Miracle-dz/UniFAR-FTUs huggingface dataset:
Speechreading using Probabilistic Models

We describe a robust method for locating and tracking lips in gray-level image sequences. Our approach learns patterns of shape variability from a training set which constrains the model during image search to only deform in ways similar to the training examples. Image search is guided by a learned gray-level model which is used to describe the large appearance variability of lips. Such variability might be due to different individuals, illumination, mouth opening, specularity, or visibility of teeth and tongue. Visual speech features are recovered from the tracking results and represent both shape and intensity information. We describe a speechreading (lip-reading) system, where the extracted features are modeled by Gaussian distributions and their temporal dependencies by hidden Markov models. Experimental results are presented for locating lips, tracking lips, and speechreading. The database used consists of a broad variety of speakers and was recorded in a natural environment with no special lighting or lip markers used. For a speaker independent digit recognition task using visual information only, the system achieved an accuracy about equivalent to that of untrained humans.
EmotiRob: An emotional interaction model

This paper presents our work on the therapeutic robotics project EmotiRob which is the continuity of the MAPH (active media for the handicap) project in emotion synthesis. Our aim is to maintain emotional interaction with children. For this, word, movement, and emotion synthesis are used. After defining a face for our robot, which is a compromise between the richness of expression and simplified mechanics, we propose the internal model of it in order to achieve and maintain emotional interaction.
The thermodynamical limit of general gl(N) spin chains: the vacuum state and densities

We study the vacuum state of spin chains where each site carries an arbitrary representation. We prove that the string hypothesis usually used to solve the Bethe ansatz equations is valid for representations characterized by rectangular Young tableaux. In these cases, we obtain the density of the centre of the strings for the vacuum. We work through different examples and, in particular, that for spin chains with a periodic array of impurities.
ALTERNATIVE MATING STRATEGIES AND THE EVOLUTION OF SEXUAL SIZE DIMORPHISM IN THE SIDE‐BLOTCHED LIZARD, UTA STANSBURIANA: A POPULATION‐LEVEL COMPARATIVE ANALYSIS

Population‐level comparative analyses can link microevolutionary processes within populations to macroevolutionary patterns of diversification. We used the comparative method to study the evolution of sexual size dimorphism (SSD) among populations of side‐blotched lizards (Uta stansburiana). Uta stansburiana is polymorphic for different male mating and female life‐history strategies in some populations, but monomorphic in others. We tested whether intrasexual selection among males, fecundity selection on females, and the presence of polymorphic strategies affected levels of SSD. We first resolved a phylogeny for 41 populations across the range of the species and documented a substantial regional structure. Our intraspecific data had significant phylogenetic signal, and correcting for phylogeny using independent contrasts had large effects on our results. Polymorphic populations had male‐biased SSD and changes in male body size, levels of tail breaks, and SSD consistent with the intrasexual selection hypothesis. Monomorphic populations had changes in female size, clutch size, and SSD consistent with the fecundity selection hypothesis. Fecundity selection is a likely cause of some monomorphic populations having no SSD or female‐biased SSD. Our results suggest that changes in mating strategies are associated with phenotypic diversification and multiple evolutionary forces can shape SSD.
AUTOMATIC SHORT MAIL CONSTRUCTION TO ENHANCE EMAIL STORAGE

Short Texts play a significant role in reviews, blogs, social networking sites etc. The replacement of featured text by short text can greatly reduce the text size, thereby allowing more information to be communicated. Emails apparently do not have large number of short texts. This paper proposes a novel idea for automatic construction of Short Text Dictionary (STD) that maps the large texts into their short texts thereby curtailing the storage space efficiently. An algorithm has been proposed to convert the featured word into a new Short Word. Normalisation in Sentimental Analysis involves pre-processing of short texts. This constructed dictionary with transliterated word processing can thus be used as a pre-processing tool.
Nonlinear Model Based Coordinated Adaptive Robust Control of Electro-hydrauli c Robotic Manipulator by Overparametrizin g Method

This paper studies the coordinated motion control of robotic manipulators driven by single-rod hydraulic actuators. Compared to conventional robot manipulators driven by electrical motors, hydraulic robot arms have a richer nonlinear dynamics and stronger couplings among various joints (or hydraulic cylinders). This paper presents a physical model based adaptive robust control (ARC) strategy to explicitly take into account the strong coupling among various hydraulic cylinders (or joints). To avoid the need of acceleration feedback for ARC backstepping design, the property, that the adjoint matrix and the determinant of the inertial matrix could be linearly parametrized by certain suitably selected parameters is utilized and overparametrizing method is employed. Theoretically, the resulting controller is able to take into account not only the effect of parametric uncertainties coming from the payload and various hydraulic parameters but also the effect of uncertain nonlinearities. Furthermore, the proposed ARC controller guarantees a prescribed output tracking transient performance and final tracking accuracy while achieving asymptotic output tracking in the presence of parametric uncertainties only. Simulation results which is based on a three degree-of-freedom (DOF) hydraulic robot arm (a scaled down version of an industrial backhoe/excavator arm) are presented to illustrate the proposed control algorithm. Experimental results will be included upon the final submission
Perspectives on : Local calcium signaling Recording single-channel activity of inositol trisphosphate receptors in intact cells with a microscope , not a patch clamp

Introduction Cytosolic Ca plays a central role as a universal intra­ cellular messenger, controlling many aspects of physi­ ology such as gene expression, electrical excitability, secretion, and synaptic plasticity in virtually all cell types (Berridge et al., 2000). The rich diversity in in­ formation encoded by Ca arises through the mecha­ nisms by which Ca signals are generated and transmitted to act over very different spatial and temporal scales. For example, locally high [Ca] can trigger neuro­ transmitter release within microseconds of Ca entry through voltage­gated channels closely apposed to active release zones (Neher, 1998), whereas globally propagating Ca waves with periods measured in min­ utes regulate gene expression (Dolmetsch et al., 1998; Bootman et al., 2001). Resting [Ca] in the cytosol is maintained at low nanomolar levels, and Ca signals are generated by Ca ions entering the cytosol both across the plasma membrane through voltage­ and ligand­gated channels and store­operated calcium chan­ nels, and by liberation of Ca from intracellular stores such as the ER through Ca release channels. The ino­ sitol trisphosphate (IP3) receptor (IP3R) is one of the two major Ca release channels located on the ER, and it is the focus of our discussion. The spatiotemporal patterning and transmission of Ca signals involve the inherent properties of the Ca channels themselves, their spatial organization, and how these channels interact via diffusion of Ca both with themselves and with effector proteins. The IP3R channel exhibits particular complexity because its open­ ing requires binding of the second messenger IP3 to­ gether with Ca to receptor sites on the cytosolic face. Gating by Ca is biphasic, such that small elevations of cytosolic Ca induce channel opening, whereas larger elevations cause inactivation (Bezprozvanny et al., 1991; Foskett et al., 2007). Thus, there exists the potential for both positive and negative feedback loops, whereby Ca
Pinpointing Mobile Malware Using Code Analysis

Mobile malware has recently become an acute problem. Existing solutions either base static reasoning on syntactic properties, such as exception handlers or configuration fields, or compute data-flow reachability over the program, which leads to scalability challenges. We explore a new and complementary category of features, which strikes a middleground between the above two categories. This new category focuses on security-relevant operations (communcation, lifecycle, etc) --- and in particular, their multiplicity and happens-before order --- as a means to distinguish between malicious and benign applications. Computing these features requires semantic, yet lightweight, modeling of the program's behavior. We have created a malware detection system for Android, MassDroid, that collects traces of security-relevant operations from the call graph via a scalable form of data-flow analysis. These are reduced to happens-before and multiplicity features, then fed into a supervised learning engine to obtain a malicious/benign classification. MassDroid also embodies a novel reporting interface, containing pointers into the code that serve as evidence supporting the determination. We have applied MassDroid to 35,000 Android apps from the wild. The results are highly encouraging with an F-score of 95% in standard testing, and >90% when applied to previously unseen malware signatures. MassDroid is also efficient, requiring about two minutes per app. MassDroid is publicly available as a cloud service for malware detection.
Identifying "good" architectural design alternatives with multi-objective optimization strategies

Architecture trade-off analysis methods are appropriate techniques to evaluate design decisions and design alternatives with respect to conflicting quality requirements. However, the identification of good design alternatives is a time consuming task, which is currently performed manually. To automate this task, this paper proposes to use evolutionary algorithms and multi-objective optimization strategies based on architecture refactorings to identify a sufficient set of design alternatives. This approach will reduce development costs and improve the quality of the final system, because an automated and systematic search will identify more and better design alternatives.
Hand Gesture Recognition Using Statistical Analysis of Curvelet Coefficients

Hand gesture tracking is very challenging task with different backgrounds and illumination conditions. However, the challenge becomes more tough when the hand gesture is moving in all 3600 direction. In the earlier work as studied in literature survey, it has been found that the location, rotation and size invariance has been the major problem to deal with. In the proposed work, efforts are made to overcome the size, rotation and location invariance using statistical tools. A discontinuity point affects all the Fourier coefficients in the frequency domain. The presented algorithm is based on statistical analysis of curvelet coefficients, and we take the minimum standard deviation, minimum class variance, minimum differential entropy and maximum PSNR.
MISER: an integrated three layer gridless channel router and compactor

In this paper, we present a new gridless three layer channel router (MISER) based on an integrated approach to routing and compaction. MISER partitions the input net-list into several sub net-lists called levels and forms a level graph. This level graph is used to guide the routing and compaction process. Compaction is done immediately after each level is routed. Experimental results show that our algorithm usually performs 5-10% better than existing three layer channel routing algorithms.
A novel ranking method of web search result using clustering and concordance count

In recent years, information on World Wide Web is exploding. Search Engine robots are used to search information on World Wide Web. However, a robot type search engine has a few problems. One problem is that, it is difficult for user to come up with an appropriate query for getting search results she/he intends. Moreover, it is difficult for users to understand contents of search results because a robot type search engine outputs too many search results in a long list format. To resolve these problems, many systems classify a robot type search engine results into clusters. Clusters are labeled and those labels are shown to the user. Cluster labels need to be appropriate words for the web site within the cluster. We have proposed a labeling method using concordance count. First, web search results are obtained by a query input, and the result is classified into clusters. We used our proposed method to assign proper labels to those clusters. To ensure that we use a novel method. We find the set of websites resulted from AND-query using an original query word and the cluster label. If this set and the member of the cluster are common, we say that the concordance count is high. If the concordance count is high, the cluster label is assigned high weight. Finally, we evaluate the accuracy of our proposed method by simulation experiments.
A novel semantic model for video concept detection

Graph-based semi-supervised learning approaches have been proven effective and efficient in solving the problem of the inefficiency of training samples in many real-world application areas, such as video annotation. As a significant factor of these algorithms, however, pairwise similarity metric has not been fully investigated. On the one hand, for general video annotation methods, the estimation of pairwise similarity between two samples relies on the spatial property of video data. On the other hand, temporal property, which is an essential character of video data, is not embedded into the pairwise similarity metric. Therefore, in this paper, a novel method, called Joint Spatio-Temporal Correlation Learning (JSTCL), is proposed to improve the accuracy of video annotation. This method is characterized by simultaneously taking into account both the spatial and temporal property of video data to well represent the pairwise similarity. Experiments conducted on the TRECVID demonstrate the efficiency of the proposed method.
Document clustering with prior knowledge

Document clustering is an important tool for text analysis and is used in many different applications. We propose to incorporate prior knowledge of cluster membership for document cluster analysis and develop a novel semi-supervised document clustering model. The method models a set of documents with weighted graph in which each document is represented as a vertex, and each edge connecting a pair of vertices is weighted with the similarity value of the two corresponding documents. The prior knowledge indicates pairs of documents that known to belong to the same cluster. Then, the prior knowledge is transformed into a set of constraints. The document clustering task is accomplished by finding the best cuts of the graph under the constraints. We apply the model to the Normalized Cut method to demonstrate the idea and concept. Our experimental evaluations show that the proposed document clustering model reveals remarkable performance improvements with very limited training samples, and hence is a very effective semi-supervised classification tool.
Poly(A) RNA and Paip2 act as allosteric regulators of poly(A)-binding protein

When bound to the 3′ poly(A) tail of mRNA, poly(A)-binding protein (PABP) modulates mRNA translation and stability through its association with various proteins. By visualizing individual PABP molecules in real time, we found that PABP, containing four RNA recognition motifs (RRMs), adopts a conformation on poly(A) binding in which RRM1 is in proximity to RRM4. This conformational change is due to the bending of the region between RRM2 and RRM3. PABP-interacting protein 2 actively disrupts the bent structure of PABP to the extended structure, resulting in the inhibition of PABP-poly(A) binding. These results suggest that the changes in the configuration of PABP induced by interactions with various effector molecules, such as poly(A) and PABP-interacting protein 2, play pivotal roles in its function.
System-Generated Requests for Rewriting Proposals

We present an online deliberation system using mutual evaluation in order to collaboratively develop solutions. Participants submit their proposals and evaluate each other's proposals; some of them may then be invited by the system to rewrite 'problematic' proposals. Two cases are discussed: a proposal supported by many, but not by a given person, who is then invited to rewrite it for making yet more acceptable; and a poorly presented but presumably interesting proposal. The first of these cases has been successfully implemented. Proposals are evaluated along two axes-understandability (or clarity, or, more generally, quality), and agreement. The latter is used by the system to cluster proposals according to their ideas, while the former is used both to present the best proposals on top of their clusters, and to find poorly written proposals candidates for rewriting. These functionalities may be considered as important components of a large scale online deliberation system.
On the effect of clustering on quality assessment measures for dimensionality reduction

Visualization techniques constitute important interfaces between the rapidly increasing digital information available today and the human user. In this context, numerous dimensionality reduction models have been proposed in the literature, see, e.g., [1, 2]. This fact recently motivated the development of frameworks for a reliable and automatic quality assessment of the produced results [3, 2]. These specialized measurements represent a means to objectively assess an overall qualitative change under spatial transformation. The rapidly increasing size of the data sets, however, causes the need to not only map given data points to low dimensionality, but to priorly compress the available information, e.g., by means of clustering techniques. While a standard evaluation measure for clustering is given by the quantization error, its counterpart, the reconstruction error, is usually infeasible for dimensionality reduction, such that one has to rely on alternatives. In this paper, we investigate in how far two existing quality measures used for dimensionality reduction are appropriate to evaluate the quality of clustering outputs, such that only one quality measure can be used for the full visualization process in the context of large data sets. In this contribution, we present empirical results from different basic clustering scenarios as a first study to analyze how structural characteristics of the data are reflected in the quality assessment.
Parameterization of a hydrological model for a large, ungauged urban catchment

Urbanization leads to the replacement of natural areas by impervious surfaces and affects the catchment hydrological cycle with adverse environmental impacts. Low impact development tools (LID) that mimic hydrological processes of natural areas have been developed and applied to mitigate these impacts. Hydrological simulations are one possibility to evaluate the LID performance but the associated small-scale processes require a highly spatially distributed and explicit modeling approach. However, detailed data for model development are often not available for large urban areas, hampering the model parameterization. In this paper we propose a methodology to parameterize a hydrological model to a large, ungauged urban area by maintaining at the same time a detailed surface discretization for direct parameter manipulation for LID simulation and a firm reliance on available data for model conceptualization. Catchment delineation was based on a high-resolution digital elevation model (DEM) and model parameterization relied on a novel model regionalization approach. The impact of automated delineation and model regionalization on simulation results was evaluated for three monitored study catchments (5.87–12.59 ha). The simulated runoff peak was most sensitive to accurate catchment discretization and calibration, while both the runoff volume and the fit of the hydrograph were less affected.
Chinese Partial Parsing with Modal Finite-State Approach

A novel finite-state approach with the practical use of modal operators for Chinese partial parsing is presented in this paper. The traditional rule-based partial parsing approaches make use of regular grammar to approximate the context free or simplified context sensitive grammar with some loss of descriptive ability. Until now, how to solve the non-categorical problem within rule-based paradigm has not been reported, except for adding probability to grammars. In the approach presented in this paper, basic modal operators, necessity and possibility, are introduced into classic regular grammar. The technique of using such grammar to partially parse the free Chinese text is also discussed in detail. The experimental results suggest that the proposed approach can achieve Chinese partial parsing well and give a considerable performance improvement compared to traditional finite-state approach. A new application in natural language processing of modal logic is revealed through this paper.
Crowdsourcing a Subjective Labeling Task: A Human-Centered Framework to Ensure Reliable Results

How can we best use crowdsourcing to perform a subjective labeling task with low inter-rater agreement? We have developed a framework for debugging this type of subjective judgment task, and for improving label quality before the crowdsourcing task is run at scale. Our framework alternately varies characteristics of the work, assesses the reliability of the workers, and strives to improve task design by disaggregating the labels into components that may be less subjective to the workers, thereby potentially improving inter-rater agreement. A second contribution of this work is the introduction of a technique, Human Intelligence Data-Driven Enquiries (HIDDEN), that uses Captcha-inspired subtasks to evaluate worker effectiveness and reliability while also producing useful results and enhancing task performance. HIDDEN subtasks pivot around the same data as the main task, but ask workers to perform less subjective judgment subtasks that result in higher inter-rater agreement. To illustrate our framework and techniques, we discuss our efforts to label high quality social media content, with the ultimate aim of identifying meaningful signal within complex results.