Finding Academic Experts on a MultiSensor Approach using Shannon's Entropy

Expert finding is an information retrieval task concerned with the search for the most knowledgeable people, in some topic, with basis on documents describing peoples activities. The task involves taking a user query as input and returning a list of people sorted by their level of expertise regarding the user query. This paper introduces a novel approach for combining multiple estimators of expertise based on a multisensor data fusion framework together with the Dempster-Shafer theory of evidence and Shannon's entropy. More specifically, we defined three sensors which detect heterogeneous information derived from the textual contents, from the graph structure of the citation patterns for the community of experts, and from profile information about the academic experts. Given the evidences collected, each sensor may define different candidates as experts and consequently do not agree in a final ranking decision. To deal with these conflicts, we applied the Dempster-Shafer theory of evidence combined with Shannon's Entropy formula to fuse this information and come up with a more accurate and reliable final ranking list. Experiments made over two datasets of academic publications from the Computer Science domain attest for the adequacy of the proposed approach over the traditional state of the art approaches. We also made experiments against representative supervised state of the art algorithms. Results revealed that the proposed method achieved a similar performance when compared to these supervised techniques, confirming the capabilities of the proposed framework

Selection of sequence motifs and generative Hopfield-Potts models for protein familiesilies

Statistical models for families of evolutionary related proteins have recently gained interest: in particular pairwise Potts models, as those inferred by the Direct-Coupling Analysis, have been able to extract information about the three-dimensional structure of folded proteins, and about the effect of amino-acid substitutions in proteins. These models are typically requested to reproduce the one- and two-point statistics of the amino-acid usage in a protein family, {\em i.e.}~to capture the so-called residue conservation and covariation statistics of proteins of common evolutionary origin. Pairwise Potts models are the maximum-entropy models achieving this. While being successful, these models depend on huge numbers of {\em ad hoc} introduced parameters, which have to be estimated from finite amount of data and whose biophysical interpretation remains unclear. Here we propose an approach to parameter reduction, which is based on selecting collective sequence motifs. It naturally leads to the formulation of statistical sequence models in terms of Hopfield-Potts models. These models can be accurately inferred using a mapping to restricted Boltzmann machines and persistent contrastive divergence. We show that, when applied to protein data, even 20-40 patterns are sufficient to obtain statistically close-to-generative models. The Hopfield patterns form interpretable sequence motifs and may be used to clusterize amino-acid sequences into functional sub-families. However, the distributed collective nature of these motifs intrinsically limits the ability of Hopfield-Potts models in predicting contact maps, showing the necessity of developing models going beyond the Hopfield-Potts models discussed here.Comment: 26 pages, 16 figures, to app. in PR

3-D Hand Pose Estimation from Kinect's Point Cloud Using Appearance Matching

We present a novel appearance-based approach for pose estimation of a human hand using the point clouds provided by the low-cost Microsoft Kinect sensor. Both the free-hand case, in which the hand is isolated from the surrounding environment, and the hand-object case, in which the different types of interactions are classified, have been considered. The hand-object case is clearly the most challenging task having to deal with multiple tracks. The approach proposed here belongs to the class of partial pose estimation where the estimated pose in a frame is used for the initialization of the next one. The pose estimation is obtained by applying a modified version of the Iterative Closest Point (ICP) algorithm to synthetic models to obtain the rigid transformation that aligns each model with respect to the input data. The proposed framework uses a "pure" point cloud as provided by the Kinect sensor without any other information such as RGB values or normal vector components. For this reason, the proposed method can also be applied to data obtained from other types of depth sensor, or RGB-D camera

Accuracy assessment of Tri-plane B-mode ultrasound for non-invasive 3D kinematic analysis of knee joints

BACKGROUND Currently the clinical standard for measuring the motion of the bones in knee joints with sufficient precision involves implanting tantalum beads into the bones. These beads appear as high intensity features in radiographs and can be used for precise kinematic measurements. This procedure imposes a strong coupling between accuracy and invasiveness. In this paper, a tri-plane B-mode ultrasound (US) based non-invasive approach is proposed for use in kinematic analysis of knee joints in 3D space. METHODS The 3D analysis is performed using image processing procedures on the 2D US slices. The novelty of the proposed procedure and its applicability to the unconstrained 3D kinematic analysis of knee joints is outlined. An error analysis for establishing the method's feasibility is included for different artificial compositions of a knee joint phantom. Some in-vivo and in-vitro scans are presented to demonstrate that US scans reveal enough anatomical details, which further supports the experimental setup used using knee bone phantoms. RESULTS The error between the displacements measured by the registration of the US image slices and the true displacements of the respective slices measured using the precision mechanical stages on the experimental apparatus is evaluated for translation and rotation in two simulated environments. The mean and standard deviation of errors are shown in tabular form. This method provides an average measurement precision of less than 0.1 mm and 0.1 degrees, respectively. CONCLUSION In this paper, we have presented a novel non-invasive approach to measuring the motion of the bones in a knee using tri-plane B-mode ultrasound and image registration. In our study, the image registration method determines the position of bony landmarks relative to a B-mode ultrasound sensor array with sub-pixel accuracy. The advantages of our proposed system over previous techniques are that it is non-invasive, does not require the use of ionizing radiation and can be used conveniently if miniaturized.This work has been supported by School of Engineering & IT, UNSW Canberra, under Research Publication Fellowship

Participatory Patterns in an International Air Quality Monitoring Initiative

The issue of sustainability is at the top of the political and societal agenda, being considered of extreme importance and urgency. Human individual action impacts the environment both locally (e.g., local air/water quality, noise disturbance) and globally (e.g., climate change, resource use). Urban environments represent a crucial example, with an increasing realization that the most effective way of producing a change is involving the citizens themselves in monitoring campaigns (a citizen science bottom-up approach). This is possible by developing novel technologies and IT infrastructures enabling large citizen participation. Here, in the wider framework of one of the first such projects, we show results from an international competition where citizens were involved in mobile air pollution monitoring using low cost sensing devices, combined with a web-based game to monitor perceived levels of pollution. Measures of shift in perceptions over the course of the campaign are provided, together with insights into participatory patterns emerging from this study. Interesting effects related to inertia and to direct involvement in measurement activities rather than indirect information exposure are also highlighted, indicating that direct involvement can enhance learning and environmental awareness. In the future, this could result in better adoption of policies towards decreasing pollution.Comment: 17 pages, 6 figures, 1 supplementary fil

Adaptivity in Single Player Video Games

Um dos principais objetivos dos jogos é poder obter satisfação e a diversão, fazendo com que o tempo gasto valha a pena. A versão digital de um jogo é um videojogo, onde o usuário interage com um computador. No entanto, cada jogador gosta de jogar à sua maneira e no seu ritmo. Esta diversidade cria diferentes níveis de perícia entre diferentes jogadores dentro do jogo, tornando a mesma dificuldade não compatível com todos. Se o jogador não se enquadra no estilo de jogo, é provável que ele não continue a jogá-lo no futuro. Uma das maneiras mais comuns de ajustar um jogo para capturar o máximo de utilizadores possível e criar uma experiência positiva é através de níveis de dificuldade. Contudo, este é um processo manual e não é uma garantia de que funcionará, já que os desenvolvedores criam diferentes dificuldades sobre o que acham que é o melhor para a maioria dos jogadores. Uma solução para este problema é automatizar o processo, utilizando algoritmos de aprendizagem de máquina que irão descobrir, associar e implementar os parâmetros de jogo que melhor se adaptam a cada tipo de jogador. A solução proposta é criar uma versão adaptada de um jogo que possa mudar seu conteúdo para se adaptar ao tipo de personalidade de cada jogador. Para isso, devemos primeiro criar um sistema que irá determinar a personalidade de um jogador dependendo do que ele sente, age e como se comporta durante o jogo. Usando estas informações, medimos a diversão e a frustração do jogador e criamos uma associação com o conteúdo alterável do jogo. Em seguida, alteramos os diferentes parâmetros de jogo para se adaptarem a um utilizador específico, fornecendo um fluxo de jogo positivo e uma experiência de jogo ideal. O resultado esperado do jogo adaptado é que os utilizadores tenham uma melhor experiência e fluidez do jogo. Este resultado pode ser observado por questionários ou pelas ações do jogador, por exemplo, tempos de jogo mais longos ou mais frequentes. A adaptatividade pode também ajudar a criar uma base de jogadores mais estável e melhorar a longevidade do jogo. Assim, esta solução proposta é relevante para qualquer videojogo que queira ampliar sua quantidade de jogadores.One of the main objectives of playing games is to achieve satisfaction and fun, making the time spent worth it. A digital version is a video game where the user interacts with a computer. However, each player likes to play the game the way they want and at a pace they prefer. This diversity creates different skills levels between different player within the game, making the same difficult not compatible with everyone. If the user cannot identify its playstyle during gameplay, it is likely he will not continue playing the same game in the future. One of the most popular ways to adjust a game to capture the most audience possible and create a positive experience is by creating difficulty levels. Furthermore, this is a manual process and is not a guarantee that it will work since the developers create different difficulties on what they think is the best for most players. A solution to this problem is to automate the process, using state of the art machine learning algorithms that will discover, associate and implement the game parameters that best fit each type of player. The solution proposed is to create an adapted version of a game that can change its content to fit each player personality type. For this, we must first create a profiling system that will determine a player's personality depending on what he feels, acts and behaves during gameplay. Using the information relative to the player and the gameplay, we measure the player's fun and frustration and create an association with the game alterable content. We then alter different game parameters to fit a specific user, ultimately delivering a positive game flow and an optimal game experience. The expected result of the adapted game is that the users have a better experience and game flow. This result can be observed by questionnaires or by the player's actions, for example, longer or more frequent playtimes. Also, adaptivity can help create a more stable player base and improve the game's longevity. Thus, this proposed solution is relevant for any video game that wants to broaden its player base