A profiling based task scheduling approach for multicore network processors

 Multicore network processors have been playing an increasingly important role in computational processes, which emphasize on scalability and parallelism of the systems, in distributed environments especially in Internet-based delay-sensitive applications. It is an important but unsolved issue, however, to efficiently schedule tasks in network processors with multicore and multithread for improving the system throughput as much as possible. Profiling can gather runtime environment information and guide the compiler to optimize programs through scheduling tasks based on the runtime context. This paper proposes a profiling-based task scheduling approach, targeting on improving the throughput of multicore network processor (Intel IXP) systems in the balanced pipeline way. In this work, we investigate a profiling-based task scheduling framework, a task scheduling algorithm, and a set of performance models. Our task allocation scheme maps tasks onto the pipeline architecture and multiple threads of network processors in parallel, which incorporates the profiling context and global thread refinement. We evaluate our task scheduling algorithm by implementing representative network applications on the Intel IXP network processor. Experimental results demonstrate that our algorithm is able to schedule tasks in a balanced pipeline fashion and achieve the high throughput and data transmission rate. Copyright © 2012 John Wiley & Sons, Ltd.

Combining multiple tracking algorithms for improved general performance

Automated tracking of objects through a sequence of images has remained one of the difficult problems in computer vision. Numerous algorithms and techniques have been proposed for this task. Some algorithms perform well in restricted environments, such as tracking using stationary cameras, but a general solution is not currently available. A frequent problem is that when an algorithm is refined for one application, it becomes unsuitable for other applications. This paper proposes a general tracking system based on a different approach. Rather than refine one algorithm for a specific tracking task, two tracking algorithms are employed, and used to correct each other during the tracking task. By choosing the two algorithms such that they have complementary failure modes, a robust algorithm is created without increased specialisation.

Identifying markov blankets using lasso estimation

Determining the causal relation among attributes in a domain is a key task in data mining and knowledge discovery. The Minimum Message Length (MML) principle has demonstrated its ability in discovering linear causal models from training data. To explore the ways to improve efficiency, this paper proposes a novel Markov Blanket identification algorithm based on the Lasso estimator. For each variable, this algorithm first generates a Lasso tree, which represents a pruned candidate set of possible feature sets. The Minimum Message Length principle is then employed to evaluate all those candidate feature sets, and the feature set with minimum message length is chosen as the Markov Blanket. Our experiment results show the ability of this algorithm. In addition, this algorithm can be used to prune the search space of causal discovery, and further reduce the computational cost of those score-based causal discovery algorithms.

End-user experience with information in Web-based electronic service : a case study

End-user experience with information presumably considered as one of the prominent factors shaping the adoption of web-based electronic services. User interfacing with large amount of information the rationale is to deduce the effect in the current web-based task environment. Understanding user’s perception on the basis of the prior experience with information may provide insights into what constitutes in driving those perceptions and their effect in the current and future task in web-based electronic services. The paper lays the theoretical context of end-user experience with information and proceeds further in an attempt to distinguish the role in web-based electronic services.

Adaptive patch size determination for patch-based image completion

Patch-based image completion proceeds by iteratively filling the target (unknown) region by the best matching patches in the source image. In most existing such algorithms, the size of the patches is either fixed and specified by a default number or simply chosen to be inversely proportional to the spatial frequency. However, it is noted that the patch size affects how well the filled patch captures the local characteristics of the source image and thus the final completion accuracy. Thus in this paper we propose a new method to compute appropriate patch sizes for image completion to improve its performance. In particular, we formulate the patch size determination as an optimization problem that minimizes an objective function involving image gradients and distinct and homogenous features. Experimental results show that our method can provide a significant enhancement to patch-based image completion algorithms.

The efficient imputation method for neighborhood-based collaborative filtering

As each user tends to rate a small proportion of available items, the resulted Data Sparsity issue brings significant challenges to the research of recommender systems. This issue becomes even more severe for neighborhood-based collaborative filtering methods, as there are even lower numbers of ratings available in the neighborhood of the query item. In this paper, we aim to address the Data Sparsity issue in the context of the neighborhood-based collaborative filtering. Given the (user, item) query, a set of key ratings are identified, and an auto-adaptive imputation method is proposed to fill the missing values in the set of key ratings. The proposed method can be used with any similarity metrics, such as the Pearson Correlation Coefficient and Cosine-based similarity, and it is theoretically guaranteed to outperform the neighborhood-based collaborative filtering approaches. Results from experiments prove that the proposed method could significantly improve the accuracy of recommendations for neighborhood-based Collaborative Filtering algorithms. © 2012 ACM.

JLMC: A clustering method based on Jordan-Form of Laplacian-Matrix

Among the current clustering algorithms of complex networks, Laplacian-based spectral clustering algorithms have the advantage of rigorous mathematical basis and high accuracy. However, their applications are limited due to their dependence on prior knowledge, such as the number of clusters. For most of application scenarios, it is hard to obtain the number of clusters beforehand. To address this problem, we propose a novel clustering algorithm - Jordan-Form of Laplacian-Matrix based Clustering algorithm (JLMC). In JLMC, we propose a model to calculate the number (n) of clusters in a complex network based on the Jordan-Form of its corresponding Laplacian matrix. JLMC clusters the network into n clusters by using our proposed modularity density function (P function). We conduct extensive experiments over real and synthetic data, and the experimental results reveal that JLMC can accurately obtain the number of clusters in a complex network, and outperforms Fast-Newman algorithm and Girvan-Newman algorithm in terms of clustering accuracy and time complexity.

Improving communication when seeking informed consent: a randomised controlled study of a computer-based method for providing information to prospective clinical trial participants

OBJECTIVE: To assess the efficacy, with respect to participant understanding of information, of a computer-based approach to communication about complex, technical issues that commonly arise when seeking informed consent for clinical research trials. DESIGN, SETTING AND PARTICIPANTS: An open, randomised controlled study of 60 patients with diabetes mellitus, aged 27-70 years, recruited between August 2006 and October 2007 from the Department of Diabetes and Endocrinology at the Alfred Hospital and Baker IDI Heart and Diabetes Institute, Melbourne. INTERVENTION: Participants were asked to read information about a mock study via a computer-based presentation (n = 30) or a conventional paper-based information statement (n = 30). The computer-based presentation contained visual aids, including diagrams, video, hyperlinks and quiz pages. MAIN OUTCOME MEASURES: Understanding of information as assessed by quantitative and qualitative means. RESULTS: Assessment scores used to measure level of understanding were significantly higher in the group that completed the computer-based task than the group that completed the paper-based task (82% v 73%; P = 0.005). More participants in the group that completed the computer-based task expressed interest in taking part in the mock study (23 v 17 participants; P = 0.01). Most participants from both groups preferred the idea of a computer-based presentation to the paper-based statement (21 in the computer-based task group, 18 in the paper-based task group). CONCLUSIONS: A computer-based method of providing information may help overcome existing deficiencies in communication about clinical research, and may reduce costs and improve efficiency in recruiting participants for clinical trials.

Space-crossing : community-based data forwarding in mobile social networks under the hybrid communication architecture

In this paper, we study two tightly coupled issues, space-crossing community detection and its influence on data forwarding in mobile social networks (MSNs). We propose a communication framework containing the hybrid underlying network with access point (AP) support for data forwarding and the base stations for managing most of control traffic. The concept of physical proximity community can be extended to be one across the geographical space, because APs can facilitate the communication among long-distance nodes. Space-crossing communities are obtained by merging some pairs of physical proximity communities. Based on the space-crossing community, we define two cases of node local activity and use them as the input of inner product similarity measurement. We design a novel data forwarding algorithm Social Attraction and Infrastructure Support (SAIS), which applies similarity attraction to route to neighbor more similar to destination, and infrastructure support phase to route the message to other APs within common connected components. We evaluate our SAIS algorithm on real-life datasets from MIT Reality Mining and University of Illinois Movement (UIM). Results show that space-crossing community plays a positive role in data forwarding in MSNs. Based on this new type of community, SAIS achieves a better performance than existing popular social community-based data forwarding algorithms in practice, including Simbet, Bubble Rap and Nguyen's Routing algorithms.

Prospective memory while driving : comparison of time- and event-based intentions

Prospective memories can divert attentional resources from ongoing activities. However, it is unclear whether these effects and the theoretical accounts that seek to explain them will generalise to a complex real-world task such as driving. Twenty-four participants drove two simulated routes while maintaining a fixed headway with a lead vehicle. Drivers were given either event-based (e.g. arriving at a filling station) or time-based errands (e.g. on-board clock shows 3:30). In contrast to the predominant view in the literature which suggests time-based tasks are more demanding, drivers given event-based errands showed greater difficulty in mirroring lead vehicle speed changes compared to the time-based group. Results suggest that common everyday secondary tasks, such as scouting the roadside for a bank, may have a detrimental impact on driving performance. The additional finding that this cost was only evident with the event-based task highlights a potential area of both theoretical and practical interest. Practitioner Summary: Drivers were given either time- or event-based errands whilst engaged in a simulated drive. We examined the effect of errands on an ongoing vehicle follow task. In contrast to previous non-driving studies, event-based errands are more disruptive. Common everyday errands may have a detrimental impact on driving performance.

A conceptual matrix model for biological data processing

This paper proposes a conceptual matrix model with algorithms for biological data processing. The required elements for constructing a matrix model are discussed. The representative matrix-based methods and algorithms which have potentials in biological data processing are presented / proposed. Some application cases of the model in biological data processing are studied, which show the applicability of this model in various kinds of biological data processing. This conceptual model established a framework within which biological data processing and mining could be conducted. The model is also heuristic to other applications.

How do teachers investigate scientific concepts in an Early Childhood Environment?

Early Childhood Educators have an important role to fulfil in aiding children's development and understandings in the science curriculum. There are many different views and opinions on how science can be taught in an Early Childhood environment, it is therefore our aim to investigate how teachers feel about teaching science concepts and promoting science in the early childhood centre. We aim to discover how everyday activities relate to the nature of science within our everyday lives. The science curriculum is important in Early Childhood settings as it provides children with various opportunities to explore the natural world. We are hoping to gain a deeper understanding of how teachers are guiding and encouraging children to make sense of their experiences. It is also important that we explore how Early Childhood Educators understand their own practice in teaching science concepts in their curriculum.

Description of project: We will be completing a small inquiry based task which will require us to compile data collected from interviews, recordings from teachers in long day and kindergarten settings around the Geelong region.

Methodology: ln order to undertake this research we will be using a socio cultural framework, focusing on language in the social environment and play (basing our ideas on the theories of Vygotsky). We will be undertaking narrative accounts to obtain data which will be collated from three different sources.

Ethical implications of projects: We do not foresee any significant risks to any participant in this study. The topic of the research is uncontroversial, and we will be taking measures to ensure anonymity or confidentiality where appropriate.

Improving data forwarding in mobile social networks with infrastructure support: a space-crossing community approach

In this paper, we study two tightly coupled issues: space-crossing community detection and its influence on data forwarding in Mobile Social Networks (MSNs) by taking the hybrid underlying networks with infrastructure support into consideration. The hybrid underlying network is composed of large numbers of mobile users and a small portion of Access Points (APs). Because APs can facilitate the communication among long-distance nodes, the concept of physical proximity community can be extended to be one across the geographical space. In this work, we first investigate a space-crossing community detection method for MSNs. Based on the detection results, we design a novel data forwarding algorithm SAAS (Social Attraction and AP Spreading), and show how to exploit the space-crossing communities to improve the data forwarding efficiency. We evaluate our SAAS algorithm on real-life data from MIT Reality Mining and University of Illinois Movement (UIM). Results show that space-crossing community plays a positive role in data forwarding in MSNs in terms of delivery ratio and delay. Based on this new type of community, SAAS achieves a better performance than existing social community-based data forwarding algorithms in practice, including Bubble Rap and Nguyen's Routing algorithms. © 2014 IEEE.

A visual-numeric approach to clustering and anomaly detection for trajectory data

This paper proposes a novel application of Visual Assessment of Tendency (VAT)-based hierarchical clustering algorithms (VAT, iVAT, and clusiVAT) for trajectory analysis. We introduce a new clustering based anomaly detection framework named iVAT+ and clusiVAT+ and use it for trajectory anomaly detection. This approach is based on partitioning the VAT-generated Minimum Spanning Tree based on an efficient thresholding scheme. The trajectories are classified as normal or anomalous based on the number of paths in the clusters. On synthetic datasets with fixed and variable numbers of clusters and anomalies, we achieve 98 % classification accuracy. Our two-stage clusiVAT method is applied to 26,039 trajectories of vehicles and pedestrians from a parking lot scene from the real life MIT trajectories dataset. The first stage clusters the trajectories ignoring directionality. The second stage divides the clusters obtained from the first stage by considering trajectory direction. We show that our novel two-stage clusiVAT approach can produce natural and informative trajectory clusters on this real life dataset while finding representative anomalies.