Unleashing the vermin : vectored route-length minimization

We have posed a simple but interesting graph theoretic problem and posited a heuristic solution procedure, which we have christened as Vectored Route-length Minimization (VeRMin). Basically, it constitutes a re-casting of the classical “shortest route” problem in a strictly Euclidean space. We have presented only a heuristic solution process hoping that a formal proof will eventually emerge as the problem receives wider exposure within mathematical circles.

Announcement effect on the credit spreads of US dollar Malaysian bonds

The main purpose of this paper is to extend the empirical research on the behavior of credit spreads on the USD denominated Malaysian bonds. We find that international political events have more influence on the changes of bond yield spreads from Malaysian USD issues than domestic events. Significant results are consistent across different issues. However, the resignation by the former Prime Minister, Dr Mahathir Mohamad, created a mixed response from the market. Using an error correction model, this study also found that the monetary policy by the US Federal Reserve has a long-term and significant impact on the behavior of the Malaysian USD issues. This study also provides further evidence that the current theoretical framework is sufficient to explain changes in the credit spread of bonds issued by the emerging market.

Volatility transmissions between stock and bond markets : evidence from Japan and the U.S.

This study attempts to investigate the transmission of market-wide volatility between the equity markets and bond markets of Japan and the U.S. To measure the volatility transmission, the BEKK (Baba, Engle, Kraft and Kroner, 1990) method, a decomposition approach of the multivariate GARCH (1,1) model, is used to examine the cross-market contemporaneous effect of information arrival. The time series analysis provides evidence to the long-run phenomena of causality in conditional variances of paired assets within the local and international markets. Within various pairings, some evidence of bi-directional volatility transmissions such as informational linkages have been observed. Our empirical results suggest that within the domestic cross markets, the volatility transmission is unidirectional from the stock market to the bond market. Evidence from international cross-market analysis is mixed, with strong evidence on volatility spillover among these international stock markets, but weak evidence between international stock and bond markets. In addition, there are significant directional volatility transmissions between DJI index and FTSE100 index, and between DJI index and DAX200 index. The volatility transmission between these two markets indicates that the international diversification of bonds is not prevalent.

Conceptual representation for multimedia information

Multimedia information is now routinely available in the forms of text, pictures, animation and sound. Although text objects are relatively easy to deal with (in terms of information search and retrieval), other information bearing objects (such as sound, images, animation) are more difficult to index. Our research is aimed at developing better ways of representing multimedia objects by using a conceptual representation based on Schank's conceptual dependencies. Moreover, the representation allows for users' individual interpretations to be embedded in the system. This will alleviate the problems associated with traditional semantic networks by allowing for coexistence of multiple views of the same information. The viability of the approach is tested, and the preliminary results reported.

Extracting common subtrees from decision trees

This paper explores an efficient technique for the extraction of common subtrees in decision trees. The method is based on a Suffix Tree string matching process and the algorithm is applied to the problem of finding common decision rules in path planning.

Finding the beat : an analysis of the rhythmic elements of motion pictures

This paper presents a new study on the application of the framework of Computational Media Aesthetics to the problem of automated understanding of film. Leveraging Film Grammar as the means to closing the "semantic gap" in media analysis, we examine film rhythm, a powerful narrative concept used to endow structure and form to the film compositionally and enhance its lyrical quality experientially. The novelty of this paper lies in the specification and investigation of the rhythmic elements that are present in two cinematic devices; namely motion and editing patterns, and their potential usefulness to automated content annotation and management systems. In our rhythm model, motion behavior is classified as being either nonexistent, fluid or staccato for a given shot. Shot neighborhoods in movies are then grouped by proportional makeup of these motion behavioral classes to yield seven high-level rhythmic arrangements that prove to be adept at indicating likely scene content (e.g. dialogue or chase sequence) in our experiments. The second part of our investigation presents a computational model to detect editing patterns as either metric, accelerated, decelerated or free. Details of the algorithm for the extraction of these classes are presented, along with experimental results on real movie data. We show with an investigation of combined rhythmic patterns that, while detailed content identification via rhythm types alone is not possible by virtue of the fact that film is not codified to this level in terms of rhythmic elements, analysis of the combined motion/editing rhythms can allow us to determine that the content has changed and hypothesize as to why this is so. We present three such categories of change and demonstrate their efficacy for capturing useful film elements (e.g. scene change precipitated by plot event), by providing data support from five motion pictures.

Learning other agents' preferences in multi-agent negotiation using the bayesian classifier

In multi-agent systems, most of the time, an agent does not have complete information about the preferences and decision making processes of other agents. This prevents even the cooperative agents from making coordinated choices, purely due to their ignorance of what others want. To overcome this problem, traditional coordination methods rely heavily on inter-agent communication, and thus become very inefficient when communication is costly or simply not desirable (e.g. to preserve privacy). In this paper, we propose the use of learning to complement communication in acquiring knowledge about other agents. We augment the communication-intensive negotiating agent architecture with a learning module, implemented as a Bayesian classifier. This allows our agents to incrementally update models of other agents' preferences from past negotiations with them. Based on these models, the agents can make sound predictions about others' preferences, thus reducing the need for communication in their future interactions.

The visitors guide a simple video reuse application

One of the results of the surge in interest in the internet is the great increase in availability of pictorial and video data. Web browsers such as Netscape give access to an enormous range of such data. In order to make use of large amounts of pictorial and video data, it is necessary to develop indexing and retrieval methods. Pictorial databases have made great progress recently, to the extent that there are now a number of commercially available products. Video databases are now being researched and developed from a number of different viewpoints. Given a general indexing scheme for video, the next step is to reuse clips in further applications. In this paper we present an initial application for the reuse of video clips. The aim of the system is to resequence video clips for a particular application. We have chosen a well-constrained application for this purpose, the aim being to produce a video tour of a campus between designated start and destination points from a set of indexed video clips. We use clips of a guide entering and leaving buildings on our campus, and when visitors select a start location and a destination, the system will retrieve clips suitable for guiding the visitor along the correct path. The system uses an index of spatial relationships of key objects for the video clips to decide which clips provide the correct sequence of motion around the campus. Although the full power of the indexing notation is unnecessary for this simple problem, the results from this initial implementation indicate that the concept could be applicable to more complex problems.

Tracking and surveillance in wide-area spatial environments using the abstract hidden markov model.

In this paper, we consider the problem of tracking an object and predicting the object's future trajectory in a wide-area environment, with complex spatial layout and the use of multiple sensors/cameras. To solve this problem, there is a need for representing the dynamic and noisy data in the tracking tasks, and dealing with them at different levels of detail. We employ the Abstract Hidden Markov Models (AHMM), an extension of the well-known Hidden Markov Model (HMM) and a special type of Dynamic Probabilistic Network (DPN), as our underlying representation framework. The AHMM allows us to explicitly encode the hierarchy of connected spatial locations, making it scalable to the size of the environment being modeled. We describe an application for tracking human movement in an office-like spatial layout where the AHMM is used to track and predict the evolution of object trajectories at different levels of detail.

Application of the DCT energy histogram for face recognition

In this paper, we investigate the face recognition problem via energy histogram of the DCT coefficients. Several issues related to the recognition performance are discussed, In particular the issue of histogram bin sizes and feature sets. In addition, we propose a technique for selecting the classification threshold incrementally. Experimentation was conducted on the Yale face database and results indicated that the threshold obtained via the proposed technique provides a balanced recognition in term of precision and recall. Furthermore, it demonstrated that the energy histogram algorithm outperformed the well-known Eigenface algorithm.

Plant extract assisted extracellular tannase production by Aspergillus sp MIK 23

An extracellular tannase (E.C. 3.1.1.20) producing fungal strain was isolated from soil and identified as Aspergillus sp MIK23. Out of various plant extracts, Terminalia chebula powder (TCP) in the optimized medium enhanced enzyme production. Maximum yield of tannase (3 IU ml-1) was obtained with glucose (10 g/L), urea (2 g/L), and yeast extract (2.5 g/L) when inoculated with 10% inoculum in 48 h. An initial medium at pH 6.0 and a cultivation temperature of 37 0C was found to be optimum for enzyme production. Metal ions Mg2+, Zn2+, Ca2+, Cu2+ and Cd2+ did not improve enzyme activity, whereas, Ca2+, Fe2+ and Hg2+ repressed enzyme activity. The enzyme was purified using ammonium sulfate precipitation followed by Q-sepharose ion-exchange chromatography. The enzyme was purified to 42-fold with an overall recovery of 20. The pH and temperature optima of the purified tannase were found to be 7.0 and 37°C, respectively.

A multi-objective sensitivity approach to training providers' evaluation and quota allocation planning

The practice of solely relying on the human resources department in the selection process of external training providers has cast doubts and mistrust across other departments as to how trainers are sourced. There are no measurable criteria used by human resource personnel, since most decisions are based on intuitive experience and subjective market knowledge. The present problem focuses on outsourcing of private training programs that are partly government funded, which has been facing accountability challenges. Due to the unavailability of a scientific decision-making approach in this context, a 12-step algorithm is proposed and tested in a Japanese multinational company. The model allows the decision makers to revise their criteria expectations, in turn witnessing the change of the training providers' quota distribution. Finally, this multi-objective sensitivity analysis provides a forward-looking approach to training needs planning and aids decision makers in their sourcing strategy.