The effectiveness of public sector asset management in Malaysia

Property is an important factor in all businesses production in order to function. Nourse (1990) quoted ¡°some businesses are real estate, all businesses use real estate¡±. In recent years, the management of property assets has become the focus of many organisations, including the non-real estate businesses. Good asset management is concerned with the effective utilisation of a property owner.s assets. It is the management process of ensuring that the portfolio of properties held meets the overall requirements of the users. In short, it is the process of identifying the user.s requirement and the rationalisation of property holdings to match that requirement best, followed by a monitoring and ongoing review of practice. In Malaysia, federal agencies and local authorities are among the largest property asset owners. Recently the federal government has released a Total Asset Management Manual (TAMM). It is at the preliminary stage of implementation. This thesis will study the international practices of asset management of public sector assets and assess the effectiveness of TAMM. This research will focus on current international practices for the effective management of public sector property assets. The current application in Malaysia will be highlighted, to determine the awareness and understanding of the current practices to the recently released TAMM. This research is an exploratory research. The basis of this research relies on the combination of qualitative and quantitative approach, whereby the qualitative approach focuses on the international practices and its application to the management of public sector property assets. Questionnaires survey will be conducted among the Malaysian public property assets managers and users in the quantitative approach to gauge the collective opinion on the current practices of TAMM and its implementation

Molecular dynamics simulation of structural characteristics in metal cluster deposition on surfaces

The deposition of small metal clusters (Cu, Au and Al) on f.c.c. metals (Cu, Au and Ni) has been studied by molecular dynamics simulation using Finnis–Sinclair (FS) potential. The impact energy varied from 0.01 to 10 eV/atom. First, the deposition of single cluster was simulated. We observed that, even at much lower energy, a small cluster with (Ih) icosahedral symmetry was reconstructed to match the substrate structure (f.c.c.) after deposition. Next, clusters were modeled to drop, one after the other, on the surface. The nanostructure was found by soft landing of Au clusters on Cu with increasing coverage, where interfacial energy dominates. While at relatively higher deposition energy (a few eV), the ordered f.c.c.-like structure was observed in the first adlayer of the film formed by Al clusters depositing on Ni substrate. This characteristic is mainly attributive to the ballistic collision. Our results indicate that the surface morphology synthesized by cluster deposition could be controlled by experimental parameters, which will be helpful for controlled design of nanostructure.

Vision-based place recognition : how low can you go?

In this paper we use the algorithm SeqSLAM to address the question, how little and what quality of visual information is needed to localize along a familiar route? We conduct a comprehensive investigation of place recognition performance on seven datasets while varying image resolution (primarily 1 to 512 pixel images), pixel bit depth, field of view, motion blur, image compression and matching sequence length. Results confirm that place recognition using single images or short image sequences is poor, but improves to match or exceed current benchmarks as the matching sequence length increases. We then present place recognition results from two experiments where low-quality imagery is directly caused by sensor limitations; in one, place recognition is achieved along an unlit mountain road by using noisy, long-exposure blurred images, and in the other, two single pixel light sensors are used to localize in an indoor environment. We also show failure modes caused by pose variance and sequence aliasing, and discuss ways in which they may be overcome. By showing how place recognition along a route is feasible even with severely degraded image sequences, we hope to provoke a re-examination of how we develop and test future localization and mapping systems.

Gelatin-methacrylamide hydrogels as potential biomaterials for fabrication of tissue-engineered cartilage constructs

Gelatin-methacrylamide (gelMA) hydrogels are shown to support chondrocyte viability and differentiation and give wide ranging mechanical properties depending on several cross-linking parameters. Polymer concentration, UV exposure time, and thermal gelation prior to UV exposure allow for control over hydrogel stiffness and swelling properties. GelMA solutions have a low viscosity at 37 °C, which is incompatible with most biofabrication approaches. However, incorporation of hyaluronic acid (HA) and/or co-deposition with thermoplastics allows gelMA to be used in biofabrication processes. These attributes may allow engineered constructs to match the natural functional variations in cartilage mechanical and geometrical properties.

Association study of calcitonin gene-related polypeptide-alpha (CALCA) gene polymorphism with migraine

Migraine is a neurological disorder that is associated with increased levels of calcitonin gene-related peptide (CGRP) in plasma. CGRP, being one of the mediators of neurogenic inflammation and a phenomenon implicated in the pathogenesis of migraine headache, is thus suggested to have an important role in migraine pathophysiology. Polymorphisms of the CALCA gene have been linked to Parkinson's disease, ovarian cancer and essential hypertension, suggesting a functional role for these polymorphisms. Given the strong evidence linking CGRP and migraine, it is hypothesised that polymorphisms in the CALCA gene may play a role in migraine pathogenesis. Seemingly non functional intronic polymorphisms are capable of disrupting normal RNA processing or introducing a splice site in the transcript. A 16 bp deletion in the first intron of the CALCA gene has been reported to be a good match for the binding site for a transcription factor expressed strongly in neural crest derived cells, AP-2. This deletion also eliminates an intron splicing enhancer (ISE) that may potentially cause exon skipping. This study investigated the role of the 16 bp intronic deletion in the CALCA gene in migraineurs and matched control individuals. Six hundred individuals were genotyped for the deletion by polymerase chain reaction followed by fragment analysis on the 3130 Genetic Analyser. The results of this study showed no significant association between the intronic 16 bp deletion in the CALCA gene and migraine in the tested Australian Caucasian population. However, given the evidence linking CGRP and migraine, further investigation of variants with this gene may be warranted.

Integrating collaborative filtering and matching-based search for product recommendations

Currently, recommender systems (RS) have been widely applied in many commercial e-commerce sites to help users deal with the information overload problem. Recommender systems provide personalized recommendations to users and, thus, help in making good decisions about which product to buy from the vast amount of product choices. Many of the current recommender systems are developed for simple and frequently purchased products like books and videos, by using collaborative-filtering and content-based approaches. These approaches are not directly applicable for recommending infrequently purchased products such as cars and houses as it is difficult to collect a large number of ratings data from users for such products. Many of the ecommerce sites for infrequently purchased products are still using basic search-based techniques whereby the products that match with the attributes given in the target user’s query are retrieved and recommended. However, search-based recommenders cannot provide personalized recommendations. For different users, the recommendations will be the same if they provide the same query regardless of any difference in their interest. In this article, a simple user profiling approach is proposed to generate user’s preferences to product attributes (i.e., user profiles) based on user product click stream data. The user profiles can be used to find similarminded users (i.e., neighbours) accurately. Two recommendation approaches are proposed, namely Round- Robin fusion algorithm (CFRRobin) and Collaborative Filtering-based Aggregated Query algorithm (CFAgQuery), to generate personalized recommendations based on the user profiles. Instead of using the target user’s query to search for products as normal search based systems do, the CFRRobin technique uses the attributes of the products in which the target user’s neighbours have shown interest as queries to retrieve relevant products, and then recommends to the target user a list of products by merging and ranking the returned products using the Round Robin method. The CFAgQuery technique uses the attributes of the products that the user’s neighbours have shown interest in to derive an aggregated query, which is then used to retrieve products to recommend to the target user. Experiments conducted on a real e-commerce dataset show that both the proposed techniques CFRRobin and CFAgQuery perform better than the standard Collaborative Filtering and the Basic Search approaches, which are widely applied by the current e-commerce applications.

Augmenting analytical CRM strategies with social BI

Large communities built around social media on the Internet offer an opportunity to augment analytical customer relationship management (CRM) strategies. The purpose of this paper is to provide direction to advance the conceptual design of business intelligence (BI) systems for implementing CRM strategies. After introducing social CRM and social BI as emerging fields of research, the authors match CRM strategies with a re-engineered conceptual data model of Facebook in order to illustrate the strategic value of these data. Subsequently, the authors design a multi-dimensional data model for social BI and demonstrate its applicability by designing management reports in a retail scenario. Building on the service blueprinting framework, the authors propose a structured research agenda for the emerging field of social BI.

Improving the PLDA based speaker verification in limited microphone data conditions

A significant amount of speech data is required to develop a robust speaker verification system, but it is difficult to find enough development speech to match all expected conditions. In this paper we introduce a new approach to Gaussian probabilistic linear discriminant analysis (GPLDA) to estimate reliable model parameters as a linearly weighted model taking more input from the large volume of available telephone data and smaller proportional input from limited microphone data. In comparison to a traditional pooled training approach, where the GPLDA model is trained over both telephone and microphone speech, this linear-weighted GPLDA approach is shown to provide better EER and DCF performance in microphone and mixed conditions in both the NIST 2008 and NIST 2010 evaluation corpora. Based upon these results, we believe that linear-weighted GPLDA will provide a better approach than pooled GPLDA, allowing for the further improvement of GPLDA speaker verification in conditions with limited development data.

Representing and discovering adversarial team behaviors using player roles

In this paper, we describe a method to represent and discover adversarial group behavior in a continuous domain. In comparison to other types of behavior, adversarial behavior is heavily structured as the location of a player (or agent) is dependent both on their teammates and adversaries, in addition to the tactics or strategies of the team. We present a method which can exploit this relationship through the use of a spatiotemporal basis model. As players constantly change roles during a match, we show that employing a "role-based" representation instead of one based on player "identity" can best exploit the playing structure. As vision-based systems currently do not provide perfect detection/tracking (e.g. missed or false detections), we show that our compact representation can effectively "denoise" erroneous detections as well as enabe temporal analysis, which was previously prohibitive due to the dimensionality of the signal. To evaluate our approach, we used a fully instrumented field-hockey pitch with 8 fixed high-definition (HD) cameras and evaluated our approach on approximately 200,000 frames of data from a state-of-the-art real-time player detector and compare it to manually labelled data.

Reliability and validity of query intent assessments

In most intent recognition studies, annotations of query intent are created post hoc by external assessors who are not the searchers themselves. It is important for the field to get a better understanding of the quality of this process as an approximation for determining the searcher's actual intent. Some studies have investigated the reliability of the query intent annotation process by measuring the interassessor agreement. However, these studies did not measure the validity of the judgments, that is, to what extent the annotations match the searcher's actual intent. In this study, we asked both the searchers themselves and external assessors to classify queries using the same intent classification scheme. We show that of the seven dimensions in our intent classification scheme, four can reliably be used for query annotation. Of these four, only the annotations on the topic and spatial sensitivity dimension are valid when compared with the searcher's annotations. The difference between the interassessor agreement and the assessor-searcher agreement was significant on all dimensions, showing that the agreement between external assessors is not a good estimator of the validity of the intent classifications. Therefore, we encourage the research community to consider using query intent classifications by the searchers themselves as test data.

Personalized ontology learning for enhancing text mining effectiveness

Over the last decade, the majority of existing search techniques is either keyword- based or category-based, resulting in unsatisfactory effectiveness. Meanwhile, studies have illustrated that more than 80% of users preferred personalized search results. As a result, many studies paid a great deal of efforts (referred to as col- laborative filtering) investigating on personalized notions for enhancing retrieval performance. One of the fundamental yet most challenging steps is to capture precise user information needs. Most Web users are inexperienced or lack the capability to express their needs properly, whereas the existent retrieval systems are highly sensitive to vocabulary. Researchers have increasingly proposed the utilization of ontology-based tech- niques to improve current mining approaches. The related techniques are not only able to refine search intentions among specific generic domains, but also to access new knowledge by tracking semantic relations. In recent years, some researchers have attempted to build ontological user profiles according to discovered user background knowledge. The knowledge is considered to be both global and lo- cal analyses, which aim to produce tailored ontologies by a group of concepts. However, a key problem here that has not been addressed is: how to accurately match diverse local information to universal global knowledge. This research conducts a theoretical study on the use of personalized ontolo- gies to enhance text mining performance. The objective is to understand user information needs by a \bag-of-concepts" rather than \words". The concepts are gathered from a general world knowledge base named the Library of Congress Subject Headings. To return desirable search results, a novel ontology-based mining approach is introduced to discover accurate search intentions and learn personalized ontologies as user profiles. The approach can not only pinpoint users' individual intentions in a rough hierarchical structure, but can also in- terpret their needs by a set of acknowledged concepts. Along with global and local analyses, another solid concept matching approach is carried out to address about the mismatch between local information and world knowledge. Relevance features produced by the Relevance Feature Discovery model, are determined as representatives of local information. These features have been proven as the best alternative for user queries to avoid ambiguity and consistently outperform the features extracted by other filtering models. The two attempt-to-proposed ap- proaches are both evaluated by a scientific evaluation with the standard Reuters Corpus Volume 1 testing set. A comprehensive comparison is made with a num- ber of the state-of-the art baseline models, including TF-IDF, Rocchio, Okapi BM25, the deploying Pattern Taxonomy Model, and an ontology-based model. The gathered results indicate that the top precision can be improved remarkably with the proposed ontology mining approach, where the matching approach is successful and achieves significant improvements in most information filtering measurements. This research contributes to the fields of ontological filtering, user profiling, and knowledge representation. The related outputs are critical when systems are expected to return proper mining results and provide personalized services. The scientific findings have the potential to facilitate the design of advanced preference mining models, where impact on people's daily lives.

A people-to-people matching system using graph mining techniques

A people-to-people matching system (or a match-making system) refers to a system in which users join with the objective of meeting other users with the common need. Some real-world examples of these systems are employer-employee (in job search networks), mentor-student (in university social networks), consume-to-consumer (in marketplaces) and male-female (in an online dating network). The network underlying in these systems consists of two groups of users, and the relationships between users need to be captured for developing an efficient match-making system. Most of the existing studies utilize information either about each of the users in isolation or their interaction separately, and develop recommender systems using the one form of information only. It is imperative to understand the linkages among the users in the network and use them in developing a match-making system. This study utilizes several social network analysis methods such as graph theory, small world phenomenon, centrality analysis, density analysis to gain insight into the entities and their relationships present in this network. This paper also proposes a new type of graph called “attributed bipartite graph”. By using these analyses and the proposed type of graph, an efficient hybrid recommender system is developed which generates recommendation for new users as well as shows improvement in accuracy over the baseline methods.

Communication Interception Technology (CIT) and its use in the fight against Transnational Organised Crime (TOC) in Australia : a review of the literature

In recent times, technology has advanced in such a manner that the world can now communicate in means previously never thought possible. Transnational organised crime groups, who have exploited these new technologies as basis for their criminal success, however, have not overlooked this development, growth and globalisation. Law enforcement agencies have been confronted with an unremitting challenge as they endeavour to intercept, monitor and analyse these communications as a means of disrupting the activities of criminal enterprises. The challenge lies in the ability to recognise and change tactics to match an increasingly sophisticated adversary. The use of communication interception technology, such as phone taps or email interception, is a tactic that when used appropriately has the potential to cause serious disruption to criminal enterprises. Despite the research that exists on CIT and TOC, these two bodies of knowledge rarely intersect. This paper builds on current literature, drawing them together to provide a clearer picture of the use of CIT in an enforcement and intelligence capacity. It provides a review of the literature pertaining to TOC, the structure of criminal enterprises and the vulnerability of communication used by these crime groups. Identifying the current contemporary models of policing it reviews intelligence-led policing as the emerging framework for modern policing. Finally, it assesses the literature concerning CIT, its uses within Australia and the limitations and arguments that exist. In doing so, this paper provides practitioners with a clearer picture of the use, barriers and benefits of using CIT in the fight against TOC. It helps to bridge the current gaps in modern policing theory and offers a perspective that can help drive future research.

Towards an authentically assessed science curriculum

The intentions of the science curriculum are very often constrained by the forms of student learning that are required by, or are currently available within, the system of education. Furthermore, little attention is given to developing new approaches to assessment that would encourage these good intentions. In this chapter, we argue that achieving this broadening of the intentions of science education will require a diversity of assessment techniques and that only a profile of each student’s achievement will capture the range of intended learnings. We explore a variety of assessment modes that match some of these new aspects of science learning and that also provide students with both formative information and a more comprehensive and authentic summative profile of their performances. Our discussion is illustrated with research-based examples of assessment practice in relation to three aspects of science education that are increasingly referred to in curriculum statements as desirable human dimensions of science: context-based science education, decision-making processes and socioscientific issues and integrated science education. We conclude with some notes on what these broader kinds of assessment mean for teachers and the support they would need to include them in their day-to-day practices in the science classrooms if, and when, the mainstream of science teaching and learning takes these curricular intentions seriously.

De Novo Transcriptome Sequence Assembly and Analysis of RNA Silencing Genes of Nicotiana benthamiana

Background: Nicotiana benthamiana has been widely used for transient gene expression assays and as a model plant in the study of plant-microbe interactions, lipid engineering and RNA silencing pathways. Assembling the sequence of its transcriptome provides information that, in conjunction with the genome sequence, will facilitate gaining insight into the plant's capacity for high-level transient transgene expression, generation of mobile gene silencing signals, and hyper-susceptibility to viral infection. Methodology/Results: RNA-seq libraries from 9 different tissues were deep sequenced and assembled, de novo, into a representation of the transcriptome. The assembly, of16GB of sequence, yielded 237,340 contigs, clustering into 119,014 transcripts (unigenes). Between 80 and 85% of reads from all tissues could be mapped back to the full transcriptome. Approximately 63% of the unigenes exhibited a match to the Solgenomics tomato predicted proteins database. Approximately 94% of the Solgenomics N. benthamiana unigene set (16,024 sequences) matched our unigene set (119,014 sequences). Using homology searches we identified 31 homologues that are involved in RNAi-associated pathways in Arabidopsis thaliana, and show that they possess the domains characteristic of these proteins. Of these genes, the RNA dependent RNA polymerase gene, Rdr1, is transcribed but has a 72 nt insertion in exon1 that would cause premature termination of translation. Dicer-like 3 (DCL3) appears to lack both the DEAD helicase motif and second dsRNA binding motif, and DCL2 and AGO4b have unexpectedly high levels of transcription. Conclusions: The assembled and annotated representation of the transcriptome and list of RNAi-associated sequences are accessible at www.benthgenome.com alongside a draft genome assembly. These genomic resources will be very useful for further study of the developmental, metabolic and defense pathways of N. benthamiana and in understanding the mechanisms behind the features which have made it such a well-used model plant. © 2013 Nakasugi et al.