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The sparseness of user-to-item rating is one of the main factors affecting the quality of a recommender system. To address the sparsity problem, several recommendation techniques have been proposed. They add auxiliary information to the recommender system to improve the accuracy of rating prediction. We observe that there are more implicit relationships besides the explicit connection between users and the items to be predicted. If these rich implicit relations are introduced into the recommendation model, the sparsity of the original data can be greatly reduced. Therefore, we propose a new recommendation algorithm via implicit relationship, which is applied to the classic movie recommendation problem. This model extracts the implicit relationship from the movie's cast data to make movie recommendations, so as to better understand the movie. Different from the existing context-based methods, our method extracts more diverse preference information of users and integrates the implicit relationship between users and other entities. This paper takes a step forward in this direction, and studies how to use rich implicit relations to alleviate the sparsity of recommendation and further improve the recommendation methods. We conduct a series of experiments on real datasets which show that our method sianificantly outperforms state-of-the-art methods.

Although exploratory search has received significant attention recently in the context of structured data, scant attention has been paid for graph-structured data. In this paper, we present two novel index structures called VACCINE and ADVISE to efficiently support exploratory subgraph search in a visual environment (VESS). VACCINE is an offline, feature-based index that stores rich information related to frequent and infrequent subgraphs in the underlying graph database and how they can be transformed from one subgraph to another. ADVISE, on the other hand, is an adaptive, compact, on-the-fly index instantiated during iterative visual formulation/reformulation of a subgraph query for exploratory search and records relevant information to efficiently support its repeated evaluation. These indexes engender more efficient and scalable visual exploratory subgraph search framework compared to a state-of-the-art technique.

In this paper, a 10-bit nonlinear analog-to-digital converter (ADC) of CMOS image sensors (CIS) is proposed for the lensless cell imaging system, aiming at cell images local contrast enhancement by nonlinear quantization. The nonlinear ADC is consisted of a single-slope ADC (SS ADC) and a configurable multi-frequency counter, which achieves a local contrast enhancement algorithm to reduce noise and power dissipation. This ADC is designed using 0.11-μm CMOS process. The simulation results show that the differential nonlinearity (DNL) of the proposed circuit is +0.34LSB/-0.34LSB and the integral nonlinearity (INL) is +0.78LSB/-0.98LSB.

Mobile app testing is challenging since each test needs to be executed in a variety of operating contexts including heterogeneous devices, various wireless networks and different locations. Crowdsourcing enables a mobile app test to be distributed as a crowdsourced task to leverage crowd workers to accomplish the test. However, high test quality and expected test context coverage are difficult to achieve in crowdsourced testing. Upon distributing a test task, mobile app providers neither know who to participate nor predict whether all the expected test contexts can be covered in the task. To address this problem, we put forward a novel research problem called Crowdsourced Testing Quality Maximization Under Context Coverage Constraint (Cocoon). Given a mobile app test task, our objective is to recommend a set of workers, from available crowd workers, such that the expected test context coverage and a high test quality can be achieved. We prove that the Cocoon problem is NP-Complete and then introduce two greedy approaches. Based on a real dataset from the largest Chinese crowdsourced testing platform, our evaluation shows the effectiveness and efficiency of the two approaches, which can be potentially used as online services in practice.

Crowdsourced testing is an emerging trend in software testing, which relies on crowd workers to accomplish test tasks. Due to the cost constraint, a test task usually involves a limited number of crowd workers. Furthermore, more workers does not necessarily result in detecting more bugs. Different workers, who may have different testing experience and expertise, may make much differences in the test outcomes. For example, some inappropriate workers may miss true bug, introduce false bugs or report duplicated bugs, which decreases the test quality. In current practice, a test task is usually dispatched in a random manner, and the quality of testing cannot be guaranteed. Therefore, it is important to select an appropriate subset of workers to perform a test task to ensure high bug detection rate. This paper introduces ExReDiv, a novel hybrid approach to select a set of workers for a test task. It consists of three key strategies: the experience strategy selects experienced workers, the relevance strategy selects workers with expertise relevant to the given test task, the diversity strategy selects diverse workers to avoid detecting duplicated bugs. We evaluate ExReDiv based on 42 test tasks from one of the largest crowdsourced testing platforms in China, and the experimental results show its effectiveness.

Host-based anomaly detection systems (HADS) serves as the second line of defense after cyber attacks have penetrated the network level defense. The major components of reliable HADS includes enriched data source (DS), computational efficient data feature retrieval (DFR), accurate and fast decision engine (DE). ADFA-LD is a recently published data set which reflects the invisible threat environment of modern computer system. The existing HADS utilizing ADFA-LD as DS, exhibits high computational DFR and inferior performance of the DE at real-time. The major drawback is inability to acquire representative features from host activities. Confronting this drawback in this paper, at DFR a character data zero watermark inspired statistical based strategy is developed for integer data to extract hidden reliable or representative features from system calls of the trace. At DE, three supervised machine learning classifiers such as support vector machine (SVM) with linear and radial bases function (RBF) kernels and k-nearest neighbor (KNN) are evaluated across detection rate (DR), false alarm rate (FAR) and computational time. The numerical trials validates that the suggested statistical feature extraction strategy at DFR and KNN at DE can attain acceptable performance at real-time

ADFA-LD is a recently released data set for evaluating host-based anomaly detection systems, aiming to substitute the existing benchmark data sets which have failed to reflect the characteristics of modern computer systems. In a previous work, we had attempted to evaluate ADFA-LD with a highly efficient frequency model but the performance is inferior. In this paper, we focus on the other typical technical category that detects anomalies with a short sequence model. In collaboration with the one-class SVM algorithm, a novel anomaly detection system is proposed for ADFA-LD. The numerical experiments demonstrate that it can not only achieve a satisfactory performance, but also reduce the computational cost largely. Refereed/Peer-reviewed

Host-based intrusion detection systems (HIDSs), especially anomaly-based, have received much attention over the past few decades. Over time, however, the existing data sets used for evaluation of a HIDS have lost most of their relevance due to the substantial development of computer systems. To fill this gap, ADFA Linux data set (ADFA-LD) is recently released, which is composed of thousands of system call traces collected from a contemporary Linux local server and expects to be a new benchmark for evaluating a HIDS. In this paper, we perform a preliminary analysis of ADFA-LD, in an attempt to extract useful information for developing new host-based anomaly detection systems (HADSs). In accordance with the general concerns arising from the community, some typical features are analysed particularly against ADFA-LD, such as length, common pattern and frequency. Furthermore, we implement a simple k nearest neighbour (kNN)-based HADS to be evaluated using ADFA-LD. The experimental results show that, although an acceptable performance can be acquired for a few types of attack, there is still a long way to fully understand the complex behaviour resulting from a modern computer system and, finally, realise more intelligent HADSs.

Online anomaly detection is critical for protecting wireless sensor networks (WSNs) from cyber-attacks and random faults, which handles the streaming data in real-time. Comparing to other techniques, histogram-based anomaly detection is cheaper in computation, which should be suitable for WSNs. However, performing histogram-based anomaly detection with an online manner in WSNs is not a straightforward issue. Most of the existing histogram-based schemes have to depend on a verification procedure, which costs a great amount of computational overhead as well as communication overhead. Thus, it almost wipes out the advantage of low complexity of histogram-based anomaly detection. This paper introduces a simple estimating approach to detect anomalies with the histogram, which takes account into the distributed manner and online manner at the same time. It also proves the error caused by the new estimate is very small, through a theoretical analysis. Moreover, the optimal parameter will be suggested by minimizing the error. Finally, a set of experiments are implemented with a real WSN dataset, which prove the new scheme is effective and efficient.

Unsupervised anomaly detection (UAD) techniques have received increasing attention in wireless sensor networks (WSNs). However, the high dimensional training data often make sensor nodes unable to sustain in computation, and result in quite expensive communication overhead. The feature reduction techniques make great sense through the reduction of the dimensionality when the features are strongly interrelated. Among these UAD techniques, distance-based anomaly detection (DB-AD) is a special one that allows to be described by a probability model. Based on this observation, DB-AD is explored deeply with a feature reduction technique, principal component analysis (PCA). Through examining the proportion of the variance explained by the first principal component (PC), a new feature reduction approach is proposed for DB-AD in WSNs, which enables to reduce the dimensionality to one in any situation. Specifically, the first PC is alone used for representing the original data as long as it retains most of the variance, otherwise, the information loss is geometrically reverted to neutralize the error. By obtaining a tradeoff between the detection error and performance overload, this approach is significant for resource-constrained WSNs, as the computational complexity and communication overhead will be reduced to a fraction of the original magnitude. Finally, this approach is evaluated with a real WSN dataset.

We propose a self-healing key distribution scheme with implicit authentication following a hash-binary-tree based key distribution scheme. The scheme reduces storage overhead without increase of communication and computation overhead. Implication authentication is subtly introduced to detect tam- per attack against broadcast messages during transmission. The scheme achieves the same security level with Dutta et al's approach which is an improved version of one of our schemes. The security of the proposed scheme is analyzed under an security model. Keywords-Self-healing key distribution, hash-binary-tree, vector space secret sharing; I. INTRODUCTION WSNs (Wireless Sensor Networks) have gained wide applications ranging from civilian to military use, where net- work infrastructure is usually not available and adversaries may launch different kinds of attacks. WSNs are prone to all kinds of attacks thus security becomes the first concern. In order to keep communication secure, sensitive data should be encrypted and authenticated. Therefore, key distribution, which is a prerequisite of encryption and authentication, should be addressed carefully. WSNs are not reliable networks, and hence the broadcast of key distribution for a particular session might never reach some users. Requiring retransmissions would contribute to the traffic on the network which might already be heavily burdened. Especially, when network scale is large, such re-transmissions could potentially exhaust the group man- ager. In addition, in some high security environments, it is suggested that only sending essential messages lest they make themselves vulnerable by revealing their location. Self- healing key distribution schemes enable large and dynamic groups of users over an unreliable network to establish group keys for secure communication over an unreliable channel in the manner that is resistant to packet loss. Hence, self- healing key distribution solutions are not only favorable but also necessary. Our contribution in this paper are two-fold. Firstly, we propose an HBT (hash binary tree) based self-healing key distribution scheme following approach of (1). The scheme

The basic principle of Artificial Neural Networks and BP algorithm was introduced in this paper. The application of BP algorithm Artificial Neural Networks in fault diagnosis of 40TM liquid-gas hammer was studied. The superiority of BP algorithm Artificial Neural Networks in fault diagnosis was proved by the MATLAB simulation and the training. The causes of faults were determined by BP algorithm Artificial Neural Networks.

With the extensive using P2P networks, P2P data stream occupied more and more bandwidth, it will affect the performance of the entire Internet. This paper studies the P2P nodes which exchange traffic disorder, through introducing XCP congestion control protocol, designs network region-based exchange unit and flow control strategy to achieve flow control. The simulation results show that the flow control method can ease the P2P data stream.

Cognitive Radio Networks (CRNs) deal with opportunistic spectrum access in order to fully utilize the scarce of spectrum resources, with the development of cognitive radio technologies to greater utilization of the spectrum. Now-a-days Cognitive Radio (CR) is a promising concept for improving the utilization of limited radio spectrum resources for future wireless communications and mobile computing. In this paper we propose two approaches. At first we propose a trust aware model to authenticate the secondary users (SUs) in CRNs which provides a reliable technique to establish trust for CRNs. Secondly, we propose stochastic approach to show the free spectrum availability for SUs in CRNs. To evaluate the proposed approaches, we have modeled and analyzed steady state availability depending on Markov model for free spectrum by adopting QoS characteristics in cognitive radio networks.

Wireless multimedia sensor networks (WMSNs) are an emerging type of sensor networks which contain sensor nodes equipped with microphones, cameras, and other sensors that producing multimedia content. These networks have the potential to enable a large class of applications ranging from military to modern healthcare. Since in WMSNs information is multimedia by nature and it uses wireless link as mode of communication so this posse's serious security threat to this network. Thereby, the security mechanisms to protect WMSNs communication have found importance lately. However given the fact that WMSN nodes are resources constrained, so the traditionally intensive security algorithm is not well suited for WMSNs. Hence in this research, we aim to a develop lightweight digital watermarking enabled techniques as a security approach to ensure secure wireless communication. Finally aim is to provide a secure communication framework for WMSNs by developing new.

For WSN, the network node energy is limited, so how to save and even the consumption of the overall energy is an important problem. Clustering is one of the design methods used to manage the network energy consumption efficiently. In this paper, we use genetic simulated annealing algorithm(GSAA) to find an optimal cluster formation and prolong network lifetime. The proposed algorithm takes the nodes position and energy consumption of the network as objective function. Simulation results show that the system's performance have further improved by using GSAA over some existing cluster-based routing protocols.

With the increasingly wide application of Peer-to-Peer, a large amount of data have been accumulated in Peerto-Peer.Nowadays,in improving the search efficiency and system performance, researchers primarily focus on improving the P2P model, these studies have became bottleneckes in some extent. In this paper, data mining technology is applied to P2P network, and some further imrovements based on SWLDRM are done, nodes are clustered based on the features of object stored by K-NN algorithm, applicable decentralized clustering algorithm is designed, new K-Chord modle was constructed, simulation experiments between K-Chord and SWLDRM are done and the result shown that K-Chord has lower maintenance cost and higher search efficiency than SWLDRM.

IP traffic prediction plays an important role in network-layout, traffic-management, as well as the emphasis of traffic-project, congestion-control and network management. Poor prediction performance would be acquired generally as a result of intense nonlinearity of networks traffic. To tackle it, a modeling method for exact representing IP trafficpsilas movement tendency and a regression algorithm with powerful nonlinear approaching ability should be employed. Consequently, chaos theory and support vector machine (SVM) win the bid. Then, an improved algorithm based-on local SVM method for small scale data-set is proposed. Experimental results demonstrate the validity of improvement by a real-life paradigm that successful forecasting with continuously daily IP traffic during a few days gathered from campus network.