Color constancy methods often struggle to generalize across different camera sensors due to varying spectral sensitivities. We present GCC, which leverages diffusion models to inpaint color checkers into images for illumination estimation. Our key innovations include (1) a single-step deterministic inference approach that inpaints color checkers reflecting scene illumination, (2) a Laplacian decomposition technique that preserves checker structure while allowing illumination-dependent color adaptation, and (3) a mask-based data augmentation strategy for handling imprecise color checker annotations. By harnessing rich priors from pre-trained diffusion models, GCC demonstrates strong robustness in challenging cross-camera scenarios. These results highlight our method’s effective generalization capability across different camera characteristics without requiring sensor-specific training, making it a versatile and practical solution for real-world applications.

We present SpectroMotion, a novel approach that combines 3D Gaussian Splatting (3DGS) with physically-based rendering (PBR) and deformation fields to reconstruct dynamic specular scenes. Previous methods extending 3DGS to model dynamic scenes have struggled to represent specular surfaces accurately. Our method addresses this limitation by introducing a residual correction technique for accurate surface normal computation during deformation, complemented by a deformable environment map that adapts to time-varying lighting conditions. We implement a coarse-to-fine training strategy significantly enhancing scene geometry and specular color prediction. It is the only existing 3DGS method capable of synthesizing photorealistic real-world dynamic specular scenes, outperforming state-of-the-art methods in rendering complex, dynamic, and specular scenes. Please see our project page at cdfan0627.github.io/spectromotion.

With the rising demand for parking spaces, effective management and allocation of parking resources have become increasingly crucial. Traditional intelligent transportation systems often rely on various sensors to gather parking space information, which is then transmitted to centralized servers for data analysis and storage. This approach poses security risks, such as single points of failure and system strain during peak periods, due to increased data transmission and processing. Additionally, the surge in user activity can degrade system performance. To address these issues, this article introduces a parking space search system that records parking information on a blockchain to ensure data integrity and incorporates fine-grained permission management. By utilizing threshold attribute-based encryption, our approach effectively reduces system burden during peak periods. Experimental results show that our system achieves a delay reduction ranging from 22% to 85.6% compared to the current schemes as the number of vehicles increases. These results underscore the practical security and feasibility of our proposed solution.

Live stream platforms have gained popularity in light of emerging social media platforms. Unlike traditional on-demand video platforms, viewers and streamers on the live stream platforms are able to interact in real-time, and this makes viewer interests and live stream topics mutually affect each other on the fly, which is the unique co-evolution phenomenon on live stream platforms. In this paper, we make the first attempt to introduce a novel next-topic recommendation problem for the streamers, LSNR, which incorporates the co-evolution phenomenon. A novel framework CENTR introducing the Co-evolutionary Sequence Embedding Structure that captures the temporal relations of viewer interests and live stream topic sequences with two stacks of self-attention layers is proposed. Instead of learning the sequences individually, a novel dual self-attention mechanism is designed to model interactions between the sequences. The dual self-attention includes two modules, LCA and LVA, to leverage viewer loyalty to improve efficiency and flexibility. Finally, to facilitate cold-start recommendations for new streamers, a collaborative diffusion mechanism is implemented to improve a meta learner. Through the experiments in real datasets, CENTR outperforms state-of-the-art recommender systems in both regular and cold-start scenarios.

The document transfer and goods delivery process in the traditional international trade process require a lot of manpower and is inefficient. Also, the accidents like damage of goods and tampering of documents may cause the difficulties in attribution of liability. In recent years, there have been related studies analyzing the characteristics of blockchain and further exploring its applications in the logistics industry. But its applications in international trade are seldom discussed. Thus, we develop a blockchain-based logistic platform for international trade on top of blockchain to simplify the process, thereby showing the potential application of the blockchain technology in the logistics industry.

To facilitate successive Point-of-Interests (POI) recommendation, the categories of POIs and the regions where POIs are located are seldom considered in existing models. In view of this, we extend a state-of-the-art model SPENT, named SPENT+, by taking the category and the region into considerations. In SPENT+, we formulate category- and region-aware check-in sequences, design the similarity trees to aggregate similar features, and finally establish the category latent vectors and region latent vectors, respectively. The above two latent vectors are aggregated as the category-region-aware latent vectors. Therefore, the category-region-latent vectors are sent to an LSTM together with conventional check-in sequences to improve successive POI recommendation. We conduct two real datasets, Gowalla and Foursquare, and compare with state-of-the-art methods in experiments. Results show that SPENT+ outperforms the baselines in terms of precision and recall.

As the popularity of cloud platforms and container grows rapidly, managing clouds has become an important issue. For example, failed containers on cloud platforms would trigger automatic restart mechanism. However, the failed containers caused by user error are not fixable by restart, and may lead to the loop between failure and restart. Therefore, the looping failure will harm the overall performance of cloud. In this paper, we propose to identify possible container failures, where the utility behavior of containers (e.g., CPU usage, GPU usage, I/O throughput, etc) are factored in, in a machine learning approach. We propose a light-weight neural network EEGNet-SE to support fast inference in real-time. In addition, EEGNet-SE is able to distinguish dynamic relations between each utility for different tasks. We conduct a real cloud container dataset from Taiwan Cloud Computing (TWCC) platform. Experimental results manifest that EEGNet-SE boosts the performance and efficiency simultaneously, and outperforms the other state-of-the-art methods in terms of accuracy.

As the coming maturity of Internet of Things, many home-networked devices with various sensors are deployed in residential environments. It is important to accommodate many devices that compete for bandwidth allocation to transmit data inwards and outwards through a certain home network domain. This paper proposes a novel mechanism which can adjust media stream qualities and transmission bandwidth allocations to different devices. This mechanism is agile against dynamic changes of data workload and finite network resources between inside and outside of a home network. Practical demonstration exhibits that the effects are able to maintain differentiated media transfer services in IoT-based home networks.

In contrast to traditional online videos, live multi-streaming supports real-time social interactions between multiple streamers and viewers, such as donations. However, donation and multi-streaming channel recommendations are challenging due to complicated streamer and viewer relations, asymmetric communications, and the tradeoff between personal interests and group interactions. In this paper, we introduce Multi-Stream Party (MSP) and formulate a new multi-streaming recommendation problem, called Donation and MSP Recommendation (DAMRec). We propose Multi-stream Party Recommender System (MARS) to extract latent features via socio-temporal coupled donation-response tensor factorization for donation and MSP recommendations. Experimental results on Twitch and Douyu manifest that MARS significantly outperforms existing recommenders by at least 38.8% in terms of hit ratio and mean average precision.

With the rapid growth of blockchain, more and more people possess cryptocurrencies or use decentralized applications (Dapp). A wallet in blockchain not only stores your assets but also represents your online identity. Nowadays, document notary using blockchain is getting mature to prevent the problem of the counterfeit certificate. In this paper, we integrate a cellphone-based wallet with a novel visional certificate verification system. By the trusted execution environment (TEE) protection, a user can isolate his/her private key without the leakage attack. Two-factor authentication is required to generate a signature. Different from existing platforms, our system adopts IPFS, a P2P network for storing and sharing data, for storing the certificate image, which offers the function of visional verification for better persuasiveness. Furthermore, the unique time-limited verification can restrict the accessing period of the verifier for better privacy protection. As a result, our verification system provides several promising features to enhance security and privacy strength.

In this article, we propose a novel temporal pattern mining problem, named high-utility temporal pattern mining , to fulfill the needs of various applications. Different from classical temporal pattern mining aimed at discovering frequent temporal patterns, high-utility temporal pattern mining is to find each temporal pattern whose utility is greater than or equal to the minimum-utility threshold. To facilitate efficient high-utility temporal pattern mining, several extension and pruning strategies are proposed to reduce the search space. Algorithm HUTPMiner is then proposed to efficiently mine high-utility temporal patterns with the aid of the proposed extension and pruning strategies. Experimental results show that HUTPMiner is able to prune a large number of candidates, thereby achieving high mining efficiency.

Computer vision based object tracking has been used to annotate and augment sports video. For automatically and systematically competition data collection and tactical analysis. The proposed project also includes research of data visualization, connected training auxiliary devices, and data warehouse. Deep learning techniques will be used to develop video-based real-time microscopic competition data collection based on broadcast competition video. Machine learning techniques will be used to develop tactical analysis. In addition, training auxiliary devices including smart badminton rackets and connected serving machines will be developed based on the IoT technology to further utilize competition data and tactical data and boost training efficiency. Especially, the connected serving machines will be developed to perform specified tactics and to interact with players in their training.

With the rise of blockchain technology, peer-to-peer network system has once again caught people's attention. Peer-to-peer (P2P) is currently being implemented on various kind of decentralized systems such as InterPlanetary File System (IPFS). However, P2P file sharing network systems is not without its flaws. Data stored in the other nodes cannot be deleted by the owner and can only be deleted by other nodes themselves. Ensuring that personal data can be completely removed is an important issue to comply with the European Union's General Data Protection Regulation (GDPR) criteria. To improve P2Ps privacy and security, we propose a monitorable peer-to-peer file sharing mechanism that synchronizes with other nodes to perform file deletion and to generate the File Authentication Code (FAC) of each IPFS nodes in order to make sure the system synchronized correctly. The proposed mechanism can integrate with a consortium Blockchain to comply with GDPR.

With advance of IoT (Internet of Things) technology, many manufacturers install several sensors to monitor the status of machines and the health of the whole manufacturing process. In addition, the sensed data are usually transmitted to a backend database for further analysis. However, the dramatic volume of data sensed by the sensors causes the problem of huge storage requirement and network traffic for the small medium manufacturers which have limited resource and budget in IT (Information Technology). To deal with this problem, we design a two-layered architecture using compression technique to reduce the network traffic. In addition, we use MongoDB, a NoSQL database, to store the compressed data due to MongoDB's excellent scale-out ability and cost-efficiency. We conduct several experiments to measure the performance of the proposed architecture with several compression methods. Experimental results show that with proper lossless compression method, the reduction ratio of the volume of the data is around 80% at the cost of slight increase in execution time.

Due to the advance in wireless communication technologies, smartphones and tablets are becoming part of our life. People can use mobile devices to take pictures to share their life with others. In consideration of privacy and usability, this paper focuses on developing a private platform for content sharing in home. To facilitate content management and fast content retrieval, we design a Content-Centric Platform (CCP) that allows users to manage and search contents according to the tags (or attributes) of contents. With CCP, family members can access shared contents by the tags of contents without knowing the exact physical locations of the contents. The CCP platform has an auto-tagging subsystem that can add annotated tags automatically for the shared contents. It then applies a data mining technique to analyze the tags of the contents and creates a Tag Tree to represent the association of contents based on the tags. Furthermore, CCP also adopts a Tag Cloud mechanism to guide users to retrieve contents of interest. The functionalities provided by CCP include device joining and leaving, content information collection and distribution, auto-tagging and tag analysis. Finally, we have implemented a prototype system to verify the effectiveness of the CCP for content sharing in home networks.

Recent technological advent in virtual reality (VR) has attracted a lot of attention to the VR shopping, which thus far is designed for a single user. In this paper, we envision the scenario of VR group shopping, where VR supports: 1) flexible display of items to address diverse personal preferences, and 2) convenient view switching between personal and group views to foster social interactions. We formulate the Multiview-Enabled Configuration Recommendation (MECR) problem to rank a set of displayed items for a VR shopping user. We design the Multiview-Enabled Configuration Ranking System (MEIRS) that first extracts discriminative features based on Marketing theories and then introduces a new coupled tensor factorization model to learn the representation of users, Multi-View Display (MVD) configurations, and multiple feedback with content features. Experimental results manifest that the proposed approach outperforms personalized recommendations and group recommendations by at least 30.8% in large-scale datasets and 63.3% in the user study in terms of hit ratio and mean average precision.

With the popularization of mobile devices and wireless networks, people are able to share their experience on points of interest (POIs) in social networks through "check-ins." Therefore, the problem of successive POI recommendation has been proposed to recommend some POIs to users so that the users are likely to check in at these POIs in the near future. In this paper, we propose a two-phase method to solve the problem of successive POI recommendation. First, we utilize the Matrix Factorization technique to analyze the interaction of users and their sequential check-in behavior with time influence and POI categories, and select the candidate categories that the user will visit. Then, after removing those POIs not belonging to the candidate categories, we fuse user preferences, temporal influence and geographical influence together and finally recommend the POIs with high scores to users. The experimental results on a real check-in dataset show that our recommendation method is better than several state-of-the-art methods in terms of precision and recall.

Thanks to the growth of cloud computing and network technology, different organizations might want to share data and resources between each other. However, cross-organizational authentication systems usually need a central control system, which must be fully trusted. Thus, we use blockchain technology to store the access control list of users due to its tamper-proof and decentralized feature. Our system also provides authentication/authorization/accounting functions by using a virtual coin exe_coin to achieve accounting function. The method of one-way hash chain is used to securely adapt to the transparency feature of blockchain. In authentication system, the transparency may lead to the linkability problem. In our scheme, attackers cannot get the linkability between the transactions and the particular user. To the best of our knowledge, our scheme is the first blockchain-based authentication system with the merits of unlinkability and accounting.

The eHealth trend has spread globally. Internet of Things (IoT) devices for medical service and pervasive Personal Health Information (PHI) systems play important roles in the eHealth environment. A cloud-based PHI system appears promising but raises privacy and information security concerns. We propose a cloud-based fine-grained health information access control framework for lightweight IoT devices with data dynamics auditing and attribute revocation functions. Only symmetric cryptography is required for IoT devices, such as wireless body sensors. A variant of ciphertext-policy attribute-based encryption, dual encryption, and Merkle hash trees are used to support fine-grained access control, efficient dynamic data auditing, batch auditing, and attribute revocation. Moreover, the proposed scheme also defines and handles the cloud reciprocity problem wherein cloud service providers can help each other avoid fines resulting from data loss. Security analysis and performance comparisons show that the proposed scheme is an excellent candidate for a cloud-based PHI system.

High utility sequential pattern (HUSP) mining is an emerging topic in pattern mining, and only a few algorithms have been proposed to address it. In practice, most sequence databases usually grow over time, and it is inefficient for existing algorithms to mine HUSPs from scratch when databases grow with a small portion of updates. In view of this, we propose the IncUSP-Miner + algorithm to mine HUSPs incrementally. Specifically, to avoid redundant re-computations, we propose a tighter upper bound of the utility of a sequence, called Tight Sequence Utility (TSU), and then we design a novel data structure, called the candidate pattern tree, to buffer the sequences whose TSU values are greater than or equal to the minimum utility threshold in the original database. Accordingly, to avoid keeping a huge amount of utility information for each sequence, a set of concise utility information is designed to be stored in each tree node. To improve the mining efficiency, several strategies are proposed to reduce the amount of computation for utility update and the scopes of database scans. Moreover, several strategies are also proposed to properly adjust the candidate pattern tree for the support of multiple database updates. Experimental results on some real and synthetic datasets show that IncUSP-Miner + is able to efficiently mine HUSPs incrementally.

We propose in this paper a classification-based elephant flow detection method for SDN environments. The proposed elephant flow detection method consists of two classifiers running on the switch and the controller, respectively. For better performance, the concept of application round is used to build the classifier running on the controller. Experimental results show that our elephant flow detection method is able to obtain high recall and F-measure.

In Real-Time Bidding (RTB) advertising, estimating the winning price is an important task in evaluating the bid cost of bid requests in Demand-Side Platforms (DSPs). The prior works utilize censored linear regression for winning price estimation by considering both winning and losing bid records. In the traditional regression models, the winning price of each bid request is based on Gaussian distribution. However, the property of Gaussian distribution is not suitable for the winning price of each bid request, and it is hard to link the physical meaning of Gaussian distribution and the winning price. Therefore, in this paper, based on our observation and analysis, the winning price of each bid request is modeled by a unique gamma distribution with respect to its features. Then we propose a gamma-based censored linear regression with regularization for winning price estimation. To derive the parameters of our proposed complicated model based on bid records, our approach is to divide this hard problem into two sub-problems, which are easier to solve. In practice, we also provide four heuristic initial parameter settings that are able to greatly reduce the computation cost when deriving the parameters. The experimental results demonstrate that our approach is highly effective for estimating the winning price compared with the state-of-the-art approaches in three real datasets.

Online advertising has been all the rage these years. Budget control and traffic prediction turn out to be important issues for the demand-side platforms (DSPs). However, DSPs cannot easily grab the information of audiences and media platforms. Although DSPs might have the information immediately, it is still hard to response the request of advertisements in real-time due to the high volume of features. Therefore, we propose a method predicting traffic of requests from perspective of DSPs. The features we used are simple to be extracted from historical data. The prediction model we chose is regression model with closed-form solution. Both the features and regression model make our prediction adaptive in real-time systems. Our method can detect traffic anomalies and prevent it from overwhelming prediction. Moreover, our method can also keep pace of the trend. Experiment results show that our method's error rate of prediction is about 0.9% in total, and 10% per time unit.

High utility sequential pattern (HUSP) mining is an emerging topic in pattern mining, and only a few algorithms have been proposed to address it. In practice, most sequence databases usually grow over time, and it is inefficient for existing algorithms to mine HUSPs from scratch when databases grow with a small portion of updates. In view of this, we propose the IncUSP-Miner algorithm to mine HUSPs incrementally. Specifically, to avoid redundant computations, we propose a tighter upper bound of the utility of a sequence, called TSU, and then design a novel data structure, called the candidate pattern tree, to maintain the sequences whose TSU values are greater than or equal to the minimum utility threshold. Accordingly, to avoid keeping a huge amount of utility information for each sequence, a set of auxiliary utility information is designed to be stored in each tree node. Moreover, for those nodes whose utilities have to be updated, a strategy is also proposed to reduce the amount of computation, thereby improving the mining efficiency. Experimental results on three real datasets show that IncUSP-Miner is able to efficiently mine HUSPs incrementally.

In this paper, we study a novel type of spatial queries, namely Nearest Window Cluster (NWC) queries. For a given query location q, NWC (q; l; w; n) retrieves n objects within a window of length l and width w, where the distance between the query location q to these n objects is the shortest. To facilitate efficient NWC query processing, we identify several properties and accordingly develop an NWC algorithm. Moreover, we propose several optimization techniques to further reduce the search cost. To validate our ideas, we conduct a comprehensive performance evaluation using both real and synthetic datasets. Experimental results show that the proposed NWC algorithm, along with the optimization techniques, is very efficient under various datasets and parameter settings.

Owing to the great advent of sensor technology, the usage data of appliances in a house can be logged and collected easily today. However, it is a challenge for the residents to visualize how these appliances are used. Thus, mining algorithms are much needed to discover appliance usage patterns. Most previous studies on usage pattern discovery are mainly focused on analyzing the patterns of single appliance rather than mining the usage correlation among appliances. In this article, a novel algorithm, namely Correlation Pattern Miner (CoPMiner), is developed to capture the usage patterns and correlations among appliances probabilistically. CoPMiner also employs four pruning techniques and a statistical model to reduce the search space and filter out insignificant patterns, respectively. Furthermore, the proposed algorithm is applied on a real-world dataset to show the practicability of correlation pattern mining.

In several applications, sequence databases generally update incrementally with time. Obviously, it is impractical and inefficient to re-mine sequential patterns from scratch every time a number of new sequences are added into the database. Some recent studies have focused on mining sequential patterns in an incremental manner; however, most of them only considered patterns extracted from time point-based data. In this paper, we proposed an efficient algorithm, Inc_TPMiner, to incrementally mine sequential patterns from interval-based data. We also employ some optimization techniques to reduce the search space effectively. The experimental results indicate that Inc_TPMiner is efficient in execution time and possesses scalability. Finally, we show the practicability of incremental mining of interval-based sequential patterns on real datasets.

Vehicular ad hoc networks (VANETs) are an emerging wireless network technology used to improve road safety. Commercial services will play an important role in drawing customers to VANETs. Therefore, service-oriented vehicular networks offer an effective and promising approach. To meet the diverse requirements of different users, fine-grained access control is essential. This paper aims to address security, privacy and billing issues in service-oriented vehicular networks. Taking advantage of a portable electronic currency, the proposed scheme mitigates the long authentication delay of the centralized AAA architecture. Variant attribute-based encryption ensures fine-grained access control and secure billing. Only vehicles possessing the proper service attributes and valid electronic currency are authorized to access the requested service file. The security properties of entity authentication, session key agreement, privacy, fraud electronic currency prevention, double-spending prevention, and nonrepudiated billing are achieved. Extensive analysis and simulations demonstrate that our scheme is a viable candidate to replace a centralized AAA architecture with a decentralized method for better scalability in service-oriented vehicular networks.

In this paper, we propose a novel integrated API service system as well as a Web API script to enable mobile mashup application developers to invoke Web APIs in a consistent way, and provide a consistent format for the responses of Web APIs, thereby relieving developers of striving to study the usage of various Web APIs.

Caching valid regions of spatial queries at mobile clients is effective in reducing the number of queries submitted by mobile clients and query load on the server. However, mobile clients suffer from longer waiting time for the server to compute valid regions. We propose in this paper a proxy-based approach to continuous nearest-neighbor (NN) and window queries. The proxy creates estimated valid regions (EVRs) for mobile clients by exploiting spatial and temporal locality of spatial queries. For NN queries, we devise two new algorithms to accelerate EVR growth, leading the proxy to build effective EVRs even when the cache size is small. On the other hand, we propose to represent the EVRs of window queries in the form of vectors, called estimated window vectors (EWVs), to achieve larger estimated valid regions. This novel representation and the associated creation algorithm result in more effective EVRs of window queries. In addition, due to the distinct characteristics, we use separate index structures, namely EVR-tree and grid index, for NN queries and window queries, respectively. To further increase efficiency, we develop algorithms to exploit the results of NN queries to aid grid index growth, benefiting EWV creation of window queries. Similarly, the grid index is utilized to support NN query answering and EVR updating. We conduct several experiments for performance evaluation. The experimental results show that the proposed approach significantly outperforms the existing proxy-based approaches.

Current mobile pay-TV systems have two types of Conditional Access Systems (CAS): group-key-based and public-key systems. The best feature of group-key-based systems is the ability to enjoy the broadcast nature in delivery multimedia contents, while the major advantage of public-key systems is consolidating the security foundation to withstand various attacks, such as collusion attacks. However, the problems of group-key-based systems include collusion attacks, lack of nonrepudiation, and troublesome key distribution. Even worse, the benefit of broadcast efficiency is confined to a group size of no more than 512 subscribers. For public-key systems, the poor delivery scalability is the major shortcoming because the unique private key feature is only suitable for one-to-one delivery. In this article, we introduce a scalable access control scheme to integrate the merits of broadcasting regardless of group size and sound security assurance, including fine-grained access control and collusion attack resistance. For subscriber revocation, a single message is broadcast to the other subscribers to get the updated key, thus significantly boosting subscriber revocation scalability. Due to mobile subscribers' dynamic movements, this article also analyzes the benefit of retransmission cases in our system. Through the performance evaluation and functionality comparison, the proposed scheme should be a decent candidate to enhance the security strength and transmission efficiency in a mobile pay-TV system.

Score reduction is a process that arranges music for a target instrument by reducing original music. In this study we present a music arrangement framework that uses score reduction to automatically arrange music for a target instrument. The original music is first analyzed to determine the type of arrangement element of each section, then the phrases are identified and each is assigned a utility according to its type of arrangement element. For a set of utility-assigned phrases, we transform the music arrangement into an optimization problem and propose a phrase selection algorithm. The music is arranged by selecting appropriate phrases satisfying the playability constraints of a target instrument. Using the proposed framework, we implement a music arrangement system for the piano. An approach similar to Turing test is used to evaluate the quality of the music arranged by our system. The experiment results show that our system is able to create viable music for the piano.

In this paper, we propose an Attribute-Based Access Control System (ABACS) for emergency services with security assurance over Vehicular Ad Hoc Networks (VANETs). ABACS aims to improve the efficiency of rescues mobilized via emergency communications over VANETs. By adopting fuzzy identity-based encryption, ABACS can select the emergency vehicles that can most appropriately deal with an emergency and securely delegate the authority to control traffic facilities to the assigned emergency vehicles. Using novel cryptographic preliminaries, ABACS realizes confidentiality of messages, prevention of collusion attacks, and fine-grained access control. As compared to the current PKI scheme, the computational delay and transmission overhead can be reduced by exploiting the advantages afforded by message broadcasting, which is heavily used in ABACS. The performance evaluation demonstrates that ABACS is a suitable candidate for realizing emergency services via VANETs.

Abstract The use of anonymous channel tickets was proposed for authentication in wireless environments to provide user anonymity and to probably reduce the overhead of re‐authentications. Recently, Yanget al.proposed a secure and efficient authentication protocol for anonymous channel in wireless systems without employing asymmetric cryptosystems. In this paper, we will show that Yanget al.'s scheme is vulnerable to guessing attacks performed by malicious visited networks, which can easily obtain the secret keys of the users. We propose a new practical authentication scheme not only reserving the merits of Yanget al.'s scheme, but also extending some additional merits including: no verification table in the home network, free of time synchronization between mobile stations and visited networks, and without obsolete anonymous tickets left in visited networks. The proposed scheme is developed based on a secure one‐way hash function and simple operations, a feature which is extremely fit for mobile devices. We provide the soundness of the authentication protocol by using VO logic. Copyright © 2010 John Wiley & Sons, Ltd.

Traditional routing protocols in wireless mesh networks establish a single data path between a source and a destination. However, the performance suffers from degradation and instability due to variation of wireless links and interference. Opportunistic routing has been proposed to overcome the problem by exploiting the broadcast nature of wireless links and multiple forwarders. Current opportunistic routing protocols with network coding improve the system performance but suffer from redundant packet transmissions because a source stops transmitting packets in the same segment until it receives an ACK message from the destination. In this paper, we propose a pipelined opportunistic routing protocol with network coding (PipelineOR) in which segments are transmitted in a pipelined manner. A source is allowed to send packets in the next segment without waiting for the ACK message from the destination. In addition, we present two mechanisms, namely ETX-based mechanism and DCounter-based mechanism, to suppress forwarders to transmit redundant packets. The simulation results show that the proposed PipelineOR outperforms MORE [7] and CodeOR [8] in terms of transmission time.

In most inertial motion tracking systems, motion directions are detected and measured by direction sensors such as magnetometers and gyroscopes. In this paper, we propose a motion tracking system, called g-sensor constellations, in which only g-sensors but no direction sensors are used. The g-sensor constellation is a loose coupling g-sensor system with rigid geometric topology. As few as three g-sensors are needed for motion tracking, including direction detection. The system is easy to be installed. No complicated calibrations are needed and the necessary information is the distances between sensors. The proposed framework can improve the accuracy of dead reckoning systems and help in the analyzing of traffic accidents and developing new human-computer interfaces. In our experiments, a g-sensor constellation composed of three g-sensors, which are located at the vertices of an equilateral triangle with edges of 0.3m and communicate with the processing unit via Bluetooth links, is built to verify the proposed technique.

We in this paper propose a new concept, repeating patterns with gap constraint, to make repeating patterns tolerate the delay of events. To mine repeating patterns with gap constraint, we first show the anti-monotonic property of repeating patterns with gap constraint and then propose a level-wise algorithm, named G-Apriori (standing for Gap with Apriori), based on the anti-monotonic property. Similar to other level-wise mining algorithms such as Apriori, algorithm G-Apriori will scan databases several times to count the number of occurrences of each candidate repeating pattern. Such phenomenon makes G-Apriori spend much time in disk I/O, thereby making G-Apriori not suitable for large databases. In view of this, we develop an index structure to record the positions of the occurrences of each repeating pattern, and then propose algorithm GwI-Apriori (standing for gap with index Apriori) to utilize the index structure to reduce the number of database scans when mining repeating patterns with gap constraint. The experimental results show that algorithm GwI-Apriori is more scalable than algorithm G-Apriori in terms of execution time.

Mining repeating patterns from music data is one of the most interesting issues of multimedia data mining. However, less work are proposed for mining polyphonic repeating patterns. Hence, two efficient algorithms, A-PRPD (Apriori-based Polyphonic Repeating Pattern Discovery) and T-PRPD (Tree-based Polyphonic Repeating Pattern Discovery), are proposed to discover polyphonic repeating patterns from music data. Furthermore, a bit-string method is developed for improving the efficiency of the proposed algorithms. Experimental results show that the proposed algorithms, A-PRPD and T-PRPD, are both effective and efficient methods for mining polyphonic repeating patterns from synthetic music data and real data.

Piano reduction is a process that arranges music for the piano by reducing the original music into the most basic components. In this study we present an automatic arrangement system for piano reduction that arranges music algorithmically for the piano while considering various roles of the piano in music. We achieve this by first analyzing the original music in order to determine the type of arrangement element performed by an instrument. Then each phrase is identified and is associated with a weighted importance value. At last, a phrase selection algorithm is proposed to select phrases with maximum importance to arrangement under the constraint of piano playability. Our experiments demonstrate that the proposed system has the ability to create piano arrangement.

We propose in this paper a design of the proxy supporting nearest-neighbor queries. We also propose several algorithms to create and extend estimate valid regions. An estimate valid regions degradation algorithm and a cache replacement policy are proposed to reclaim cache space when the cache space is insufficient. To evaluate the performance of the proposed architecture and algorithms, several experiments are conducted. The experimental results show that the our design and algorithms are able to improve proxy hit rate and reduce average waiting time.

Since only a modest improvement in battery lifetime is expected in the next few years, energy conservation is raised as a key factor in the design of mobile devices. In view of this, we propose in this paper an energy-conserving on-demand data broadcasting system that employs the data indexing technique. Different from prior work, the power consumption of turning on and turning off the wireless network interfaces is considered. In addition, we also employ a server cache to reduce the effect of the time to retrieve data items from the corresponding data servers. Specifically, we first analyze the access and tuning times of data requests, and propose an adaptive index and data organizing algorithm (AIDOA) to adjust the degree of buckets according to the system workload. Several experiments are then conducted to evaluate the performance of algorithm AIDOA. The experimental results show that algorithm AIDOA is able to greatly reduce the power consumption at the cost of a slight increase in the average access time and dynamically adjust the index and data organization to adapt to the change of system workload.

In recent years, TinyOS and nesC are gradually becoming the de facto software development platform for implementing sensor applications. However, developing sensor applications is difficult for programmers since the programming paradigm used in nesC is different from that used in other popular programming languages. In view of this, we propose in this paper a virtual machine-based programming environment for rapid sensor application development. With the proposed programming environment, sensor applications are implemented in TinyJava, which is a subset of Java programming language. Developing sensor applications is very similar as developing a traditional Java applications. Therefore, we believe that the proposed programming environment is able to speedup the development of sensor applications.

Most research works in transcoding proxies in mobile computing environments are on the basis of the traditional client-server architecture and do not employ the data broadcast technique. In addition, the issues of QoS provision and energy conservation are also not addressed in the prior studies. In view of this, we design in this paper a QoS-aware and energy-conserving transcoding proxy by utilizing the on-demand broadcasting technique. We first propose a QoS-aware and energy-conserving transcoding proxy architecture, abbreviated as QETP, and model it as a queuing network consisting of three queues. By analyzing the queuing network, three lemmas are derived to estimate the load of these queues. We then propose a version decision policy and a service admission control scheme to provide QoS in QETP. The derived lemmas are used to guide the execution of the proposed version decision policy and service admission control scheme to achieve the given QoS requirement. In addition, we also propose a data indexing method to reduce the power consumption of clients. To measure the performance of the proposed architecture, three experiments are conducted. Experimental results show that the average access time reduction of the proposed scheme over the traditional client-server architecture ranges from 45 percent to 75 percent. Experimental results also show that the proposed scheme is more scalable than the traditional client-server architecture and is able to effectively control the system load to attain the given QoS requirements. In addition, the proposed scheme is able to greatly reduce the average tuning time of clients at the cost of a slight increase (around 5 percent in our experiments) in average access time.

We propose in this paper a two-phase algorithm, named algorithm layered-cutting, to address the problem of broadcast program generation in a multisystem heterogeneous overlayed wireless network. The experimental results show that algorithm layered-cutting is able to efficiently generate broadcast programs of high quality for a multisystem heterogeneous overlayed wireless network

The growth in wireless communication technologies attracts a considerable amount of attention in mobile ad hoc networks. Since mobile hosts in an ad hoc network usually move freely, the topology of the network changes dynamically and disconnection occurs frequently. These characteristics make it likely for a mobile ad hoc network to be separated into several disconnected partitions, and the data accessibility is hence reduced. Several schemes are proposed to alleviate the reduction of data accessibility by replicating data items. However, little research effort was elaborated upon exploiting the group mobility where the group mobility refers to the phenomenon that several mobile nodes tend to move together. In this paper, we address the problem of replica allocation in a mobile ad hoc network by exploring group mobility. We first analyze the group mobility model and derive several theoretical results. In light of these results, we propose a replica allocation scheme to improve the data accessibility. Several experiments are conducted to evaluate the performance of the proposed scheme. The experimental results show that the proposed scheme is able to not only obtain higher data accessibility, but also produce lower network traffic than prior schemes.

In a mobile computing environment, the combined use of broadcast and on-demand channels can utilize the bandwidth effectively for data dissemination. We explore in this paper the problem of dynamic data and channel allocation with the number of communication channels and the number of data items given. We first derive the analytical models of the average access time when the data items are requested through the broadcast and on-demand channels. Then, we transform this problem into a guided search problem. In light of the theoretical properties derived, we devise algorithm SOM to obtain the optimal allocation of data and channels. Algorithm SOM is a composite algorithm which will cooperate with 1) a search strategy and 2) a broadcast program generation algorithm. According to the analytical mode, we devise scheme BIS-incremental on the basis of algorithm SOM, which is able to obtain solutions of high quality efficiently by employing binary interpolation search. In essence, scheme BIS-incremental is guided to explore the search space with higher likelihood to be the optimal first, thereby leading to an efficient and effective search. It is shown by our simulation results that the solution obtained by scheme BIS-incremental is of very high quality and is in fact very close to the optimal one. A sensitivity study on several parameters, including the number of data items and the number of communication channels, is conducted. The experimental results show that scheme BIS-incremental is of very good scalability, which is particularly important for its practical use in a mobile computing environment.

We propose in this paper an energy-conserved on-demand data broadcasting system by employing the data indexing technique. We also propose algorithm AIDOA to adjust the degree of buckets according to system workload. Ex-perimental results show that algorithm AIDOA is able to greatly reduce power consumption at the cost of slight in-crement in average access time and adjust the index and data organization dynamically to adapt to change of system workload.

The high diversity in the capabilities of various mobile devices such as display capabilities and computation power makes the design of mobile information systems more challenging. A transcoding proxy is placed between a client and an information server to coordinate the mismatch between what the server provides and what the client prefers. However, most research works in transcoding proxies in mobile computing environments are under the traditional client-server architecture and do not employ the data broadcast technique which is has been deemed a promising technique to design a power conservation, high scalable and high band-width utilization. In addition, the issue of QoS provision is also not addressed. In view of this, we design A QoS-aware transcoding proxy by utilizing the on-demand broadcasting technique. We first propose a QoS-aware transcoding proxy architecture, abbreviated as QTP, and model it as a queueing network. By analyzing the queueing network, several theoretical results are derived. We then propose a version decision policy and a service admission control scheme to provide QoS in QTP. The derived results are used to guide the execution of the proposed version decision policy and service admission control scheme to achieve the given QoS requirement. To measure the performance of QTP, several experiments are conducted. Experimental results show that the proposed scheme is more scalable than traditional client-server systems. In addition, the proposed scheme is able to effectively control the system load to attain the desired QoS.

Data broadcast is a promising technique to improve the bandwidth utilization and conserve the power consumption in a mobile computing environment. In many applications, the data items broadcast are dependent upon one another. However, most prior studies on broadcasting dependent data are restricted to a single broadcast channel environment, and as a consequence, the results are of limited applicability to the upcoming mobile environments. In view of this, we relax this restriction and explore the problem of broadcasting dependent data in multiple broadcast channels. By analyzing the model of dependent data broadcasting, we derive several theoretical properties for the average access time in a multiple channel environment. In light of the theoretical results, we develop a genetic algorithm to generate broadcast programs. Our experimental results show that the theoretical results derived are able to guide the search of the genetic algorithm very effectively, thus leading to broadcast programs of very high quality.

The growth in wireless communication technologies attracts a considerable amount of attention in mobile ad-hoc networks. Since mobile hosts in an ad-hoc network usually move freely, the topology of the network changes dynamically and disconnection occurs frequently. These characteristics make a mobile ad-hoc network be likely to be separated into several disconnected partitions, and the data accessibility is hence reduced. Several schemes are proposed to alleviate the reduction of data accessibility by replicating data items. However, little research effort was elaborated upon exploiting the group mobility where the group mobility refers to the phenomenon that several mobile nodes tend to move together. In this paper, we address the problem of replica allocation in a mobile ad-hoc network by exploring group mobility. We first analyze the group mobility model and derive several theoretical results. In light of these results, we propose a replica allocation scheme to improve the data accessibility. Several experiments are conducted to evaluate the performance of the proposed scheme. The experimental results show that the proposed scheme is able to not only obtain higher data accessibility but also produce lower network traffic than prior schemes.

In several mobile applications, the data items broadcast are dependent upon one another. However, most prior studies on broadcasting dependent data mainly consider single broadcast channel environments. In view of this, we explore the problem of broadcasting dependent data in multiple broadcast channels. By analyzing the model of dependent data broadcasting, we derive several theoretical properties for the average access time in a multiple channel environment. In light of the theoretical results, we develop a genetic algorithm to generate broadcast programs.

In this paper we propose and evaluate a new NAS based system which is a consolidated file system in a network environment. The consolidated file system utilizes storage boxes cooperating as peers to provide all file system services, and presents itself to its NFS clients as a unified storage server. We have prototyped several key operations in this system. To provide more performance insights, we build a comprehensive system simulator and conduct extensive performance studies for the system via the simulator This simulator has been run against a workload of a real Web server and the system behavior of this workload is studied. In light of the experimental results, a consolidated file system is being implemented in full scale.

Data broadcasting is a promising technique to improve the bandwidth utilization and conserve the power consumption in a mobile computing environment. In many applications, the data items broadcast are dependent upon one another. However, most prior studies on broadcasting dependent data are restricted to a single broadcast channel environment which imposes an impractical limitation. In view of this, we explore in this paper the problem of broadcasting dependent date in multiple broadcast channels. By analyzing the model of dependent data broadcasting, we derive several theoretical properties for the average access time in a multiple channel environment. In light of the theoretical results, we develop a genetic algorithm to generate broadcast programs. Our experimental results show that the theoretical results derived are able to guide the search of the genetic algorithm very effectively, thus leading to solution broadcast programs of very high quality.

We explore in this paper the problem of dynamic data and channel allocations with the number of communication channels and the number of data items given. It is noted that the combined use of broadcast and on-demand channels can utilize the bandwidth effectively for data dissemination in a mobile computing environment. We first derive the an-alytical models of the expected delays when the data are requested through the broadcast and on-demand channels. Then, we transform this problem into to a guided search problem. In light of the theoretical properties derived, we devise an algorithm based on binary interpolation search, referred to as algorithm BIS, to obtain solutions of high quality efficiently. In essence, algorithm BIS is guided to explore the solution space with higher likelihood to be the optimal first, thereby leading to an efficient and effective search. It is shown by our simulation results that the solution obtained by algorithm BIS is of very high quality and is in fact very close to the optimal one. Sensitivity analysis on several parameters, including the number of data items and the number of communication channels, is conducted.

The flexibility and rich application frameworks of the Web model make Web applications more prevalent in both Internet and intranet environments. Programmers enjoy various Web application frameworks with support ranging from simple user interactions based on the plain client-server model, to complicated distributed-object computations based on CORBA. The variety gives users the flexibility to decide a proper framework, and leads to demands for new support tools and a testing framework to test and maintain Web applications. This paper presents an architecture containing several supporting tools which enhance traditional software testing architecture to fit common Web application frameworks. The architecture suits current Web models and reuses several software patterns and architectures from traditional testing environments. In addition, a prototype Web application testing environment is constructed for demonstration.