Rough set-based reasoning and pattern mining for information filtering

An information filtering (IF) system monitors an incoming document stream to find the documents that match the information needs specified by the user profiles. To learn to use the user profiles effectively is one of the most challenging tasks when developing an IF system. With the document selection criteria better defined based on the users’ needs, filtering large streams of information can be more efficient and effective. To learn the user profiles, term-based approaches have been widely used in the IF community because of their simplicity and directness. Term-based approaches are relatively well established. However, these approaches have problems when dealing with polysemy and synonymy, which often lead to an information overload problem. Recently, pattern-based approaches (or Pattern Taxonomy Models (PTM) [160]) have been proposed for IF by the data mining community. These approaches are better at capturing sematic information and have shown encouraging results for improving the effectiveness of the IF system. On the other hand, pattern discovery from large data streams is not computationally efficient. Also, these approaches had to deal with low frequency pattern issues. The measures used by the data mining technique (for example, “support” and “confidences”) to learn the profile have turned out to be not suitable for filtering. They can lead to a mismatch problem. This thesis uses the rough set-based reasoning (term-based) and pattern mining approach as a unified framework for information filtering to overcome the aforementioned problems. This system consists of two stages - topic filtering and pattern mining stages. The topic filtering stage is intended to minimize information overloading by filtering out the most likely irrelevant information based on the user profiles. A novel user-profiles learning method and a theoretical model of the threshold setting have been developed by using rough set decision theory. The second stage (pattern mining) aims at solving the problem of the information mismatch. This stage is precision-oriented. A new document-ranking function has been derived by exploiting the patterns in the pattern taxonomy. The most likely relevant documents were assigned higher scores by the ranking function. Because there is a relatively small amount of documents left after the first stage, the computational cost is markedly reduced; at the same time, pattern discoveries yield more accurate results. The overall performance of the system was improved significantly. The new two-stage information filtering model has been evaluated by extensive experiments. Tests were based on the well-known IR bench-marking processes, using the latest version of the Reuters dataset, namely, the Reuters Corpus Volume 1 (RCV1). The performance of the new two-stage model was compared with both the term-based and data mining-based IF models. The results demonstrate that the proposed information filtering system outperforms significantly the other IF systems, such as the traditional Rocchio IF model, the state-of-the-art term-based models, including the BM25, Support Vector Machines (SVM), and Pattern Taxonomy Model (PTM).

A novel nonrepudiable threshold multi-proxy multi-signature scheme with shared verification

Tzeng et al. proposed a new threshold multi-proxy multi-signature scheme with threshold verification. In their scheme, a subset of original signers authenticates a designated proxy group to sign on behalf of the original group. A message m has to be signed by a subset of proxy signers who can represent the proxy group. Then, the proxy signature is sent to the verifier group. A subset of verifiers in the verifier group can also represent the group to authenticate the proxy signature. Subsequently, there are two improved schemes to eliminate the security leak of Tzeng et al.’s scheme. In this paper, we have pointed out the security leakage of the three schemes and further proposed a novel threshold multi-proxy multi-signature scheme with threshold verification.

Seismic performance of brick infilled RC frame structures in low and medium rise buildings in Bhutan

The construction of reinforced concrete buildings with unreinforced infill is common practice even in seismically active country such as Bhutan, which is located in high seismic region of Eastern Himalaya. All buildings constructed prior 1998 were constructed without seismic provisions while those constructed after this period adopted seismic codes of neighbouring country, India. However, the codes have limited information on the design of infilled structures besides having differences in architectural requirements which may compound the structural problems. Although the influence of infill on the reinforced concrete framed structures is known, the present seismic codes do not consider it due to the lack of sufficient information. Time history analyses were performed to study the influence of infill on the performance of concrete framed structures. Important parameters were considered and the results presented in a manner that can be used by practitioners. The results show that the influence of infill on the structural performance is significant. The structural responses such as fundamental period, roof displacement, inter-storey drift ratio, stresses in infill wall and structural member forces of beams and column generally reduce, with incorporation of infill wall. The structures designed and constructed with or without seismic provision perform in a similar manner if the infills of high strength are used.

Low-fat oxidation may be a factor in obesity among men with schizophrenia

Objective: Obesity associated with atypical antipsychotic medications is an important clinical issue for people with schizophrenia. The purpose of this project was to determine whether there were any differences in resting energy expenditure (REE) and respiratory quotient (RQ) between men with schizophrenia and controls. Method: Thirty-one men with schizophrenia were individually matched for age and relative body weight with healthy, sedentary controls. Deuterium dilution was used to determine total body water and subsequently fat-free mass (FFM). Indirect calorimetry using a Deltatrac metabolic cart was used to determine REE and RQ. Results: When corrected for FFM, there was no significant difference in REE between the groups. However, fasting RQ was significantly higher in the men with schizophrenia than the controls. Conclusion: Men with schizophrenia oxidised proportionally less fat and more carbohydrate under resting conditions than healthy controls. These differences in substrate utilisation at rest may be an important consideration in obesity in this clinical group.

Low Vision

The care of low-vision patients is termed vision rehabilitation, and optometrists have an essential role to play in the provision of vision rehabilitation services. Ideally, if patients stay with one optometrist or practice, their low-vision care becomes part of a continuum of eye care, from the time when they had normal vision. If progressive vision loss occurs, the role of the optometrist changes from primary eye care only to one of monitoring vision loss and gradually introducing low-vision care, especially magnification and advice on lighting and contrast, in conjunction with other vision rehabilitation professionals.

Design and evaluation of an image analysis platform for low-power, low-bandwidth camera networks

We describe the design and evaluation of a platform for networks of cameras in low-bandwidth, low-power sensor networks. In our work to date we have investigated two different DSP hardware/software platforms for undertaking the tasks of compression and object detection and tracking. We compare the relative merits of each of the hardware and software platforms in terms of both performance and energy consumption. Finally we discuss what we believe are the ongoing research questions for image processing in WSNs.

Groundwater Visualisation System (GVS): a software framework for developing low-end, scalable and robust software for 3D visualisation and animation of groundwater conceptual models

Effective management of groundwater requires stakeholders to have a realistic conceptual understanding of the groundwater systems and hydrological processes.However, groundwater data can be complex, confusing and often difficult for people to comprehend..A powerful way to communicate understanding of groundwater processes, complex subsurface geology and their relationships is through the use of visualisation techniques to create 3D conceptual groundwater models. In addition, the ability to animate, interrogate and interact with 3D models can encourage a higher level of understanding than static images alone. While there are increasing numbers of software tools available for developing and visualising groundwater conceptual models, these packages are often very expensive and are not readily accessible to majority people due to complexity. .The Groundwater Visualisation System (GVS) is a software framework that can be used to develop groundwater visualisation tools aimed specifically at non-technical computer users and those who are not groundwater domain experts. A primary aim of GVS is to provide management support for agencies, and enhancecommunity understanding.