A Random ID Update Scheme to Protect Location Privacy in RFID-Based Student Administration Systems
Recently the RFID has been received great attentions and gotten wide applications in many different areas including some administration systems for automatically managing students in a campus. However, a RFID tag may infringe on its owner's location privacy because of its traceability. Therefore, location privacy problems in RFID-based student administration systems become a critical issue in applying such systems to the practical. Three representative schemes to solve these problems are explained and analyzed. Moreover, a safe, fast and low cost scheme which offers better privacy protections is proposed. In this scheme, a hash value is generated from a secret ID and a random number in a RFID tag by using a cheap hash circuit, and it is used for an identifier of a user. The secret ID is updated by using the hash circuit when a reader for updating is used. Finally, some comparisons between the proposed scheme and the other three typical schemes are presented in terms of safety, speed and cost

title: A Smart Ambient Sound Aware Environment for Be Quiet Reminding
abstract: This paper presents a prototype of a smart sound aware environment that captures ambient sounds, analyzes sound situations and gives a be-quiet reminder to someone who generates sound exceeding certain volume level and disturbing others in the same site. It shows the microphone net to sense sound data and the speaker net to send be-quiet reminders. All computers are interconnected with a P2P network atop of the TOMSCOP platform. It discusses how sound data is captured and processed, and when a be-quiet reminder decision is made with analyzing contexts of sounds and users in a room. Some preliminary experimental results are given

title: Designing a Context-Aware System to Detect Dangerous Situations in School Routes for Kids Outdoor Safety Care
abstract: Ubiquitous computing is targeted at services and applications of computer and communication technologies in the real world. This research, as a part of UbicKids Project, is focused on designing a context-aware system that dynamically detects the possible dangerous situations in the routes where kids go to and return from schools, and provides prompt advices to kids who may encounter some dangerous situations. Based on analyses of typical dangerous situations in school routes, the paper then shows the system architecture and discusses about danger-related context information processing including context description, representation and presentation. Security and privacy issues and possible solutions are also explained. A preliminary system prototype has been implemented and some GUIs are explained. Related work is discussed with comparisons to other research work.

title: Smart hyperspaces and project Ubikids
abstract: A smart space is an electronics-enhanced physical environment that can sense the existence of users and other entities, and provide them the right services, in the right way, at the right time. Such senses and services are based on collaborative work of many interconnected devices and computers, which may be small enough to be embedded to everyday objects in the space. So far, many creative researches have been conducted to develop and experiment a variety of smart spaces. The next research issue to emerge, we believe, is to interconnect and integrate these isolated smart spaces together into a higher level space known as a hyperspace. In this paper, after briefly reviewing the smart space concepts and related research, we describe what a smart hyperspace is all about and what related challenging issues exist. We then explain our vision and motivations towards building a specific smart hyperspace, called Ubikids, to assist parents and provide services to make them more convenient, prompt, reliable, precise, and can remotely take care of their kids. Finally, we show the fundamental approaches, conceptual designs, crucial problems and so on, regarding the project Ubikids.

title: A Java-Based RFID Service Framework with Semantic Data Binding Between Real and Cyber Spaces
abstract: Recently there are getting more and more systems and applications using RFID. There is a necessity of a framework for them. This paper presents a Java-based framework that offers a set of general services to support various RFID systems for different purposes and application scenarios. The framework emphasizes on the semantic data binding for contextual information mapping between real and cyber spaces. The Java interface classes are provided to support general communications among a RFID, a reader and an application. Real and cyber spaces are synchronized via dynamic and real-time mapping from symbolic strings or IDs to the semantic XML data representations that are more effectively and efficiently useable by RFID applications. In this paper, the architecture and functional modules of the Java-based RFID service framework are explained in detail.

title: Efficient Training of RBF Networks Via the BYY Automated Model Selection Learning Algorithms
abstract: Radial basis function (RBF) networks of Gaussian activation functions have been widely used in many applications due to its simplicity, robustness, good approximation and generalization ability, etc.. However, the training of such a RBF network is still a rather difficult task in the general case and the main crucial problem is how to select the number and locations of the hidden units appropriately. In this paper, we utilize a new kind of Bayesian Ying-Yang (BYY) automated model selection (AMS) learning algorithm to select the appropriate number and initial locations of the hidden units or Gaussians automatically for an input data set. It is demonstrated well by the experiments that this BYY-AMS training method is quite efficient and considerably outperforms the typical existing training methods on the training of RBF networks for both clustering analysis and nonlinear time series prediction.

title: The capacity of time-delay recurrent neural network for storing spatio-temporal sequences
abstract: We investigate the capacity of a type of discrete-time recurrent neural network, called time-delay recurrent neural network, for storing spatio-temporal sequences. By introducing the order of a spatio-temporal sequence, the match law between a time-delay recurrent neural network and a spatio-temporal sequence has been established. It has been proved that the full order time-delay recurrent neural network of l-step feedback is able to learn and memorize (or store) any bipolar (or binary) spatio-temporal sequence of the order k if k≤l, and that the asymptotic memory capacity of the first-order time-delay recurrent neural network of one-step feedback is not less than C1(n)=n+1, where n is the number of processing neurons in the network. Moreover, we substantiate the theoretical results by simulation experiments.

title: A Robust Error Concealment Algorithm in Wireless Networks
abstract: Compressed video sequences are very vulnerable to channel disturbances when transmitted through the burst-packet-loss wireless channels. Transmission errors not only corrupt the current frame, but may also propagate to succeeding frames. We find that a number of current proposed post-processing error concealment methods exploit the spatial/temporal redundancy in the video stream can not actually conceal the transmission errors effectively, because they ignored the fact that the neighboring blocks can be easily lost by the burst packet losses and the correlation was considered only in the current frame, which all limited the ability to obtain an optimal recovery. In this paper, a novel error concealment algorithm is proposed for video transmissions in a wireless environment. Simulation proves that the proposed algorithm can be chosen as good trade-off between video quality and computational load in wireless networks.

title: The Mahalanobis Distance Based Rival Penalized Competitive Learning Algorithm
abstract: The rival penalized competitive learning (RPCL) algorithm has been developed to make the clustering analysis on a set of sample data in which the number of clusters is unknown, and recent theoretical analysis shows that it can be constructed by minimizing a special kind of cost function on the sample data. In this paper, we use the Mahalanobis distance instead of the Euclidean distance in the cost function computation and propose the Mahalanobis distance based rival penalized competitive learning (MDRPCL) algorithm. It is demonstrated by the experiments that the MDRPCL algorithm can be successful to determine the number of elliptical clusters in a data set and lead to a good classification result.

title: Unexpected means of protocol inference
abstract: Network managers are inevitably called upon to associate network traffic with particular applications. Indeed, this operation is critical for a wide range of management functions ranging from debugging and security to analytics and policy support. Traditionally, managers have relied on application adherence to a well established global port mapping: Web traffic on port 80, mail traffic on port 25 and so on. However, a range of factors - including firewall port blocking, tunneling, dynamic port allocation, and a bloom of new distributed applications - has weakened the value of this approach. We analyze three alternative mechanisms using statistical and structural content models for automatically identifying traffic that uses the same application-layer protocol, relying solely on flow content. In this manner, known applications may be identified regardless of port number, while traffic from one unknown application will be identified as distinct from another. We evaluate each mechanism's classification performance using real-world traffic traces from multiple sites.