Exploring probabilistic follow relationship to prevent collusive peer-to-peer piracy

P2P collusive piracy, where paid P2P clients share the content with unpaid clients, has drawn significant concerns in recent years. Study on the follow relationship provides an emerging track of research in capturing the followee (e.g., paid client) for the blocking of piracy spread from all his followers (e.g., unpaid clients). Unfortunately, existing research efforts on the follow relationship in online social network have largely overlooked the time constraint and the content feedback in sequential behavior analysis. Hence, how to consider these two characteristics for effective P2P collusive piracy prevention remains an open problem. In this paper, we proposed a multi-bloom filter circle to facilitate the time-constraint storage and query of P2P sequential behaviors. Then, a probabilistic follow with content feedback model to fast discover and quantify the probabilistic follow relationship is further developed, and then, the corresponding approach to piracy prevention is designed. The extensive experimental analysis demonstrates the capability of the proposed approach.

Effects of training peer tutors in content knowledge versus tutoring skills on giving feedback to help tutees’ complex tasks

This study aims to investigate the effectiveness of training tutors in content knowledge of a particular domain versus training them in tutoring skills of pedagogical knowledge when tutoring on a complex tutee task. Forty-seven tutor-tutee pairs of fourth year secondary school students were created and assigned to one of two treatments. Twenty-two tutors received training in content knowledge and the other twenty-five tutors in tutoring skills. Tutors formulated written feedback immediately after the training. Tutees first interpreted the tutor feedback and then used it to revise their research questions. The results showed that tutors trained in tutoring skills formulated more effective feedback than tutors trained in content knowledge. In addition, tutees helped by tutoring-skills tutors found the feedback more motivating than those helped by content- knowledge tutors. However, no differences were found in tutee performance on revision. The findings are discussed in terms of the set-up of this study and implications for improving the effectiveness of peer tutoring.

A new re-ranking method using enhanced pseudo-relevance feedback for content-based medical image retrieval

We propose a novel re-ranking method for content-based medical image retrieval based on the idea of pseudo-relevance feedback (PRF). Since the highest ranked images in original retrieval results are not always relevant, a naive PRF based re-ranking approach is not capable of producing a satisfactory result. We employ a two-step approach to address this issue. In step 1, a Pearson's correlation coefficient based similarity update method is used to re-rank the high ranked images. In step 2, after estimating a relevance probability for each of the highest ranked images, a fuzzy SVM ensemble based approach is adopted to re-rank the images. The experiments demonstrate that the proposed method outperforms two other re-ranking methods.

A noisy-smoothing relevance feedback method for content-based medical image retrieval

In this paper, we address a new problem of noisy images which present in the procedure of relevance feedback for medical image retrieval. We concentrate on the noisy images, caused by the users mislabeling some irrelevant images as relevant ones, and a noisy-smoothing relevance feedback (NS-RF) method is proposed. In NS-RF, a two-step strategy is proposed to handle the noisy images. In step 1, a noisy elimination algorithm is adopted to identify and eliminate the noisy images. In step 2, to further alleviate the influence of noisy images, a fuzzy membership function is employed to estimate the relevance probabilities of retained relevant images. After noisy handling, the fuzzy support vector machine, which can take into account different relevant images with different relevance probabilities, is adopted to re-rank the images. The experimental results on the IRMA medical image collection demonstrate that the proposed method can deal with the noisy images effectively.

Pedagogy of feedback on student academic writing : research supervisor practices for the end of the research degree candidature

All academic writing is advanced with the benefit of feedback about the writing. In the case of the academic writing genres of the research proposal and the dissertation, feedback is usually provided by the research supervisor. Given that academic writing development is a process, and in the case of the research proposal and dissertation, writing which develops over time, it seems likely that the nature of feedback on drafts written early in the candidature may be different from feedback provided by the research supervisor later in a student’s candidature. ----- ----- When a research supervisor has been reading a student’s writing over a period of time, their own familiarity with the writing generates a risk to their ability to provide critical and objective feedback. Particularly by the end of a student’s candidature, the research supervisor’s familiarity with the work may cause them to miss elements of writing improvement. ----- ----- The author, as a research supervisor, has developed a feedback grid to facilitate feedback on the final drafts of a dissertation. This feedback grid is generated by the embedded promises in the early sections of the dissertation, which are then used to audit the content of the final sections of the dissertation to ascertain whether promises made have been fulfilled. This provides a strategy for the research supervisor to step back from the work and read the dissertation with the agenda of a dissertation examiner. ----- ----- The grid is one strategy within a broader pedagogy of providing feedback on writing samples.

Overview of the INEX 2011 relevance feedback track

The INEX 2011 Relevance Feedback track offered a refined approach to the evaluation of Focused Relevance Feedback algorithms through simulated exhaustive user feedback. Run in largely identical fashion to the Relevance Feedback track in INEX 2010[2], we simulated a user-in-the loop by re-using the assessments of ad-hoc retrieval obtained from real users who assess focused ad-hoc retrieval submissions. We present the evaluation methodology, its implementation, and experimental results obtained for four submissions from two participating organisations. As the task and evaluation methods did not change between INEX 2010 and now, explanations of these details from the INEX 2010 version of the track have been repeated verbatim where appropriate.

Setting measureable goals with young people : qualitative feedback from the Goal Attainment Scale in youth mental health

Introduction Measuring occupational performance is an essential part of clinical practice; however, there is little research on service user perceptions of measures. The aim of this investigation was to explore the acceptability and utility of one occupational performance outcome measure, Goal Attainment Scaling, with young people (12–25 years old) seeking psychological help. Method Semi-structured interviews were conducted with ten young people seeking help from a youth mental health clinic. Interviews were audio taped and a field diary kept. Interviews were transcribed verbatim and analysed using content analysis. Results were verified by member checking. Results All participants were able to engage in using Goal Attainment Scaling to set goals for therapy, and reported the process to be useful. The participants identified the physical location and ownership of the scale was important to help motivate them to work on their goals. Conclusion Young help-seekers see Goal Attainment Scaling as an acceptable tool to facilitate the establishment of functional goals. Young service users were particularly keen to maintain control over the physical location of completed forms.

Toward the adaptive generation of bespoke game content

In this chapter, we explore methods for automatically generating game content—and games themselves—adapted to individual players in order to improve their playing experience or achieve a desired effect. This goes beyond notions of mere replayability and involves modeling player needs to maximize their enjoyment, involvement, and interest in the game being played. We identify three main aspects of this process: generation of new content and rule sets, measurement of this content and the player, and adaptation of the game to change player experience. This process forms a feedback loop of constant refinement, as games are continually improved while being played. Framed within this methodology, we present an overview of our recent and ongoing research in this area. This is illustrated by a number of case studies that demonstrate these ideas in action over a variety of game types, including 3D action games, arcade games, platformers, board games, puzzles, and open-world games. We draw together some of the lessons learned from these projects to comment on the difficulties, the benefits, and the potential for personalized gaming via adaptive game design.

Defining the InstaBooth: Facilitating debate and content creation from situated users

Through ubiquitous computing and location-based social media, information is spreading outside the traditional domains of home and work into the urban environment. Digital technologies have changed the way people relate to the urban form supporting discussion on multiple levels, allowing more citizens to be heard in new ways (Fredericks et al. 2013; Houghton et al. 2014; Caldwell et al. 2013). Face-to-face and digitally mediated discussions, facilitated by tangible and hybrid interaction, such as multi-touch screens and media façades, are initiated through a telephone booth inspired portable structure: The InstaBooth. The InstaBooth prototype employs a multidisciplinary approach to engage local communities in a situated debate on the future of their urban environment. With it, we capture citizens’ past stories and opinions on the use and design of public places. The way public consultations are currently done often engages only a section of the population involved in a proposed development; the more vocal citizens are not necessarily the more representative of the communities (Jenkins 2006). Alternative ways to engage urban dwellers in the debate about the built environment are explored at the moment, including the use of social media or online tools (Foth 2009). This project fosters innovation by providing pathways for communities to participate in the decision making process that informs the urban form. The InstaBooth promotes dialogue and mediation between a bottom-up and a top-down approach to urban design, with the aim of promoting community connectedness with the urban environment. The InstaBooth provides an engagement and discussion platform that leverages a number of locally developed display and interaction technologies in order to facilitate a dialogue of ideas and commentary. The InstaBooth combines multiple interaction techniques into a hybrid (digital and analogue) media space. Through the InstaBooth, urban design and architectural proposals are displayed encouraging commentary from visitors. Inside the InstaBooth, visitors can activate a multi-touch screen in order to browse media, write a note, or draw a picture to provide feedback. The purpose of the InstaBooth is to engage with a broader section of society, including those who are often marginalised. The specific design of the internal and external interfaces, the mutual relationship between these interfaces with regards to information display and interaction, and the question how visitors can engage with the system, are part of the research agenda of the project.

Periodic oscillations in feedback systems with combined pulse modulation

This paper presents a systematic method of investigating the existence of limit cycle oscillations in feedback systems with combined integral pulse frequency-pulse width (IPF-P/V) modulation. The method is based on the non-linear discrete equivalence of the continuous feedback system containing the IPF-PW modulator.

Construction of stability multipliers for non-linear feedback systems

This paper is concerned with the analysis of the absolute stability of a non-linear autonomous system which consists of a single non-linearity belonging to a particular class, in an otherwise linear feedback loop. It is motivated from the earlier Popovlike frequency-domain criteria using the ' multiplier ' eoncept and involves the construction of ' stability multipliers' with prescribed phase characteristics. A few computer-based methods by which this problem can be solved are indicated and it is shown that this constitutes a stop-by-step procedure for testing the stability properties of a given system.

On the stabilization of uniformly decoupled systems by state variable feedback

Conditions under which the asymptotic stabilization of uniformly decoupled time-varying multivariate systems is possible are explored. This is accomplished by developing a canonical form for integrator uniformly decoupled system in which the coefficient matrices have a simple structure. The procedures developed rely on certain conditions on the given system and yield explicit expressions for the stabilization compensators.

Efficient content-based image retrieval based on multi-feature fusion

The increased availability of image capturing devices has enabled collections of digital images to rapidly expand in both size and diversity. This has created a constantly growing need for efficient and effective image browsing, searching, and retrieval tools. Pseudo-relevance feedback (PRF) has proven to be an effective mechanism for improving retrieval accuracy. An original, simple yet effective rank-based PRF mechanism (RB-PRF) that takes into account the initial rank order of each image to improve retrieval accuracy is proposed. This RB-PRF mechanism innovates by making use of binary image signatures to improve retrieval precision by promoting images similar to highly ranked images and demoting images similar to lower ranked images. Empirical evaluations based on standard benchmarks, namely Wang, Oliva & Torralba, and Corel datasets demonstrate the effectiveness of the proposed RB-PRF mechanism in image retrieval.

Rendering stiffer walls: a hybrid haptic system using continuous and discrete time feedback

Instability in conventional haptic rendering destroys the perception of rigid objects in virtual environments. Inherent limitations in the conventional haptic loop restrict the maximum stiffness that can be rendered. In this paper we present a method to render virtual walls that are much stiffer than those achieved by conventional techniques. By removing the conventional digital haptic loop and replacing it with a part-continuous and part-discrete time hybrid haptic loop, we were able to render stiffer walls. The control loop is implemented as a combinational logic circuit on an field-programmable gate array. We compared the performance of the conventional haptic loop and our hybrid haptic loop on the same haptic device, and present mathematical analysis to show the limit of stability of our device. Our hybrid method removes the computer-intensive haptic loop from the CPU-this can free a significant amount of resources that can be used for other purposes such as graphical rendering and physics modeling. It is our hope that, in the future, similar designs will lead to a haptics processing unit (HPU).

A neural network based CBIR system using STI features and relevance feedback

Multimedia mining primarily involves, information analysis and retrieval based on implicit knowledge. The ever increasing digital image databases on the Internet has created a need for using multimedia mining on these databases for effective and efficient retrieval of images. Contents of an image can be expressed in different features such as Shape, Texture and Intensity-distribution(STI). Content Based Image Retrieval(CBIR) is an efficient retrieval of relevant images from large databases based on features extracted from the image. Most of the existing systems either concentrate on a single representation of all features or linear combination of these features. The paper proposes a CBIR System named STIRF (Shape, Texture, Intensity-distribution with Relevance Feedback) that uses a neural network for nonlinear combination of the heterogenous STI features. Further the system is self-adaptable to different applications and users based upon relevance feedback. Prior to retrieval of relevant images, each feature is first clustered independent of the other in its own space and this helps in matching of similar images. Testing the system on a database of images with varied contents and intensive backgrounds showed good results with most relevant images being retrieved for a image query. The system showed better and more robust performance compared to existing CBIR systems