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.

The complete nucleotide sequence of subterranean clover mottle virus

The complete nucleotide sequence of Subterranean clover mottle virus (SCMoV) genomic RNA has been determined. The SCMoV genome is 4,258 nucleotides in length. It shares most nucleotide and amino acid sequence identity with the genome of Lucerne transient streak virus (LTSV). SCMoV RNA encodes four overlapping open reading frames and has a genome organisation similar to that of Cocksfoot mottle virus (CfMV). ORF1 and ORF4 are predicted to encode single proteins. ORF2 is predicted to encode two proteins that are derived from a -1 translational frameshift between two overlapping reading frames (ORF2a and ORF2b). A search of amino acid databases did not find a significant match for ORF1 and the function of this protein remains unclear. ORF2a contains a motif typical of chymotrypsin-like serine proteases and ORF2b has motifs characteristically present in positive-stranded RNA-dependent RNA polymerases. ORF4 is likely to be expressed from a subgenomic RNA and encodes the viral coat protein. The ORF2a/ORF2b overlapping gene expression strategy used by SCMoV and CfMV is similar to that of the poleroviruses and differ from that of other published sobemoviruses. These results suggest that the sobemoviruses could now be divided into two distinct subgroups based on those that express the RNA-dependent RNA polymerase from a single, in-frame polyprotein, and those that express it via a -1 translational frameshifting mechanism.

Two cycles of voluminous pyroclastic volcanism and sedimentation related to episodic granite emplacement during the late Archean : Eastern Yilgarn Craton, Western Australia

The thick piles of late-Archean volcaniclastic sedimentary successions that overlie the voluminous greenstone units of the eastern Yilgarn Craton, Western Australia, record the important transition from the cessation in mafic-ultramafic volcanism to cratonisation between about 2690 and 2655 Ma. Unfortunately, an inability to clearly subdivide the superficially similar sedimentary successions and correlate them between the various geological terranes and domains of the eastern Yilgarn Craton has led to uncertainty about the timing and nature of the region's palaeogeographic and palaeotectonic evolution. Here, we present the results of some 2025 U–Pb laser-ablation-ICP-MS analyses and 323 Sensitive High-Resolution Ion Microprobe (SHRIMP) analyses of detrital zircons from 14 late-Archean felsic clastic successions of the eastern Yilgarn Craton, which have enabled correlation of clastic successions. The results of our data, together with those compiled from previous studies, show that the post-greenstone sedimentary successions include two major cycles that both commenced with voluminous pyroclastic volcanism and ended with widespread exhumation and erosion associated with granite emplacement. Cycle One commences with an influx of rapidly reworked feldspar-rich pyroclastic debris. These units, here-named the Early Black Flag Group, are dominated by a single population of detrital zircons with an average age of 2690–2680 Ma. Thick (up to 2 km) dolerite bodies, such as the Golden Mile Dolerite, intrude the upper parts of the Early Black Flag Group at about 2680 Ma. Incipient development of large granite domes during Cycle One created extensional basins predominantly near their southeastern and northwestern margins (e.g., St Ives, Wallaby, Kanowna Belle and Agnew), into which the Early Black Flag Group and overlying coarse mafic conglomerate facies of the Late Black Flag Group were deposited. The clast compositions and detrital-zircon ages of the late Black Flag Group detritus match closely the nearby and/or stratigraphically underlying successions, thus suggesting relatively local provenance. Cycle Two involved a similar progression to that observed in Cycle One, but the age and composition of the detritus were notably different. Deposition of rapidly reworked quartz-rich pyroclastic deposits dominated by a single detrital-zircon age population of 2670–2660 Ma heralded the beginning of Cycle Two. These coarse-grained quartz-rich units, are name here the Early Merougil Group. The mean ages of the detrital zircons from the Early Merougil Group match closely the age of the peak in high-Ca (quartz-rich) granite magmatism in the Yilgarn Craton and thus probably represent the surface expression of the same event. Successions of the Late Merougil Group are dominated by coarse felsic conglomerate with abundant volcanic quartz. Although the detrital zircons in these successions have a broad spread of age, the principal sub-populations have ages of about 2665 Ma and thus match closely those of the Early Merougil Group. These successions occur most commonly at the northwestern and southeastern margins of the granite batholiths and thus are interpreted to represent resedimented units dominted by the stratigraphically underlying packages of the Early Merougil Group. The Kurrawang Group is the youngest sedimentary units identified in this study and is dominated by polymictic conglomerate with clasts of banded iron formation (BIF), granite and quartzite near the base and quartz-rich sandstone units containing detrital zircons aged up to 3500 Ma near the top. These units record provenance from deeper and/or more-distal sources. We suggest here that the principal driver for the major episodes of volcanism, sedimentation and deformation associated with basin development was the progressive emplacement of large granite batholiths. This interpretation has important implication for palaeogeographic and palaeotectonic evolution of all late-Archean terranes around the world.

Odometry-driven inference to link multiple exemplars of a location

A major challenge for robot localization and mapping systems is maintaining reliable operation in a changing environment. Vision-based systems in particular are susceptible to changes in illumination and weather, and the same location at another time of day may appear radically different to a system using a feature-based visual localization system. One approach for mapping changing environments is to create and maintain maps that contain multiple representations of each physical location in a topological framework or manifold. However, this requires the system to be able to correctly link two or more appearance representations to the same spatial location, even though the representations may appear quite dissimilar. This paper proposes a method of linking visual representations from the same location without requiring a visual match, thereby allowing vision-based localization systems to create multiple appearance representations of physical locations. The most likely position on the robot path is determined using particle filter methods based on dead reckoning data and recent visual loop closures. In order to avoid erroneous loop closures, the odometry-based inferences are only accepted when the inferred path's end point is confirmed as correct by the visual matching system. Algorithm performance is demonstrated using an indoor robot dataset and a large outdoor camera dataset.

Characterizing transport through a crowded environment with different obstacle sizes

Transport through crowded environments is often classified as anomalous, rather than classical, Fickian diffusion. Several studies have sought to describe such transport processes using either a continuous time random walk or fractional order differential equation. For both these models the transport is characterized by a parameter α, where α = 1 is associated with Fickian diffusion and α < 1 is associated with anomalous subdiffusion. Here, we simulate a single agent migrating through a crowded environment populated by impenetrable, immobile obstacles and estimate α from mean squared displacement data. We also simulate the transport of a population of such agents through a similar crowded environment and match averaged agent density profiles to the solution of a related fractional order differential equation to obtain an alternative estimate of α. We examine the relationship between our estimate of α and the properties of the obstacle field for both a single agent and a population of agents; we show that in both cases, α decreases as the obstacle density increases, and that the rate of decrease is greater for smaller obstacles. Our work suggests that it may be inappropriate to model transport through a crowded environment using widely reported approaches including power laws to describe the mean squared displacement and fractional order differential equations to represent the averaged agent density profiles.

Large-scale analysis of formations in soccer

Due to the demand for better and deeper analysis in sports, organizations (both professional teams and broadcasters) are looking to use spatiotemporal data in the form of player tracking information to obtain an advantage over their competitors. However, due to the large volume of data, its unstructured nature, and lack of associated team activity labels (e.g. strategic/tactical), effective and efficient strategies to deal with such data have yet to be deployed. A bottleneck restricting such solutions is the lack of a suitable representation (i.e. ordering of players) which is immune to the potentially infinite number of possible permutations of player orderings, in addition to the high dimensionality of temporal signal (e.g. a game of soccer last for 90 mins). Leveraging a recent method which utilizes a "role-representation", as well as a feature reduction strategy that uses a spatiotemporal bilinear basis model to form a compact spatiotemporal representation. Using this representation, we find the most likely formation patterns of a team associated with match events across nearly 14 hours of continuous player and ball tracking data in soccer. Additionally, we show that we can accurately segment a match into distinct game phases and detect highlights. (i.e. shots, corners, free-kicks, etc) completely automatically using a decision-tree formulation.

Predicting shot locations in tennis using spatiotemporal data

Over the past decade, vision-based tracking systems have been successfully deployed in professional sports such as tennis and cricket for enhanced broadcast visualizations as well as aiding umpiring decisions. Despite the high-level of accuracy of the tracking systems and the sheer volume of spatiotemporal data they generate, the use of this high quality data for quantitative player performance and prediction has been lacking. In this paper, we present a method which predicts the location of a future shot based on the spatiotemporal parameters of the incoming shots (i.e. shot speed, location, angle and feet location) from such a vision system. Having the ability to accurately predict future short-term events has enormous implications in the area of automatic sports broadcasting in addition to coaching and commentary domains. Using Hawk-Eye data from the 2012 Australian Open Men's draw, we utilize a Dynamic Bayesian Network to model player behaviors and use an online model adaptation method to match the player's behavior to enhance shot predictability. To show the utility of our approach, we analyze the shot predictability of the top 3 players seeds in the tournament (Djokovic, Federer and Nadal) as they played the most amounts of games.

Towards condition-invariant, top-down visual place recognition

In this paper we present a novel place recognition algorithm inspired by recent discoveries in human visual neuroscience. The algorithm combines intolerant but fast low resolution whole image matching with highly tolerant, sub-image patch matching processes. The approach does not require prior training and works on single images (although we use a cohort normalization score to exploit temporal frame information), alleviating the need for either a velocity signal or image sequence, differentiating it from current state of the art methods. We demonstrate the algorithm on the challenging Alderley sunny day – rainy night dataset, which has only been previously solved by integrating over 320 frame long image sequences. The system is able to achieve 21.24% recall at 100% precision, matching drastically different day and night-time images of places while successfully rejecting match hypotheses between highly aliased images of different places. The results provide a new benchmark for single image, condition-invariant place recognition.

Slicing big data - Twitter, gambling and time sensitive information

Big Data presents many challenges related to volume, whether one is interested in studying past datasets or, even more problematically, attempting to work with live streams of data. The most obvious challenge, in a ‘noisy’ environment such as contemporary social media, is to collect the pertinent information; be that information for a specific study, tweets which can inform emergency services or other responders to an ongoing crisis, or give an advantage to those involved in prediction markets. Often, such a process is iterative, with keywords and hashtags changing with the passage of time, and both collection and analytic methodologies need to be continually adapted to respond to this changing information. While many of the data sets collected and analyzed are preformed, that is they are built around a particular keyword, hashtag, or set of authors, they still contain a large volume of information, much of which is unnecessary for the current purpose and/or potentially useful for future projects. Accordingly, this panel considers methods for separating and combining data to optimize big data research and report findings to stakeholders. The first paper considers possible coding mechanisms for incoming tweets during a crisis, taking a large stream of incoming tweets and selecting which of those need to be immediately placed in front of responders, for manual filtering and possible action. The paper suggests two solutions for this, content analysis and user profiling. In the former case, aspects of the tweet are assigned a score to assess its likely relationship to the topic at hand, and the urgency of the information, whilst the latter attempts to identify those users who are either serving as amplifiers of information or are known as an authoritative source. Through these techniques, the information contained in a large dataset could be filtered down to match the expected capacity of emergency responders, and knowledge as to the core keywords or hashtags relating to the current event is constantly refined for future data collection. The second paper is also concerned with identifying significant tweets, but in this case tweets relevant to particular prediction market; tennis betting. As increasing numbers of professional sports men and women create Twitter accounts to communicate with their fans, information is being shared regarding injuries, form and emotions which have the potential to impact on future results. As has already been demonstrated with leading US sports, such information is extremely valuable. Tennis, as with American Football (NFL) and Baseball (MLB) has paid subscription services which manually filter incoming news sources, including tweets, for information valuable to gamblers, gambling operators, and fantasy sports players. However, whilst such services are still niche operations, much of the value of information is lost by the time it reaches one of these services. The paper thus considers how information could be filtered from twitter user lists and hash tag or keyword monitoring, assessing the value of the source, information, and the prediction markets to which it may relate. The third paper examines methods for collecting Twitter data and following changes in an ongoing, dynamic social movement, such as the Occupy Wall Street movement. It involves the development of technical infrastructure to collect and make the tweets available for exploration and analysis. A strategy to respond to changes in the social movement is also required or the resulting tweets will only reflect the discussions and strategies the movement used at the time the keyword list is created — in a way, keyword creation is part strategy and part art. In this paper we describe strategies for the creation of a social media archive, specifically tweets related to the Occupy Wall Street movement, and methods for continuing to adapt data collection strategies as the movement’s presence in Twitter changes over time. We also discuss the opportunities and methods to extract data smaller slices of data from an archive of social media data to support a multitude of research projects in multiple fields of study. The common theme amongst these papers is that of constructing a data set, filtering it for a specific purpose, and then using the resulting information to aid in future data collection. The intention is that through the papers presented, and subsequent discussion, the panel will inform the wider research community not only on the objectives and limitations of data collection, live analytics, and filtering, but also on current and in-development methodologies that could be adopted by those working with such datasets, and how such approaches could be customized depending on the project stakeholders.

Self-organization processes in field-invasion team sports

In nature, the interactions between agents in a complex system (fish schools; colonies of ants) are governed by information that is locally created. Each agent self-organizes (adjusts) its behaviour, not through a central command centre, but based on variables that emerge from the interactions with other system agents in the neighbourhood. Self-organization has been proposed as a mechanism to explain the tendencies for individual performers to interact with each other in field-invasion sports teams, displaying functional co-adaptive behaviours, without the need for central control. The relevance of self-organization as a mechanism that explains pattern-forming dynamics within attacker-defender interactions in field-invasion sports has been sustained in the literature. Nonetheless, other levels of interpersonal coordination, such as intra-team interactions, still raise important questions, particularly with reference to the role of leadership or match strategies that have been prescribed in advance by a coach. The existence of key properties of complex systems, such as system degeneracy, nonlinearity or contextual dependency, suggests that self-organization is a functional mechanism to explain the emergence of interpersonal coordination tendencies within intra-team interactions. In this opinion article we propose how leadership may act as a key constraint on the emergent, self-organizational tendencies of performers in field-invasion sports.

Science of winning soccer : emergent pattern-forming dynamics in association football

Quantitative analysis is increasingly being used in team sports to better understand performance in these stylized, delineated, complex social systems. Here we provide a first step toward understanding the pattern-forming dynamics that emerge from collective offensive and defensive behavior in team sports. We propose a novel method of analysis that captures how teams occupy sub-areas of the field as the ball changes location. We used the method to analyze a game of association football (soccer) based upon a hypothesis that local player numerical dominance is key to defensive stability and offensive opportunity. We found that the teams consistently allocated more players than their opponents in sub-areas of play closer to their own goal. This is consistent with a predominantly defensive strategy intended to prevent yielding even a single goal. We also find differences between the two teams' strategies: while both adopted the same distribution of defensive, midfield, and attacking players (a 4:3:3 system of play), one team was significantly more effective both in maintaining defensive and offensive numerical dominance for defensive stability and offensive opportunity. That team indeed won the match with an advantage of one goal (2 to 1) but the analysis shows the advantage in play was more pervasive than the single goal victory would indicate. Our focus on the local dynamics of team collective behavior is distinct from the traditional focus on individual player capability. It supports a broader view in which specific player abilities contribute within the context of the dynamics of multiplayer team coordination and coaching strategy. By applying this complex system analysis to association football, we can understand how players' and teams' strategies result in successful and unsuccessful relationships between teammates and opponents in the area of play.