Reviewerly/exHarmony huggingface dataset:
Real-time social recommendation based on graph embedding and temporal context

With the rapid proliferation of online social networks, personalized social recommendation has become an important means to help people discover their potential friends or interested items in real-time. However, the cold-start issue and the special properties of social networks, such as rich temporal dynamics, heterogeneous and complex structures, render the most commonly used recommendation approaches (e.g. Collaborative Filtering) inefficient. In this paper, we propose a novel dynamic graph-based embedding (DGE) model for social recommendation which is capable of recommending relevant users and interested items. In order to support real-time recommendation, we construct a heterogeneous user-item (HUI) network and incrementally maintain it as the social network evolves. DGE jointly captures the temporal semantic effects, social relationships and user behavior sequential patterns in a unified way by embedding the HUI network into a shared low dimensional space. Then, with simple search methods or similarity calculations, we can use the encoded representation of temporal contexts to generate recommendations. We conduct extensive experiments to evaluate the performance of our model on two real large-scale datasets, and the experimental results show its advantages over other state-of-the-art methods.
3D Object Retrieval Based on Multi-View Latent Variable Model

View-based 3D object retrieval, in which multiple views are used for representation and retrieval, has attracted increasing attention due to its great flexibility. In this paper, we propose a discriminative multi-view latent variable model (MVLVM) for this task. Specifically, we design MVLVM to have an undirected graph structure in which the view set of a given 3D object is treated as the observations from which to discover the latent visual and spatial contexts. Then, we detail the learning and inference process of MVLVM for view-based 3D object retrieval. The proposed MVLVM has the following beneficial features: 1) it jointly learns visual and spatial contexts for 3D object modelling and 2) it avoids the difficulty of representative view extraction for model representation. Consequently, it can support flexible 3D model retrieval for real applications by avoiding camera array constraints, which severely constrain traditional methods. We report extensive experiments conducted on single-modal datasets (the NTU and ITI datasets) and a multi-modal dataset (MVRED-RGB and MVRED-Depth). These comparative experiments demonstrate the superiority of the proposed method.
Design Rule Spaces: A New Model for Representing and Analyzing Software Architecture

In this paper, we propose an architecture model called Design Rule Space (DRSpace). We model the architecture of a software system as multiple overlapping DRSpaces, reflecting the fact that any complex software system must contain multiple aspects, features, patterns, etc. We show that this model provides new ways to analyze software quality. In particular, we introduce an Architecture Root detection algorithm that captures DRSpaces containing large numbers of a project's bug-prone files, which are called Architecture Roots (ArchRoots). After investigating ArchRoots calculated from 15 open source projects, the following observations become clear: from 35 to 91 percent of a project's most bug-prone files can be captured by just 5 ArchRoots, meaning that bug-prone files are likely to be architecturally connected. Furthermore, these ArchRoots tend to live in the system for significant periods of time, serving as the major source of bug-proneness and high maintainability costs. Moreover, each ArchRoot reveals multiple architectural flaws that propagate bugs among files and this will incur high maintenance costs over time. The implication of our study is that the quality, in terms of bug-proneness, of a large, complex software project cannot be fundamentally improved without first fixing its architectural flaws.
FCSS: Fully Convolutional Self-Similarity for Dense Semantic Correspondence

We present a descriptor, called fully convolutional self-similarity (FCSS), for dense semantic correspondence. Unlike traditional dense correspondence approaches for estimating depth or optical flow, semantic correspondence estimation poses additional challenges due to intra-class appearance and shape variations among different instances within the same object or scene category. To robustly match points across semantically similar images, we formulate FCSS using local self-similarity (LSS), which is inherently insensitive to intra-class appearance variations. LSS is incorporated through a proposed convolutional self-similarity (CSS) layer, where the sampling patterns and the self-similarity measure are jointly learned in an end-to-end and multi-scale manner. Furthermore, to address shape variations among different object instances, we propose a convolutional affine transformer (CAT) layer that estimates explicit affine transformation fields at each pixel to transform the sampling patterns and corresponding receptive fields. As training data for semantic correspondence is rather limited, we propose to leverage object candidate priors provided in most existing datasets and also correspondence consistency between object pairs to enable weakly-supervised learning. Experiments demonstrate that FCSS significantly outperforms conventional handcrafted descriptors and CNN-based descriptors on various benchmarks.
Multiobjective Semisupervised Classifier Ensemble

Classification of high-dimensional data with very limited labels is a challenging task in the field of data mining and machine learning. In this paper, we propose the multiobjective semisupervised classifier ensemble (MOSSCE) approach to address this challenge. Specifically, a multiobjective subspace selection process (MOSSP) in MOSSCE is first designed to generate the optimal combination of feature subspaces. Three objective functions are then proposed for MOSSP, which include the relevance of features, the redundancy between features, and the data reconstruction error. Then, MOSSCE generates an auxiliary training set based on the sample confidence to improve the performance of the classifier ensemble. Finally, the training set, combined with the auxiliary training set, is used to select the optimal combination of basic classifiers in the ensemble, train the classifier ensemble, and generate the final result. In addition, diversity analysis of the ensemble learning process is applied, and a set of nonparametric statistical tests is adopted for the comparison of semisupervised classification approaches on multiple datasets. The experiments on 12 gene expression datasets and two large image datasets show that MOSSCE has a better performance than other state-of-the-art semisupervised classifiers on high-dimensional data.
Patch-Sparsity-Based Image Inpainting Through a Facet Deduced Directional Derivative

This paper presents a patch-sparsity-based image inpainting algorithm through a facet deduced directional derivative. The algorithm could ensure the continuity of boundaries of the inpainted region and achieve a better performance on restoring the missing structure of an image. In this paper, two improvements are proposed. First, the facet model is introduced to get direction features of the image, which could efficiently reduce the effect of noises. The first-order directional derivatives, along with pixel values, are used to measure the difference between patches. Consequently, a more reliable and accurate matching result is promised. At the same time, the local patch consistency constraint of sparse representation of the target patch is also rewritten in the form of the first-order directional derivative. Therefore, a more precise sparse linear combination could be obtained under constraints for both color and derivative information. Second, the value of patch confidence in the traditional exemplar-based inpainting algorithms drops sharply in the late stage so that the data term or structure sparsity has little influence on priority function. Aiming at this problem, the algorithm makes a modification to the calculating of priority. Thus, the filling order decided by priority function appears more reasonable as a result of a better balance between the values of modified confidence and structure sparsity. Experiments on different types of damages to images show the superiority of the algorithm.
GuideR: A guided separate-and-conquer rule learning in classification, regression, and survival settings

This article presents GuideR, a user-guided rule induction algorithm, which overcomes the largest limitation of the existing methods-the lack of the possibility to introduce user's preferences or domain knowledge to the rule learning process. Automatic selection of attributes and attribute ranges often leads to the situation in which resulting rules do not contain interesting information. We propose an induction algorithm which takes into account user's requirements. Our method uses the sequential covering approach and is suitable for classification, regression, and survival analysis problems. The effectiveness of the algorithm in all these tasks has been verified experimentally, confirming guided rule induction to be a powerful data analysis tool.
A 3.3 Gbps CCSDS 123.0-B-1 Multispectral &amp; Hyperspectral Image Compression Hardware Accelerator on a Space-Grade SRAM FPGA

The explosive growth of data volume from next generation high-resolution and high-speed hyperspectral remote sensing systems will compete with the limited on-board storage resources and bandwidth available for the transmission of data to ground stations making hyperspectral image compression a mission critical and challenging on-board payload data processing task. The Consultative Committee for Space Data Systems (CCSDS) has issued recommended standard CCSDS-123.0-B-1 for lossless multispectral and hyperspectral image compression. In this paper, a very high data-rate performance hardware accelerator is presented implementing the CCSDS-123.0-B-1 algorithm as an IP core targeting a space-grade FPGA. For the first time, the introduced architecture based on the principles of C-slow retiming, exploits the inherent task-level parallelism of the algorithm under BIP ordering and implements a reconfigurable fine-grained pipeline in critical feedback loops, achieving high throughput performance. The CCSDS-123.0-B-1 IP core achieves beyond the current state-of-the-art data-rate performance with a maximum throughput of 213 MSamples/s (3.3 Gbps @ 16-bits) using 11 percent of LUTs and 27 percent of BRAMs of the Virtex-5QV FPGA resources for a typical hyperspectral image, leveraging the full throughput of a single SpaceFibre lane. To the bes t of our knowledge, it is the fastest implementation of CCSDS-123.0-B-1 targeting a space-grade FPGA to date.
Quantitative separation logic: a logic for reasoning about probabilistic pointer programs

We present quantitative separation logic ($\mathsf{QSL}$). In contrast to classical separation logic, $\mathsf{QSL}$ employs quantities which evaluate to real numbers instead of predicates which evaluate to Boolean values. The connectives of classical separation logic, separating conjunction and separating implication, are lifted from predicates to quantities. This extension is conservative: Both connectives are backward compatible to their classical analogs and obey the same laws, e.g. modus ponens, adjointness, etc. Furthermore, we develop a weakest precondition calculus for quantitative reasoning about probabilistic pointer programs in $\mathsf{QSL}$. This calculus is a conservative extension of both Reynolds' separation logic for heap-manipulating programs and Kozen's / McIver and Morgan's weakest preexpectations for probabilistic programs. Soundness is proven with respect to an operational semantics based on Markov decision processes. Our calculus preserves O'Hearn's frame rule, which enables local reasoning. We demonstrate that our calculus enables reasoning about quantities such as the probability of terminating with an empty heap, the probability of reaching a certain array permutation, or the expected length of a list.
Efficient Privacy-Preserving Machine Learning in Hierarchical Distributed System

With the dramatic growth of data in both amount and scale, distributed machine learning has become an important tool for the massive data to finish the tasks as prediction, classification, etc. However, due to the practical physical constraints and the potential privacy leakage of data, it is infeasible to aggregate raw data from all data owners for the learning purpose. To tackle this problem, the distributed privacy-preserving learning approaches are introduced to learn over all distributed data without exposing the real information. However, existing approaches have limits on the complicated distributed system. On the one hand, traditional privacy-preserving learning approaches rely on heavy cryptographic primitives on training data, in which the learning speed is dramatically slowed down due to the computation overheads. On the other hand, the complicated system architecture becomes a barrier in the practical distributed system. In this paper, we propose an efficient privacy-preserving machine learning scheme for hierarchical distributed systems. We modify and improve the collaborative learning algorithm. The proposed scheme not only reduces the overhead for the learning process but also provides the comprehensive protection for each layer of the hierarchical distributed system. In addition, based on the analysis of the collaborative convergency in different learning groups, we also propose an asynchronous strategy to further improve the learning efficiency of hierarchical distributed system. At the last, extensive experiments on real-world data are implemented to evaluate the privacy, efficacy, and efficiency of our proposed schemes.
A Comprehensive Study of Sparse Representation Techniques for Offline Signature Verification

In this paper, a feature extraction method for offline signature verification is presented that harnesses the power of sparse representation (SR) in order to deliver state-of-the-art verification performance in several signature datasets like CEDAR, MCYT-75, GPDS, and UTSIG. Beyond the accuracy improvements, several major parameters associated with SR; such as selected configuration, dictionary size, sparsity level, and positivity priors are investigated. Besides, it is evinced that second-order statistics of the sparse codes is a powerful pooling function for the formation of the global signature descriptor. Also, a thorough evaluation of the effects of preprocessing is introduced by an automated algorithm in order to select the optimum thinning level. Finally, a segmentation strategy which employs a special form of spatial pyramid tailored to the problem of SR is presented along with the enhancing of the produced descriptor on meaningful areas of the signature as emerged from the binary robust invariant scalable key-point detection mechanism. The obtained state-of-the-art results on the most challenging signature datasets provide a strong indication toward the benefits of learned features, even in writer dependent scenarios with a unique model for each writer and only a few available reference samples of him/her.
Automatic blur type classification via ensemble SVM

Automatic classification of blur type is critical to blind image restoration. In this paper, we propose an original solution for blur type classification of digital images using ensemble Support Vector Machine (SVM) structure. It is assumed that each image is subject to at most one of three blur types: haze, motion, and defocus In the proposed technique, 35 blur features are first calculated from image spatial and transform domains, and then ranked using the SVM-Recursive Feature Elimination (SVM-RFE) method, which is also adopted to optimize the parameters of the Radial Basis Function (RBF) kernel of SVMs. Moreover, Support Vector Rate (SVR) is used to quantify the optimal number of features to be included in the classifiers. Finally, the bagging random sampling method is utilized to construct the ensemble SVM classifier based on a weighted voting mechanism to classify the types of blurred images. Numerical experiments are conducted over a sample dataset to be called Beihang Univ. Blur Image Database (BHBID) that consists of 1188 simulated blurred images and 1202 natural blurred images collected from popular national and international websites (Baidu.com, Flicker.com, Pabse.com, etc.). The experiments demonstrate the superior performance of the proposed ensemble SVM classifier by comparing it with single SVM classifiers as well as other state-of-the-art blur classification methods.
Blur image identification with ensemble convolution neural networks

Blur image classification is a key step to image recovery in image processing. In this article, an ensemble convolution neural network (CNN) is designed to identify and classify four types of blur images: defocus blur, Gaussian blur, haze blur, and motion blur. To achieve this, a two-stage pipeline, comprised of deep compression and ensemble technique, is proposed to enhance model discriminability without incurring additional computing burden. Specifically, our method first prunes the well-known networks, Alexnet and GoogleNet, by an appropriate compression ratio. The pruned networks are denoted as Simplified-Fast-Alexnet (SFA) and Simplified-Fast-GoogleNet (SFGN). Next, we employ an ensemble policy to integrate the SFA with SFGN as SFA+SFGN by assigning their respective weights based on a voting mechanism. In addition, to provide a benchmark set of blur image samples for training and testing blur classification models, we create a new public blur image dataset (available online at http://doip.buaa.edu.cn/info/1092/1073.htm) containing 80,000+ patch-level, naturally blurred photographs, constructed using the improved super-pixel segmentation method, and 200,000+ artificially blurred images. Numerical experiments demonstrate the superior performance of the proposed approach in comparison with the original Alexnet and GoogleNet, as well as other state-of-the-art methods.
Do I really belong?: Impact of employment status on information security policy compliance

With recent incidents of temporary employees such as seasonal employees, contractors, and subcontractors engaging in information security violations, once again the focus lies on motivating the people within the "organization's wall" to comply with information security. This research paper studies the impact of employment status on organizational commitment and perceived organizational support of employees and how those will affect behavioral intention to comply with Information Security Policies (ISP). Employment status, specifically permanent versus temporary status, can result in different levels of organizational commitment and perceived organizational support. The result of the study supports the notion that the effect of organizational commitment and perceived organizational support on behavioral intention to use ISP will be stronger among permanent employees. The research also found that perceived response cost of performing a behavior would negatively impact intention to comply with ISP.
Semantic Aware Attention Based Deep Object Co-segmentation

Object co-segmentation is the task of segmenting the same objects from multiple images. In this paper, we propose the Attention Based Object Co-Segmentation for object co-segmentation that utilize a novel attention mechanism in the bottleneck layer of the deep neural network for the selection of semantically related features. Furthermore, we take the benefit of attention learner and propose an algorithm to segment multi-input images in linear time complexity. Experiment results demonstrate that our model achieves state of the art performance on multiple datasets, with a significant reduction of computational time.
How Well Do Change Sequences Predict Defects? Sequence Learning from Software Changes

Software defect prediction, which aims to identify defective modules, can assist developers in finding bugs and prioritizing limited quality assurance resources. Various features to build defect prediction models have been proposed and evaluated. Among them, process metrics are one important category. Yet, existing process metrics are mainly encoded manually from change histories and ignore the sequential information arising from the changes during software evolution. Are the change sequences derived from such information useful to characterize buggy program modules? How can we leverage such sequences to build good defect prediction models? Unlike traditional process metrics used for existing defect prediction models, change sequences are mostly vectors of variable length. This makes it difficult to apply such sequences directly in prediction models that are driven by conventional classifiers. To resolve this challenge, we utilize Recurrent Neural Network (RNN), which is a deep learning technique, to encode features from sequence data automatically. In this paper, we propose a novel approach called Fences, which extracts six types of change sequences covering different aspects of software changes via fine-grained change analysis. It approaches defects prediction by mapping it to a sequence labeling problem solvable by RNN. Our evaluations on 10 open source projects show that Fences can predict defects with high performance. In particular, our approach achieves an average F-measure of 0.657, which improves the prediction models built on traditional metrics significantly. The improvements vary from 31.6 to 46.8 percent on average. In terms of AUC, Fences achieves an average value of 0.892, and the improvements over baselines vary from 4.2 to 16.1 percent. Fences also outperforms the state-of-the-art technique which learns semantic features automatically from static code via deep learning.
Bayesian estimation of generalized Gamma mixture model based on variational EM algorithm

In this paper, we propose a Bayesian inference method for the generalized Gamma mixture model (GΓMM) based on variational expectation-maximization algorithm. Specifically, the shape parameters, the inverse scale parameters, and the mixing coefficients in the GΓMM are treated as random variables, while the power parameters are left as parameters without assigning prior distributions. The help function is designed to approximate the lower bound of the variational objective function, which facilitates the assignment of the conjugate prior distributions and leads to the closed-form update equations. On this basis, the variational E-step and the variational M-step are alternatively implemented to infer the posteriors of the variables and estimate the parameters. The computational demand is reduced by the proposed method. More importantly, the effective number of components of the GΓMM can be determined automatically. The experimental results demonstrate the effectiveness of the proposed method especially in modeling the asymmetric and heavy-tailed data.