Smartphones are changing humans' lifestyles. Mobile applications (apps) on smartphones serve as entries for users to access a wide range of services in our daily lives. The apps installed on one's smartphone convey lots of personal information, such as demographics, interests, and needs. This provides a new lens to understand smartphone users. However, it is difficult to compactly characterize a user with his/her installed app list. In this article, a user representation framework is proposed, where we model the underlying relations between apps and users with Boolean matrix factorization (BMF). It builds a compact user subspace by discovering basic components from installed app lists. Each basic component encapsulates a semantic interpretation of a series of special-purpose apps, which is a reflection of user needs and interests. Each user is represented by a linear combination of the semantic basic components. With this user representation framework, we use supervised and unsupervised learning methods to understand users, including mining user attributes, discovering user groups, and labeling semantic tags to users. Extensive experiments were conducted on three data subsets from a large-scale real-world dataset for evaluation, each consisting of installed app lists from over 10 000 users. The results demonstrated the effectiveness of our user representation framework.

This article proposes a facial expression removal method to recover a 3D neutral face from a single 3D expressional or non-neutral face. We treat a 3D non-neutral face as the sum of its neutral one and the residual. This can be satisfied if the correspondence between 3D vertices of expressional faces and those of neutral faces is established. We propose a non-rigid deformation method to establish the correspondence between 3D faces. Then, according to algebra inequality, the minimization of a neutral face model can be replaced by the minimization of its upper bound, i.e., the errors of an expressional face model and a residual model. Thus, we co-optimize the representation errors of the latter two models and build the relationship between the representation coefficients of the two models. Given an expressional face as the input, its corresponding neutral face can be inferred by the associative representation parameters in these two models. In the testing stage, we use an iterative joint fitting scheme to obtain a more accurate recovery. Extensive experiments are conducted to evaluate our method. The results show that our method obtains considerably better performance than existing methods in terms of average root mean square errors and recognition rates, and also better visual effects.

Forecasting price trend of bulk commodities is important in international trade, not only for markets participants to schedule production and marketing plans but also for government administrators to adjust policies. Previous studies cannot support accurate fine-grained short-term prediction, since they mainly focus on coarse-grained long-term prediction using historical data. Recently, cross-domain open data provides possibilities to conduct fine-grained price forecasting, since they can be leveraged to extract various direct and indirect factors of the price. In this article, we predict the price trend over upcoming days, by leveraging cross-domain open data fusion. More specifically, we formulate the price trend into three classes (rise, slight-change, and fall), and then we predict the specific class in which the price trend of the future day lies. We take three factors into consideration: (1) supply factor considering sources providing bulk commodities,<?brk?> (2) demand factor focusing on vessel transportation with reflection of short time needs, and (3) expectation factor encompassing indirect features (e.g., air quality) with latent influences. A hybrid classification framework is proposed for the price trend forecasting. Evaluation conducted on nine real-world cross-domain open datasets shows that our framework can forecast the price trend accurately, outperforming multiple state-of-the-art baselines.

As more business workflow systems are being deployed in modern enterprises and organizations, more employee-activity log data are being collected and analyzed. In this paper, we develop a latent ability model (LAM) as a generative probabilistic learning framework for workforce analytics over employee-activity logs. The LAM development is novel in three aspects. First, we introduce the concept of latent ability variables to model hidden relations between employees and activities in terms of job performance, such as the set of skills provided by an employee and the set of skills required by an activity, and how well they matchup in employee-activity assignment. Second, we construct the latent ability model by learning latent ability parameters from the employee-activity log data using expectation-maximization and gradient descent. Finally, we leverage LAM to build inference and prediction models for employee performance prediction, employee ability comparison, and employee-activity matchup quality estimation. We evaluate the accuracy and efficiency of our approach using real log datasets collected from a workflow system deployed in the government of the city of Hangzhou, China, which consists of 5,287,621 log records over two years involving 744 activities and 1,725 employees. We show that LAM approach outperforms existing representative methods in both accuracy and efficiency.

With the explosive growth of services, including Web services, cloud services, APIs and mashups, discovering the appropriate services for consumers is becoming an imperative issue. The traditional service discovery approaches mainly face two challenges: 1) the single source of description documents limits the effectiveness of discovery due to the insufficiency of semantic information; 2) more factors should be considered with the generally increasing functional and nonfunctional requirements of consumers. In this paper, we propose a novel framework, called SMS, for effectively discovering the appropriate services by incorporating social media information. Specifically, we present different methods to measure four social factors (semantic similarity, popularity, activity, decay factor) collected from Twitter. Latent Semantic Indexing (LSI) model is applied to mine semantic information of services from meta-data of Twitter Lists that contains them. In addition, we assume the target query-service matching function as a linear combination of multiple social factors and design a weight learning algorithm to learn an optimal combination of the measured social factors. Comprehensive experiments based on a real-world dataset crawled from Twitter demonstrate the effectiveness of the proposed framework SMS, through some compared approaches.

NgramCNN is a deep convolutional neural network developed for classification of graphs based on common substructure patterns and their latent relationships in the collection of graphs. Our NgramCNN deep learning framework consists of three novel components: (1) The concept of n-gram graph block to transform each raw graph object into a sequence of n-gram blocks connected through overlapping regions. (2) The diagonal convolution layer to extract local patterns and connectivity features hidden in the n-gram blocks by performing n-gram normalization before conducting deep learning through the network of convolution layers. (3) The extraction of deeper global patterns based on the local patterns and the ways that they respond to overlapping regions by building a n-gram deep convolutional neural network. Extensive evaluation of NgramCNN using five real graph repositories from bioinformatics and social networks domains show the effectiveness of NgramCNN over the existing state of art methods with high accuracy and comparable performance.

Spiking neural networks (SNNs) are considered to be biologically plausible and power-efficient on neuromorphic hardware. However, unlike the brain mechanisms, most existing SNN algorithms have fixed network topologies and connection relationships. This paper proposes a method to jointly learn network connections and link weights simultaneously. The connection structures are optimized by the spike-timing-dependent plasticity (STDP) rule with timing information, and the link weights are optimized by a supervised algorithm. The connection structures and the weights are learned alternately until a termination condition is satisfied. Experiments are carried out using four benchmark datasets. Our approach outperforms classical learning methods such as STDP, Tempotron, SpikeProp, and a state-of-the-art supervised algorithm. In addition, the learned structures effectively reduce the number of connections by about 24%, thus facilitate the computational efficiency of the network.

Energy consumed by Cloud datacenters has dramatically increased, driven by rapid uptake of applications and services globally provisioned through virtualization. By applying energy-aware virtual machine scheduling, Cloud providers are able to achieve enhanced energy efficiency and reduced operation cost. Energy consumption of datacenters consists of computing energy and cooling energy. However, due to the complexity of energy and thermal modeling of realistic Cloud datacenter operation, traditional approaches are unable to provide a comprehensive in-depth solution for virtual machine scheduling which encompasses both computing and cooling energy. This paper addresses this challenge by presenting an elaborate thermal model that analyzes the temperature distribution of airflow and server CPU. We propose GRANITE - a holistic virtual machine scheduling algorithm capable of minimizing total datacenter energy consumption. The algorithm is evaluated against other existing workload scheduling algorithms MaxUtil, TASA, IQR and Random using real Cloud workload characteristics extracted from Google datacenter tracelog. Results demonstrate that GRANITE consumes 4.3-43.6 percent less total energy in comparison to the state-of-the-art, and reduces the probability of critical temperature violation by 99.2 with 0.17 percent SLA violation rate as the performance penalty.

Due to the limits of battery capacity of mobile devices, how to select cloud services to invoke in order to reduce energy consumption in mobile environments is becoming a critical issue. This paper addresses the problem of mobile service selection for composition in terms of energy consumption. It formally models this problem and constructs energy consumption computation models. Energy consumption aggregation rules for composite services with different structures are presented. It adopts the genetic algorithm to resolve it. A replanning mechanism is also proposed to deal with the changeable conditions and user behavior. A series of experiments are conducted to evaluate the performance of our method. The results show that our service selection method significantly outperforms traditional methods. Even if the conditions or user behavior is changeable, this method is still effective to recommend services. Moreover, the service selection method performs good scalability as the experimental scale increases.

This paper presents a system that utilizes process recommendation technology to help design new business processes from scratch in an efficient and accurate way. The proposed system consists of two phases: 1) offline mining and 2) online recommendation. At the first phase, it mines relations among activity nodes from existing processes in repository, and then stores the extracted relations as patterns in a database. At the second phase, it compares the new process under construction with the premined patterns, and recommends proper activity nodes of the most matching patterns to help build a new process. Specifically, there are three different online recommendation strategies in this system. Experiments on both real and synthetic datasets are conducted to compare the proposed approaches with the other state-of-the-art ones, and the results show that the proposed approaches outperform them in terms of accuracy and efficiency.

Convolutional Neural Network (CNN) has gained attractions in image analytics and speech recognition in recent years. However, employing CNN for classification of graphs remains to be challenging. This paper presents the Ngram graph-block based convolutional neural network model for classification of graphs. Our Ngram deep learning framework consists of three novel components. First, we introduce the concept of n-gram block to transform each raw graph object into a sequence of n-gram blocks connected through overlapping regions. Second, we introduce a diagonal convolution step to extract local patterns and connectivity features hidden in these n-gram blocks by performing n-gram normalization. Finally, we develop deeper global patterns based on the local patterns and the ways that they respond to overlapping regions by building a n-gram deep learning model using convolutional neural network. We evaluate the effectiveness of our approach by comparing it with the existing state of art methods using five real graph repositories from bioinformatics and social networks domains. Our results show that the Ngram approach outperforms existing methods with high accuracy and comparable performance.

With the emergence of online social networks, the social network-based recommendation approach is popularly used. The major benefit of this approach is the ability of dealing with the problems with cold-start users. In addition to social networks, user trust information also plays an important role to obtain reliable recommendations. Although matrix factorization (MF) becomes dominant in recommender systems, the recommendation largely relies on the initialization of the user and item latent feature vectors. Aiming at addressing these challenges, we develop a novel trust-based approach for recommendation in social networks. In particular, we attempt to leverage deep learning to determinate the initialization in MF for trust-aware social recommendations and to differentiate the community effect in user's trusted friendships. A two-phase recommendation process is proposed to utilize deep learning in initialization and to synthesize the users' interests and their trusted friends' interests together with the impact of community effect for recommendations. We perform extensive experiments on real-world social network data to demonstrate the accuracy and effectiveness of our proposed approach in comparison with other state-of-the-art methods.

Neuromorphic computing has become an important emerging research area in recent years. By emulating computational principles and architecture found in neural systems, neuromorphic computing has led to the development of neuromorphic sensors, processors, and sensory motor systems for robotic agents. It has also led to rapid progress in related areas covering computational theories of sensory coding, synaptic computing, learning, and signal processing algorithms, circuit designs, and implementations. The work in these areas shows neuromorphic approaches with appealing computational advantages over conventional approaches, but at the same time, neuromorphic systems still pose many research challenges. Neuromorphic computing overlaps with another area called cyborg intelligence which is dedicated to integrating artificial intelligence (AI) with biological intelligence closely and deeply by connecting computer systems and biological beings. Cyborg intelligence aims to compensate for the weaknesses of both systems by combining the computational power of machines with the perceptive and cognitive abilities of biological systems. Recently, many of the advances in cyborg intelligence methods, systems, and applications have demonstrated the trend of the rapid integration of cyborg intelligence with neuromorphic computing in both breadth and depth. These areas pose innumerable interesting and significant questions for AI and could fundamentally change the landscape of AI research.

Nowadays, numerous enterprises are migrating their applications into cloud computing environments. Typically, the applications are composed of several dependent service components that span many hosts and network devices. In light of this, exploring the dependency between service components can be beneficial for achieving fast network application response time. Moreover, it is significant to consolidate service components according to resource constraints, service dependency, and network structure. However, it is a tedious task to discover the dependency among service components without expert knowledge of the running application. In this paper, we propose CloudScout, a non-intrusive approach that is capable of automatically discovering dependent service components. CloudScout analyzes the correlation among service components based on the time-series information from system monitoring logs. We address two key challenges in CloudScout: service distance calculation and dependent service clustering. We conduct experiments on five applications with 290 service components that span 20 physical hosts across two data centers. The experimental results demonstrate that CloudScout can successfully discover the dependency among service components and facilitate reducing the network latency of network applications and distributed applications.

Modern service industry (MSI) is becoming a leading and pillar industry in recent years. Its theory construction, however, has not kept up with the industry development. Specifically, the business process designing and reconstructing, the critical part in business transformation and competition of modern service enterprise, are still handled manually. The existing models, e.g., BPMN and EPC, mainly adopt the business activities and events without considering the resource and data generated and consumed in interaction with business collaborators, which is ubiquitous in MSI business process. Hence, the deficiency of resource and data in these models hindered them from popularization in MSI. In this paper, with a systematic analysis of the above issues, we introduce the service pattern of MSI business process from the point view of resource and data with formalized description and some concrete basic patterns. To support the process designing and reconstructing better, we propose a pattern-centered formalization language, called Service Pattern Description Language (SPDL). Furthermore, a service pattern matching approach, in the Constructing-As-Identifying style, is studied and a service process designing tool, called SPDL-Editor, is developed. Additionally, a case of the famous video-on-demand service (Youku) in China is given, though out of this paper, for a better understanding of our theories.

Inevitable tradeoff between read performance and space saving always shows up when applying offline deduplication for primary storage. We propose Mudder, a multi-tiered and dynamic SLA-driven deduplication framework to address such challenge. Based on specific Dedup-SLA configurations, Mudder conducts multi-tiered deduplication process combining Global File-level Deduplication (GFD), Local Chunk-level Deduplication (LCD) and Global Chunk-level Deduplication (GCD). More importantly, Mudder dynamically regulates deduplication processes according to instant workload status and predefined Dedup-SLA during runtime.

Bike sharing is booming globally as a green transportation mode, but the occurrence of over-demand stations that have no bikes or docks available greatly affects user experiences. Directly predicting individual over-demand stations to carry out preventive measures is difficult, since the bike usage pattern of a station is highly dynamic and context dependent. In addition, the fact that bike usage pattern is affected not only by common contextual factors (e.g., time and weather) but also by opportunistic contextual factors (e.g., social and traffic events) poses a great challenge. To address these issues, we propose a dynamic cluster-based framework for over-demand prediction. Depending on the context, we construct a weighted correlation network to model the relationship among bike stations, and dynamically group neighboring stations with similar bike usage patterns into clusters. We then adopt Monte Carlo simulation to predict the over-demand probability of each cluster. Evaluation results using real-world data from New York City and Washington, D.C. show that our framework accurately predicts over-demand clusters and outperforms the baseline methods significantly.

Understanding smartphone users is fundamental for creating better smartphones, and improving the smartphone usage experience and generating generalizable and reproducible research. However, smartphone manufacturers and most of the mobile computing research community make a simplifying assumption that all smartphone users are similar or, at best, constitute a small number of user types, based on their behaviors. Manufacturers design phones for the broadest audience and hope they work for all users. Researchers mostly analyze data from smartphone-based user studies and report results without accounting for the many different groups of people that make up the user base of smartphones. In this work, we challenge these elementary characterizations of smartphone users and show evidence of the existence of a much more diverse set of users. We analyzed one month of application usage from 106,762 Android users and discovered 382 distinct types of users based on their application usage behaviors, using our own two-step clustering and feature ranking selection approach. Our results have profound implications on the reproducibility and reliability of mobile computing studies, design and development of applications, determination of which apps should be pre-installed on a smartphone and, in general, on the smartphone usage experience for different types of users.

Temperature prediction can enhance datacenter thermal management towards minimizing cooling power draw. Traditional approaches achieve this through analyzing task-temperature profiles or resistor-capacitor circuit models to predict CPU temperature. However, they are unable to capture task resource heterogeneity within multi-tenant environments and make predictions under dynamic scenarios such as virtual machine migration, which is one of the main characteristics of Cloud computing. This paper proposes virtual machine level temperature prediction in Cloud datacenters. Experiments show that the mean squared error of stable CPU temperature prediction is within 1.10, and dynamic CPU temperature prediction can achieve 1.60 in most scenarios.

The development of mobile computing and cloud computing enables people to invoke various services from their mobile devices. This paper focuses on the problem of service composition with temporal and QoS constraints in mobile cloud computing. This problem indeed becomes dramatically tough due to the mobility environment, local-time constraints and global QoS constraints as well. This study aims to form such a service composition that not only satisfies both the time constraints and QoS constraints in a mobile service composition, but also ensures the composition to be executed successfully to the greatest extent in the uncertain mobile environment. Firstly, it formally defines the problem and then transfers the problem into a constrained optimization problem and proves it to be an NP-hard problem. To solve this problem, it proposes a differential evolutionary for constraint driven service composition algorithm (DE4CDSC). It first utilizes a constraints based service filtering process to reduce the searching space and then adopts a differential evolutionary based algorithm to form a service combination by maximizing its successful probability after considering service providers' mobility. To evaluate the proposed approach, a series of simulation experiments and comparisons are conducted to demonstrate the effectiveness of the proposed approach.

The advances in mobile technologies enable us to consume or even provide services through powerful mobile devices anytime and anywhere. Services running on mobile devices within limited range can be composed to coordinate together through wireless communication technologies and perform complex tasks. However, the mobility of users and devices in mobile environment imposes high risk on the execution of the tasks. This paper targets reducing this risk by constructing a dependable service composition after considering the mobility of both service requesters and providers. It first proposes a risk model and clarifies the risk of mobile service composition; and then proposes a service composition approach by modifying the simulated annealing algorithm. Our objective is to form a service composition by selecting mobile services under the mobility model and to ensure the service composition have the best quality of service and the lowest risk. The experimental results demonstrate that our approach can yield near-optimal solutions and has a nearly linear complexity with respect to a problem size.

Container ports are generally measured and compared using performance indicators such as container throughput and facility productivity. Being able to measure the performance of container ports quantitatively is of great importance for researchers to design models for port operation and container logistics. Instead of relying on the manually collected statistical information from different port authorities and shipping companies, we propose to leverage the pervasive ship GPS traces and maritime open data to derive port performance indicators, including ship traffic, container throughput, berth utilization, and terminal productivity. These performance indicators are found to be directly related to the number of container ships arriving at the terminals and the number of containers handled at each ship. Therefore, we propose a framework that takes the ships' container-handling events at terminals as the basis for port performance measurement. With the inferred port performance indicators, we further compare the strengths and weaknesses of different container ports at the terminal level, port level, and region level, which can potentially benefit terminal productivity improvement, liner schedule optimization, and regional economic development planning. In order to evaluate the proposed framework, we conduct extensive studies on large-scale real-world GPS traces of container ships collected from major container ports worldwide through the year, as well as various maritime open data sources concerning ships and ports. Evaluation results confirm that the proposed framework not only can accurately estimate various port performance indicators but also effectively produces port comparison results such as port performance ranking and port region comparison.

We address the problem of traffic sign recognition in a light detection and ranging (LIDAR)-equipped vehicle. With the help of 3-D LIDAR points, the 2-D multiview sign images will be easily detected from the captured images of street signs. After detection, the sign recognition problem is formulated as a multiview object recognition task. We develop a metric-learning-based template matching approach for this task and learn a distance metric between the captured images and the corresponding sign templates. For each sign, recognition is done via soft voting by the recognition results of its corresponding multiview images. We propose a latent structural support vector machine (SVM)-based weakly supervised metric learning (WSMLR) method to learn the metric and a reliability classifier. The reliability classifier is used to determine each image's reliability, which serves as each image's weight in both the learning and soft voting procedure. We evaluate the proposed method for multiview traffic sign recognition on a multiview traffic sign data set with 112 categories and observe very encouraging results compared with other state-of-the-art methods. In addition, the method can be customized to solve the single-view sign recognition. The performance of our method for single-view sign recognition is tested on two public data sets, showing that our method is comparable with other competitive ones.

Sparse principal component analysis (sparse PCA) aims at finding a sparse basis to improve the interpretability over the dense basis of PCA, while still covering the data subspace as much as possible. In contrast to most existing work that addresses the problem by adding sparsity penalties on various objectives of PCA, we propose a new method, sparse PCA via rotation and truncation (SPCArt), which finds a rotation matrix and a sparse basis such that the sparse basis approximates the basis of PCA after the rotation. The algorithm of SPCArt consists of three alternating steps: 1) rotating the PCA basis; 2) truncating small entries; and 3) updating the rotation matrix. Its performance bounds are also given. The SPCArt is efficient, with each iteration scaling linearly with the data dimension. Parameter choice is simple, due to explicit physical explanations. We give a unified view to several existing sparse PCA methods and discuss the connections with SPCArt. Some ideas from SPCArt are extended to GPower, a popular sparse PCA algorithm, to address its limitations. Experimental results demonstrate that SPCArt achieves the state-of-the-art performance, along with a good tradeoff among various criteria, including sparsity, explained variance, orthogonality, balance of sparsity among loadings, and computational speed.

Service mashups are applications created by combining single-functional services (or APIs) dispersed over the web. With the development of cloud computing and web technologies, service mashups are becoming more and more widely used and a large number of mashup platforms have been produced. However, due to the proliferation of services on the web, how to select component services to create mashups has become a challenging issue. Most developers pay more attention to the quality of service (QoS) and cost of services. Beside service selection, mashup deployment is another pivotal process, as the platform can significantly affect the quality of mashups. In this paper, we focus on creating service mashups from the perspective of developers. A genetic algorithm-based method, genetic algorithm for mashup creation (GA4MC), is proposed to select component services and deployment platforms in order to create service mashups with optimal cost performance. A series of experiments are conducted to evaluate the performance of GA4MC. The results show that the GA4MC method can achieve mashups whose cost performance is extremely close to the optimal. Moreover, the execution time of GA4MC is in a low order of magnitude and the algorithm performs good scalability as the experimental scale increases.

Mobile business is becoming a reality due to ubiquitous Internet connectivity, popular mobile devices, and widely available cloud services. However, characteristics of the mobile environment, such as mobility, unpredictability, and variation of mobile network's signal strength, present challenges in selecting optimal services for composition. Traditional QoS-aware methods that select individual services with the best QoS may not always result in the best composite service because constant mobility makes the performance of service invocation unpredictable and location-based. This paper discusses the challenges of this problem and defines it in a formal way. To solve this new research problem, we propose a mobility model, a mobility-aware QoS computation rule, and a mobility-enabled selection algorithm with teaching-learning-based optimization. The experimental simulation results demonstrate that our approach can obtain better solutions than current standard composition methods in mobile environments. The approach can obtain near-optimal solutions and has a nearly linear algorithmic complexity with respect to the problem size.

Modern service industry (MSI) is an information and knowledge intensive service industry, relying on information technology and modern management philosophy. The development of MSI is of high significance for promoting rapid global economy, accelerating social progress, and building an innovation-oriented society and harmonious realm. This paper first presents the history and trends of MIS in China in terms of the worldwide MSI development status. It then proposes the concept of Crossover Services based upon a survey of 62 MSI-related listed firms in China, whose business models, products, and services evidently exploit the concept. After elaborating on the crossover, convergence, and complex characteristics of crossover services, it proposes a technical framework that facilities addressing the scientific issues and technical challenges on crossover service realization. Finally, it illustrates how a new cloud-based middleware platform, named JTang++, supports the realization of crossover services.

Learning latent features of Web services will greatly boost the ability of search engine to discover relevant services. Extracted information from Web Service Description Language (WSDL) documents of services is less efficient due to the limited usage of data source. Recently, a number of ongoing works have indicated incorporating service tag, a textual symbol provides additional contextual and semantic information, helps to enhance the process of service discovery. However, a large number of relevant tags for Web services are difficult to obtain in practice. In this paper, we propose a Web service Tag Learning system to address this issue. WT Learning system adopts sparse learning technique to fully understand the structure of high dimensional textual information extracted from WSDL documents and tags. Meanwhile, our proposed system implements Alternative Direction Method of Multiplier (ADMM) strategy, which accelerates solving process in Big Data environment. Extensive experiments are conducted based on real-world dataset, which consists of 24,569 Web services. The results demonstrate the effectiveness of WT Learning system. Specifically, our system outperforms other state-of-the-art frameworks in tag classification and recommendation tasks, with 29.6% and 27.1% performance gaining respectively.

Gait, as a promising biometric for recognizing human identities, can be nonintrusively captured as a series of acceleration signals using wearable or portable smart devices. It can be used for access control. Most existing methods on accelerometer-based gait recognition require explicit step-cycle detection, suffering from cycle detection failures and intercycle phase misalignment. We propose a novel algorithm that avoids both the above two problems. It makes use of a type of salient points termed signature points (SPs), and has three components: 1) a multiscale SP extraction method, including the localization and SP descriptors; 2) a sparse representation scheme for encoding newly emerged SPs with known ones in terms of their descriptors, where the phase propinquity of the SPs in a cluster is leveraged to ensure the physical meaningfulness of the codes; and 3) a classifier for the sparse-code collections associated with the SPs of a series. Experimental results on our publicly available dataset of 175 subjects showed that our algorithm outperformed existing methods, even if the step cycles were perfectly detected for them. When the accelerometers at five different body locations were used together, it achieved the rank-1 accuracy of 95.8% for identification, and the equal error rate of 2.2% for verification.

A social event is an occurrence that involves lots of people and is accompanied by an obvious rise in human flow. Analysis of social events has real-world importance because events bring about impacts on many aspects of city life. Traditionally, detection and impact measurement of social events rely on social investigation, which involves considerable human effort. Recently, by analyzing messages in social networks, researchers can also detect and evaluate country-scale events. Nevertheless, the analysis of city-scale events has not been explored. In this article, we use human flow dynamics, which reflect the social activeness of a region, to detect social events and measure their impacts. We first extract human flow dynamics from taxi traces. Second, we propose a method that can not only discover the happening time and venue of events from abnormal social activeness, but also measure the scale of events through changes in such activeness. Third, we extract traffic congestion information from traces and use its change during social events to measure their impact. The results of experiments validate the effectiveness of both the event detection and impact measurement methods.

Planning an itinerary before traveling to a city is one of the most important travel preparation activities. In this paper, we propose a novel framework called TripPlanner, leveraging a combination of location-based social network (i.e., LBSN) and taxi GPS digital footprints to achieve personalized, interactive, and traffic-aware trip planning. First, we construct a dynamic point-of-interest network model by extracting relevant information from crowdsourced LBSN and taxi GPS traces. Then, we propose a two-phase approach for personalized trip planning. In the route search phase, TripPlanner works interactively with users to generate candidate routes with specified venues. In the route augmentation phase, TripPlanner applies heuristic algorithms to add user's preferred venues iteratively to the candidate routes, with the objective of maximizing the route score while satisfying both the venue visiting time and total travel time constraints. To validate the efficiency and effectiveness of the proposed approach, extensive empirical studies were performed on two real-world data sets from the city of San Francisco, which contain more than 391 900 passenger delivery trips generated by 536 taxis in a month and 110 214 check-ins left by 15 680 Foursquare users in six months.

Taxi service strategies, as the crowd intelligence of massive taxi drivers, are hidden in their historical time-stamped GPS traces. Mining GPS traces to understand the service strategies of skilled taxi drivers can benefit the drivers themselves, passengers, and city planners in a number of ways. This paper intends to uncover the efficient and inefficient taxi service strategies based on a large-scale GPS historical database of approximately 7600 taxis over one year in a city in China. First, we separate the GPS traces of individual taxi drivers and link them with the revenue generated. Second, we investigate the taxi service strategies from three perspectives, namely, passenger-searching strategies, passenger-delivery strategies, and service-region preference. Finally, we represent the taxi service strategies with a feature matrix and evaluate the correlation between service strategies and revenue, informing which strategies are efficient or inefficient. We predict the revenue of taxi drivers based on their strategies and achieve a prediction residual as less as 2.35 RMB/h,1which demonstrates that the extracted taxi service strategies with our proposed approach well characterize the driving behavior and performance of taxi drivers.

Improving energy efficiency of data centers has become increasingly important nowadays due to the significant amounts of power needed to operate these centers. An important method for achieving energy efficiency is server consolidation supported by virtualization. However, server consolidation may incur significant degradation to workload performance due to virtual machine (VM) co-location and migration. How to reduce such performance degradation becomes a critical issue to address. In this paper, we propose a profiling-based server consolidation framework which minimizes the number of physical machines (PMs) used in data centers while maintaining satisfactory performance of various workloads. Inside this framework, we first profile the performance losses of various workloads under two situations: running in co-location and experiencing migrations. We then design two modules: (1) consolidation planning module which, given a set of workloads, minimizes the number of PMs by an integer programming model, and (2) migration planning module which, given a source VM placement scenario and a target VM placement scenario, minimizes the number of VM migrations by a polynomial time algorithm. Also, based on the workload performance profiles, both modules can guarantee the performance losses of various workloads below configurable thresholds. Our experiments for workload profiling are conducted with real data center workloads and our experiments on our two modules validate the integer programming model and the polynomial time algorithm.

With the rapid development of big data and cloud computing, big data analytics as a service in the cloud is becoming increasingly popular. More and more individuals and organizations tend to rent virtual cluster to store and analyze data rather than building their own data centers. However, in virtualization environment, whether scaling out using a cluster with more nodes to process big data is better than scaling up by adding more resources to the original virtual machines (VMs) in cluster is not clear. In this paper, we study the scalability performance issues of hadoop virtual cluster with cost consideration. We first present the design and implementation of VirtualMR platform which can provide users with scalable hadoop virtual cluster services for the MapReduce based big data analytics. Then we run a series of hadoop benchmarks and real parallel machine learning algorithms to evaluate the scalability performance, including scale-up method and scale-out method. Finally, we integrate our platform with resource monitoring module and propose a system tuner. By analyzing the monitored data, we dynamically adjust the parameters of hadoop framework and virtual machine configuration to improve resource utilization and reduce rent cost. Experimental results show that the scale-up method outperforms the scale-out method for CPU-bound applications, and it is opposite for I/O-bound applications. The results also verify the efficiency of system tuner to increase resource utilization and reduce rent cost.

Unsupervised feature learning with deep networks has been widely studied in the recent years. Despite the progress, most existing models would be fragile to non-Gaussian noises and outliers due to the criterion of mean square error (MSE). In this paper, we propose a robust stacked autoencoder (R-SAE) based on maximum correntropy criterion (MCC) to deal with the data containing non-Gaussian noises and outliers. By replacing MSE with MCC, the anti-noise ability of stacked autoencoder is improved. The proposed method is evaluated using the MNIST benchmark dataset. Experimental results show that, compared with the ordinary stacked autoencoder, the R-SAE improves classification accuracy by 14% and reduces the reconstruction error by 39%, which demonstrates that R-SAE is capable of learning robust features on noisy data.

Motor brain-machine interface (BMI) has great potentials in neural motor prostheses and has received increasing attention during the past decades in the neural engineering field. It requires an approach to decode neural activities that represents desired movements. Much of the progress in decoding algorithms has been driven by the availability of neural data, e.g. spike trains, in some research groups having animal laboratories and capable of performing surgery and building BMI systems. However, researchers in the neural signal processing field often face a dilemma of lacking neural data. To continue the innovation in decoding algorithms, this paper introduces a public neural dataset, the ZJU Neural Decoding Dataset (ZJUNDD). We give the detailed paradigm of the BMI system on monkey, including the experimental setup and the collection of 96-channel motor cortical activities. The dataset contains spike rates of neurons obtained by a consistent spike sorting method. To improve the data quality and reduce outliers, the spike data are carefully selected according to the quality of hand movements of the monkey. A standard protocol is provided for the assessment of decoding algorithms on the dataset, including the partition of training and testing sets, and the evaluation metrics. We also build an online evaluation system in order to enable a fair comparison between decoding approaches. Further, we benchmark several existing algorithms, which provides a basic performance of the methods. To the best of our knowledge, this is the first public dataset of spike trains for the decoding research of motor cortical activities.

As pairwise constraints are usually easier to access than label information, pairwise constraint propagation attracts more and more attention in semi-supervised learning. Most existing pairwise constraint propagation methods are based on canonical graph propagation model, which heavily depends on the edge weights in the graph and cannot preserve local and global consistency simultaneously. In order to address this drawback, we cast cross-view pairwise constraint propagation into a problem of low rank matrix completion and propose a Matrix Completion method for cross-view Pairwise Constraint Propagation(MCPCP). With low rank requirement and graph regularization, our MCPCP can preserve local and global consistency simultaneously. We develop an algorithm based on alternating direction method of multipliers(ADMM) to solve the optimization problem. Finally, the effectiveness of MCPCP is demonstrated in cross-view multimedia retrieval.

The research on the business models is a hot topic in recent years. It is an interesting problem to study the business model of the service. There are three kinds of models related: classical service models, Business process (BP) models and the enterprise business (EB) models in management. However, none of them covers all the properties of the service business model. In this paper, we define the business model of the service as the combination of four kinds of strategies and name it as the service pattern. We also propose a language named Service Pattern Description Language (SPDL) covering all the elements involved in these strategies. We formulate the language syntax and two basic extraction rules assisting economic analysis. Furthermore, we extend Business Process Model Notation (BPMN) to support SPDL, which is named BPMN for Service Pattern (BPMN4SP). The example of Mobile Application Platform is studied in detail for a better understanding of SPDL.

The shapes of speakers’ vocal organs change under their different emotional states, which leads to the deviation of the emotional acoustic space of short-time features from the neutral acoustic space and thereby the degradation of the speaker recognition performance. Features deviating greatly from the neutral acoustic space are considered as mismatched features, and they negatively affect speaker recognition systems. Emotion variation produces different feature deformations for different phonemes, so it is reasonable to build a finer model to detect mismatched features under each phoneme. However, given the difficulty of phoneme recognition, three sorts of acoustic class recognition—phoneme classes, Gaussian mixture model (GMM) tokenizer, and probabilistic GMM tokenizer—are proposed to replace phoneme recognition. We propose feature pruning and feature regulation methods to process the mismatched features to improve speaker recognition performance. As for the feature regulation method, a strategy of maximizing the between-class distance and minimizing the within-class distance is adopted to train the transformation matrix to regulate the mismatched features. Experiments conducted on the Mandarin affective speech corpus (MASC) show that our feature pruning and feature regulation methods increase the identification rate (IR) by 3.64% and 6.77%, compared with the baseline GMM-UBM (universal background model) algorithm. Also, corresponding IR increases of 2.09% and 3.32% can be obtained with our methods when applied to the state-of-the-art algorithm i-vector.

Brain-computer interfaces (BCIs) can provide direct bidirectional communication between the brain and a machine. Recently, the BCI technique has been used in seizure control. Usually, a closed-loop system based on BCI is set up which delivers a therapic electrical stimulus only in response to seizure onsets. In this way, the side effects of neurostimulation can be greatly reduced. In this paper, a new BCI-based responsive stimulation system is proposed. With an efficient morphology-based seizure detector, seizure events can be identified in the early stages which trigger electrical stimulations to be sent to the cortex of the brain. The proposed system was tested on rats with penicillin-induced epileptic seizures. Online experiments show that 83% of the seizures could be detected successfully with a short average time delay of 3.11 s. With the therapy of the BCI-based seizure control system, most seizures were suppressed within 10 s. Compared with the control group, the average seizure duration was reduced by 30.7%. Therefore, the proposed system can control epileptic seizures effectively and has potential in clinical applications.

Brain-machine interfaces (BMIs) aim at building a direct communication pathway between the brain and an external device, and represent an area of research where significant progress has been made during the past decade. Based on BMIs, mind information can be read out by neural signals to control machine actuators, and machine-coded sensory information can be delivered to specific areas in the brain. BMI techniques provide the opportunity to integrate machine intelligence with biological intelligence at multiple levels to develop a more powerful intelligent system, thereby creating a new field called ‘cyborg intelligence’.

Quality-of-Service (QoS)-aware web service composition is of great importance to assemble individual services into a composite one meeting functional and nonfunctional requirements. Given a large number of candidate services, automatic composition is essential so as to derive a composite service efficiently. Most existing methods return one solution that is optimal in some given criteria. This is somewhat rigid in terms of flexibility. In case some component service in the optimal composition becomes unavailable, the composition algorithm has to run again to find another optimal solution. Also, in a lot of circumstances users prefer multiple alternatives over a single one. Therefore, providing top- k service compositions according to their QoS is becoming more desirable. On another aspect, from the perspective of computation efficiency, due to the explosion of the searching space, single-threaded methods are usually not capable of handling a large number of candidate services. This paper tackles these two issues together, i.e., large-scale, QoS-based services composition yielding top- k solutions. The composition algorithm is based on the combination of backtrack search and depth-first search, which can be executed in a parallel way. Experiments are carried out based on the datasets provided by the WS-Challenge competition 2009 and China Web Service 2011. The results show that our approach can not only find the same optimal solution as the winning systems from these competitions, but also provide alternative solutions together with the optimal QoS.

Matrix factorization based techniques, such as nonnegative matrix factorization and concept factorization, have attracted great attention in dimensionality reduction and data clustering. Previous studies show that both of them yield impressive results on image processing and document clustering. However, both of them are essentially unsupervised methods and cannot incorporate label information. In this paper, we propose a novel semisupervised matrix decomposition method for extracting the image concepts that are consistent with the known label information. With this constraint, we call the new approach constrained concept factorization. By requiring that the data points sharing the same label have the same coordinate in the new representation space, this approach has more discriminating power. The experimental results on several corpora show good performance of our novel algorithm in terms of clustering accuracy and mutual information.

In modern commerce, both frequent changes of custom demands and the specialization of the business process require the capacity of modeling business processes for enterprises effectively and efficiently. Traditional methods for improving business process modeling, such as workflow mining and process retrieval, still requires much manual work. To address this, based on the structure of a business process, a method called workflow recommendation technique is proposed in this paper to provide process designers with support for automatically constructing the new business process that is under consideration. In this paper, with the help of the minimum depth-first search (DFS) codes of business process graphs, we propose an efficient method for calculating the distance between process fragments and select candidate node sets for recommendation purpose. In addition, a recommendation system for improving the modeling efficiency and accuracy was implemented and its implementation details are discussed. At last, based on both synthetic and real-world datasets, we have conducted experiments to compare the proposed method with other methods and the experiment results proved its effectiveness for practical applications.

Learning sparse representation of high-dimensional data is a state-of-the-art method for modeling data. Matrix factorization-based techniques, such as nonnegative matrix factorization and concept factorization (CF), have shown great advantages in this area, especially useful for image representation. Both of them are linear learning problems and lead to a sparse representation of the images. However, the sparsity obtained by these methods does not always satisfy locality conditions. For example, the learned new basis vectors may be relatively far away from the original data. Thus, we may not be able to achieve the optimal performance when using the new representation for other learning tasks, such as classification and clustering. In this paper, we introduce a locality constraint into the traditional CF. By requiring the concepts (basis vectors) to be as close to the original data points as possible, each datum can be represented by a linear combination of only a few basis concepts. Thus, our method is able to achieve sparsity and locality simultaneously. We analyze the complexity of our novel algorithm and demonstrate the effectiveness in comparison with the state-of-the-art approaches through a set of evaluations based on realworld applications.

Policy-based management is a very effective method to protect sensitive information. However, the overclaim of privileges is widespread in emerging applications, including mobile applications and social network services, because the applications' users involved in policy administration have little knowledge of policy-based management. The overclaim can be leveraged by malicious applications, then lead to serious privacy leakages and financial loss. To resolve this issue, this paper proposes a novel policy administration mechanism, referred to as collaborative policy administration (CPA for short), to simplify the policy administration. In CPA, a policy administrator can refer to other similar policies to set up their own policies to protect privacy and other sensitive information. This paper formally defines CPA and proposes its enforcement framework. Furthermore, to obtain similar policies more effectively, which is the key step of CPA, a text mining-based similarity measure method is presented. We evaluate CPA with the data of Android applications and demonstrate that the text mining-based similarity measure method is more effective in obtaining similar policies than the previous category-based method.

Web service composition helps users integrate services to create new large-granularity and value-added composite services. Most recent studies have focused on automatic AI-Planning-based static or dynamic composition at functional- or process-level. However in industry, most business applications are still composed manually or semi-automatically with abundant domain expertise. Consequently, to build a good and reliable composite service is really a time-consuming and professional task. Inspired by the Instant Search of Google, we propose an Instant recommendation approach to provide optimal suggestions while a composition process incrementally proceeds. In our model, we fully utilize the execution log of composite services, and intend to identify appropriate services which have been proved to be more reliable and robust, therefore those services have higher probability to fulfill users' demands. To find the top-k possible composite services in real-time, we adopt the A* search algorithm with various pruning heuristics to dynamically expand the search space efficiently. Experiments on a real-world dataset with 15,959 real Web services crawled from the Internet demonstrate the effectiveness and efficiency of the proposed approach.

Chaos of data is the total unpredictability of all the data elements, and can by quantified by Shannon entropy. In this paper, we firstly propose an entropy based theoretic framework for machine learning, which states that chaos in sample data will decrease and rule will advance as learning progresses. However, it is usually time consuming to apply the theoretic framework because groups of rule need to be trained iteratively and data chaos will be recalculated during each iteration. To implement the theoretic framework for scalable learning, we propose a MapReduce based distributed computational framework. In a case study of classification, the framework parallelly trains multiple classifiers and calculats chaos of the sample set during each iteration, and then resamples a small sample subset with the highest entropy for training of the next iteration, reducing chaos in sample data as quickly as possible. With typical classification benchmarks, our experiment presents entropy in sample data, and proves that the theoretic framework is rational and can help improve the accuracy of machine learning. Meanwhile, the computational framework shows high performance including high efficiency and scalability for large scale learning on hadoop cluster.

Recently, the emerging accumulation of biomedical data on the Web (e.g. vast amounts of protein sequences, genes, gene products, drugs, diseases and chemical compounds, etc.) has shaped a big network of isolated professional knowledge. Embedded with domain knowledge from different disciplines all regarding to human biological systems, the decentralized data repositories are implicitly connected by human expert knowledge. Lots of biomedical data sources are published separately in the form of semantic ontologies represented by Web Ontology Language (OWL) syntax, which is naturally based on linked graphs. When we are faced with such massive, disparate and interlinked data, biomedical data analysis becomes a challenge. In this paper, we present a general OWL reasoning framework for the analysis of big biomedical data and implement a MapReduce-based property chain reasoning prototype system. OWL reasoning method is ideally suitable for problems involved complex semantic associations because it is able to infer logical consequences based on a set of asserted rules or axioms. MapReduce framework is used to solve the problem of scalability. In our experiment, we focus on the discovery of associations between Traditional Chinese Medicine (TCM) and Western Medicine (WM). The results show the system achieves high performance, accuracy and scalability.

Establishing a dense vertex-to-vertex anthropometric correspondence between 3D faces is an important and fundamental problem in 3D face research, which can contribute to most applications of 3D faces. This paper proposes a sparse facial deformable model to automatically achieve this task. For an input 3D face, the basic idea is to generate a new 3D face that has the same mesh topology as a reference face and the highly similar shape to the input face, and whose vertices correspond to those of the reference face in an anthropometric sense. Two constraints: 1) the shape constraint and 2) correspondence constraint are modeled in our method to satisfy the three requirements. The shape constraint is solved by a novel face deformation approach in which a normal-ray scheme is integrated to the closest-vertex scheme to keep high-curvature shapes in deformation. The correspondence constraint is based on an assumption that if the vertices on 3D faces are corresponded, their shape signals lie on a manifold and each face signal can be represented sparsely by a few typical items in a dictionary. The dictionary can be well learnt and contains the distribution information of the corresponded vertices. The correspondence information can be conveyed to the sparse representation of the generated 3D face. Thus, a patch-based sparse representation is proposed as the correspondence constraint. By solving the correspondence constraint iteratively, the vertices of the generated face can be adjusted to correspondence positions gradually. At the early iteration steps, smaller sparsity thresholds are set that yield larger representation errors but better globally corresponded vertices. At the later steps, relatively larger sparsity thresholds are used to encode local shapes. By this method, the vertices in the new face approach the right positions progressively until the final global correspondence is reached. Our method is automatic, and the manual work is needed only in training procedure. The experimental results on a large-scale publicly available 3D face data set, BU-3DFE, demonstrate that our method achieves better performance than existing methods.

Detailed land use, which is difficult to obtain, is an integral part of urban planning. Currently, GPS traces of vehicles are becoming readily available. It conveys human mobility and activity information, which can be closely related to the land use of a region. This paper discusses the potential use of taxi traces for urban land-use classification, particularly for recognizing the social function of urban land by using one year's trace data from 4000 taxis. First, we found that pick-up/set-down dynamics, extracted from taxi traces, exhibited clear patterns corresponding to the land-use classes of these regions. Second, with six features designed to characterize the pick-up/set-down pattern, land-use classes of regions could be recognized. Classification results using the best combination of features achieved a recognition accuracy of 95%. Third, the classification results also highlighted regions that changed land-use class from one to another, and such land-use class transition dynamics of regions revealed unusual real-world social events. Moreover, the pick-up/set-down dynamics could further reflect to what extent each region is used as a certain class.

Quality-of-service-based (QoS) service selection is an important issue of service-oriented computing. A common premise of previous research is that the QoS values of services to target users are supposed to be all known. However, many of QoS values are unknown in reality. This paper presents a neighborhood-based collaborative filtering approach to predict such unknown values for QoS-based selection. Compared with existing methods, the proposed method has three new features: 1) the adjusted-cosine-based similarity calculation to remove the impact of different QoS scale; 2) a data smoothing process to improve prediction accuracy; and 3) a similarity fusion approach to handle the data sparsity problem. In addition, a two-phase neighbor selection strategy is proposed to improve its scalability. An extensive performance study based on a public data set demonstrates its effectiveness.

How to improve the modeling efficiency and accuracy has become a burning problem. The popularization of recommendation technique in E-Commerce provide us new trajectories that can be used for addressing the problem. In this paper, we propose a graph-based workflow recommendation for improving business process modeling. The start point is so-called "workflow repository" including a set of already developed process models. Graph mining method is used to extract the process patterns from the repository. Based on graph edit distance (GED) [2], we calculate the distance between patterns and the partial business process, viewed as reference model, which is under modeling and select the candidate nodes with smaller distances for recommendation. The performance study show its feasibility for practical uses.

As a central problem in computer vision and pattern recognition, data representation has attracted great attention in the past years. Non-negative matrix factorization (NMF) which is a useful data representation method makes great contribution on finding the latent structure of the data and leads to a parts-based representation by decomposing the data matrix into a few bases and encodings with nonnegative constraints. However, non-negative constraint is insufficient for getting more robust data representation. In this paper, we propose a novel method, called A-Optimal Non-negative Projection (ANP) for image data representation and further analysis. ANP imposes a constraint on the encoding factor as a regularizer during matrix factorization. In this way, the learned data representation leads to a stable linear model no matter what kind of data label is selected for further processing. Thus, it can preserve more intrinsic characteristics of the data regardless of any specific labels. We demonstrate the effectiveness of this novel algorithm through a set of evaluations on real world applications.

Smart environments require to percept conditions of people. Current context-aware systems mainly model limited user situations, which constrains their coverage and effect in real world usage. This paper proposes an encyclopedic knowledge network to enable practical context inference in our daily life by: 1) expressing essential semantics of contextual concepts and relations into a well-informed relational network, and 2) exploiting relational semantics to infer various contexts simultaneously. The performance of the approach is validated in real challenging problems and compared with inference of human being.

Predicting the Quality of Service (QoS) values is important since they are widely applied to Service-Oriented Computing (SOC) research domain. Previous research works on this problem do not consider the influence of user location information carefully, which we argue would contribute to improving prediction accuracy due to the nature of Web services invocation process. In this paper, we propose a novel collaborative QoS prediction framework with location-based regularization (LBR). We first elaborate the popular Matrix Factorization (MF) model for missing values prediction. Then, by taking advantage of the local connectivity between Web services users, we incorporate geographical information to identify the neighborhood. Different neighborhood situations are considered to systematically design two location-based regularization terms, i.e. LBR1 and LBR2. Finally we combine these regularization terms in classic MF framework to build two unified models. The experimental analysis on a large-scale real-world QoS dataset shows that our methods improve 23.7% in prediction accuracy compared with other state-of-the-art algorithms in general cases.

Nonnegative matrix factorization (NMF) is a popular technique for finding parts-based, linear representations of nonnegative data. It has been successfully applied in a wide range of applications such as pattern recognition, information retrieval, and computer vision. However, NMF is essentially an unsupervised method and cannot make use of label information. In this paper, we propose a novel semi-supervised matrix decomposition method, called Constrained Nonnegative Matrix Factorization (CNMF), which incorporates the label information as additional constraints. Specifically, we show how explicitly combining label information improves the discriminating power of the resulting matrix decomposition. We explore the proposed CNMF method with two cost function formulations and provide the corresponding update solutions for the optimization problems. Empirical experiments demonstrate the effectiveness of our novel algorithm in comparison to the state-of-the-art approaches through a set of evaluations based on real-world applications.

Technologies keep evolving to strengthen and further people's abilities in many aspects. For instance, vehicles expand the range of human moving while mobile phones boost the range of human communication. In this video, we develop a novel mini unmanned aerial vehicle (mini-UAV) named FlyingBuddy to augment human mobility and perceptibility. This prototype is made up of off the shelf components AR. Drone and iPhones with customized software. With help of the built-in magnetometer, GPS, and cameras, as well as Bluetooth, Wi-Fi and 3G connectivity, FlyingBuddy is capable of both manual controlled and self-piloted flying. It provides four typical services: flying to buy, flying to see, flying to report accident, and flying to take pictures.

Matrix factorization based techniques, such as nonnegative matrix factorization (NMF) and concept factorization (CF), have attracted great attention in dimension reduction and data clustering. Both of them are linear learning problems and lead to a sparse representation of the data. However, the sparsity obtained by these methods does not always satisfy locality conditions, thus the obtained data representation is not the best. This paper introduces a locality-constrained concept factorization method which imposes a locality constraint onto the traditional concept factorization. By requiring the concepts (basis vectors) to be as close to the original data points as possible, each data can be represented by a linear combination of only a few basis concepts. Thus our method is able to achieve sparsity and locality at the same time. We demonstrate the effectiveness of this novel algorithm through a set of evaluations on real world applications.

This paper proposes a new approach to improve multiclass classification performance by employing Stacked Generalization structure and One-Against-One decomposition strategy. The proposed approach encodes the outputs of all pairwise classifiers by implicitly embedding two-class discriminative information in a probabilistic manner. The encoded outputs, called Meta Probability Codes (MPCs), are interpreted as the projections of the original features. It is observed that MPC, compared to the original features, has more appropriate features for clustering. Based on MPC, we introduce a cluster-based multiclass classification algorithm, called MPC-Clustering. The MPC-Clustering algorithm uses the proposed approach to project an original feature space to MPC, and then it employs a clustering scheme to cluster MPCs. Subsequently, it trains individual multiclass classifiers on the produced clusters to complete the procedure of multiclass classifier induction. The performance of the proposed algorithm is extensively evaluated on 20 datasets from the UCI machine learning database repository. The results imply that MPC-Clustering is quite efficient with an improvement of 2.4% overall classification rate compared to the state-of-the-art multiclass classifiers.

Non-negative matrix factorization (NMF), as a useful decomposition method for multivariate data, has been widely used in pattern recognition, information retrieval and computer vision. NMF is an effective algorithm to find the latent structure of the data and leads to a parts-based representation. However, NMF is essentially an unsupervised method and can not make use of label information. In this paper, we propose a novel semi-supervised matrix decomposition method, called Constrained Non-negative Matrix Factorization, which takes the label information as additional constraints. Specifically, we require that the data points sharing the same label have the same coordinate in the new representation space. This way, the learned representations can have more discriminating power. We demonstrate the effectiveness of this novel algorithm through a set of evaluations on real world applications.

This paper focuses on the task of recovering the neutral 3D face of a person when given his/her 3D face model with facial expression. We propose a learning-based expression removal framework to tackle this task. Our basic idea is to model expression residue from samples, and then use the inferred expression residue from the input expressional face model to recover the neutral one. A two-step non-rigid alignment method is introduced to make all the face models topologically share a common structure. Then we construct two spaces, normal space and expression residue space, for modeling expression. Therefore, the expression removal problem can be formalized as the inference of expression residue from normal spaces. The neutral face model can be generated in a Poisson-based framework by the inferred expression residue. The experimental results on BU-3DFED database demonstrate the effectiveness of our approach.

Electronic textiles are regarded as one of the most important computation platforms for future computer-assisted health-monitoring applications. In these novel systems, multiple batteries are used in order to prolong their operational lifetime, which is a significant metric for system usability. However, due to the nonlinear features of batteries, computing systems with multiple batteries cannot achieve the same battery efficiency as those powered by a monolithic battery of equal capacity. In this paper, we propose an algorithm aiming to maximize battery efficiency globally for the computer-assisted health-care systems with multiple batteries. Based on an accurate analytical battery model, the concept of weighted battery fatigue degree is introduced and the novel battery-scheduling algorithm called predicted weighted fatigue degree least first (PWFDLF) is developed. Besides, we also discuss our attempts during search PWFDLF: a weighted round-robin (WRR) and a greedy algorithm achieving highest local battery efficiency, which reduces to the sequential discharging policy. Evaluation results show that a considerable improvement in battery efficiency can be obtained by PWFDLF under various battery configurations and current profiles compared to conventional sequential and WRR discharging policies.

Electronic textiles (e-textiles), known as computational fabrics, offer an emerging platform for constructing ambient intelligent applications. Computational nodes in e-textiles are driven by batteries. Unlike wireless sensor networks, not each computational node in e-textiles has its own battery. Instead, many computational nodes in e-textiles share a battery. Existing e-textiles use one fixed battery to drive a fixed set of computation nodes (or power consuming electronic components). The fixed battery-component connection may result in electronic components stopping functioning and/or energy waste in batteries when link connection problems occur. In this paper, we propose a new infrastructure of the power networks for e-textiles: flexible power network (FPN). Under the FPN infrastructure, a power consuming node (PCN) is not just connected to one single fixed battery. Instead, it is connected to multiple batteries and can obtain power energy from one of the available battery nodes (BNs) with the help of a battery selector. The electrical features of battery selectors and overcurrent protectors that protect the batteries from wasting the charge when short-circuit faults occur are illustrated. Moreover, by modeling the number of fault occurrence at conductive wires and nodes stochastically, an evaluation algorithm is proposed to analyze the reliability of FPN and to compare the metrics of different design schemes under the perspective of both the BNs and the PCNs. Experimental results show that our FPN is more dependable than some common e-textile electric networks published before with the occurrence of short- and/or open-circuit faults.

This paper attempts to model pervasive computing environments as a user-centric ldquoSmartShadowrdquo using the BDP (belief-desire-plan) user model, which maps pervasive computing environments into a dynamic virtual user space. SmartShadow will follow the user to provide him with pervasive services, just like his shadow in the physical world. In the BDP model, desires of a user are inferred from his belief set, and plans are made to satisfy each desire. Pervasive service is introduced to describe computing resources in the cyberspace, which can be organized by the user's BDP to accomplish his desires. The composition process maps pervasive services into a user's SmartShadow. The model is logically natural and simple, and can flexibly model dynamics of pervasive computing spaces. In addition, we implement a simulation system to verify and evaluate the SmartShadow model.

With the proliferation of data APIs, it is not uncommon that users who have no clear ideas about data APIs will encounter difficulties to build Mashups to satisfy their requirements. In this paper, we present a semantic-based mashup navigation system, sMash that makes mashup building easy by constructing and visualizing a real-life data API network. We build a sample network by gathering more than 300 popular APIs and find that the relationships between them are so complex that our system will play an important role in navigating users and give them inspiration to build interesting mashups easily. The system is accessible at: http://www.dart.zju.edu.cn/mashup.

The Semantic Web technology enables integration of heterogeneous data on the World Wide Web by making the semantics of data explicit through formal ontologies. In this article, we survey the feasibility and state of the art of utilizing the Semantic Web technology to represent, integrate and analyze the knowledge in various biomedical networks. We introduce a new conceptual framework, semantic graph mining, to enable researchers to integrate graph mining with ontology reasoning in network data analysis. Through four case studies, we demonstrate how semantic graph mining can be applied to the analysis of disease-causal genes, Gene Ontology category cross-talks, drug efficacy analysis and herb-drug interactions analysis.

We present sMash, a system for facilitating users to mashup Web data. The aspects emphasized by the demo are: (1) how to help novice users master data APIs and relationships amongst them easily; (2) how to inspire various users to build more amazing Web data mashups. First, a real-life data API network is constructed and visualized to enable users to surf and mashup. Second, two kinds of recommendations are generated dynamically based on a comprehensive analysis of the network, user's traces and a repository of mashups to provide navigation.

An increasing number of real-time applications like railway signaling control systems and medical electronics systems require high quality of security to assure confidentiality and integrity of information. Therefore, it is desirable and essential to fulfill security requirements in security-critical real-time systems. This paper addresses the issue of optimizing quality of security in real-time systems. To meet the needs of a wide variety of security requirements imposed by real-time systems, a group-based security service model is used in which the security services are partitioned into several groups depending on security types. While services within the same security group provide the identical type of security service, the services in the group can achieve different quality of security. Security services from a number of groups can be combined to deliver better quality of security. In this study, we seamlessly integrate the group-based security model with a traditional real-time scheduling algorithm, namely earliest deadline first (EDF). Moreover, we design and develop a security-aware EDF schedulability test. Given a set of real-time tasks with chosen security services, our scheduling scheme aims at optimizing the combined security value of the selected services while guaranteeing the schedulability of the real-time tasks. We study two approaches to solve the security-aware optimization problem. Experimental results show that the combined security values are substantially higher than those achieved by alternatives for real-time tasks without violating real-time constraints.

This paper demonstrates a novel car-following model focused on passenger comfort, for example, a rapid deceleration will make passengers uncomfortable. The brake comfort model of car following was set up according to the relationship between vehicle deceleration and passenger comfort levels. The model calculates the controlled car's acceleration by measuring the distance between the controlled car and its preceding car, as well as the velocity of the controlled car. By controlling the car's acceleration, the model is able to keep riders feeling comfortable. The friction coefficient between the car and the road surface is also considered. Experiments show that the model is highly compatible with real cases.

Recent advances in Web and information technologies have resulted in many e-learning resources. There is an emerging requirement to manage and reuse relevant resources together to achieve on-demand e-learning in the Web. Ontologies have become a key technology for enabling semantic-driven resource management. We argue that to meet the requirements of semantic-based resource management for Web-based e-learning, one should go beyond using domain ontologies statically. In this paper, we provide a semantic mapping mechanism to integrate e-learning databases by using ontology semantics. Heterogeneous e-learning databases can be integrated under a mediated ontology. Taking into account the locality of resource reuse, we propose to represent context-specific portions from the whole ontology as sub-ontologies. We present a sub-ontology-based approach for resource reuse by using an evolutionary algorithm. We also conduct simulation experiments to evaluate the approach with a traditional Chinese medicine e-learning scenario and obtain promising results.

This paper focuses on hallucinating a facial shape from a low-resolution 3D facial shape. Firstly, we give a constrained conformal embedding of 3D shape in R2, which establishes an isomorphic mapping between curved facial surface and 2D planar domain. With such conformal embedding, two planar representations of 3D shapes are proposed:Gaussiancurvatureimage(GCI) for a facial surface, andsurfacedisplacementimage(SDI) for a pair of facial surfaces. The conformal planar representation reduces the data complexity from 3D irregular curved surface to 2D regular grid while preserving the necessary information for hallucination. Then, hallucinating a low resolution facial shape is formalized as inference of SDI from GCIs by modeling the relationship between GCI and SDI by RBF regression. The experiments on USF HumanID 3D face database demonstrate the effectiveness of the approach. Our method can be easily extended to hallucinate those category-specific 3D surfaces sharing with similar geometric structures.

We bring forward a two-phase semantic service discovery mechanism which supports both the operation matchmaking and operation-composition matchmaking. A serial of experiments on a service management framework show that the mechanism gains better performance on both discovery recall rate and precision than a traditional matchmaker.

We conduct the first systematical adoption of the Semantic Web solution in the integration, management, and utilization of TCM information and knowledge resources. As the results, the largest TCM Semantic Web ontology is engineered as the uniform knowledge representation mechanism; the ontology-based query and search engine is deployed, mapping legacy and heterogeneous relational databases to the Semantic Web layer for query and search across database boundaries; the first global herb-drug interaction network is mapped through semantic integration, and the semantic graph mining methodology is implemented for discovering and interpreting interesting patterns from this network. The platform and underlying methodology are proved effective in TCM-related drug usage, discovery, and safety analysis.

Three-dimensional human face recognition in the presence of expression is a big challenge, since the shape distortion caused by facial expression greatly weakens the rigid matching. This paper proposes a guidance-based constraint deformation(GCD) model to cope with the shape distortion by expression. The basic idea is that, the face model with non-neutral expression is deformed toward its neutral one under certain constraint so that the distortion is reduced while inter-class discriminative information is preserved. The GCD model exploits the neutral 3D face shape to guide the deformation, meanwhile applies a rigid constraint on it. Both steps are smoothly unified in the Poisson equation framework. The GCD approach only needs one neutral model for each person in the gallery. The experimental results, carried out on the large 3D face databases-FRGC v2.0, demonstrate that our method significantly outperforms ICP method for both identification and authentication mode. It shows the GCD model is promising for coping with the shape distortion in 3D face recognition.

We present a real-time liveness detection approach against photograph spoofing in face recognition, by recognizing spontaneous eyeblinks, which is a non-intrusive manner. The approach requires no extra hardware except for a generic webcamera. Eyeblink sequences often have a complex underlying structure. We formulate blink detection as inference in an undirected conditional graphical framework, and are able to learn a compact and efficient observation and transition potentials from data. For purpose of quick and accurate recognition of the blink behavior, eye closity, an easily-computed discriminative measure derived from the adaptive boosting algorithm, is developed, and then smoothly embedded into the conditional model. An extensive set of experiments are presented to show effectiveness of our approach and how it outperforms the cascaded Adaboost and HMM in task of eyeblink detection.

Service discovery becomes a key to hastening the evolution of web services as the number of services is expected to increase dramatically. In this paper, we propose to index all the ontology-annotated outputs in registered services. For each ontology-annotated output, there is a service list which records all the services in the registry that deliver the output. Based on the indexing, we propose a composition-oriented service discovery algorithm, which greatly accelerates the filtering of irrelevant atomic services by making use of the inverted indexing, and increases the likelihood of finding a possible candidate by exploring service composition. Experimental results show that the proposed algorithm provides a better performance on response time than the sequential matchmaking, and a better recall rate than the algorithms without the exploration of composition.

Online travel continues to be one of the most successful e-commerce categories on the Internet. As increasing number of people book online, existing e-ticket systems can not satisfy customers' growing demand. Customers complain about most of the current e-ticket systems for the lack of "reservation" functionality. They also complain that these systems are not "intelligent" enough as a traditional travel agent who can deal with more complicated personal preferences. In this paper, we propose a transaction model for e-commerce system to support reservation functionality while balancing the benefits and risks of both customers and airlines. We introduce the theory of hypothetical query to avoid unnecessary transactions and rollbacks on underlying database. Hypothetical queries can be generated automatically and transformed into relational algebra queries which can be optimized with conventional techniques. We extend the lazy approach for evaluating hypothetical queries with integrity constraints to deal with customers' personal preferences. We also provide the "watch" and "remind" mechanism to help customers to purchase their most desirable tickets. Although we have chosen ticket booking system as our target application, the framework presented in this paper is also applicable to other e-commerce systems.

BPEL can automate orchestrations for cross-organizational Web services; however, it meets a serious challenge from modeling human-intensive business activities, especially from addressing access control for human coordination considering complex interpersonal relationship in modern business. This paper analyzes the importance of human-intensive processes and introduces several additional types of BPEL constructs, then discusses RBAC Model extracted from BPEL process, finally uses improved FOAF to enhance RBAC Model in BPEL. The goal of our work is to enhance human coordination capability in BPEL-based business processes by using RBAC model and improved FOAF.

BACKGROUND: Recent advances in Web and information technologies with the increasing decentralization of organizational structures have resulted in massive amounts of information resources and domain-specific services in Traditional Chinese Medicine. The massive volume and diversity of information and services available have made it difficult to achieve seamless and interoperable e-Science for knowledge-intensive disciplines like TCM. Therefore, information integration and service coordination are two major challenges in e-Science for TCM. We still lack sophisticated approaches to integrate scientific data and services for TCM e-Science. RESULTS: We present a comprehensive approach to build dynamic and extendable e-Science applications for knowledge-intensive disciplines like TCM based on semantic and knowledge-based techniques. The semantic e-Science infrastructure for TCM supports large-scale database integration and service coordination in a virtual organization. We use domain ontologies to integrate TCM database resources and services in a semantic cyberspace and deliver a semantically superior experience including browsing, searching, querying and knowledge discovering to users. We have developed a collection of semantic-based toolkits to facilitate TCM scientists and researchers in information sharing and collaborative research. CONCLUSION: Semantic and knowledge-based techniques are suitable to knowledge-intensive disciplines like TCM. It's possible to build on-demand e-Science system for TCM based on existing semantic and knowledge-based techniques. The presented approach in the paper integrates heterogeneous distributed TCM databases and services, and provides scientists with semantically superior experience to support collaborative research in TCM discipline.

The Semantic Web technologies provide enhanced capabilities that allow data and the meaning of the data to be shared and reused across application, enterprise, and community boundaries, better enabling integrative research and more effective knowledge discovery. This special issue is intended to give an introduction of the state-of-the-art of Semantic Web technologies and describe how such technologies would be used to build the e-Science infrastructure for biomedical communities. Six papers have been selected and included, featuring different approaches and experiences in a variety of biomedical domains.

Compared with smart spaces addressed previously, the smart vehicle space is quite special. First, unlike room, it is a high mobile space. Second, it requires frequent information exchange with the outer environment; for instance, it may need local traffic information and other local services. The complex vehicle space needs a software infrastructure of high adaptation to meet the complex and easy variational situations. This paper proposes a semantic and adaptive middleware platform, i.e., ScudWare, for smart vehicle space. In ScudWare, techniques of multiagent, context-aware, and adaptive component management are smoothly synthesized. It consists of three key components, namely 1) semantic virtual agents; 2) a semantic context management service; and 3) an adaptive component management service. This enables entities in smart vehicle space to interact autonomously, provides semantic-integration context awareness, and supports component-based adaptability and scalability. A test bed of smart car space is built to evaluate the ScudWare, which demonstrates the effectiveness and efficiency of ScudWare

Improving real-time is one of the most essential problems in studies of real-time operating system (RTOS). The time of task context switching is an important factor that affects the real-time of RTOS. This paper concentrates on the improvement of task context switching of one kind of RTOS-OSEK operating system. According to different task states, we apply different context switching strategies to reduce the average time of context switching. This method has been successfully implemented and evaluated in our OSEK compatible operating system-SmartOSEK OS.

Trust plays a central role for efficient and secure interactions on the semantic Web. Nowadays, more and more research has focused on this topic. In this paper, we introduce RCSW, a reputation-chain trust model for the semantic Web. We illustrate the basic definitions and mechanisms of RCSW and present the algorithm to calculate trust ratings through the chain of the trust network. RCSW incorporates pairwise trust values and reliable factors of acquaintances and constructs an edge-weighted graph to calculate trust ratings. We also define classes of trust functions to allow users to individualize their trust calculation. Our proposed model can deal with trust management effectively and can quickly adapt to the changing environment.

A Web service is a basic software component that can be accessed by standard Internet protocols. It provides a new approach to cooperative and federated computing among different organizational units. There are many specifications which can describe the elements of Web services and make the end-users interact with each other. However, they are remaining at the descriptive level, without supporting any kind of mechanisms or tools for the verifying the specified attributes of the Web services. In the paper, we provide a mathematical scheme, type theory, to describe the basic elements of Web services and the specified attributes. We also present the mechanism to deduce the automated programs in other languages (for example ML) from the type theory. Thus we can verify the behavioral properties of a Web service as well as analyzing and verifying Web services composition

With the growing population of Web services, the discovery of services is a key to the development of Web services. While extensive researches focus mainly on service matchmaking algorithms, service classification that is also a meaningful approach to accelerating service discovery only receives little attention. In this paper, we propose to use adaptive back-propagation neural network model (BPM) to perform service recognition. During the training process, the feature vectors of training services and their categories are learned by the BPM. The element in the feature vector is the semantic similarity between the feature word in the system dictionary and the occurrence in the feature set for a service. During the recognition process, the characteristics of the test service are analyzed by the BPM and the output shows the category of the test service. Furthermore, the BPM is adapted with correctly recognized test services, which results in better modeling over time. Based on extensive experiments, we show that using the adaptive BPM is a promising way to realize automatic service classification, and the adaptive BPM using semantic similarity as the element in feature vector provides better performance than the general BPM using word frequency

This paper introduces SmartC, a language designed for programming automotive electronics embedded systems such as engine control systems. SmartC is a hierarchical modeling language and implements the SmartOSEK operating system model. The SmartC models are classified into four levels, namely module level, task level, subtask level and component level. In the SmartC models, control-flow oriented models and data-flow oriented models are integrated in the hybrid SmartC models. At the task level, the model is constructed based on the control flow, whereas the component level model is constructed based on the dataflow. In SmartC programs, all inter-task communication, task triggering mechanisms, and access to guarded global variables, are automatically generated by the SmartC generator which generates the C code from the SmartC code. Having well-structured concurrency mechanisms, SmartC greatly reduces the risk of concurrency errors, such as deadlock and race conditions. The SmartC language is implemented on the automated manual transmission (AMT) control system and is compatible with the OSEK/VDXspecifications. We use a continuous time (CT) model as an example to illustrate the effectiveness of the language

This paper introduces SmartC, a language designed for programming automotive electronics embedded systems such as engine control systems. SmartC is a hierarchical modeling language and implements the SmartOSEK operating system model. The SmartC models are classified into four levels, namely module level, task level, subtask level and component level. In the SmartC models, control-flow oriented models and data-flow oriented models are integrated in the hybrid SmartC models. At the task level, the model is constructed based on the control flow, whereas the component level model is constructed based on the data flow. In SmartC programs, all inter-task communication, task triggering mechanisms, and access to guarded global variables, are automatically generated by the SmartC generator which generates the C code from the SmartC code. Having well-structured concurrency mechanisms, SmartC greatly reduces the risk of concurrency errors, such as deadlock and race conditions. The SmartC language is implemented on the automated manual transmission (AMT) control system and is compatible with the OSEK/VDX specifications. We use a continuous time (CT) model as an example to illustrate the effectiveness of the language

Electronic control units (ECUs) are widely used to improve the comfort and reliability of vehicles. Due to the increasing degree of distribution and interaction of ECUs, many approaches are adopted to design the in-vehicle networks to ensure the stability and reliability of the holistic network, but none of them support the synchronous development of in-vehicle networks and software in ECUs. This paper put forward a model-based approach to design the in-vehicle networks and the software in ECUs with presenting a dependable platform compliant with OSEK/VDX specifications. The paper presents a tool-chain that covers the entire system development life-cycle including system modeling, model transformation, model analysis, network simulation, code generation, document generation and runtime instrumentation. In addition, a case study is presented to demonstrate the application of the approach. The contribution of the approach is threefold. First, the approach applies the theory of model-based design with OSEK/VDX standard in automotive electronics domain, and implements the transformation between SysML models and OSEK/VDX models through an efficient method. Second, the approach presents a method of software co-design among distributed ECUs. Third, the approach bring forward a simulation model for the system models with in-vehicle networks and provides the designer with the simulation results to optimize the design at the early time of the development

One of the largest challenges in speaker recognition applications is dealing with speaker-emotion variability. In this paper, we further investigate the rules based feature modification for robust speaker recognition with emotional speech. Specifically, we learn the rules of prosodic features modification from a small amount of the content matched source-target pairs. Features with emotion information are adapted from the prevalent neutral features by applying the modification rules. The converted features are trained together with the neutral features to build the speaker models. The effects of individual and combined modifications of duration, pitch and amplitude are also studied using EPST dataset recorded by 8 professional actors with 14 kinds of emotion expressiveness. It demonstrates that duration modifications play the most important role; and that, pitch modifications are more effective than amplitude modifications. Promising result with an improved identification rate by 7.83% is achieved compared to the traditional speaker recognition

We study the problem of answering queries through a RDF/RDFS ontology, given a set of view-based mappings between one or more relational schemas and this target ontology. Particularly, we consider a set of RDFS semantic constraints such as rdfs:subClassof, rdfs:subPropertyof, rdfs:domain, and rdfs:range, which are present in RDF model but neither XML nor relational models. We formally define the query semantics in such an integration scenario, and design a novel query rewriting algorithm to implement the semantics. On our approach, we highlight the important role played by RDF Blank Node in representing incomplete semantics of relational data. A set of semantic tools supporting relational data integration by RDF are also introduced. The approach have been used to integrate 70 relational databases at China Academy of Traditional Chinese Medicine.

In this paper, we present an improved method of anchor models for speaker verification. Anchor model is the method that represent a speaker by his relativity of a set of other speakers, called anchor speakers. It was firstly introduced for speaker indexing in large audio database. We suggest a rank based metric for the measurement of speaker character vectors in anchor model. Different from conventional metric methods which consider each anchor speaker equally and compare the log likelihood scores directly, in our method the relative order of anchor speakers is exploited to characterize target speaker. We have taken experiments on the YOHO database. The results show that EER of our method is 13.29% lower than that of conventional metric. Also, our method is more robust against the mismatching between test set and anchor set

Electronic textiles, known as computational fabrics, offer an emerging method for constructing wearable and large area applications. Because e-textiles are battery-driven and fault-prone systems, there is a need for developing a dependable infrastructure of the electric networks for e-textiles. In this paper, a new infrastructure of the power networks for e-textiles, flexible power network (FPN), is presented. Instead of drawing power from a fixed battery as in the conventional electric networks, the power consuming nodes in a FPN can obtain power energy from one of the choices of batteries available with the help of the battery selectors. We also introduce the over current protectors into the battery nodes (BN) to protect the batteries from wasting the charge when short-circuit faults occur. The electric features of battery selectors and over current protectors, the two types of important electric devices used in FPNs, are illustrated in the paper. We have performed simulation experiments and the results show that our FPNs are more dependable than some common electric networks published before in the cases of short- and open-circuit faults.

With the development of mobile devices and wireless communication technologies, users and devices will face an environment full of changes. It is indispensable for a system to adapt to the environment changes, which we call context-awareness. This paper introduces the adaptive mechanisms for embedded systems from the point of view of the data. The data in a context-aware system are in three different forms in different stage: the sensor data, the context, the situation. This paper introduces the context model which consists of three layers and the adaptive mechanisms in different layer to enable the system adaptive to contexts. Besides, we develop an example to show how the clustering and reasoning technology are used to recognize and derive the higher level context

Generating multi-threading implementation of embedded software from design models, i.e. implementation synthesis, is a critical stage during embedded software development, where many nonfunctional requirements are implemented and optimized. Currently, almost all research work on implementation synthesis of embedded software assumes that the underlying operating systems use the priority-based fully preemptive scheduling strategy. However, real-world embedded operating systems may support many kinds of scheduling strategies for different requirements. This paper presents an implementation synthesis method under operating systems that support the group-based scheduling model, which extends the application scope of implementation synthesis. The implementation synthesis method involves task grouping, priority assignment for task groups, task generation and priority assignment. Experimental evaluation shows our method can yield multi-threading embedded software effectively while satisfying real-time requirements.

Service discovery becomes a key to accelerating the evolution of Web services as the number of services is expected to increase dramatically. Foregoing work on service discovery is primarily based on the interfaces of services through the use of ontology. Ongoing work targets at service behavior, with not only individual message exchanges being captured, but also constraints between these message exchanges. In this paper, we propose a formal approach to expressing the service and query behavior using pi-calculus for service matchmaking. The resulting pi-calculus expressions of services and queries are precise in defining single operations involving message exchanges as well as execution sequence between operations. Based on the formalizations, service matchmaking between a service query and a service description is reasoned through the capability of pi-calculus. Expressing service behavior using pi-calculus is expected to be a promising way to realize intelligent service discovery

Abstract In the presence of a Database Grid where a huge number of highly diverse, widely distributed, autonomously managed databases can be involved in a sharing cycle, database tools and middleware should be well suited for schema mediation and query processing in a semantically meaningful way. In this paper, an implemented system called DartGrid is presented. DartGrid is intended to provide a semantic infrastructure for building database grid applications. We explore the essential and fundamental roles played by Resource Description Framework (RDF) semantics for database grids and implement a set of semantically enabled tools and grid services such as semantic browser, semantic mapping tools, ontology service, semantic query service and semantic registration service. We propose an RDF‐View‐based approach for relational schema mediation and describe the view‐based semantic query rewriting algorithm implemented in DartGrid. DartGrid has been used to build a real database grid application for Traditional Chinese Medicine in China. Copyright © 2006 John Wiley & Sons, Ltd.

There has been a rapid growth in the amount and the importance of software in automobiles in the past two decades. This trend makes us view automobile space as a set of smart services. The basic platform for managing these services is a real-time operating system. Due to the limited resource in ECU, this operating system must be efficient in some aspects especially in memory utilization. We have implemented an operating system SmartOSEK, an OSEK-compliant OS. Some features and optimizations to reduce the RAM needed by the operating system are presented in this paper. First, an upper bound for the register set of each task that has to be stored can be calculated off-line. It also calls for the extension of the compiler to allocate registers across tasks for task contexts. We provide an experimental implementation with its result shown in this paper. Second, the stack space is saved based on some properties of tasks and interrupt handling.

With the rapid development of network technology, current workflow system has become service-based. But the services in current service-based workflow system are either totally public or hidden. The paper presents a service-oriented workflow model which is composed of two types of workflow service: private and public. The private service is the full definition of a workflow service as common. The public one is simplified from the private service according to a principle - interface equivalence. After taking an overview of the service-oriented workflow model, it provides several definitions which are required in interface equivalence. Given the property of interface equivalence, it proposes an algorithm to simplified public service from private service based on the interface equivalence.

Today's industries exhibit a growing trend towards benefiting from Web services. However, management complexity also grows with the number and type of services deployed. How to help users discover an appropriate service from tremendous ones, how to provide security access to target service, how to supply reliable service, and how to integrate commerce into services executing process become significant challenges. Therefore, companies soon require facilities that support and automate their management efforts. Furthermore, existing Web services management facilities, which lack structure and computer understandable metadata, prohibit users from managing Web services effectively. To address above issues, this paper elaborates a semantic Web technologies based service-management solution, called Dart-Man, towards making Web service management more flexible and automated.

Super resolution technique could produce a higher resolution image than the originally captured one. However, nearly all super-resolution algorithms arm at 2D images. In this paper, we focus on generating the 3D face model of higher resolution from one input of 3D face model. In our method, the 3D face models firstly are all regularized via resampling in cylindrical representation. The super resolution then performs in the regular domain of cylindrical coordinate. The experiments using USF HumanID 3D face database of 137 3D face models are carried out, and demonstrate the presented algorithm is promising.

In this paper, we introduce an elaborate utterance detection algorithm to enhance speaker segmentation. Silence detector, further divider and audio type classifier are employed in this elaborate utterance detection, to make this algorithm adaptive for both silent and noisy environments. Open-set verification testing has taken on the Hub4-NE broadcasts database. The experiment results show that this enhanced segmentation method can provide better information for speaker models.

We study the problem of answering queries through a target RDF ontology, given a set of view-based mappings between one or more source relational schemas and this target ontology. We design a novel query rewriting algorithm that can efficiently rewrites a RDF query into relational queries using a set of RDF views. With our approach, we highlight the important role played by RDF blank node in representing incomplete semantics of relational data when integrating them using RDF.

In order to meet the on-demand requirement of service composition at a large scale, one should go beyond the use of static domain ontologies but allow different focused aspects of the ontologies to be distributed as sub-ontologies. To demonstrate the feasibility of the sub-ontology idea, we suggest a possible implementation using the semantic Web technology and apply it to distributed e-learning.

In this paper, the mixture of support vector machines is proposed and applied to text-independent speaker recognition. The mixture of experts is used and is implemented by the divide-and-conquer approach. The purpose of adopting this idea is to deal with the large scale speech data and improve the performance of speaker recognition. The principle is to train several parallel SVMs on the subsets of the whole dataset and then combine them in the distance or probabilistic fashion. The experiments have been run on the YOHO database, and the results show that the mixture model is superior to the basic Gaussian mixture model.

Segmentation of continuous audio is an important processing in many applications. In speaker indexing, the reliability of speaker model depends much on segmentation. Commonly used methods are based on the Bayesian information criteria (BIC), which is however not so capable when dealing with short utterances. In this paper, we present a pitch-based speech segmentation method, which can detect frequent speaker changes accurately and rapidly. In our algorithm, pitch is introduced in speaker segmentation. Firstly, utterance segments are detected by pitch. Then distances of pitch are computed, and compared with a self-adaptable threshold. Speaker changes are finally decided among utterance segments. We applied our method and three comparative methods on the HUB4-NE broadcast data. Speaker indexing experiments have been taken following each algorithm. We also suggested two indicators as complements of false alarm and missing rate in the evaluation of segmentation. The experiment results show that our algorithm works faster and better, with most of short time speaker changes detected. Speaker indexing equal error rate of our method is 10.43%, which is much lower than 12.94%, 25.84% and 15.91% of other methods.

Wireless networked sensors raise a great number of programming challenges in applications. In this paper, we present AntC, a systems programming language, which focuses on the super-micro embedded system that supports wireless networked sensors. It provides four broad characteristics: it is a component-oriented application design; it integrates an embedded OS with two-level dispatcher; it improves the fault tolerance in run-time; furthermore, the improvement on multiplatform adaptability allows AntC program to be easily transplanted to other platforms with different embedded processors.

Context-aware computing is a computing paradigm in which applications can discover and take advantage of context information. Faced with the diverse mix of distributed and heterogeneous sensors, embedded devices and mobile devices in the ubiquitous computing environments, it is fairly difficult for users to develop context-aware applications. This paper proposes ScudWare, a middleware to support the development of context-aware applications. We introduce the developing language, compiling and assembling of a context-aware application. To illustrate the developing process, we design an example which shows how the application adapts to changes in the environments.

In recent years, there has been much research on ubiquitous computing, including context aware computing that is an important field in ubiquitous computing. The primary focus of this paper is on context modeling. We study the characteristics of context and so the requirements on the modeling. Besides, we also take into account the development requirements of next generation context-aware application. We study the context modeling approaches of some famous existing projects and find that few models meet all these requirements. In this paper, we propose a comprehensive model, which meets these requirements. We implement this model in a middleware called Scudware.

Fixed-priority with preemption threshold (FPPT) is an important form of real-time scheduling algorithm, which fills the gap between fixed-priority preemptive (FPP) and fixed-priority non-preemptive (FPNP). However, this scheduling scheme together with the requirements of fault tolerance makes the prediction of a real-time system's behaviors more difficult than traditional scheduling scheme. The major contribution of this work is twofold. First, we present an appropriate schedulability analysis, based on response time analysis, for supporting fault-tolerant FPPT scheduling in hard real-time systems. The error-recovery techniques are considered to be used to carry out fault tolerance. Second, we propose the optimal priority assignment algorithm which can be used, together with the schedulability analysis, to improve system fault resilience.

Temporal behaviors prediction is very critical for real-time control systems. This work presents a method to analyse the worst-case response time of OSEK/VDX compliant hard real-time distributed control systems. OSEK/VDX is an industry standard for an open-ended architecture for distributed control units in vehicles. Traditional time analysis methods based on the fixed priority preemptive scheduling model do not account for the problems occur in practical systems. Based on the current time analysis technologies, we propose an accurate time analysis method considering the practical effects of resource access synchronization, mixed preemptive and non-preemptive scheduling, as well as the overheads of OSEK OS. Furthermore, we also propose a scheme to efficiently integrate time analysis tools into the current development process for electronic control units in vehicles.

Smart sensors are sensing nodes that contain local intelligence to receive commands and take actions. They act as feelers of ubiquitous computing environments. Their software platform has to meet the requirements of concurrent operations, real-time constraint, adaptability and dependability. Gamma-OS is an OS kernel designed for these smart devices. This work analyzes the typical task model of smart sensors and presents the implementation of scheduling, resource access and dynamic admission control schemes in gamma-OS. Its primary and subordinate two-level task-scheduling scheme can efficiently isolate the scheduling of time-critical and time-uncritical tasks. Fault tolerance strategies including FTRMS admission control, fault detection, and fault recovery are also extended in gamma-OS to tolerate transient faults in executions of tasks.

Most state-of-the-art speaker recognition systems do improve their performance when utilizing glottal information. Although they successfully model its changes as features for recognition task, they do not take into account the spectral variations caused by it. A method that can lessen this influence, using both long-term and short-term glottal information, is proposed. Spectral features behave more discriminative in text-independent automatic speaker recognition (ASR) through this recuperation. Our method was applied to YOHO corpus and our SRMC corpus. The experimental works show promising results.

Wireless sensor networks (WSNs) are an area of emerging networking research. One obstacle is its limited supply of energy. Therefore, minimizing energy consumption and maximizing system lifetime have been a major design goal for WSNs. This paper presents an energy-efficient event-driven clustering routing algorithm (EDC algorithm) based on unique features of the event-driven data model of WSNs. The algorithm can decide which nodes would become cluster-head nodes according to the maximum remainder energy of nodes, which are sensing an event occurred and are firstly switched to the active state if their several components are in the sleeping state. This strategy can keep sensor nodes with lower remainder energy out of being used up quickly. Detailed simulations of sensor network environments demonstrate that EDC algorithm saves node energy, prolongs system lifetime, and improves evenness of dissipated network energy.

We present a novel approach for automatic 3D face verification from range data. The method consists of range data registration and comparison. There are two steps in the registration procedure: a coarse step conducting normalization by exploiting a priori knowledge of the human face and facial features; a fine step aligning the input data with the model stored in the database by the partial directed Hausdorff distance. To speed up the registration, a simplified version of the model is generated for each model in the model database. During the face comparison, the partial Hausdorff distance is employed as the similarity metric. Experiments have been carried out on a database with 30 individuals, and the best EER of 3.24% is achieved.

This paper presents a novel approach to automatic speaker recognition using dynamic Bayesian network (DBN). DBNs have a precise and well-understood probabilistic semantics, and the ability to incorporate prior knowledge, to represent arbitrary non-linearities, and to handle hidden variables and missing data in a principled way with high extensibility. Experimental evaluation over YOHO corpus shows promising results compared to other classical methods.

In this paper, we presented a novel approach for automatic 3D face verification from range data. The method consists of range data registration and comparison. There are two steps in registration procedure: the coarse step conducting the normalization by exploiting a priori knowledge of the human face and facial features, and the fine step aligning the input data with the model stored in the database by the partial directed Hausdorff distance. To speed up the registration, a simplified version of the model is generated for each model in the model database. During the face comparison, the partial Hausdorff distance is employed as the similarity metric. The experiments are carried out on a database with 30 individuals and the best EER of 3.24% is achieved.

This paper presents a novel approach to automatic speaker recognition using dynamic Bayesian network (DBN). DBNs have a precise and well-understand probabilistic semantics, and it has the ability to incorporate prior knowledge, to represent arbitrary non-linearities, and to handle hidden variables and missing data in a principled way with high extensibility. Experimental evaluation over YOHO corpus shows promising results compared to other classical methods.

Human experience is a special kind of knowledge, which captures previously experienced, similar problem situations (case) for human problem solving. This paper describes a case-based approach for knowledge sharing and management with regard to the semantic web settings. We define a RDF-based Case Markup Language (CaseML) for experience knowledge representation and develop a generic architecture for the case based reasoning in the open, distributed environment with the emphasis on the integration of case knowledge with web ontologies. An application scenario from traditional Chinese medicine field is introduced to evaluate our methodology. With our application experience, we claim that, in contrast with logic-based knowledge representation formalisms, case is a more suitable for knowledge sharing in the semantic Web, because case based reasoning is similarity-based and there is weak demand on semantic consistent in such reasoning processes.

In this paper, we presented an approach for automatic face verification from range data. The method consists of profile and surface matching. The profile is extracted on the basis of symmetry of human face, and a global profile matching method based on k-th Hausdorff distance is used to align and compare profiles, without detection of fiducial points that is often unreliable. For each individual, a statistical model of facial surface is built to represent the distinct discriminative capability of the different parts in the facial surface. Then the model is incorporated into a weighted distance function to measure similarity of surfaces. Finally two experts are combined to give a decision. The comparable experimental results are obtained on a database with 180 pieces of range data of 30 individuals.

A novel approach to text-independent speaker recognition using a new classifier, called principal component space (PCS) is proposed in this work. This classifier uses the subspaces spanned by the principal components as the criteria. Together with other PCA classifier, it forms a hybrid classifier which is another technique presented here. All of these classifiers were applied to speaker recognition in particular on YOHO corpus. The experimental works show promising results.

The negotiation process is a necessary precondition for many wide-area based applications which involve interactions across multiple autonomous entities. In the case where database resources are explicitly exposed to the distributed computing context like the Grid, it is necessary for unauthorized database users to get authorized so that they can be granted with certain privileges to access the desirable database resources. The facility to establish a legal identity for database user is one of the functionalities that the database resource management framework should provide. In this paper, we give a handshaking protocol which is applied between database clients and database service providers to achieve the database-specific negotiation. It's a six-phased protocol, which is described in terms of service operations in our database resource management middleware, which is an OGSA compliant service-based system.

Semantic Web provides us with a new paradigm of Web based knowledge sharing and management. In this paper, an open knowledge service architecture (OKSA) is described, which emphasizes how to organize, discover, utilize, and manage web knowledge resources. Three kinds of knowledge service (KS) are identified, they are: OntoKS for retrieving, browsing and managing Web ontologies, RuleKS for facilitating the building of rule-based expert system and CaseKS for case-based experience knowledge management. These KSes provides value-added Web services from distributed Web knowledge resources. We use an application scenario from the traditional Chinese medicine area to clarify the design rationale of the KS model.

A grid architecture, called TCM-grid for traditional Chinese medicine, is presented. Based on the open grid service architecture (OGSA), we develop a set of database grid services for discovering and accessing TCM database resources remotely and transparently. Upon these database grid services, a set of knowledge services are also specified to support TCM knowledge sharing in the Web environment. With our application experience, we argue that OGSA be promoted to support finely granular data sharing and knowledge-intensive tasks.

This thesis presents a method of extracting a new speaker's voice features for the purpose of synthetically using the voice of the donor speaker. In the small speaker set, it seems good to recognize speaker by their voice by means of the traditional feature extraction. Nevertheless, the performance of recognizer usually depressed owning to the limited feature space, it is hard to deal with the increasing of speaker set to be recognized. Accordingly it proposes a novel feature extraction method, further feature extract (FFE), which is based on some measures such as weight, differential, combination and selection, are taken to explore those voice characteristics that can be used to distinguish different speakers. Experiment based on 138-person YOHO database demonstrates that better performance can be achieved by the proposed method.

In this paper we investigate the discriminative capability of facial profile extracted from range data for 3D face recognition. A robust symmetry plane detection method is proposed for profile extracting. A global profile matching approach based on k-th Hausdorff metric is presented to align and compare profiles, without detection of fiducial points that is often unreliable. The experiment are carried out on a low-quality database with 180 pieces of range data of 30 individuals acquired by a structured light system. Based on the experimental results, we observe that the profile is quite valuable information in 3D face recognition, and the best EER of 2.22% is achieved.

This paper, firstly, presents a new signature representation for point in range data, called curgram. Curgram serves to describe the structural neighborhood of a point and establish a signature for each of the given 3D data points rather than information of this point position. It yields invariance under rigid motions and mirror imaging. Secondly, its application to detection of facial features from range data is performed by incorporating the metric for curgram into the conventional appearance-based face detection framework. Experimental results with fifteen face range images have demonstrated the validity and effectiveness of the proposed method.

Expansions of mobile services and private data have required increased level of protection. Speaker recognition, one of the biometric technologies, arises lots of research interests for its simple, cheap and convenient characteristics. In this paper, a robust speaker recognition system which facilitates reliable authentication with multi-channel voices is presented. A large multi-channel corpus, including mobile phone, personal digital assistant (PDA), telephone and microphone, is collected to evaluate the system performance.

Knowledge Base Grid enables worldwide sharing and coordinated using of knowledge base resources, which distributed around the Internet. By constructing large-scale web knowledge bases, Knowledge Base Grid supports on-demand and intelligent services satisfying personal needs and expectations such as personal health consultation. In this paper, we introduce a generic knowledge base grid for Traditional Chinese Medicine (TCM). Its framework consists of three main parts: Virtual Open Knowledge Base in which TCM knowledge has been well organized, Knowledge Base Index that indexes the TCM knowledge services, and a Semantic Browser that supports TCM concepts browsing and TCM knowledge management. The implementation of TCM Knowledge Base Grid includes TCM knowledge representation and service hierarchy.

Knowledge Base Grid realizes worldwide sharing, discovery, and coordinated using of knowledge base resources. Ontology acts as the public standard and interchange format in Knowledge Base Grid. One of the most important effects of ontology is that it makes the grid reasoning possible. In this paper, we introduce the Distributed Petri Net, which is used to enable Knowledge Base Grid reasoning. Distributed Petri Net is the improvement of the basic Petri Net. A URI is added into the Petri Net model, which connects the distributed sub Petri Nets of reasoning. In the option of Distributed Petri Net, the Knowledge Base Grid is a hierarchical network. Distributed Petri Net models the Knowledge Base Grid at a high abstract level, therefore it fits not only the entire network model but also part of it, which is also a hierarchical sub-network.

In general, cepstra are used to manifest the shape of the vocal tract. But it ignores the various stochastic errors of the vocal tract, which can be observed from different speech signals is obtained from the same word pronounced repeatedly even if ignoring the noise and the speaking rate. Now we make use of a method to eliminate the various stochastic errors to some extent, and the feature vectors can represent the essential characteristics of the vocal tract more effectively. In this paper, we analyze the speech-production process and utilize a segment model to implement the elimination of the various stochastic errors. The experiments show: besides the elimination of the stochastic errors, the proposed method also can remove the noise in the environment.

In a speaker identification system, training speaker models (e.g. Gaussian mixture model, GMM) is computationally expensive, especially when the dimension of feature vectors is large. Principal component analysis (PCA) method is an optimal linear dimension reduction technique in the mean-square sense, which can reduce the computational overhead of the subsequent processing stages. In this paper, a new speaker identification framework is proposed, with PCA embedded in after feature extraction step. Experiments are conducted to investigate PCA de-correlation and dimension reduction properties. The robust ability of PCA transform is also examined. Some promising results are found.

The few-color images are often synthetic graphics without complicated color and texture variation, which makes the embedding of invisible digital watermark difficult. This paper proposes a data hiding method that can hide a moderate amount of data in a few-color image, such as cartoon images, binary images, without introducing noticeable artifacts. To achieve least visual quality reduction, the prioritized pattern matching scheme is employed to embed the invisible data in the pixels those are close to the boundaries. The block permutation is also exploited before embedding. No additional color is introduced and the palette keeps unchanged after embedding. Extracting of the hidden data does not require the knowledge of the original image. The experimental results show that the approach achieves a quite great performance in visibility transparency. It is applicable to invisible annotation, covert communication etc.

There are many cases where an organization needs to monitor the data and operations of its supervised departments, especially un-owned departments, which are managed by its own information systems. A distributed heterogeneous inspecting system (DHIS) is a system the organization uses to monitor its supervised departments by inspecting their information systems. In DHIS, the inspected systems are generally distributed, heterogeneous, and constructed by different companies. DHIS has three key processes-to abstract core data sets and core operation sets, to collect these sets, and to inspect these collected sets. We present the concept of DHIS, mathematical definition of DHIS, a metadata method to solve the interoperability, security strategy for data transfer, and a middleware-based solution of DHIS. We also describe an example of the inspecting system at Wenzhou custom.

A new classification using support vectors is presented. Support vector machines that learn classification problem are specific to use hyperplane. We propose a novel approach that contains support vectors describing the hypersphere to separate the samples. We also generalize it in multi-class classification phrase. The experiment results on UCI datasets are presented.

With the amount of information exploding, the number of databases and information stored in the database increases very quickly. Those databases are more likely to be heterogeneous. There are many distributed databases systems to resolve this problem, but the main drawback is that they don't do well in database cooperation. We provide a general architecture of a distributed multidatabase query system: TCMMDB (traditional Chinese medicine multidatabase). We discuss key techniques such as distributed query plan generation, distributed query synchronization and distributed error handling in TCMMDB in relation to distributed databases.