Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel User-Specific Face and Palmprint Feature Level Fusion

Recent years, people pay more and more attention to multi-modal biometric recognition because of its higher security. In this paper, face and palmprint are fused in feature level and a novel user-specific weighting rule is used to adapt different people's need and improve recognition accuracy. According to respective characteristic of face and palmprint, different feature extraction methods are adopted. For face, phase congruency is used to extract its energy feature meanwhile Gabor transformation is used to extract texture feature of palmprint. Then the two modals are weighted according to users' specific in feature level. Experimental results show good performance.
Joint Demodulation in DS / CDMA Systems Exploiting theSpace and Time Diversity of the Mobile Radio

|A demodulation algorithm for a base station receiver in a direct sequence (DS) spread spectrum code division multiple access (CDMA) communication system is proposed which performs joint multiuser detection and estimation using the output signals of an antenna array in the absence of a tight power control. The scheme is a combination of a multistage (MS) detector for data recovery and a space-alternating generalized expectation-maximization (SAGE) algorithm for channel parameter estimation. Monte-Carlo simulations show that the scheme is able to overcome the near-far problem while simultaneously combining the signal energy spread over time and space in a nearly optimum fashion.
Discovery of SOA patterns via model checking

Design pattern recovery has been proved to be an useful mechanism to assess the quality of object-oriented systems, to facilitate their comprehension, and to help identifying reusable assets. The diffusion of Service Oriented Architectures (SOA) is fostering the introduction of new patterns, realizing recurring mechanisms that service-oriented systems often realize. Examples are proxies used to enable dynamic binding or to ensure fault tolerance, service compositions, and loggers used for monitoring purposes. This paper describes how SOA patterns can be identified by analyzing SOAP messages collected by monitoring a service oriented system. Namely, the paper presents a model checking-based approach where patterns, described by parametric selective mu-calculus logic formulae, are verified on a synthetized model of the system.
No : 133050073 Under the guidance of Prof

Sentiment Analysis refers to the problem of automatically determining the polarity of the opinion expressed by an author in a text. Two competing approaches to solve this problem are the classifier-based and resource-based approaches. The resource-based approach involves creation of dictionaries of lexical units of a language with their sentiment values and using these dictionaries for sentiment analysis of a text. This report covers the resource-based approach, including background work that led to the lexicons, a few case-studies covering the structure of the lexicons and the process of their creation, and recent developments, including those at IIT Bombay. The report concludes with several directions that can be taken in the future.
Expression of androgen receptors in primary breast cancer.

BACKGROUND To investigate the clinicopathological significance of androgen receptor (AR) expression in primary breast cancers. PATIENTS AND METHODS We evaluated AR using immunohistochemistry from 413 whole sections from January 2008 to March 2009 and analyzed the relationship between AR and clinicopathological parameters. Tumors with >/=10% nuclear-stained cells were considered to be positive for AR. The differences among variables were calculated by chi-square test. RESULTS The expression rate of AR was 72.9% higher than those of estrogen receptors (ER) and progesterone receptors. AR expression was significant in patients with no elevated preoperative serum cancer antigen 15-3 levels, smaller tumor size, lower histologic grade and hormone receptor-positive and non-triple-negative breast cancer. However, AR expression was observed in 35% of triple-negative cancers. Metaplastic, medullary and mucinous types of carcinomas showed less AR expression. In the ER-negative subgroup, AR was significantly correlated with human epidermal growth factor receptor type 2 (HER-2) overexpression. CONCLUSIONS AR is expressed in a significant number of breast cancers and is associated with lower tumor burden and favorable differentiation. There are many issues to be further investigated such as whether AR is an independent prognostic factor, whether it is a therapeutic target for the triple-negative breast cancers and whether it is associated with HER-2 signaling in ER-negative tumors.
Lossy hyperspectral image compression tuned for spectral mixture analysis applications on NVidia graphics processing units

In this paper, we develop a computationally efficient approach for lossy compression of remotely sensed hyperspectral images which has been specifically tuned to preserve the relevant information required in spectral mixture analysis (SMA) applications. The proposed method is based on two steps: 1) endmember extraction, and 2) linear spectral unmixing. Two endmember extraction algorithms: the pixel purity index (PPI) and the automatic morphological endmember extraction (AMEE), and a fully constrained linear spectral unmixing (FCLSU) algorithm have been considered in this work to devise the proposed lossy compression strategy. The proposed methodology has been implemented in graphics processing units (GPUs) of NVidiaTM type. Our experiments demonstrate that it can achieve very high compression ratios when applied to standard hyperspectral data sets, and can also retain the relevant information required for spectral unmixing in a computationally efficient way, achieving speedups in the order of 26 on a NVidiaTM GeForce 8800 GTX graphic card when compared to an optimized implementation of the same code in a dual-core CPU.
Max-margin clustering: Detecting margins from projections of points on lines

Given a unlabelled set of points X ∊ RN belonging to k groups, we propose a method to identify cluster assignments that provides maximum separating margin among the clusters. We address this problem by exploiting sparsity in data points inherent to margin regions, which a max-margin classifier would produce under a supervised setting to separate points belonging to different groups. By analyzing the projections of X on the set of all possible lines L in RN, we first establish some basic results that are satisfied only by those line intervals lying outside a cluster, under assumptions of linear separability of clusters and absence of outliers. We then encode these results into a pair-wise similarity measure to determine cluster assignments, where we accommodate non-linearly separable clusters using the kernel trick. We validate our method on several UCI datasets and on some computer vision problems, and empirically show its robustness to outliers, and in cases where the exact number of clusters is not available. The proposed approach offers an improvement in clustering accuracy of about 6% on the average, and up to 15% when compared with several existing methods.
A philosophy of minimalist biomimetic robotics

We present our approach to building intelligent robots, which we call minimalist biomimetic robotics. In this philosophy, biology is used as a source of inspiration and Ockham’s razor is applied to obtain minimal solutions. The goal is to create embodied and situated agents that are efficient at performing their tasks, but have enough redundancy in sensors, actuators and control architecture to be very robust and adaptive. Exploiting physical properties of the world is a key issue, as this often enables simple solutions to work well. Our philosophy was applied in the development of the zeppelin robot Lemon, a lightweight flying robot equipped with several sensor modalities inspired largely on biology.
It's all the same to me: prosodic discrimination across speakers and face areas

Visual cues to speech prosody are available from a speaker’s face; however the form and/or location of such cues are likely to be inconsistent across speakers. Given this, the question arises as to whether such cues are general enough to signal the same prosody information across speakers, and if so, where and what these cues are. To investigate this, this study used visual-visual and auditory-visual matching tasks requiring participants to select pairs of stimuli that were produced with the same prosody withinand across-speakers when visual information was limited to the upper or lower face. Experiment 1 tested within-speaker prosody matching when the speaker's lower face was presented. The results showed highly accurate matching performance. Taken together with the results of our previous study which presented the upper face in the same tasks [1], these data provided a baseline for which to evaluate cross-speaker prosody matching (Experiment 2). In Experiment 2, both lower and upper face stimuli were presented. In comparison to within-speaker matching, performance was lower for cross-speaker matching but still greater than chance. Overall, the results suggest that both the upper and lower face provide general non-speaker specific as well as speaker-specific visual cues to prosody.
Fast Generation of Large Scale Social Networks with Clustering

A key challenge within the social network literature is the problem of network generation - that is, how can we create synthetic networks that match characteristics traditionally found in most real world networks? Important characteristics that are present in social networks include a power law degree distribution, small diameter and large amounts of clustering; however, most current network generators, such as the Chung Lu and Kronecker models, largely ignore the clustering present in a graph and choose to focus on preserving other network statistics, such as the power law distribution. Models such as the exponential random graph model have a transitivity parameter, but are computationally difficult to learn, making scaling to large real world networks intractable. In this work, we propose an extension to the Chung Lu ran- dom graph model, the Transitive Chung Lu (TCL) model, which incorporates the notion of a random transitive edge. That is, with some probability it will choose to connect to a node exactly two hops away, having been introduced to a 'friend of a friend'. In all other cases it will follow the standard Chung Lu model, selecting a 'random surfer' from anywhere in the graph according to the given invariant distribution. We prove TCL's expected degree distribution is equal to the degree distribution of the original graph, while being able to capture the clustering present in the network. The single parameter required by our model can be learned in seconds on graphs with millions of edges, while networks can be generated in time that is linear in the number of edges. We demonstrate the performance TCL on four real- world social networks, including an email dataset with hundreds of thousands of nodes and millions of edges, showing TCL generates graphs that match the degree distribution, clustering coefficients and hop plots of the original networks.
A ug 2 00 8 Supersymmetric isolated horizons in ADS spacetime

We discuss various physical aspects of nonextremal, extremal and supersymmetric black holes in asymptotically anti-de Sitter (ADS) spacetimes. Specifically, we discuss how the isolated horizon (IH) framework leads to an ambiguityfree description of rotating black holes in these spacetimes. We then apply this framework to investigate the properties of supersymmetric isolated horizons (SIHs) in four-dimensional N = 2 gauged supergravity. Among other results we find that they are necessarily extremal, that rotating SIHs must have nontrivial electromagnetic fields, and that non-rotating SIHs necessarily have constant curvature horizon cross sections and a magnetic (though not electric) charge. PACS: 04.50. Gh; 04.70. Bw; 11.30. Pb Electronic mail: ibooth at math.mun. ca Electronic mail: liko at gravity.psu. edu
Purdue E-pubs Daylight Glare Probability Measurements and Correlation with Indoor Illuminances in a Full- Scale Office with Dynamic Shading Controls Daylight Glare Probability Measurements and Correlation with Indoor Illuminance in a Full-scale Office with Dynamic Shading Controls

Daylight glare evaluation has been the recent focus of research on visual comfort since newer office buildings have large glass facades offering daylight provision and outdoor views. Available glare indices are related to source luminance size and location, view direction and background luminance. The Daylight Glare Probability index that considers vertical illuminance at the eye level, has been identified as one of the reliable metrics, since it was based on experiments with real human subjects. In this study, extensive experiments were conducted in a full-scale private office environment with dynamic shading controls, to measure interior luminance and illuminance conditions under variable sky conditions and shading control strategies. A high dynamic range camera with a fish eye lens was used to capture the luminance in the field of view, while horizontal and vertical illuminance was measured at different positions. The images were processed for calculating DGP values based on the " evalglare " method. The results are used to evaluate the efficiency of control strategies in terms of glare probability, while correlations between indoor illuminances, sky conditions and DGP may lead to simplified criteria and guidelines for controlling daylight glare in office spaces. Finally, the experimental results can be used for daylight model validation for spaces with dynamic facades.
Trends in executive gender pay gaps : the role of females ’ risk aversion and board composition

Prior research has documented pay gaps between female and male executives. We document recent trends in the gender pay gaps and explore the relative effect of females’ appetite for compensation incentives and gender bias in the boardroom as possible explanations for the gaps. Using data from the 1996-2010 period, we find evidence that female executives’ pay and incentive gaps are gradually converging towards their male counterparts, but that the wedge between genders still exists because females accept contracts with lower equity compensation incentives. We also find evidence that female representation on boards, and compensation committees in particular, has significantly contributed to mitigating the gaps. We gratefully acknowledge the financial support of Boston College and London Business School.
On a mathematical relation between the Eigen model and the asexual Wright-Fisher model

We show that the Eigen model and the asexual Wright-Fisher model can be obtained as different limit cases of a unique stochastic model. This derivation makes clear which are the exact differences between these two models. The two key concepts introduced with the Eigen model, the error threshold and the quasispecies, are not affected by these differences, so that they are naturally present also in population genetics models. According to this fact, in the last part of the paper, we use the classical diploid mutation-selection equation and the single peak fitness approximation to obtain the error threshold for sexual diploids. Finally, we compare the results with the asexual case.
A Caching Algorithm using Evolutionary Game Theory in a File-Sharing System

In a P2P file-sharing system, a node finds and retrieves its desired file. If multiple nodes cache the same file to provide others, we can achieve a file-sharing system with low latency and high file availability. However, a node has to spend costs, e.g., processing load or storage capacity, on caching of a file. Consequently, a node may selfishly behave and hesitate to cache a file. In such a case, there is a possibility that unpopular files disappear from the system. In this paper, we aim to accomplish effective caching in the whole system that emerges from autonomous and selfish node behavior. We first discuss relationship between selfish node behavior and system dynamics according to evolutionary game theory. As a result, we show that a file-sharing system can be robust to file disappearance depending on a cost and demand model for caching even if nodes behave selfishly.
Model words-driven approaches for duplicate detection on the web

The detection of product duplicates is one of the many challenges that Web shop product aggregators are facing. This paper presents two new methods to solve the problem of product duplicate detection. Both methods extend a state-of-the-art approach that uses the found model words in product titles to detect product duplicates. The first proposed method uses several distance measures to calculate distances between product attribute keys and values to find duplicate products when no matching product title is found. The second proposed method detects matching model words in all product attribute values in order to find duplicate products when no matching product title is found. Based on our experimental results on real-world data gathered from two existing Web shops, we show that the second proposed method significantly outperforms the existing state-of-the-art method in terms of F1-measure, while the first method outperforms the existing state-of-the-art method in terms of F1-measure, but not significantly.
Global IT Multisourcing: Objectives, Challenges And Requirements In Multinational Insurance Companies

In recent times, information technology (IT) multisourcing has emerged as a key strategy in the field of IT outsourcing. Most of the current scholarly and practitioner-related literature acknowledges the importance of multisourcing, but linear extensions of the dyadic client-vendor relationship are insufficient to capture its nuances. Multinational financial services providers (such as banks and insurance companies) have been pioneers in the field of IT outsourcing. However, there is a lack of experience-based research about the implementation of global IT multisourcing. In this article we present findings from a cross-case study investigating the implementation of global IT multisourcing in four leading insurance companies. First, the article presents the objectives of the insurers in pursuing global IT multisourcing. Second, the research reveals challenges the companies are facing in implementing global IT multisourcing. Finally, requirements for implementing IT multisourcing are derived. The insights of these real-life examples extend existing literature on IT outsourcing and provide helpful orientation for organizations that are implementing global IT multisourcing.
Understanding Maturity Models Results of a Structured Content Analysis

Maturity models are popular instruments used, e.g., to rate capabilities of maturing elements and select appropriate actions to take the elements to a higher level of maturity. Their application areas are wide spread and range from cognitive science to business applications and engineering. Although there are many maturity models reported in scientific and non-scientific literature, the act of how to develop a maturity model is for the most part unexplored. Many maturity models simply - and vaguely - build on their, often well-known, predecessors without critical discourse about how appropriate the assumptions are that form the basis of these models. This research sheds some light on the construction of maturity models by analysing 16 representative maturity models with the help of a structured content analysis. The results are transformed into a set of questions which can be used for the (re-)creation of maturity models and are answered with the help of the case example of a knowledge maturity model. Furthermore, a definition of the term maturity model is developed from the study's results.
Neural Networks for Multi-Instance Learning

Multi-instance learning originates from the investigation on drug activity prediction, where the task is to predict whether an unseen molecule could be used to make some drug. Such a problem is difficult because a molecule may have many alternative shapes with low energy, yet only one of those shapes may be responsible for the qualification of the molecule to make the drug. Because of its unique characteristics and extensive existence, multi-instance learning is regarded as a new machine learning framework parallel to supervised learning, unsupervised learning, and reinforcement learning. In this paper, an open problem of this area is addressed. That is, a popular neural network algorithm is adapted for multi-instance learning through employing a specific error function. Experiments show that the adapted algorithm achieves good result on the drug activity prediction data.
The Open University ’ s repository of research publications and other research outputs Multimedia resource discovery Book Chapter

Copyright and Moral Rights for the articles on this site are retained by the individual authors and/or other copyright owners. For more information on Open Research Online's data policy on reuse of materials please consult the policies page. 1 Introduction Resource discovery is more than just search: it is browsing, searching, selecting, assessing and evaluating, ie, ultimately accessing information. Giving users access to collections is one of the defining tasks of a library. For thousands of years the traditional methods of resource discovery have been facilitated by librarians: they create reference cards with meta-data that are put into catalogues (nowadays, databases); they also place the objects in physical locations that follow certain classification schemes and they answer questions at the reference desk. The advent of digital documents has radically changed the organisation principles; now it is possible to automatically index and search document collections as big as the worldwide webà la Google and browse collections utilising author-inserted links. It is almost as if automated processing has turned the traditional library access paradigm upside down. Instead of searching meta-data catalogues in order to retrieve the document, web search engines search the full content of documents and retrieve their meta-data, ie, the location where documents can be found. Not all manual intervention has been abandoned, though. For example, the Yahoo! directory is an edited classification scheme of submitted web sites that are put into a browsable directory structure akin to library classification schemes. Undoubtedly, however, it is the automated approaches that have made all the difference to the way the vast worldwide web can be used. While the automated indexing of text documents has been successfully applied to collections as large as the worldwide web for over a decade now, multimedia indexing by content involves different, still less mature and less scalable technologies. Figure 1 shows a matrix of different search engines depending on the query (columns) and document repository (rows). Entry a) in this matrix corresponds to a traditional text search engine with a completely different technology than entry b), a system that allows you to express musical queries by humming a tune and that then plays the corresponding song; the three c) entries in Figure 1 correspond to a multi-modal video search engine allowing search by motion with example images and text queries, eg, find me video shots of the Houses of Parliament in London zooming into its tower's clock …