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With the increasing popularity of social networks, massive digital footprints of individuals in online service platforms are generated. As a result, an emerging technology namely personality trait analysis has drawn much attention. The prediction and analysis of personality trait is an efficient way to voting prediction, review analysis, decision analysis and marketing. The existing studies generally employ classification models while ignore the temporal property of digital footprints, which may lead to unsatisfactory results. To make an improvement, this paper proposes an effective method to predict the personality traits by taking the temporal factors into account through the use of Attention Recurrent Neural Network (AttRNN). The experimental results based on the dataset of 19000 Facebook volunteers suggest the proposed method is effective for predicting personality traits.

Joint fraud is one of the most common fraud types existing in medical fraud. However, joint fraud detection is a difficult problem because fraudsters take only a very small part of the population and fraudsters do everything to bypass fraud detection constraints. Most existing fraud detection studies focus on finding normal behavior patterns and treat those who possess behaviors that violate behavior patterns as fraudsters. However, these methods generally have high false positives because normal people may also sometime behave contrary to normal behavior patterns. To address this issue, we propose an abnormal group-based joint fraud detection method named abnormal group-based joint fraud detection method. This method can distinguish suspicious fraudsters from normal persons who have unusual behaviors by abnormal group mining in person similarity adjacency graph so that the occurrence of false positives caused by non-fraudulent abnormal behavior can be reduced. The extensive experiments using medical insurance data show that our approach has improved the precision of joint fraud detection by more than 10% compared with the conventional methods.

With the development of mobile Internet, various mobile applications have become increasingly popular. Many people are being benefited from the mobile healthcare services. Compared with the traditional healthcare services, patients’ medical behavior trajectories can be recorded by mobile healthcare services meticulously. They monitor the entire healthcare services process and help to improve the quality and standardization of healthcare services. By tracking and analyzing the patients’ medical records, they provide real-time protection for the patients’ healthcare activities. Therefore, medical fraud can be avoided and the loss of public health funds can be reduced. Although mobile healthcare services can provide a large amount of timely data, an effective real-time online algorithm is needed due to the timeliness of detecting the medical insurance fraud claims. However, because of the complex granularity of medical data, existing fraud detection approaches tend to be less effective in terms of monitoring the healthcare services process. In this paper, we propose an approach to deal with these problems. By means of the proposed SSIsomap activity clustering method, SimLOF outlier detection method, and the Dempster–Shafer theory-based evidence aggregation method, our approach is able to detect unusual categories and frequencies of behaviors simultaneously. Our approach is applied to a real-world data set containing more than 40 million medical insurance claim activities from over 40 000 users. Compared with two state-of-the-art approaches, the extensive experimental results show that our approach is significantly more effective and efficient. Our approach agent which provides decision support for the approval sender during the medical insurance claim approval process is undergoing trial in mobile healthcare services.

Crowdsourcing emerges as a promising means of solution generation, which creates tremendous value by leveraging the intelligence of crowds in the web services. With the rise of the business of crowdsourcing services, both crowdsourcers and workers expect to gain better Quality of Experience, as well as more profits. But there is a contradiction between the incentives of the crowdsourcer and the quality of result of the crowds. In order to balance this conflict, the paper develops a profit optimization model for all parties in crowdsourcing by employing Tullock Contests. The model consists of two parts. Firstly, optimized incentives are provided by crowdsourcer to encourage workers to achieve a better quality of result. Secondly, an optimal fee schedule is provided as guidance to workers. The visualization of the equilibria of benefits is helpful to reach a win-win situation for crowdsourcer and crowds, which in turn impacts the development of crowdsourcing services. In addition, we simulate the acquisition of Nash-equilibrium as a repeated crowdsourcing task. The effectiveness of our model is testified by the experimental results.

A novel method of facial expression recognition based on convolutional neural network is proposed in this paper, we design a new convolutional neural network and expand the expression database. On the basis of CK+ and JAFFE databases, adding 200 sleepy expressions and focused expressions. The sleepy expression and the focus expression is the most common expression in the students’ learning process, recognizing that can help teacher know the students’ learning situation and change the point of teaching. In comparison to the traditional method of facial expression, the experimental results show that the proposed network is more accurate and efficient.

In mathematics teaching, it is very important to present students with abstract mathematical concepts in a simple visual way, thus promoting students’ understanding and mastering knowledge. Visual teaching has always been an important component of education. Visual models combined with mathematical language help to express mathematical ideas. For mathematics teaching, the visual model is not only descriptive, educational and instructive, but also practical. One of the significant technologies to help to visualize in education is 3D printing. In this paper, we illustrate how 3D printing can help students and teachers in abstract conceptual understanding and mathematical proof in mathematics teaching. While discussing the close relationship between 3D printing technology and mathematics teaching, we propose a comprehensive mathematics education plan with 3D printing.

Fraud detection is an important issue in the area of data science, and it has a lot of practical applications in related fields, such as business, health, and environment. Most traditional methods detect fraud based on rulemaking. Unfortunately, it is not always useful in the medical field since the boundary of fraud detection is vague. As a result, outlier detection is a promising method. This paper develops an outlier detection method of analyzing the correlation of patients to detect fraud. We construct a heterogeneous information network which bridges the medicines used and diseases of patients. In light of the network, we calculate the correlation score of different patients and design a discriminant rule. Through the discriminating rule, fraudulent patients represented by the abnormal nodes can be found. Our experiments use real medical insurance data sets and the results confirm that our method is accurate and effective.

Improving efficiency and performance is an important topic in the world today. As it is well-known, cooperative computing is an effective and traditional approach, and it is widely used in various fields. Inspired by this idea, take E-commerce for example, Security is one of its important indicators. In E-commerce, the security technology has become a major issue restricting the rapid development and popularization of E-commerce. Existing solutions leverage blockchain protocols to improve the credibility of transactions, but most of them have some limitations, such as a lower throughput and higher consensus latency, and these problems make blockchain technology difficult to be widely used. This paper presents a trusted framework (ETT F) using blockchain protocol in E-commerce to achieve a higher credible trading. ETTF includes a peer blockchain protocol (PBP) based on a peer blockchain architecture to support the storage of massive transactions and instant transactions. In PBP, the throughput scales are nearly linearly increased with the computation: the more computing power available, the more blocks are selected per unit time. Besides, in order to ensure a higher security of transactions we have introduced a strong consensus algorithm(ECA) in E-commerce. ETTF is also efficient because the number of messages it requires is nearly linear in the network size. Compared to Bitcoin-derived blockchain, ETTF shows better performance on throughput, latency, and capacity in E-commerce.

Globalization makes innovation talent more important. The country's economy, technology and culture need to be driven by innovation, which makes the U.S. government promote STEM education to a strategic. In the new era background, STEM education should be adapted to this development to form “STEM+” education. 3D printing and innovative education integration analysis, on the one hand, can provide a platform for the implementation of STEM education, on the other hand, can promote the development of STEM education, and accelerate the formation of “STEM+” education model. Additionally, the specific curriculum implementation scheme is given, which provides a reference for teaching.

One difficulty of teaching in mathematics is to present abstract mathematical concepts or laws to students in a simple visual way to promote students' understanding and mastery. Traditionally mathematical teachers tried to explain abstract mathematical concepts and formulas by means of some simple teaching tools and some mathematical models presented by some mathematical software, such as Matlab, Mathematica, etc. As an integrated modern discipline, 3D printing has become more and more important in scientific research and technological developments. This paper intends to explore the close relationship between mathematics teaching and 3D printing technology, and propose a comprehensive programme of mathematical education based on the introduction of 3D printer manufacture, mathematical formula, 3D printing models, logo adding to 3D models, and 3D outcomes. This scenario can well solve the problem of visualization of abstract mathematical concepts. Teachers can easily explain complex mathematical expressions by the visual 3D tools. Students' learning interests of mathematics can be stimulated and their understanding abilities of space and graphics can be greatly enhanced by means of the real practice of mathematical 3D printing.

Addressing transmission errors in underwater acoustic channels is a key challenge for the real-time video communication between an autonomous underwater vehicle and a surface station. In this paper, we propose an error-resilient video compression technique based on hybrid multiple descriptions and redundant pictures to overcome impact of packet loss in underwater acoustic transmission. Video sequences are split into two descriptions with selective redundant pictures in order to achieve a balance between coding efficiency and error resiliency. Experiments with underwater video sequences are presented to assess the performance of the proposed approach under various packet loss rates, in comparison to state-of-the-art single and multiple description coding techniques.

The lack of credibility of transactions has become major issue restricting the rapid development and popularization of E-commerce. Existing solutions adopt blockchain protocols to improve the credibility of transactions. Most of them still have significant scalability barrier, such as instant transaction and throughput. By analyzing how fundamental and circumstantial bottlenecks in Bitcoin-derived blockchain limit the ability of its current peer-to-peer overlay network, this paper presents Permissioned Blockchain Framework (PBF). PBF aims at achieving trusted trading and supporting instant transaction in E-commerce. To guarantee a higher credibility of transactions, this paper presents a global consensus algorithm - Permissioned Trusted Trading Network Consensus Algorithm. In order to fulfill the demand of instant transactions without sacrificing credibility, this paper presents a Peer Inner Blockchain Protocol. The comparison between PBF and Bitcoin-derived blockchain in E-commerce, suggests better performance on throughput, latency, capacity.

Execution time is an important indicator of Service Level Agreement. Software as a Service providers expect to finish all queries before their respective deadlines and avoid penalties in violation of agreements. Hence, caching frequently occupied multi-tenant data is an efficient way of reducing execution time and penalty cost. However, it is difficult to minimize penalty cost through choosing cached data from massive multi-tenant data since it hardly finds a quantitative relationship between cached data and penalty cost. To address the challenge, we develop a mechanism of penalty cost minimization for multi-tenant queries based on cache optimization. We generate the cached data by analyzing cost space of multi-tenant data and the violation of queries of each node, and dispatch the incoming queries on the node with the shortest execution time according to multi-tenant cached data distribution. Experimental results suggest that penalty cost by employing the developed mechanism reduces 30% than that of the benchmark solution. Further, the developed mechanism is capable of limiting the dispatching time of queries within 1ms.

Big data is now rapidly expanding into various domains such as banking, insurance and e-commerce. Data analysis and related studies have attracted more attentions. In health insurance, abuse of diagnosis is one of the key fraud problems, which damages the interests of insured people. To address this issue, numbers of studies have focused on this topic. This paper develops a healthcare fraud detection approach based on the trustworthiness of doctors to distinguish fraud cases from normal records. Compared to conventional methods, our approach can detect healthcare fraud in a good accuracy by only little feature information from healthcare data without the violation of privacy. This approach combines a weighted HITS algorithm with a frequent pattern mining algorithm to calculate a rational treatment model of a certain disease. In addition, this paper also introduces the copy precision behavior in the treatment sequences of patients, which is a critical metric to learn the trustworthiness of doctors. The numerical validation with a healthcare dataset demonstrates that healthcare fraud by misdiagnosis in healthcare treatments can be successfully detected by employing the developed fraud detection approach.

E-commerce applications is a typical scale collaborative application, which includes many complex trading processes and small applications, such as portals of buyers and sellers, product management, online payment and billing, logistics system, and so on. But it is a challenge to develop such large-scale collaborative application using traditional method starting from scratch, since many application collaboration technologies should be solved, such as application collaboration relationship management and customization, collaboration application execution scheduling, and so on. Cloud and PaaS (Platform as a Service) service model provide a new opportunity for this problem solution. So, a method of trading process construction based on PaaS (Platform as a Service) platform supporting applications collaboration is proposed. Both ISV and ecommerce operator can construct collaborative applications easily. This method contains two aspects: one is the application collaboration semantic meta-model, the other is the method of trading process construction. The application collaboration semantic meta-model is used to be a container in e-commerce PaaS to specify each kind of E-commerce application attributes and relationships between one application and the others. Based on the application collaboration semantic meta-model, PaaS platform can automatically construct a whole customized e-commerce collaboration application according to e-commerce operators' requirements. This method is based on HTN (Hierarchical Task Network) planning theory. In the end, through cases analysis, the proposed PaaS platform and method is proved to construct a variety of collaboration applications easily based on the application collaboration semantic meta-model.

For SaaS applications, multi-tenant data storage can be mixed to make full use of storage resources, which makes the single tenant's data may be spread across multiple data nodes. For queries based on shard key, they can be located directly to the relevant nodes. However, for queries based on non-shard key, they need to implement a global parallel retrieval even if there are indices on the conditions. In response to these issues, the paper introduces global index to multi-tenant data storage model. By publishing tenant's the local index to establish and maintain a global index entries, it can reduce the number of computing nodes involved in the query operation. Experiments and analysis shows that the global index effectively reduces the resource usage of parallel search operation to improve the query efficiency.

In this paper, a new video compression technique based on adaptive hybrid wavelets and directional filter banks is proposed to achieve both high coding efficiency and good reconstruction quality at very low-bit rates. A key application is the real-time transmission of video from an autonomous underwater vehicle to a surface station, e.g., for man-in-the-loop monitoring and inspection operations, through acoustic channels that have limited bandwidth. The proposed method can maintain details in texture regions at relatively low bit rates, while overcoming the ringing artifacts within smooth regions, for intra-frame coding. For inter-frame coding, improved efficiency is achieved by making use of: 1) a new spatio-temporal just-noticeable-distortion model to remove perceptual redundancy; 2) motion interpolation to reduce bit rate; 3) variable-precision in quantizing the residual error; and 4) block inter-leaver to reduce transmission errors. Experiments with underwater video sequences are presented to assess the effectiveness of the proposed approach, in comparison to traditional wavelet-based techniques.

With the rapid progress in data mining and outlier detection, outlier detection methods have been widely used in various domains. The density based LOF method is the commonly used outlier detection method. In big data, the size and dimensions of data is very large, and the data is sparse. Those features make the LOF not suitable for big data. According to the features of big data, we propose a novel SLOF method. We use vectors to denote the complex high dimensional objects in dataset. We compute the distances between objects based on the concept of vector similarity. We introduce the idea of feature bagging approach, to make the SLOF method robust and accurate. We compare the performance of SLOF, LOF and the PINN methods. The experimental results show that SLOF scores' distribution is more stable, the recall rate and precision of SLOF is much better than LOF and PINN methods.

In big data epoch, one of the major challenges is the large volume of mixed structured and unstructured data, which comes in heterogeneous sources. Because of different form, structured and unstructured data are often considered apart from each other. However, they may speak about the same entities of the world. If a query involve both structured data and its unstructured counterpart, it is inefficient to execute it separately. The paper presents a novel index structure tailored towards the combinations of structured and unstructured data. The combined index is a joint index over structured database and unstructured document, based on entity co-occurrences. It is also a semantic index which describes the semantic relationships between entities and their multiple resources. We store the index as RDF graphs and queries are SPARQL-like. Experiments show that the associated index can not only provide apposite information but also execute queries efficiently.

We present an efficient compression method based on the hybrid wavelets and directional filter banks (HWD), to achieve high compression efficiency while keeping visually pleasing reconstruction quality for underwater images. According to the characteristics of underwater images and the human vision system (HVS), an improved just noticeable distortion (JND) model is initially employed to adaptively remove the visual redundancy of underwater images in the HWD domain. The low-frequency coefficients are then quantized in the fixed length and encoded losslessly. The high-frequency coefficients are quantized by variable precision and fixed length method, and are coded by the difference reduction algorithm based on HWD trees. The experimental results show that the proposed compression algorithm provides both high coding efficiency and satisfactory reconstruction quality, which is highly desired for the transmission of underwater images at very low-bit rates.

Mashup is becoming a powerful approach for end-users to meet their ad-hoc requirements based on existing services. Quite a few researches have been performed to achieve rapid, on-demand, intuitive development of mashups, which mainly focus on finding suitable quality components from a large number of available services. However, for mashups with procedure and context features, it is more crucial and difficult to construct an effective mashup structure, rather than selecting individual components. In this paper, we propose a context-based autonomous construction approach for procedural mashup based on pattern mining. In our approach, the mashup composition process is divided into 2 phases: schema construction phase and component binding phase. First, context-based mashup schemas with probability are extracted and recovered by applying pattern mining tasks to historical mashup logs. Then, according to user goal and awareness of user context, an optimal mashup schema is composed progressively by top-k recommendations for the next behavior/activity, which will be grounded to Web-API based components later. The proposed approach can autonomously generate context-based mashup schema with quality components and high probability of success without dependence on user professionalism.

Relevance search in heterogeneous information networks is a basic and crucial operation which is usually used in recommendation, clustering and anomaly detection. Nowadays most existing relevance search methods focus on objects in homogeneous information networks. In this paper, we propose a method to find the top-k most relevant objects to a specific one in heterogeneous networks. It is a two phase process that we get the initial relevance score based on the method of pair wise random walk along given meta-paths, which is a meta-level description of the path instances in heterogeneous information networks, and then take user preference into consideration to calculate the weights combination of meta-paths and model the problem into a multi-objective linear planning problem which can be solved with the method of generic algorithm. Besides, to ensure the efficiency, we use matrix computation and selective materialization to avoid the recursive computation of pair wise random walk. What's more, we propose an effective pruning method to skip unnecessary objects computations. The experiments on IMDB and DBLP dataset show that the method can gain a better accuracy and efficiency.

The problem of prediction is an important task in network analysis, which has attracted more attention from computer science communities. In this paper, prediction in signed heterogeneous networks is addressed, which contains two aspects, link prediction and sign prediction. Most of previous studies focus on non-signed networks that have only positive links or homogeneous networks that have only one type of nodes. However, there are many signed heterogeneous networks in which the nodes and links belong to different types and links can be either positive (indicating relationships such as trust, preferences, friendship, and etc) or negative (indicating relationships such as distrust, dislike, opposition, and etc) in real world. For link prediction, a rule-based methodology called RulePredict is proposed in the paper. In RulePredict, we first extract all features systematically which contain positive features that promote the existence of links and negative ones that reduce the possibility reversely. Then, the weights associated with different features will be learned by a supervised method based on generalized least squares (GLS). For sign prediction, we put forward a new method called HeteSign to calculate the polarity of the links based on the similarity of two objects depends on their linked objects in heterogeneous networks. Experiments are conducted on real networks, the IMDB and Epinions networks, which demonstrate that our approach gets better performance in terms of accuracy.

Interference management in femtocell network is an important issue to achieve the network throughput gain as well as not degrading the throughput of the macrocell tier. In this paper we consider a MIMO femtocell network and propose an interference mitigation strategy using interference alignment (IA) to mitigate the interference at the macrocell users (MUEs) caused by femtocell base stations (FBSs). Since the resource of the macrocell tier is scarcer than the femtocell tier, the transceiver design algorithm is proposed to guarantee the performance of the macrocell tier. Numerical simulations are given and the results show that by introducing IA algorithm the proposed algorithm achieve better femtocell tier throughput performance especially with dense femtocell deployment, with only a small degradation in macrocell tier throughput.

We develop a novel method for enhancing object distinction problem, which is to distinct different objects with identical names. The traditional approach is based on the attributes or contexts of the objects, not using the information of some attribute which may propagate between the objects and the attributes. To this end, we propose an approach which uses probabilistic topic model to improve the accuracy. We first model the attribute which has unstructured information and get its topics which may propagate to object as a new attribute, and then calculate the similarity values of the context attributes of the two objects with identical names, then we use these similarity values to build a decision tree model based on the training set. For the problem of object distinction for people, we combine the affiliation similarity with other context attributes similarity to judge whether the two people who share the same name correspond to the same people in real life. Experiments show that our method using Probabilistic Topic Model can achieve high accuracy.

Web integration systems (WIS) need to collect web objects belong to a specific domain from different websites effectively. Most WIS defines entity schemas beforehand by domain experts. Due to the essence of diversity and variability of web, it is hard to model the web entity comprehensively beforehand, furthermore, wrong annotations happen when align object values from different websites into the WIS. In order to avoid the limitations, we propose an integrated annotating method combining the matching strategy and machine learning technology to dynamically discover synonyms for predefined attribute labels and new attribute labels for a specified type of web entity. Experimental results using real-world data in book and job domains show that the proposed approach is effective in enriching web entity schema to enhance the performance of data collection process in a WIS.

In deep web, the properties and constraint on different interfaces are various. The heterogeneity and autonomy make the query between interfaces can not be translated equally. Zhang et al. build up the query translation on three level: property, prediction, query, and constructs a framework to handle query translation between two random interfaces in the same domain. But their works left to be furthered. Hence, we propose one algorithm with refined criteria to obtain minimal subsuming prediction mapping set based on query "materialization". We also propose a method for query rewriting, which can quantify the query materialization.

SaaS (Software as a Service) is widely used as a model for selling software. SaaS applications are on the software provider and can be accessed via the Internet. The SaaS applications have the same basic functions in the template file and the template file can be configured by tenant administrators to meet the individual demands. Workflow is used in SaaS applications and business processes need to be customized to adapt individual requirement. This paper represents a process customization framework that can customize and configure processes while guarantee the tenancy isolation. After the customization, there must be multiple versions of processes based on one basic template process. The collection of customized processes is necessary for ISVs because customized processes reflect demanding information of tenants.

MapReduce is emerging as an important programming model for massive data processing. A key challenge in MapReduce environments is the ability to efficiently control resource allocation and task scheduling for achieving Service Level Objectives (SLOs) of MapReduce jobs. However, there are few effective task scheduling methods to guarantee MapReduce jobs' SLOs. Therefore, we address this challenge by proposing a SLO-driven task scheduling mechanism in this paper. Based on the MapReduce performance model we build, our mechanism dynamically adjusts resource allocation and task scheduling in order to guarantee the SLOs of jobs and improve global job utility. Experimental results show that our SLO-driven task scheduler effectively meets the specified job latency SLOs and enhances job utility on tested MapReduce programs.

As one of the most important patterns of cloud service, PaaS provides an entire development, deployment and runtime platform for SaaS applications. In a specific PaaS environment such as e-commerce platform, there are similar functionalities among multiple applications, such as "searching items". In this paper, we focus on enhancing general PaaS platform with the mechanism of shared component-based application construction. We discuss some key issues of constructing such abilities with large number of heterogeneous components in the PaaS platform. A component layer with a semantic-enabled multi-level abstract component model is constructed and added to the general PaaS platform. We also give a description of the prototype of our enhanced e-commerce PaaS platform, which can efficiently simplify SaaS application constructing with reusable components.

We develop a method for matching unstructured reviews to database objects in data integration, where each object has a set of attributes. To this end, we propose a Labeled Latent Dirichlet Allocation model. We model reviews as if they were generated by a two-stage stochastic process. Each review is represented by a probability distribution over attributes, and each attribute is represented as a probability distribution over words for that attribute. We introduce the label for each attribute, and then the model integrates object information. We use an unsupervised manner to estimate the model parameters, and use this model to find, given a review, the most likely object to be the topic of the review. Experiments in multiple domains show that our method is superior to the TFIDF method as well as a recent RLM method for the review matching problem.

In this paper, optimization of the sensing time and the number of cooperative users for OR-Rule cooperative spectrum sensing in cognitive radio network is investigated. A cognitive network with multiple users and one fusion centre is considered. The frame structure of cooperative spectrum sensing is proposed, which is divided into multiple transmission slots and one sensing slot including local energy detection and cooperative overhead. By selecting the optimal sensing time and number of cooperative users, an optimization problem is formulated, which seeks to maximize the throughput of cognitive radio subject to the constraints of both the false alarm probability and the detection probability. A joint optimization algorithm is also proposed to solve this optimization problem. The simulation results show that the significant improvement on the throughput of cognitive radio can be achieved through the proposed joint optimization algorithm.

In this paper, we investigate the resource allocation problem for the scenario where a satellite based primary network and an orthogonal frequency division multiplexing (OFDM) based multiuser cognitive radio (CR) secondary network coexist. The resource allocation aims to maximize the throughput of CR users, and we develop a resource allocation algorithm based on game theory, which seeks to improve the spectrum utilization in a distributed fashion under the constraints of the transmit power and symbol error rate limits of CR users. The primary user interference and spectrum sensing errors are also taken into consideration. A gradient projection based algorithm is used to solve the distributed game and a compressive sensing technique is used to acquire the channel and interference parameters needed for resource allocation. Simulation results show that although implemented in a distributed way, the performances of the proposed algorithm are comparable to a centralized heuristic allocation method which represents the optimal allocation.

As a key task for the implementation of cognitive radio (CR) systems, spectrum sensing confronts several technical challenges in the wideband CR networks, such as high sampling rates, limited hardware resources and wireless fading channels. To overcome these challenges, a distributed collaborative compressive spectrum sensing algorithm is developed in this paper. Each CR performs local compressive sensing to scan the wideband spectrum at affordable data acquisition costs. To achieve spatial diversity against wireless fading, CRs collaborate via one-hop communications only, and percolate the exchanged information across the multi-hop network to reach global convergence on the support set. All CRs share the same support set in the local sparse signal reconstruction, and thus joint sparsity is exploited to achieve reliable spectrum detection. Simulation results show that our proposed algorithm achieves effective spectrum detection at sub-Nyquist sampling rates, and has near-optimal detection performance in the absence of a fusion center.

In this work, robust transceiver optimization algorithms are proposed for multi-user multiple-input multiple-output (MIMO) interference network in which only imperfect channel state information (CSI) is available at both transmitters and receivers. The errors of the CSI are assumed to be norm bounded, and the mean square errors (MSE) are served as quality of service targets to be minimized. Considering the impact of channel uncertainty, robust algorithms that minimize the maximum sum MSE and minimize the maximum per-user MSE with per transmitter power constraint are proposed. Each transceiver design algorithm can be decomposed into two subproblems, and the optimization alternates between the transmitters and receivers. Iterative algorithms that design one precoder or decoder each time while leave others as fixed are proposed. Such problem can be recast into convex semidefinite programming (SDP) problems. Numerical results are presented which show the effectiveness and robustness of proposed algorithms when CSI errors exist.

The cognitive radio technology is considered as a suitable way to improve the utilization of the radio spectrum and a promising solution to solve the coexistence problem of two wireless networks operating on the same frequency band. In this paper, a satellite-based multi-resolution compressive spectrum detection algorithm is proposed to help the coexistence of a mobile satellite system and an infrastructured wireless terrestrial network. With the help of compressive sensing techniques, sub-Nyquist rate samples are acquired, and utilized to detect frequency subbands via a wavelet-based edge detector. Then a double-threshold energy detector is used to realize the multi-resolution spectrum detection. Simulation results show that at sub-Nyquist rates, the proposed algorithm has near-optimal detection performance without sensing the whole band at the maximum resolution.

Interference alignment (IA) is a multi-user wireless communication strategy that enables several transmitters simultaneous communicate with their paired receivers without interfering each other. IA is a multiplexing gain optimal strategy at the expense of requiring the global channel state information (CSI) at the transmitters. Most previous works assume the availability of perfect CSI which is not possible in practice. The performance of IA algorithms based on perfect CSI drop dramatically in circumstance where CSI errors exist. In this paper we propose a robust minimum mean square error (MMSE) IA algorithm with imperfect CSI. Based on a Gaussian statistic channel error model, we give the mean square error objective function with per transmitter power constraint. The objective function is not joint convex in precoders and decoders, but convex in precoders with fixed decoders and vice versa. We convert the problem to two convex sub problems and iteratively find the solutions. Numerical results are presented to validate the effectiveness of proposed algorithm when CSI errors exist.

With the tremendous growth of the Web, it has become a huge challenge for the single-process crawlers to locate the resources that are precise and relevant to some topics in an appropriate amount of time, so it is increasingly important to use the parallel crawler. However, due to the parallelism of crawlers, one headache problem we have to face is how to distribute the URLs to crawlers to make the parallel system work coordinately and thereby make sure that the Web pages fetched are of high quality. In this paper, a novel URL assignment model for the parallel crawler is described, which is based on multi-objective decision making method and considers multiple factors synthetically such as load balance, overlap and so on. Extensive experiments test and validate our techniques.

SaaS provides multi-tenant technology allowing database to share storage but coming with irregular load, untraceable transaction characteristics and so on. Lock technology is important to ensure transaction concurrency control, but if multi-tenant database uses traditional granularity database locks, it will cause numerous problems because of the untraceable transaction characteristics and other reasons. To satisfy requirements in multi-tenant database, we propose TOL mechanism. It provides a lock of one granularity between the upper coarse-grained granularity and the lower fine-grained granularity for each tenant. Then we establish the transaction model of multi-tenant database to give comprehensive analysis of the transaction conflict, deadlock, and throughput and lock number of TOL. Meanwhile, Cluster and Diffusion methods are proposed to support the granularity adjustment for a single tenant. Combined with the analysis in the transaction model, a lock granularity adjustment strategy is built. It focuses on evaluating an approximate suitable granularity for one tenant between upper coarse-grained and lower fine-grained. In addition, we give experiments indicating that the performance of TOL is excellent and the lock granularity adjustment algorithm is feasible.

We proposed a new cognitive radio (CR) model in this paper whereby CR senses the environment periodically and then forwards the sensing results one at a time and through such process CR develops an efficiency function to reflect the performance and efficiency of the proposed model. In addition we proposed a joint algorithm of sensing time and a cooperative user in order to obtain the maximal efficiency of the CR network. Results obtained from simulations process shows that the efficiency of the proposed algorithm is predominant and when SNR=0dB, it holds 0.4W more residual power than that of sensing-throughput tradeoff model. And lastly we shows that cooperative detection can improve the efficiency of the model, however the results also shows that the efficiency of the model may degrade when large numbers of cooperative CRs are involved in the process.