Dividing a lecture speech into segments and providing those segments as learning objects are quite general and convenient way to construct e-learning resources. However it is difficult to assign an appropriate title to each object that reflects its content. Since there are various aspects of analyzing discourse segments, it is inevitable that researchers will face the diversity when describing the "meanings" of discourse segments. In this paper, we propose the assignment of discourse segment titles from the representation of their "meanings." In this assigning procedure, we focus on the speaker's evaluation for the event or the speech object. To verify the effectiveness of our idea, we examined identification of the segment boundaries from the titles that were described in our procedure. We confirmed that the result of the identification was more accurate than that of intuitive identification.

An application profile specifies a set of terms, drawn from one or more standard namespaces, for annotation of data, and constrains their usage and interpretations in a particular local application. An approach to representation of and reasoning with application profiles based on the OWL and OWL/XDD languages is proposed. The former is a standard Web ontology language, while the latter is a definite-clause-style rule language that employs XML expressions as its underlying data structure. Semantic constraints are defined in terms of rules, which are represented as XDD clauses. Application of the approach to defining application profiles with fine-grained semantic constraints, involving implicit properties of metadata elements, is illustrated. A prototype application profile development environment equipped with metadata validation features has been implemented based on the proposed framework.

The monitoring of performance in VoIP traffic has become vital because users generally expect VoIP service quality that is as high as that of PSTN services. A lightweight method of processing by extracting VoIP flows from Internet traffics is proposed in this paper. Estimating delay variations and the packet loss ratio using knowledge about specific features and the characteristics of VoIP flows, i.e., the inter-packet gap (IPG) which is constant in VoIP flows, is also proposed. Simulation with actual traffic trace is used to evaluate the method, and this revealed that delay variations (IPG variance) can be accurately estimated by monitoring only a few percentage of all flows. The proposed method can be used as a first-alert tool to monitor large amounts of flows to detect signs of degradation in VoIP flows. The method can be used by ISPs to estimate whether VoIP flow performance is adequate within their networks and at ingress from other ISPs.

Machine learning techniques have long been applied in many fields and have gained a lot of success. The purpose of learning processes is generally to obtain a set of parameters based on a given data set by minimizing a certain objective function which can explain the data set in a maximum likelihood or minimum estimation error sense. However, most of the learned parameters are highly data dependent and rarely reflect the true physical mechanism that is involved in the observation data. In order to obtain the inherent knowledge involved in the observed data, it is necessary to combine physical models with learning process rather than only fitting the observations with a black box model. To reveal underlying properties of human speech production, we proposed a learning process based on a physiological articulatory model and a coarticulation model, where both of the models are derived from human mechanisms. A two-layer learning framework was designed to learn the parameters concerned with physiological level using the physiological articulatory model and the parameters in the motor planning level using the coarticulation model. The learning process was carried out on an articulatory database of human speech production. The learned parameters were evaluated by numerical experiments and listening tests. The phonetic targets obtained in the planning stage provided an evidence for understanding the virtual targets of human speech production. As a result, the model based learning process reveals the inherent mechanism of the human speech via the learned parameters with certain physical meaning.

From lessons learned in medical data mining projects we show that integration of advanced computation techniques and human inspection is indispensable in medical data mining. We proposed an integrated approach that merges data mining and text mining methods plus visualization support for expert evaluation. We also appropriately developed temporal abstraction and text mining methods to exploit the collected data. Furthermore, our visual discovery system D2MS allowed to actively and effectively working with physicians. Significant findings in hepatitis study were obtained by the integrated approach.

Several approaches have been studied to cope with the exceptional features of non-segmented languages. When there is no explicit information about the boundary of a word, segmenting an input text is a formidable task in language processing. Not only the contemporary word list, but also usages of the words have to be maintained to cover the use in the current texts. The accuracy and efficiency in higher processing do heavily rely on this word boundary identification task. In this paper, we introduce some statistical based approaches to tackle the problem due to the ambiguity in word segmentation. The word boundary identification problem is then defined as a part of others for performing the unified language processing in total. To exhibit the ability in conducting the unified language processing, we selectively study the tasks of language identification, word extraction, and dictionary-less search engine.

In this paper, we present a method to automatically acquire a large-scale vocabulary of evaluative expressions from a large corpus of blogs. For the purpose, this paper presents a semi-supervised method for classifying evaluative expressions, that is, tuples of subjects, their attributes, and evaluative words, that indicate either favorable or unfavorable opinions towards a specific subject. Due to its characteristics, our semi-supervised method can classify evaluative expressions in a corpus by their polarities, starting from a very small set of seed training examples and using contextual information in the sentences the expressions belong to. Our experimental results with real Weblog data as our corpus show that this bootstrapping approach can improve the accuracy of methods for classifying favorable and unfavorable opinions. We also show that a reasonable amount of evaluative expressions can be really acquired.

An energy management circuit is proposed for self-powered ubiquitous sensor modules using vibration-based energy. With the proposed circuit, the sensor modules work with low duty cycle operation. Moreover, a two-tank circuit as a part of the energy management circuit is utilized to solve the problem that the average power density of ambient energy always varies with time while the power consumption of the sensor modules is constant and larger than it. In addition, the long start-up time problem is also avoided with the timing control of the proposed energy management circuit. The CMOS implementation and silicon verification results of the proposed circuit are also presented. Its validity is further confirmed with a vibration-based energy generation. The sensor module is used to supervise the vibration of machines and transfer the vibration signal discontinuously. A piezoelectric element acts as the vibration-to-electricity converter to realize battery-free operation.

In this paper, we propose an intrablog-based informal communication encouraging system named "Attractiblog." It has been pointed out that daily informal communications at a shared public space play very important role in information sharing in an organization. However, in most cases, the communications are often mere chats. To make the communications more informative, it is necessary to feed some common and beneficial topics there. Attractiblog is a system that extracts some articles posted in an intrablog considering who are in the shared space, and show them on a large-sized display that is located in the space. Thus, Attractiblog attempts to seamlessly link on-line communications to off-line communications. We conducted user studies and confirmed that Attractiblog can achieve a natural correspondence between topics in face-to-face informal communications and issues related to the activities of an organization as given in its intrablog.

In a community which consists of a large number of people interacting with each other, social dilemma is an important problem. This problem occurs when the payoff of each person conflicts with the total payoff of the community which he/she belongs to. Evolutionary game theory has been used as a powerful mathematical framework to analyze such a social problem. Recently, the authors have proposed replicator dynamics with reallocation of payoffs. In this model, the government is willing to lead the population to a desirable goal state by using collections and reallocations of payoffs. In this paper, we investigate this model, and show conditions for the goal state to be a locally or a globally asymptotically stable equilibrium point, respectively. We also propose a government's strategy depends on population states which can stabilize the goal state globally.

Correctness of Java programs is important because they are executed in distributed computing environments. The object initialization scheme in the Java programming language is complicated, and this complexity may lead to undesirable semantic bugs. Various tools have been developed for detecting program patterns that might cause errors during program execution. However, current tools cannot identify code patterns in which an uninitialized field is accessed when an object is initialized. We refer to such erroneous patterns as uninitialized field references. In this paper, we propose a static pattern detection algorithm for identifying uninitialized field references. We design a sound analysis for this problem and implement an analyzer using the Soot framework. In addition, we apply our algorithm to some real Java applications. From the experiments, we identify 12 suspicious field references in the applications, and among those we find two suspected errors by manual inspection.

Petri nets are known as a modeling language for concurrent and distributed systems. In recent years, various object-oriented Petri nets were proposed, and we are proposing a kind of object-oriented Petri nets, called multi agent nets (MANs). In this letter, we consider the reachability analysis of MANs. We propose an algorithm for generating an abstract state space of a multi agent net, and report results of computational experiments.

The major application of digital data hiding techniques is to deliver confidential data secretly via public but unreliable computer networks. Most of the existing data hiding schemes, however, exploit the raw data of cover images to perform secret communications. In this paper, a novel data hiding scheme was presented with the manipulation of images based on the compression of side-match vector quantization (SMVQ). This proposed scheme provided adaptive alternatives for modulating the quantized indices in the compressed domain so that a considerable quantity of secret data could be artfully embedded. As the experimental results demonstrated, the proposed scheme indeed provided a larger payload capacity without making noticeable distortions in comparison with schemes proposed in earlier works. Furthermore, this scheme also presented a satisfactory compression performance.

Wireless sensor networks are expected to play an essential role as a social infrastructure to realize our safe and secure living environment. In such a network, critical information must be transmitted faster and more reliably than other information. We propose a distributed transmission mechanism which enables emergency packets to be carried with high reliability and low latency along a preferential path, which is called an "assured corridor." In this self-organizing assured corridor mechanism (ACM), which works above the network layer and does not depend on any specific routing or MAC protocol, a corridor is gradually established as the first packet containing urgent information propagates to the base station. The nodes surrounding the corridor suppress the transmission of non-urgent information and nodes in the corridor are kept awake to forward emergency packets. ACM avoids packet loss and possible delay caused by collisions in the wireless transmission and normal sleep scheduling. An acknowledgment and retransmission scheme is incorporated into ACM in order to improve reliability of transmission of urgent information. Simulation experiments showed that, when only one node transmitted urgent information, the retransmission contributed to establish a corridor quickly and that ACM improved the delivery ratio and the delay of the urgent information transmission once a corridor is established. It was proved that ACM was effective to improve the reliability and the latency of urgent information as well in the cases where multiple nodes sent urgent information at once.

To meet users' multi-service requests under dynamic and heterogenous environment with high-assurance, the Autonomous Network-Based Integration System based on Faded Information Field (FIF) has been proposed, which permits to actively integrate the correlated information services according to the current situation of the system. However, the increase in the total number of users' requests and changes in users' preferences cause the unbalancing load in the system and the overload in the locality. In this paper, based on the autonomous access distribution in the locality, a new approach of autonomous correlated services access is proposed to reduce the load of the system and achieve the adaptability and timeliness of correlated services utilization. We proved the effectiveness of the proposed technology through the simulation and the results show that the system can improve the average response time not only for joint requests of correlated services, but also for separate requests of each service under changing environments.

This paper analyzes the throughput and the optimal announcement traffic indication message (ATIM) window of the IEEE 802.11 Distributed Coordination Function (DCF) in the power saving mode. An analytical model based on Markov chain model is proposed to express the throughput and the optimal ATIM window in a mathematical form; it is validated by the simulation. The optimal ATIM window size is obtained to maximize the throughput and minimize the power consumption while solving the fairness problem.

This study initiates a systematic evaluation of web search engine performance using queries written in Thai. Statistical testing indicates that there are some significant differences in the performance of search engines. In addition to compare the search performance, an analysis of the returned results is carried out. The analysis of the returned results shows that the majority of returned results are unique to a particular search engine and each system provides quite different results. This encourages the use of metasearch techniques to combine the search results in order to improve the performance and reliability in finding relevant documents. We examine several metasearch models based on the Borda count and Condorcet voting schemes. We also propose the use of Evolutionary Programming (EP) to optimize weight vectors used by the voting algorithms. The results show that the use of metasearch approaches produces superior performance compared to any single search engine on Thai queries.

We show the equivalence between the conventional frame synchronization in single-carrier systems and integer part estimation of frequency offset in OFDM systems and propose an efficient synchronization scheme. The proposed scheme achieves both OFDM symbol/frame timing and frequency offset estimation with only one well-designed OFDM training symbol, while previous synchronization algorithms need two OFDM training symbols at least. Numerical analysis shows that the proposed frequency estimator nearly achieves the Cramér-Rao lower bound for the variance of the frequency offset estimate, despite the reduction in the training sequence length.

Distributed Denial-of-Service attack (DDoS) is one of the most outstanding menaces on the Internet. A DDoS attack generally attempts to overwhelm the victim in order to deny their services to legitimate users. A number of approaches have been proposed for defending against DDoS attacks accurately in real time. However, existing schemes have limits in terms of detection accuracy and delay if the IDRS (Intrusion Detection and Response System) deployed only at a specific location detects and responds against attacks. As in this case, it is not able to catch the characteristic of the attack which is distributed in large-scale. Moreover, the existing detection schemes have vulnerabilities to intellectual DDoS attacks which are able to avoid its detection threshold or delay its detection time. This paper suggests the effective DDoS defense system which uses the collaborative scheme among distributed IDRSs located in the vicinity of the attack source or victim network. In proposed scheme, both victim and source-end IDRS work synergistically to identify the attack and avoid false alarm rate up to great extent. Additionally, we propose the duplicate detection window scheme to detect various attacks dynamics which increase the detection threshold gradually in early stage. The proposed scheme can effectively detect and respond against these diverse DDoS attack dynamics.

We propose a packet sampling strategy called fixed-period sampling, which selects at most one packet in every fixed-length period. Under the fixed-period sampling, the number of flow-cache lookups during a unit of time or the number of entries in a flow cache is bounded by a constant, which is simply expressed by a few tuning parameters. As an application of the fixed-period sampling, we also focus on the flow-rate estimation from fixed-period sampled packet streams. In particular, we propose a simple estimator based solely on the sampling frequency. We have conducted simulation experiments using two real traces to show basic characteristics of the fixed-period sampling for the comparison with the fixed-period sampling. We also show the accuracy of the proposed flow-rate estimator through simulations.