Privacy preservation is a primary concern in social networkswhich employ a variety of privacy preservations mechanisms to preserve and protect sensitive user information including age, location, education, interests, and others. The task of matching user identities across different social networks is considered a challenging task. In this work, we propose an algorithm to reveal user identities as a set of linked accounts from different social networks using limited user profile data, i.e., user-name and friendship. Thus, we propose a framework, ExpandUIL, that includes three standalone algorithms based on (i) the percolation graph matching in ExpandFullName algorithm, (ii) a supervised machine learning algorithm that works with the graph embedding, and (iii) a combination of the two, ExpandUserLinkage algorithm. The proposed framework as a set of algorithms is significant as, (i) it is based on the network topology and requires only name feature of the nodes, (ii) it requires a considerably low initial seed, as low as one initial seed suffices, (iii) it is iterative and scalable with applicability to online incoming stream graphs, and (iv) it has an experimental proof of stability over a real ground-truth dataset. Experiments on real datasets, Instagram and VK social networks, show upto 75% recall for linked accounts with 96% accuracy using only one given seed pair.
As cloud computing technology turning to mature, cloud services have become a trust-based service. Users’ distrust of the security and performance of cloud services will hinder the rapid deployment and development of cloud services. So cloud service providers (CSPs) urgently need a way to prove that the infrastructure and the behavior of cloud services they provided can be trusted. The challenge here is how to construct a novel framework that can effective verify the security conformance of cloud services, which focuses on fine-grained descriptions of cloud service behavior and security service level aggreements (SLAs). In this paper, we propose a novel approach to verify cloud service security conformance, which reduces the description gap between the CSP and users through modeling cloud service behavior and security SLA, these models enable a systematic integration of security constraints and service behavior into cloud while using UPPAAL to check the performance and security conformance. The proposed approach is validated through case study and experimentswith real cloud service based on Open- Stack, which illustrates CloudSec approach effectiveness and can be applied on realistic cloud scenario.
Cloud computing provides the capability to connect resource-constrained clients with a centralized and shared pool of resources, such as computational power and storage on demand. Large matrix determinant computation is almost ubiquitous in computer science and requires largescale data computation. Currently, techniques for securely outsourcing matrix determinant computations to untrusted servers are of utmost importance, and they have practical value as well as theoretical significance for the scientific community. In this study, we propose a secure outsourcing method for large matrix determinant computation. We employ some transformations for privacy protection based on the original matrix, including permutation and mix-row/mixcolumn operations, before sending the target matrix to the cloud. The results returned from the cloud need to be decrypted and verified to obtain the correct determinant. In comparison with previously proposed algorithms, our new algorithm achieves a higher security levelwith greater cloud efficiency. The experimental results demonstrate the efficiency and effectiveness of our algorithm.
In the early design stage, automotive modeling should both meet the requirements of aesthetics and engineering. Therefore, a vehicle CAD (computer aided design) model that can be easily adjusted by feedbacks is necessary. Based on CE-Bézier surface, this paper presents a set of algorithms for parametric segmentation and fairing surface generation in a car model. This model is defined by a simplified automotive template and relevant control points, shape parameters and segmentation parameters, which can be modified to alter the car form efficiently. With this model and the corresponding adjustment method, more than fifty various vehicle models are established in this research according to different parameters. And two methods for calculating similarity index between car models are constructed, which are suitable for brand design trend analysis and modelling design decisionmaking.
Pipeline processing is applied to multiple flow tables (MFT) in the switch of software-defined network (SDN) to increase the throughput of the flows. However, the processing time of each flow increases as the size or number of flow tables gets larger. In this paper we propose a novel approach called PopFlow where a table keeping popular flow entries is located up front in the pipeline, and an express path is provided for the flow matching the table. A Markov model is employed for the selection of popular entries considering the match latency and match frequency, and Queuing theory is used to model the flow processing time of the existing MFTbased schemes and the proposed scheme. Computer simulation reveals that the proposed scheme substantially reduces the flow processing time compared to the existing schemes, and the difference gets more significant as the flow arrival rate increases.
It is essential to provide responses to queries within time deadlines, even if not exact and complete. To reduce the query latency, systems usually partition large-scale data computations as a series of tasks over many processes and aggregate them to reduce the response time by using aggregation trees. An obstacle is that the involved processes of a query usually differ in their speeds, thus not all processes can complete their tasks in time. This would directly degrade the response quality (the number of outputs received by the root of an aggregation tree). In this paper, we propose a general aggregation tree model, Tarot, to maximize the response quality by systematically addressing the following challenging issues: (1) fine-grained partition of the query deadline along the multi-level aggregation tree; (2) learning the distribution of durations at each level in the aggregation tree to optimize the wait durations at aggregators; (3) adaptively reassigning tasks over processes according to their status; (4) performing periodic aggregation of received outputs from the low level to avoid missing the deadline. The prior model does not consider the four aspects simultaneously. Extensive evaluations indicate that Tarot can adapt to multi-level trees and considerably improve the response quality compared to prior work while guaranteeing the query deadline.
True random number generators (TRNG) are important counterparts to pseudorandom number generators (PRNG), especially for high security applications such as cryptography. They produce unpredictable, non-repeatable random sequences. However, most TRNGs require specialized hardware to extract entropy from physical phenomena and tend to be slower than PRNGs. These generators usually require post-processing algorithms to eliminate biases but in turn, reduces performance. In this paper, a new post-processing method based on hyperchaos is proposed for software-based TRNGs which not only eliminates statistical biases but also provides amplification in order to improve the performance of TRNGs. The proposed method utilizes the inherent characteristics of chaos such as hypersensitivity to input changes, diffusion, and confusion capabilities to achieve these goals. Quantized bits of a physical entropy source are used to perturb the parameters of a hyperchaotic map, which is then iterated to produce a set of random output bits. To depict the feasibility of the proposed post-processing algorithm, it is applied in designing TRNGs based on digital audio. The generators are analyzed to identify statistical defects in addition to forward and backward security. Results indicate that the proposed generators are able to produce secure true random sequences at a high throughput,which in turn reflects on the effectiveness of the proposed post-processing method.
A k-CNF (conjunctive normal form) formula is a regular (k, s)-CNF one if every variable occurs s times in the formula, where k≥2 and s>0 are integers. Regular (3, s)- CNF formulas have some good structural properties, so carrying out a probability analysis of the structure for random formulas of this type is easier than conducting such an analysis for random 3-CNF formulas. Some subclasses of the regular (3, s)-CNF formula have also characteristics of intractability that differ from random 3-CNF formulas. For this purpose, we propose strictly d-regular (k, 2s)-CNF formula, which is a regular (k, 2s)-CNF formula for which d≥0 is an even number and each literal occurs
Overlapping community detection has become a very hot research topic in recent decades, and a plethora of methods have been proposed. But, a common challenge in many existing overlapping community detection approaches is that the number of communities K must be predefinedmanually. We propose a flexible nonparametric Bayesian generative model for count-value networks, which can allow K to increase as more and more data are encountered instead of to be fixed in advance. The Indian buffet process was used to model the community assignment matrix Z, and an uncollapsed Gibbs sampler has been derived.However, as the community assignment matrix Z is a structured multi-variable parameter, how to summarize the posterior inference results and estimate the inference quality about Z, is still a considerable challenge in the literature. In this paper, a graph convolutional neural network based graph classifier was utilized to help to summarize the results and to estimate the inference quality about Z. We conduct extensive experiments on synthetic data and real data, and find that empirically, the traditional posterior summarization strategy is reliable.
Network representation learning called NRL for short aims at embedding various networks into lowdimensional continuous distributed vector spaces. Most existing representation learning methods focus on learning representations purely based on the network topology, i.e., the linkage relationships between network nodes, but the nodes in lots of networks may contain rich text features, which are beneficial to network analysis tasks, such as node classification, link prediction and so on. In this paper, we propose a novel network representation learning model, which is named as Text-Enhanced Network Representation Learning called TENR for short, by introducing text features of the nodes to learn more discriminative network representations, which come from joint learning of both the network topology and text features, and include common influencing factors of both parties. In the experiments, we evaluate our proposed method and other baseline methods on the task of node classification. The experimental results demonstrate that our method outperforms other baseline methods on three real-world datasets.
Image matting is an essential image processing technology due to its wide range of applications. Samplingbased image matting is one of the main branches of image matting research that estimates alpha mattes by selecting the best pixel pairs. It is essentially a large-scale multi-peak optimization problem of pixel pairs. Previous study shows that particle swarm optimization (PSO) can effectively optimize the pixel pairs. However, it still suffers from premature convergence problem which often occurs in pixel pair optimization that involves a large number of local optima. To address this problem, this work presents a parameter-free strategy for PSO called adaptive convergence speed controller (ACSC). ACSC monitors and conditionally controls the particles by competitive pixel pair recombination operator (CPPRO) and pixel pair reset operator (PPRO) during the iteration. ACSC performs CPPRO to improve the competitiveness of a particle when the performance of most of the pixel pairs is worse than that of the best-so-far solution. PPRO is performed to avoid premature convergence when the alpha mattes regarding two selected particles are highly similar. Experimental results show that ACSC significantly enhances the performance of PSO for image matting and provides competitive alpha mattes comparing with state-of-the-art evolutionary algorithms.
Regarding extreme value theory, the unseen novel classes in the open-set recognition can be seen as the extreme values of training classes. Following this idea, we introduce the margin and coverage distribution to model the training classes. A novel visual-semantic embedding framework – extreme vocabulary learning (EVoL) is proposed; the EVoL embeds the visual features into semantic space in a probabilistic way. Notably, we adopt the vast open vocabulary in the semantic space to help further constraint the margin and coverage of training classes. The learned embedding can directly be used to solve supervised learning, zero-shot learning, and open set recognition simultaneously. Experiments on two benchmark datasets demonstrate the effectiveness of the proposed framework against conventional ways.
Quality assessment is a critical component in crowdsourcing-based software engineering (CBSE) as software products are developed by the crowd with unknown or varied skills and motivations. In this paper, we propose a novel metric called the project score to measure the performance of projects and the quality of products for competitionbased software crowdsourcing development (CBSCD) activities. To the best of our knowledge, this is the first work to deal with the quality issue of CBSE in the perspective of projects instead of contests. In particular, we develop a hierarchical quality evaluation framework for CBSCD projects and come up with two metric aggregation models for project scores. The first model is a modified squale model that can locate the software modules of poor quality, and the second one is a clustering-based aggregationmodel, which takes different impacts of phases into account. To test the effectiveness of the proposed metrics, we conduct an empirical study on TopCoder, which is a famous CBSCD platform. Results show that the proposed project score is a strong indicator of the performance and product quality of CBSCD projects.We also find that the clustering-based aggregation model outperforms the Squale one by increasing the percentage of the performance evaluation criterion of aggregation models by an additional 29%. Our approach to quality assessment for CBSCD projects could potentially facilitate software managers to assess the overall quality of a crowdsourced project consisting of programming contests.