Reviewerly/exHarmony huggingface dataset:
Learning discriminative compact binary face descriptor for kinship verification

In this paper, we propose a weakly-supervised feature learning method called discriminative compact binary face descriptor (D-CBFD) for facial kinship verification. Unlike most existing kinship verification methods where hand-crafted features are used for face representation, our D-CBFD learns discriminative face representation from a set of weakly-labeled samples. Given a face image, we first compute pixel difference vectors (PDVs) at local patches. Then, we learn a discriminative projection to map each PDV into a low-dimensional binary feature space, where the energy information of the PDV can be well preserved and the distance between the positive pair is smaller and that between the negative pair is larger. Lastly, we pool these binary feature vectors within each face into a histogram feature as the final representation. Experimental results on three kinship datasets are presented to show the effectiveness of the proposed method.
Learning Linear Regression via Single-Convolutional Layer for Visual Object Tracking

Learning a large-scale regression model has proven to be one of the most successful approaches for visual tracking as in recent correlation filter (CF)- based trackers. Different from the conventional CF-based algorithms in which the regression model is solved based on circulant training samples, we propose learning linear regression models via a single-convolutional layer with the gradient descent (GD) technique. In our convolution-based approach, the samples are cropped from an image in a sliding-window manner rather than being circularly shifted from one base sample. As a result, the abundant background context in the images can be fully exploited to learn a robust tracker. The proposed tracker is based on two independent regression models: a holistic regression model and a texture regression model. The holistic regression model is trained based on the entire object patch to predict the object location, whereas the texture regression model is trained based on the local object textures. The foreground map outputted by the texture regression model is not only helpful to boost the location prediction in the case of large variations, but is also an important clue for estimating the object size. With the foreground map outputted by the texture regression model, we are able to estimate the object size by optimizing a novel objective function based on object-background contrast. Our extensive experiments on four popular visual tracking datasets OTB-50, OTB-100, VOT-2016, and TempleColor have proved that the proposed algorithm achieves outstanding performance and outperforms most CF-based trackers.
PrivateGraph: Privacy-Preserving Spectral Analysis of Encrypted Graphs in the Cloud

Big graphs, such as user interactions in social networks and customer rating matrices in collaborative filters, possess great values for both businesses and research. They are not only big but often keep evolving, which requires a large amount of computing resources to maintain. With the wide deployment of public cloud resources, owners of big graphs may want to use cloud resources to obtain storage and computation scalability. However, privacy and ownership of the graphs in the cloud has become a major concern. In this paper, we study privacy-preserving algorithms for one of the important graph analysis techniques-graph spectral analysis for outsourced graph in the cloud. The core operation: eigendecomposition of large matrix is also important to many data mining algorithms. We consider a cloud-centric framework with three collaborative parties: data contributors, data owner, and cloud provider. Graphs are represented as matrices such as adjacency matrix and Laplacian matrix, the elements of which are encrypted and submitted by distributed contributors. The data owner then interacts with the cloud-side programs to conduct spectral analysis, while protecting data privacy from the honest-but-curious cloud provider. For a N × N graph matrix, we aim to design algorithms with the cloud handling expensive storage and computation in O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) complexity, while data owner and data contributors' algorithms take only O(N). To achieve this goal, we develop the privacy-preserving versions of the two approximate eigendecomposition algorithms: the Lanczos algorithm and the Nyström algorithm, considering two encryption methods: additive homomorphic encryption (AHE) methods and somewhat homomorphic encryption (SHE) methods. Both dense and sparse matrices are studied, while sparse matrices also involve a differentially private data submission protocol to allow the trade-off between data sparsity and privacy. Experimental results show that the Nyströ algorithm with sparse encoding can dramatically reduce data owners' costs. SHE-based methods have lower computational time while AHE-based methods have lower communication/storage costs.
Exploiting data centres energy flexibility in smart cities: Business scenarios

In this paper, we have considered Data Centres (DCs) as computing facilities functioning at the crossroad of electrical, thermal and data networks and have defined optimisation techniques to exploit their energy flexibility. Our methods are leveraging on non-electrical cooling devices such as thermal storage and heat pumps for waste heat reuse and IT workload execution time shifting and spatial relocation in federated DCs. To trade energy flexibility we have defined an Energy Marketplace which allows DCs to act as active energy players integrated into the smart grid, contributing to smart city-level efficiency goals. Reinforcing this vision, we have proposed four innovative business scenarios that enable next generation smart Net-zero Energy DCs acting as energy prosumers at the interface with smart energy grids within smart city environments. Simulation experiments are conducted to determine the DCs potential electrical and thermal energy flexibility in meeting various network level goals and to assess the financial viability of the defined business scenarios. The results show that DCs have a significant amount of energy flexibility which may be shifted and traded to interested stakeholders thus allowing them to gain new revenue streams not foreseen before.
Dialogue Systems and Conversational Agents for Patients with Dementia: The Human–Robot Interaction

This study aimed to identify and describe the fundamental characteristics of spoken dialogue systems, and their role in supporting human-robot interaction and enabling the communication between socially assistive robots and patients with dementia. First, this work provides an overview of spoken dialogue systems by considering the underlying technologies, approaches, methods, and general issues. Then, the analysis focuses on studies, systems, and approaches that have investigated the role of dialogue systems and conversational agents in the interaction with elderly people with dementia by presenting the results of a literature review. While the overview of spoken dialogue systems relies on existing surveys and reviews, a research was conducted to identify existing works in the literature that have investigated the role of conversational agents and dialogue systems in the elderly and people with cognitive impairments. Inclusion criteria were as follows: (1) use of conversational agents, dialogue systems, or language processing tools for people with cognitive impairments; (2) age ≥60 years; (3) diagnosis of dementia according to National Institute on Aging-Alzheimer's Association (NIAAA) criteria; (4) presence of tests or experiments with qualitative or quantitative results. Initially 125 studies published between 2000 and 2017 were identified, of which 12 met the inclusion criteria. The review identifies the issues and challenges that are reported when conversational agents and speech-based interfaces have been used for interacting with people with cognitive impairments. In addition, the review led to the identification of studies that have investigated speech processing and natural language processing capabilities to assess the cognitive status of people with dementia.
ICFS Clustering With Multiple Representatives for Large Data

With the prevailing development of Cyber-physical-social systems and Internet of Things, large-scale data have been collected consistently. Mining large data effectively and efficiently becomes increasingly important to promote the development and improve the service quality of these applications. Clustering, a popular data mining technique, aims to identify underlying patterns hidden in the data. Most clustering methods assume the static data, thus they are unfavorable for analyzing large, unbalanced dynamic data. In this paper, to address this concern, we focus on incremental clustering by extending the novel [clustering by fast search (CFS) and find of density peaks] method to incrementally handle large-scale dynamic data. Specifically, we first discuss two challenges, i.e., assignment of new arriving objects and dynamic adjustment of clusters, in incremental CFS (ICFS) clustering. We then propose two ICFS clustering algorithms, ICFS with multiple representatives (ICFSMR) and the enhanced ICFSMR (E_ICFSMR) to tackle the two challenges. In ICFSMR, we explore the convex hull theory to modify the representatives identified for each cluster. E_ICFSMR improves the generality and effectiveness of ICFSMR by exploring one-time cluster adjustment strategy after integration of each data chunk. We evaluate the proposed methods with extensive experiments on four benchmark data sets, as well as the air quality and traffic monitoring time series, with comparisons to CFS and other three state-of-the-art incremental clustering methods. Experimental results demonstrate that the proposed methods outperform the compared methods in terms of both effectiveness and efficiency.
BDPK: Bayesian Dehazing Using Prior Knowledge

Atmospheric scattering model (ASM) has been widely used in hazy image restoration. However, the recovered albedo might deviate from the real scene once the input hazy image cannot fully satisfy the model's assumptions such as the homogeneous atmosphere and even illumination. In this paper, we break these limitations and redefine a more reliable ASM (RASM) that is extremely adaptable for various practical scenarios. Benefiting from RASM, a simple yet effective Bayesian dehazing algorithm (BDPK) is further proposed based on the prior knowledge. Our strategy is to convert the single image dehazing problem into a maximum a-posteriori probability one that can be approximated as an optimization function using the existing priori constraints. To efficiently solve this optimization function, the alternating minimizing technique is introduced, which enables us to directly restore the scene albedo. Experiments on a number of challenging images reveal the power of BDPK on removing haze and verify its superiority over several state-of-the-art techniques in terms of quality and efficiency.
Quantum experiments and graphs II: Quantum interference, computation, and state generation

We present an approach to describe state-of-the-art photonic quantum experiments using graph theory. There, the quantum states are given by the coherent superpositions of perfect matchings. The crucial observation is that introducing complex weights in graphs naturally leads to quantum interference. This viewpoint immediately leads to many interesting results, some of which we present here. First, we identify an experimental unexplored multiphoton interference phenomenon. Second, we find that computing the results of such experiments is #P-hard, which means it is a classically intractable problem dealing with the computation of a matrix function Permanent and its generalization Hafnian. Third, we explain how a recent no-go result applies generally to linear optical quantum experiments, thus revealing important insights into quantum state generation with current photonic technology. Fourth, we show how to describe quantum protocols such as entanglement swapping in a graphical way. The uncovered bridge between quantum experiments and graph theory offers another perspective on a widely used technology and immediately raises many follow-up questions.
Evolutionary game theory approach to materialized view selection in data warehouses

The data warehouse contains a number of views that are used to respond to the system queries. On the one hand, the time consuming process of responding to analytical queries of the data warehouse requires to store intermediate views for efficient query answering, and on the other hand, large numbers and high volumes of intermediate views, make the storage of all views impossible. Hence, choosing the optimal set of views for materialization is one of the most important decisions in the data warehouses design, in order to increase efficiency of query answering. Since the search space of the problem is very large, searching among the collections of all possible views of a data warehouse is very expensive and thus, it is necessary to use methods to solve the problem in an acceptable time. Random methods, such as game theory-based optimization approaches, try to increase the speed of selecting materialized views by finding near optimal solutions. In this article, an evolutionary game theory-based method to materialized view selection in the data warehouse is represented which exploits the multiple view processing plan structure to represent the search space of the problem. In this method, a population of players is created, each of which is a solution to the problem. Three strategies are considered for each player and at each repetition of the game, players attempt to choose the best strategy for themselves. At the end of the game, the final solution is calculated according to the strategies selected by the players. Our empirical evaluations revealed that the proposed method has appropriate convergence for large data warehouses and its execution time is very good. It is also shown that the quality of the solutions of the proposed method is more appropriate than other similar random methods.
Incomplete Multi-view Clustering via Graph Regularized Matrix Factorization

Clustering with incomplete views is a challenge in multi-view clustering. In this paper, we provide a novel and simple method to address this issue. Specially, the proposed method simultaneously exploits the local information of each view and the complementary information among views to learn the common latent representation for all samples, which can greatly improve the compactness and discriminability of the obtained representation. Compared with the conventional graph embedding methods, the proposed method does not introduce any extra regularization term and corresponding penalty parameter to preserve the local structure of data, and thus does not increase the burden of extra parameter selection. By imposing the orthogonal constraint on the basis matrix of each view, the proposed method is able to handle the out-of-sample. Moreover, the proposed method can be viewed as a unified framework for multi-view learning since it can handle both incomplete and complete multi-view clustering and classification tasks. Extensive experiments conducted on several multi-view datasets prove that the proposed method can significantly improve the clustering performance.
Graph model-based salient object detection using objectness and multiple saliency cues

Recent years have witnessed increasing interest in salient object detection, which aims at stimulating the human visual attention mechanism to detect and segment the most attractive object in natural scenes, and can be widely applied in numerous computer vision tasks. In this paper, by considering both objectness cue and saliency detection, we propose a graph model-based bottom-up salient object detection framework by fusing multiple saliency maps using low-level features and objectness features under a manifold ranking framework. Specifically, for each feature, we utilize geodesic distance between any two superpixels to construct the affinity matrix and un-normalized Laplacian matrix of the graph. Then, we apply saliency optimization to refine each saliency map generated by manifold ranking with the first-stage query, and integrate saliency maps corresponding to different features by multilayer cellular automata in the final stage. Extensive experimental results demonstrate that our method can deliver promising performance in comparison to several state-of-the-art bottom-up methods on many benchmark datasets.
Accurate vessel extraction via tensor completion of background layer in X-ray coronary angiograms

This paper proposes an effective method for accurately recovering vessel structures and intensity information from the X-ray coronary angiography (XCA) images of moving organs or tissues. Specifically, a global logarithm transformation of XCA images is implemented to fit the X-ray attenuation sum model of vessel/background layers into a low-rank, sparse decomposition model for vessel/background separation. The contrast-filled vessel structures are extracted by distinguishing the vessels from the low-rank backgrounds by using a robust principal component analysis and by constructing a vessel mask via Radon-like feature filtering plus spatially adaptive thresholding. Subsequently, the low-rankness and inter-frame spatio-temporal connectivity in the complex and noisy backgrounds are used to recover the vessel-masked background regions using tensor completion of all other background regions, while the twist tensor nuclear norm is minimized to complete the background layers. Finally, the method is able to accurately extract vessels' intensities from the noisy XCA data by subtracting the completed background layers from the overall XCA images. We evaluated the vessel visibility of resulting images on real X-ray angiography data and evaluated the accuracy of vessel intensity recovery on synthetic data. Experiment results show the superiority of the proposed method over the state-of-the-art methods.
Unsupervised feature extraction by low-rank and sparsity preserving embedding

Manifold based feature extraction has been proved to be an effective technique in dealing with the unsupervised classification tasks. However, most of the existing works cannot guarantee the global optimum of the learned projection, and they are sensitive to different noises. In addition, many methods cannot catch the discriminative information as much as possible since they only exploit the local structure of data while ignoring the global structure. To address the above problems, this paper proposes a novel graph based feature extraction method named low-rank and sparsity preserving embedding (LRSPE) for unsupervised learning. LRSPE attempts to simultaneously learn the graph and projection in a framework so that the global optimal projection can be obtained. Moreover, LRSPE exploits both global and local information of data for projection learning by imposing the low-rank and sparse constraints on the graph, which promotes the method to obtain a better performance. Importantly, LRSPE is more robust to noise by imposing the l2,1 sparsity norm on the reconstruction errors. Experimental results on both clean and noisy datasets prove that the proposed method can significantly improve classification accuracy and it is robust to different noises in comparison with the state-of-the-art methods.
DEFT: Dynamic Fault-Tolerant Elastic scheduling for tasks with uncertain runtime in cloud

With the widespread use of clouds, the reliability and efficiency of cloud have been the main concerns of the service providers and users. Thus, fault tolerance has become a hotspot in both industry and academia, especially for real-time applications. To achieve fault tolerance in cloud, a great number of in-depth researches have been conducted. Nevertheless, for addressing the issue of fault tolerance, few studies have taken into account the uncertainty of task runtime, which is however more practical and really needs urgent attention. In this paper, we introduce the uncertainty to our task runtime estimation model and we propose a fault-tolerant task allocation mechanism that strategically uses two fault tolerant task scheduling models while the uncertainty is considered. Moreover, we employ the overlapping mechanism to improve the resource utilization of cloud. Based on the two mechanisms, we propose an innovative Dynamic Fault-Tolerant Elastic scheduling algorithm-DEFT for scheduling real-time tasks in the cloud where the system performance volatility should be considered. The purpose of DEFT is to achieve both fault tolerance and resource utilization efficiency. We compare DEFT with three baseline algorithms: NDRFT, DRFT, and NWDEFT. The results from our extensive experiments on the workload of the Google tracelogs show that DEFT can guarantee fault tolerance while achieving high resource utilization.
A clonal selection algorithm for dynamic multimodal function optimization

The objective of dynamic multimodal optimization problems (DMMOPs) is to find all global optima in a dynamic environment. Although dynamic optimization problems (DOPs) have been widely studied in the field of nature-inspired computation, DMMOPs have not yet been paid significant attention yet. It is a challenging task for the classic clonal selection algorithm (CSA) to track all the moving global optima of dynamic multimodal optimization problems. The population of the classic CSA tends to converge to a single optimum, and the shortage of population diversity prevents the classic CSA from adapting to environmental changes. To address these limitations, this paper proposes a so-called dynamic multimodal clonal selection algorithm (DMMCSA). DMMCSA incorporates a niching method called nearest-better clustering, and adapts the scale factor of the hypermutation operator according to the distances among individuals. Experiments on benchmark problems show that DMMCSA significantly outperforms CSA in terms of tracking all the global optima of DMMOPs.
An Efficient and Privacy-Preserving Disease Risk Prediction Scheme for E-Healthcare

Big data mining-driven disease risk prediction has become one of the important topics in the field of e-healthcare. However, without the security and privacy assurances, disease risk prediction cannot continue to flourish. To address this challenge, in this paper, an efficient and privacy-preserving disease risk prediction scheme for e-healthcare is proposed, hereafter referred to as EPDP. Compared with the up-to-date works, the proposed EPDP comprehensively achieves two phases of disease risk prediction, i.e., disease model training and disease prediction, while ensuring the privacy preservation. Specifically, a super-increasing sequence is combined with a homomorphic cryptographic algorithm to efficiently extract the symptom set of each disease in the phase of disease model training. Bloom filter technique is introduced to compute the prediction result in the phase of disease risk prediction. Besides, extensive performance evaluations demonstrate that our proposed EPDP attains outstanding efficiency advantage over the state-of-the-art in terms of both computational and communication overheads, and hence our EPDP is more suitable for real-time e-healthcare, especially medical emergency.