed authentication mechanism, while having many known limitations. As a result, there is a need to enhance the security of FinTech authentication against cyber-criminals. As an alternative, graphical passwords (GPs) are considered as one promising solution to complement traditional password-based systems. In the literature, various GP schemes were proposed such as PassPoints, DAS, Cued Click Points, GeoPass, etc. In this work, we identify that multiple password inference has become a challenge for most GP schemes, and thus design RouteMap, a map-and route-based GP to further improve the security of FinTech applications. This scheme requires users to create a route on a world map as their credentials. In the evaluation, we involved a total of 120 participants, among which 60 of them have financial (FinTech) background, and investigated the performance of RouteMap by comparing it with two similar schemes. Our results demonstrate that participants can achieve better performance using RouteMap in the aspects of both authentication accuracy and multiple password memory. Our effort attempts to complement existing studies and stimulate more research on the combination of GP and FinTech.
Correlated utility-based pattern mining

Recently, a new research field called utility-oriented mining has attracted great attention. However, previous studies have shown a limitation in that they rarely consider the inherent correlation of items among patterns. For example, considering the purchase behaviors of consumers, a high-utility group of products (w.r.t. multi-products) may contain several very high-utility products with some low-utility products. However, it is considered to be a valuable pattern even if this behavior/pattern may not be highly correlated, or even if it happens by chance. In light of these challenges, we propose an efficient utility-mining approach, called non-redundant Correlated high-Utility Pattern Miner (CoUPM) by considering the positive correlation and profitable value. The derived patterns with high utility and strong positive correlation can lead to more insightful availability than those patterns that only have high profitable values. The utility-list structure is revised and applied to store the necessary information of both correlation and utility. Several pruning strategies are further developed to improve the efficiency for discovering the desired patterns. Experimental results showed that the non-redundant correlated high-utility patterns have more effectiveness than some other kinds of interesting patterns. Moreover, the efficiency of the proposed CoUPM algorithm significantly outperformed the state-of-the-art algorithm.
Creatures Great and SMAL: Recovering the Shape and Motion of Animals from Video

We present a system to recover the 3D shape and motion of a wide variety of quadrupeds from video. The system comprises a machine learning front-end which predicts candidate 2D joint positions, a discrete optimization which finds kinematically plausible joint correspondences, and an energy minimization stage which fits a detailed 3D model to the image. In order to overcome the limited availability of motion capture training data from animals, and the difficulty of generating realistic synthetic training images, the system is designed to work on silhouette data. The joint candidate predictor is trained on synthetically generated silhouette images, and at test time, deep learning methods or standard video segmentation tools are used to extract silhouettes from real data. The system is tested on animal videos from several species, and shows accurate reconstructions of 3D shape and pose.
The Chain of Implicit Trust: An Analysis of the Web Third-party Resources Loading

The Web is a tangled mass of interconnected services, where websites import a range of external resources from various third-party domains. The latter can also load resources hosted on other domains. For each website, this creates a dependency chain underpinned by a form of implicit trust between the first-party and transitively connected third-parties. The chain can only be loosely controlled as first-party websites often have little, if any, visibility on where these resources are loaded from. This paper performs a large-scale study of dependency chains in the Web, to find that around 50% of first-party websites render content that they did not directly load. Although the majority (84.91%) of websites have short dependency chains (below 3 levels), we find websites with dependency chains exceeding 30. Using VirusTotal, we show that 1.2% of these third-parties are classified as suspicious - although seemingly small, this limited set of suspicious third-parties have remarkable reach into the wider ecosystem.
Bayesian Deep Net GLM and GLMM

Deep feedforward neural networks (DFNNs) are a powerful tool for functional approximation. We describe flexible versions of generalized linear and generalized linear mixed models incorporating basis functions formed by a DFNN. The consideration of neural networks with random effects is not widely used in the literature, perhaps because of the computational challenges of incorporating subject specific parameters into already complex models. Efficient computational methods for high-dimensional Bayesian inference are developed using Gaussian variational approximation, with a parsimonious but flexible factor parameterization of the covariance matrix. We implement natural gradient methods for the optimization, exploiting the factor structure of the variational covariance matrix in computation of the natural gradient. Our flexible DFNN models and Bayesian inference approach lead to a regression and classification method that has a high prediction accuracy, and is able to quantify the prediction uncertainty in a principled and convenient way. We also describe how to perform variable selection in our deep learning method. The proposed methods are illustrated in a wide range of simulated and real-data examples, and compare favorably to a state of the art flexible regression and classification method in the statistical literature, the Bayesian additive regression trees (BART) method. User-friendly software packages in Matlab, R, and Python implementing the proposed methods are available at https://github.com/VBayesLab.
Recommending Comprehensive Solutions for Programming Tasks by Mining Crowd Knowledge

Developers often search for relevant code examples on the web for their programming tasks. Unfortunately, they face two major problems. First, the search is impaired due to a lexical gap between their query (task description) and the information associated with the solution. Second, the retrieved solution may not be comprehensive, i.e., the code segment might miss a succinct explanation. These problems make the developers browse dozens of documents in order to synthesize an appropriate solution. To address these two problems, we propose CROKAGE (Crowd Knowledge Answer Generator), a tool that takes the description of a programming task (the query) and provides a comprehensive solution for the task. Our solutions contain not only relevant code examples but also their succinct explanations. Our proposed approach expands the task description with relevant API classes from Stack Overflow Q&A threads and then mitigates the lexical gap problems. Furthermore, we perform natural language processing on the top quality answers and then return such programming solutions containing code examples and code explanations unlike earlier studies. We evaluate our approach using 97 programming queries, of which 50% was used for training and 50% was used for testing, and show that it outperforms six baselines including the state-of-art by a statistically significant margin. Furthermore, our evaluation with 29 developers using 24 tasks (queries) confirms the superiority of CROKAGE over the state-of-art tool in terms of relevance of the suggested code examples, benefit of the code explanations and the overall solution quality (code + explanation).
Deep Object Co-segmentation

This work presents a deep object co-segmentation (DOCS) approach for segmenting common objects of the same class within a pair of images. This means that the method learns to ignore common, or uncommon, background stuff and focuses on common objects. If multiple object classes are presented in the image pair, they are jointly extracted as foreground. To address this task, we propose a CNN-based Siamese encoder-decoder architecture. The encoder extracts high-level semantic features of the foreground objects, a mutual correlation layer detects the common objects, and finally, the decoder generates the output foreground masks for each image. To train our model, we compile a large object co-segmentation dataset consisting of image pairs from the PASCAL dataset with common objects masks. We evaluate our approach on commonly used datasets for co-segmentation tasks and observe that our approach consistently outperforms competing methods, for both seen and unseen object classes.
Frequency domain triangulation for full-field 3D operating-deflection-shape identification

The use of high-speed camera systems in vibration measurements is typically limited to identifying motion, transversal to the optical axis, due to an inherent limitation of 2D imaging systems. Depth information, lost in the imaging process, can be recovered by using the well-established 3D DIC technique, but is still limited to a single face of the object, observed by the stereo pair. In this research a full-field 3D operating-deflection-shape measurement technique, based on frequency-domain triangulation of image-data, is presented. A mathematical model of frequency-domain perspective transformation of small harmonic motion is introduced. This model is used to relate multiview image data to spatial amplitude spectra of the observed displacement. Using the developed method, spatial small harmonic motion of arbitrary-shaped specimen can be identified in the frequency domain using only a single, moving high-speed camera, extending the field-of-view of the established image-based vibration measurement methods.
Mobile learning in dentistry: usage habits, attitudes and perceptions of undergraduate students

The aim of this study was to evaluate usage habits, attitudes and perceptions towards mobile learning (m-learning), as well as to identify variables related to those attitudes amongst undergraduate dental students.The study consists of 81 dental undergraduate students who who volunteered to participate. The data collection tool consists of an m-learning attitude scale, a questionnaire, and open-ended questions. To compare the total scores and factors of m-learning attitude scale for demographic information and mobile technology usage habits of the students; the Mann-Whitney U test was used for two independent groups such as gender, presence of electronic devices, and places of Internet usage. The Kruskal-Wallis test was also used to compare the total scores and factors of m-learning attitude scale for more than two independent groups including internet usage purposes and opinions. Spearman's correlation coefficient was performed, and linear regression analysis was used to predict the change in total score according to the purposes of Internet usage.The majority of students thought that the use of mobile devices in dentistry courses was useful and their attitudes towards m-learning were high. The students generally use the Internet for online shopping, connecting to social networks, and communication. They tend to use mobile technologies for personal use, followed by educational purposes. There were significant differences found in the m-learning attitudes for gender, having a portable power supply and use of mobile devices in dentistry courses. Communication was found significant in predicting the change in total score for the m-learning attitude scale according to the purpose of Internet usage.Dental students have generally positive attitudes towards m-learning. Students raise awareness towards the promise of m-learning in order to apply their individual technology use and learning behaviours. Designing learning materials and applications for mobile devices may increase students' performances.
Research on the on-line dimensional accuracy measurement method of conical spun workpieces based on machine vision technology

An on-line dimensional accuracy measurement method was proposed based on machine vision technology to realize the real-time measurement of straightness and roundness of spun workpieces during spinning. An image acquisition system was developed to obtain the real-time images of conical spun workpiece and the camera calibration was conducted. The edges of the conical spun workpiece were extracted by region of interest (ROI) extraction, image deblurring, denoising and edge detection. The straightness and roundness of the spun workpieces were automatically calculated by writing the calculation program. The reliability of on-line dimensional accuracy measurement system was verified by comparing with measurement results of 3D measuring machine. The results show that the straightness and roundness calculated by measurement system confirm well with that measured by 3D measuring machine, the relative errors between which are less than 10%, and the average detection time of straightness and roundness is 98.5 ms and 69.3 ms, respectively.
Privacy-preserving aware data transmission for IoT-based e-health

Internet of Things (IoT) is an emerging networking paradigm that aims to improve human daily life. It includes a huge number of interactive nodes that generate, aggregate and exchange sensitive data. Thus, security and privacy challenges must be addressed. Various studies on privacy in the area of IoT were proposed. However, none has considered all privacy requirements. To meet such requirements, we propose a novel privacy-preserving IoT-based e-health solution. This solution satisfies content and contextual privacy requirements. It is based on a specific communication scenario and a novel identity-based encryption scheme, respecting the limited resource nature of smart-things. To validate our proposal, an extensive security analysis is provided. Its effectiveness is also demonstrated through a performance evaluation.
Detecting Phishing Websites Using Machine Learning

Phishing website is one of the internet security problems that target the human vulnerabilities rather than software vulnerabilities. It can be described as the process of attracting online users to obtain their sensitive information such as usernames and passwords. In this paper, we offer an intelligent system for detecting phishing websites. The system acts as an additional functionality to an internet browser as an extension that automatically notifies the user when it detects a phishing website. The system is based on a machine learning method, particularly supervised learning. We have selected the Random Forest technique due to its good performance in classification. Our focus is to pursue a higher performance classifier by studying the features of phishing website and choose the better combination of them to train the classifier. As a result, we conclude our paper with accuracy of 98.8% and combination of 26 features.
Learning Shape-Motion Representations from Geometric Algebra Spatio-Temporal Model for Skeleton-Based Action Recognition

Skeleton-based action recognition has been widely applied in intelligent video surveillance and human behavior analysis. Previous works have successfully applied Convolutional Neural Networks (CNN) to learn spatio-temporal characteristics of the skeleton sequence. However, they merely focus on the coordinates of isolated joints, which ignore the spatial relationships between joints and only implicitly learn the motion representations. To solve these problems, we propose an effective method to learn comprehensive representations from skeleton sequences by using Geometric Algebra. Firstly, a frontal orientation based spatio-temporal model is constructed to represent the spatial configuration and temporal dynamics of skeleton sequences, which owns the robustness against view variations. Then the shape-motion representations which mutually compensate are learned to describe skeleton actions comprehensively. Finally, a multi-stream CNN model is applied to extract and fuse deep features from the complementary shape-motion representations. Experimental results on NTU RGB+D and Northwestern-UCLA datasets consistently verify the superiority of our method.
Moving Ground Target Detection With Seismic Signal Using Smooth Pseudo Wigner–Ville Distribution

The main challenging task in moving ground target detection with seismic signals is the extraction of robust feature vectors for accurate localization of a seismic event. Toward this goal, a new feature extraction technique is proposed for seismic event detection based on time-frequency analysis implemented with the Smooth pseudo Wigner-Ville distribution (SPWVD). The time-frequency coefficients are utilized to constitute a Renyi entropy vector exploiting a probability distribution function. This entropy vector gives information on the localized measurement of a possible seismic event resulting from moving ground vehicles. The likelihood of an actual seismic event is then ensured by using a constant false alarm rate (CFAR) detector which dynamically adjusts the threshold to minimize false alarms. The proposed algorithm is tested on the benchmarked data set SITEX02 consisting of seismic signals generated by moving tracked and wheeled vehicles. Additionally, the algorithm is also applied on a data set generated by collecting the seismic signature of a moving wheeled vehicle, i.e., bus within the campus. The results are also compared with pseudo Wigner-Ville distribution (PWVD) and short-time Fourier transform (STFT)-based seismic event detection technique. Significantly improved detection results are observed for SPWVD technique. An F-score improvement of ~24% as well as lead time enhancement of twofold is observed in comparison with the PWVD for relatively weak seismic signals generated by wheeled Dragon Wagon (DW) vehicles. Similarly, the F-score improvement is of ~13% and the lead enhancement is approximately 10 s in comparison with the STFT method.
A high-efficiency adaptive artificial bee colony algorithm using two strategies for continuous optimization

It has always been a problem faced by Artificial Bee Colony (ABC) algorithm that how to adjust exploration and exploitation dynamically in the evolution process. In order to overcome this problem, this paper presents a highly efficient variant of ABC algorithm which is two-strategy adaptive. Among the two proposed search strategies, one has strong exploration ability and the other has strong exploitation ability; Based on the adaptability of the two search strategies to the problem solving and the search process, the selection probability of each search strategy is dynamically adjusted according to success rate, and then the cooperative optimization of the two search strategies is realized to improve the performance of the algorithm. It can be seen that the improved algorithm is enhanced significantly on accuracy of solution and success rate from comparing experiment results with the other state-of-the-art ABC algorithms.
PSO image thresholding on images compressed via fuzzy transforms

We present a new multi-level image thresholding method in which a Chaotic Darwinian Particle Swarm Optimization algorithm is applied on images compressed by using Fuzzy Transforms. The method requires a partition of the pixels of the image under several thresholds which are obtained by maximizing a fuzzy entropy. The usage of compressed images produces benefits in terms of execution CPU times. In a pre-processing phase the best compression rate is found by comparing the grey level histograms of the source and compressed images. Comparisons with the classical Darwinian Particle Swarm Optimization multi-level image thresholding algorithm and other meta-heuristic algorithms are presented in terms of quality of the segmented image via PSNR and SSIM.
An efficient certificateless aggregate signature scheme for the Internet of Vehicles

Abstract In recent years, the research of Internet of vehicles (IoV) has received extensive attention. In IoV, vehicles can make intelligent decisions by exchanging the real‐time traffic information between other vehicles and IoV infrastructures, thereby reducing the probability of traffic jams and accidents. Although IoV has many advantages, it is necessary to ensure that the data are not edited, forged, or disclosed during the transmission. Also, the certificateless signature (CLS) seems to be the solution to this problem. While in the high‐intensity data transmission environment, the CLS scheme is inefficient for application. To improve the efficiency, the function of aggregation was introduced into the CLS. The certificateless aggregate signature (CL‐AS) can aggregate multiple signatures into a brief signature, reducing the computational cost and the size of signature. Nevertheless, most of the recent proposed CL‐AS schemes have problems of security or efficiency. In this paper, we presented an advanced efficient CL‐AS scheme with elliptic curve cryptography for the IoV environment. In addition, the proposed scheme uses pseudonyms in communications to prevent vehicles from revealing their identity. We proved the security of our proposal in the random oracle based on computational Diffie‐Hellman assumption. Also, the efficiency analysis and simulation show the superiority of our scheme.