In this paper, a super-resolution enhancement model based on skip residual dense net(SRDN) is proposed. We design a model with a two-channel skip residual dense nets to extract deeper feature information. The two channels have the same network structure and are connected by skip connections. The model first divide the input image into two components by a guided filtering. The features of two components is learned by one convolution layer and a three layers double-channel SRDN network respectively. Then model uses the concatenation operation to combine two channels’ feature. Finally, the super-resolution image is obtained by the up-sampling network and one convolution operation. Different to the classical loss function, we define a joint loss functions for training, which is consisted of content loss, perceptual loss and color discrimination loss. The experimental results show that the proposed algorithm achieves better super-resolution visual results ans objective evaluation indicators.

Community search has been wildly studied to retrieve relevant cluster from attributed graph for a given set of sample nodes. However, existing well-studied methods aim at searching a community containing sample nodes, and fail to capture communities without sample nodes but are similar with user preference deduced from the given sample nodes. To this end, we propose a community search method that is capable of finding multi-communities with user's preference using few given sample nodes and simultaneously identify outliers in attributed network. The method is termed as Integrating user Preference into Multi-community and Outliers Search (IPMOS), which collaborates user's preference into the process of searching to find interesting clusters of the entire network. Particularly, the strategy of truncated random walk is first used to expand few sample nodes. And then, the average partition similarity is defined based on exploring strategy to infer attribute subspace as user's latent interest. Finally, multiple communities and outliers in the whole network are detected via fractional-core and structural constraints. Extensive experiments on several synthetic networks and real-world networks with different scales and subjects demonstrate the effectiveness and efficiency of our approach.

Blockchains (BCs) are claimed to have immutability, distributed consensus, established trust, distributed identity and eternal verifiable, and sound like the ultimate security unim-peachable technology. At the time, however, new age security attacks on the key components of BCs are emerging, which are very sophisticated and can cause huge irreparable damages, including network-based attacks, consensus & ledger-based at-tacks, smart contract-based attacks, and wallet-based attacks. This paper proposes to use bigraph theory to model BC attack meta-model, and automatically generate attack graphs for BC security evaluation. Bigraphical sorting mechanism is used to depict configuration of BC systems, and bigraphical reaction rules are designed to characterize attack templates and attacker behaviours. Adaptive exploit flow approach is proposed to reduce the complexity of matching algorithm guided by interested attack exploits, and probability is introduced into bigraphs to measure the capability of attackers. Preliminary experiments have shown the validity of the proposed approach.

In large scale data centers, with the increasing amount of user data, Triple Disk Failure Tolerant arrays (3DFTs) gain much popularity due to their high reliability and low monetary cost. With the development of cloud computing, scalability becomes a challenging issue for disk arrays like 3DFTs. Although previous solutions improves the efficiency of RAID scaling, they suffer many problems (high I/O overhead and long migration time) in 3DFTs. It is because that existing approaches have to cost plenty of migration I/Os on balancing the data distribution according to the complex layout of erasure codes. To address this problem, we propose a novel Balanced Data Redistribution scheme (BDR) to accelerate the scaling process, which can be applied on XOR-based 3DFTs. BDR migrates proper data blocks according to a global point of view on a stripe set, which guarantees uniform data distribution and a small number of data movements. To demonstrate the effectiveness of BDR, we conduct several evaluations and simulations. The results show that, compared to typical RAID scaling approaches like Round-Robin (RR), SDM and RS6, BDR reduces the scaling I/Os by up to 77.45%, which speeds up the scaling process of 3DFTs by up to 4.17×, 3.31×, 3.88×, respectively.

This paper is concerned with mining link spams (e.g., link farm and link exchange) from search engine optimization (SEO) forums. To provide quality services, it is critical for search engines to address web spam. Several techniques such as TrustRank, BadRank, and SpamRank have been proposed for this purpose. Most of these methods try to downgrade the effects of the spam websites by identifying specific link patterns of them. However, spam websites have appeared to be more and more similar to normal or even good websites in their link structures, by reforming their spam techniques. As a result, it is very challenging to automatically detect link spams from the Web graph. In this paper, we propose a different approach, which detects link spams by looking at how web spammers make link spam happen. We find that web spammers usually ally with each other, and SEO forum is one of the major means for them to form the alliance. We therefore propose mining suspicious link spams directly from the posts in the SEO forums. However, the task is non-trivial because there are also other information and even noises contained in these posts, in addition to useful clues of link spam. To tackle the challenges, we first extract all the URLs contained in the posts of the SEO forums. Second, we extract features for the URLs from their relationships with forum users (potential spammers) and from their link structure in the web graph. Third, we build a semi-supervised learning framework to calculate the spam scores for the URLs, which encodes several heuristics such as spam websites usually linking to each other, and good websites seldom linking to spam websites. We tested our approach on seven major SEO forums. A lot of spam websites were identified, a significant proportion of which cannot be detected by conventional anti-spam methods. It indicates that the proposed approach can be a good complement of existing anti-spam techniques.

Software outsourcing has become a common method used in many IT companies to reduce development cost, increase development efficiency and concentrate limited resource on mainstream business and technology. One of the important software engineering problems under this background is how to form an outsourcing software development process, which sum up all the people, artifacts, methods, working flows in outsourcing development field to provide instructive process guidance. On the analysis of different kinds of software outsourcing patterns, this paper puts up a RUP based software outsourcing process framework, which include outsourcing patterns exiting in main stages in software life circle, such as requirement outsourcing, design outsourcing, implement outsourcing, testing outsourcing etc. Because of the particularity in the field of software outsourcing, RUP is not completely suit for the outsourcing software development, an extension and customization to RUP is necessary. A detail requirement and design process description is illustrated in this paper.