Miracle-dz/UniFAR-FTUs huggingface dataset:
An Opportunistic Resource Sharing and Topology-Aware mapping framework for virtual networks

Network virtualization provides a promising way to overcome Internet ossification. A major challenge is virtual network mapping, i.e., how to embed multiple virtual network requests with resource constraints into a substrate network, such that physical resources are utilized in an efficient and effective manner. Since this problem is known to be NP-complete, a variety of heuristic algorithms have been proposed. In this paper, we re-examine this problem and propose a virtual network mapping framework, ORS TA, which is based on Opportunistic Resource Sharing and Topology-Aware node ranking. Opportunistic resource sharing is taken into consideration at the entire network level for the first time and we develop an online approximation algorithm, FFA, for solving the corresponding time slot assignment problem. To measure the topology importance of a substrate node, a node ranking method, MCRank, based on Markov chain is presented. We also devise a simple and practical method to estimate the residual resource of a substrate node/link. Extensive simulation experiments demonstrate that the proposed framework enables the substrate network to achieve efficient physical resource utilization and to accept many more virtual network requests over time.
THE IMPACT OF WEB BROWSING ON CLASSROOM LEARNING PERFORMANCE

In a growing number of classes, students have internet access through laptop or laboratory computers. Internet access could enhance student learning by allowing them to read online course materials provided by the instructor plus other material relevant to the course. However, students may be distracted from the class by surfing the Internet, chatting online, checking emails, and so on. Therefore, one may wonder, on balance, whether or not Internet access is beneficial to learning. This study employed a controlled experiment to investigate how Internet access influences classroom learning. Browsing non-class web sites during class time had a detrimental effect on students’ learning, but student access to class-related web sites improved it. Therefore instructors should consider using control mechanisms to block access to nonclass web sites during class lectures. Students should be encouraged to browse through slides while listening to the lecture.
Simulating the formation of the local galaxy population

We simulate the formation and evolution of the local galaxy population, starting from initial conditions with a smoothed linear density field which matches that derived from the IRAS 1.2-Jy galaxy survey. Our simulations track the formation and evolution of all dark matter haloes more massive than 1011 M⊙ out to a distance of 8000 km s−1 from the Milky Way. We implement prescriptions similar to those of Kauffmann et al. to follow the assembly and evolution of the galaxies within these haloes. We focus on two variants of the CDM cosmology: a ΛCDM model and a τCDM model. Galaxy formation in each is adjusted to reproduce the I-band Tully–Fisher relation of Giovanelli et al. We compare the present-day luminosity functions, colours, morphology and spatial distribution of our simulated galaxies with those of the real local population, in particular with the Updated Zwicky Catalog, with the IRAS PSCz redshift survey, and with individual local clusters such as Coma, Virgo and Perseus. We also use the simulations to study the clustering bias between the dark matter and galaxies of differing type. Although some significant discrepancies remain, our simulations recover the observed intrinsic properties and the observed spatial distribution of local galaxies reasonably well. They can thus be used to calibrate methods which use the observed local galaxy population to estimate the cosmic density parameter or to draw conclusions about the mechanisms of galaxy formation. To facilitate such work, we publicly release our z=0 galaxy catalogues, together with the underlying mass distribution.
Transliterating Urdu for a Broad-Coverage Urdu/Hindi LFG Grammar

In this paper, we present a system for transliterating the Arabic-based script of Urdu to a Roman transliteration scheme. The system is integrated into a larger system consisting of a morphology module, implemented via finite state technologies, and a computational LFG grammar of Urdu that was developed with the grammar development platform XLE (Crouch et al. 2008). Our long-term goal is to handle Hindi alongside Urdu; the two languages are very similar with respect to syntax and lexicon and hence, one grammar can be used to cover both languages. However, they are not similar concerning the script -- Hindi is written in Devanagari, while Urdu uses an Arabic-based script. By abstracting away to a common Roman transliteration scheme in the respective transliterators, our system can be enabled to handle both languages in parallel. In this paper, we discuss the pipeline architecture of the Urdu-Roman transliterator, mention several linguistic and orthographic issues and present the integration of the transliterator into the LFG parsing system.
1 THE MEASUREMENT OF EMOTION INTENSITY : A PSYCHOPHYSICAL APPROACH !

Despite its phenomenal salience, the intensity of emotions remains a neglected issue of emotion research. The very notion has long been shown subtle and elusive, never precisely matched by physiological arousal or, on the subjective side, by the activation dimension so common among emotion space models. This paper argues in favor of reframing the whole matter into a psychophysical measurement approach. An experiment focusing on emotion words related to seven basic discrete emotions is reported, that relies on a cross-modal matching paradigm to check the validity and quantitative properties of the scaling achieved. Considerations that put the method’s contribution in perspective are also offered. A surprising feature of emotion studies is the enduring neglect of intensity issues. As underlined by Frijda et. al. (1992), the fact is all the more surprising as it contends with the well-acknowledge relevance of felt intensity to the overall experience of emotion. Moreover, it also collides with the central role assigned to an intensity component, on more or less a priori grounds, by several influential theories of emotions (Plutchick, 1982; Reisenzein, 1994). Two severe consequences of this neglect are (1) the risk of a widespread confounding of intensity effects all across the field of emotion studies (see Levenson, 1988, for a description of the problem within the scope of ANS physiological research on emotions) and (2) the constraints imposed by resorting to an unmeasured independent variable, whenever the subject matter turns out to be the impact of emotions on other variables. The experiment here reported was meant as a first exploratory step towards a quantified approach of emotion intensity. Focusing on emotion words, it asks the following question: Can subjects validly and consistently rate overall intensity of a given expressed emotion? This question serves two functions: (1) it addresses the meaningfulness of the very concept of expressed overall emotional intensity, hereby recasted as a measurement problem, and (2) it embodies the view that the measurement of overall intensity stands as an empirical issue to be addressed on its own right, not to be dismissed by a priori assumptions regarding the «loose» multidimensionality of emotions. This research was partially supported by the Foundation for Science and Technology of Portugal. Grant BD 19487 to Francisco M. Cardoso.
The Thyroid Hormone Receptors Modulate the Skin Response to Retinoids

Background Retinoids play an important role in skin homeostasis and when administered topically cause skin hyperplasia, abnormal epidermal differentiation and inflammation. Thyroidal status in humans also influences skin morphology and function and we have recently shown that the thyroid hormone receptors (TRs) are required for a normal proliferative response to 12-O-tetradecanolyphorbol-13-acetate (TPA) in mice. Methodology/Principal findings We have compared the epidermal response of mice lacking the thyroid hormone receptor binding isoforms TRα1 and TRβ to retinoids and TPA. Reduced hyperplasia and a decreased number of proliferating cells in the basal layer in response to 9-cis-RA and TPA were found in the epidermis of TR-deficient mice. Nuclear levels of proteins important for cell proliferation were altered, and expression of keratins 5 and 6 was also reduced, concomitantly with the decreased number of epidermal cell layers. In control mice the retinoid (but not TPA) induced parakeratosis and diminished expression of keratin 10 and loricrin, markers of early and terminal epidermal differentiation, respectively. This reduction was more accentuated in the TR deficient animals, whereas they did not present parakeratosis. Therefore, TRs modulate both the proliferative response to retinoids and their inhibitory effects on skin differentiation. Reduced proliferation, which was reversed upon thyroxine treatment, was also found in hypothyroid mice, demonstrating that thyroid hormone binding to TRs is required for the normal response to retinoids. In addition, the mRNA levels of the pro-inflammatory cytokines TNFα and IL-6 and the chemotactic proteins S1008A and S1008B were significantly elevated in the skin of TR knock-out mice after TPA or 9-cis-RA treatment and immune cell infiltration was also enhanced. Conclusions/significance Since retinoids are commonly used for the treatment of skin disorders, these results demonstrating that TRs regulate skin proliferation, differentiation and inflammation in response to these compounds could have not only physiological but also therapeutic implications.
Semi-Automatic Processing of Real-time MR Image Sequences for Speech Production Studies

This paper describes algorithms and data processing procedures developed to process real-time magnetic resonance images for speech production studies. These techniques allow visualization of the teeth, contour tracking over time of active and passive articulators, and vocal tract aperture recovery. These techniques can facilitate speech production studies requiring, for example, the computation of the resonance frequencies of the moving vocal tract or an examination of intergestural timing. [Supported by NIH]
A Domain Based Approach to Social Relation Recognition

Social relations are the foundation of human daily life. Developing techniques to analyze such relations from visual data bears great potential to build machines that better understand us and are capable of interacting with us at a social level. Previous investigations have remained partial due to the overwhelming diversity and complexity of the topic and consequently have only focused on a handful of social relations. In this paper, we argue that the domain-based theory from social psychology is a great starting point to systematically approach this problem. The theory provides coverage of all aspects of social relations and equally is concrete and predictive about the visual attributes and behaviors defining the relations included in each domain. We provide the first dataset built on this holistic conceptualization of social life that is composed of a hierarchical label space of social domains and social relations. We also contribute the first models to recognize such domains and relations and find superior performance for attribute based features. Beyond the encouraging performance of the attribute based approach, we also find interpretable features that are in accordance with the predictions from social psychology literature. Beyond our findings, we believe that our contributions more tightly interleave visual recognition and social psychology theory that has the potential to complement the theoretical work in the area with empirical and data-driven models of social life.
The two books prior to the scientific revolution

The relationship between the revelation of God through nature and through Scripture is here studied,1 by focusing on the metaphor of “the Two Books” as it was used from the Fathers of the Church up to the seventeenth century. According to the majority of the Fathers, the book of nature is as universal as the book of Scripture, and the content of each is to some extent equivalent. The authors of the Middle Ages emphasize that the capability of human reason to recognize God through the book of nature has been weakened by sin. Thus, it becomes necessary the reading of a “third” book, the book of the Cross. The work of Raymond Sebond plays an important role to understand the historical evolution the metaphor underwent during the Renaissance and the Modern Age. The autonomy of the book of nature with respect to the book of Scripture will increase accordingly, including the possibility to have access to an image of God different from that conveyed by sacred Scripture. The way in which the metaphor is used during the Renaissance will pave the way to deism in the eighteenth century and to naturalism in the nineteenth century.
Anomaly Detection over Concept Drifting Data Streams

Outlier detection over data streams has attracted attention for many emerging applications, such as network intrusion detection, web click stream and aircraft health anomaly detection. Since the data stream is likely to change over time, it is important to be able to modify the outlier detection model appropriately with the evolution of the stream. Most existing approaches were using incremental or periodical models to deal with concept-drifting data streams. However, in these approaches, model updates were either more frequently and risk wasting resources on insignificant changes or more infrequently and risk model inaccuracy. In this paper, a hybrid framework by combining LOF (Local outlier Factor) and BPNN (Back propagation Neural Network), appropriate for detecting outliers in data streams, is proposed. The proposed framework provides equivalent detection performance as the iterated static LOF algorithm, while requiring significantly less computational time. Our experiments, performed on several synthetic and real life data sets, have shown that our proposed technique may achieve comparable detection performance to the static LOF model with much lower cost
Monitoring civil structures with a wireless sensor network

Structural health monitoring (SHM) is an active area of research devoted to systems that can autonomously and proactively assess the structural integrity of bridges, buildings, and aerospace vehicles. Recent technological advances promise the eventual ability to cover a large civil structure with low-cost wireless sensors that can continuously monitor a building's structural health, but researchers face several obstacles to reaching this goal, including high data-rate, data-fidelity, and time-synchronization requirements. This article describes two systems the authors recently deployed in real-world structures.
Joint Threshold Adjustment and Power Allocation for Cognitive Target Tracking in Asynchronous Radar Network

In this paper, a joint threshold adjustment and power allocation (JTAPA) algorithm is developed for target tracking in asynchronous radar network (ARN). The basis of the JTAPA strategy is to feed back the target track information from the fusion center to local radar sites to enhance both the target detection capability and the resource utilization efficiency of the ARN. For the detector, we develop a threshold adjustment (TA) algorithm for better detection performance, based on the predicted target location information fed back from the fusion center. For the transmitter, we build an asynchronous power allocation (APA) model based on the perceptual information, and use an optimization technique to control the limited power resource for the next period of time. The goal of the APA scheme is to achieve better target tracking accuracy with a given power budget. The Bayesian Cramér–Rao lower bound is derived, normalized, and subsequently utilized, as the optimization criterion for the APA strategy. The resulting nonconvex optimization problem is solved through relaxation incorporating the spectral projected gradient technique. Simulation results demonstrate that the integration of the TA and APA processes can evidently improve the tracking performance.
A Virtual Machine for Multi-Language Execution

This paper presents the architecture of a virtual machine designed specifically for the execution of multiple languages, which we call Seam. The architecture consists of a number of generic components, usable by all languages, and of a number of interfaces for which implementations have to be provided by language implementors. Our contribution is the identification of the generic services and the clean design for the parameterization over their language-specific aspects. The goal of Seam is to provide both for ample reuse and simple language implementation, concerning both compilers and runtime components, and to be a platform for language interoperation. We have implemented a prototype version of Seam and validated it with two language implementations. We present a full running implementation of Alice and a naive implementation of a Java Virtual Machine running on Seam. The paper presents first implementation effort and performance results for the prototype.
Serialized unsupervised classifier for adaptative color image segmentation: application to digitized ancient manuscripts

This paper presents an adaptative algorithm for the segmentation of color images suited for document image analysis. The algorithm is based on a serialization of the k-means algorithm that is applied sequentially by using a sliding window over the image. The algorithm reuses information about the clusters computed by the previous classification and automatically adjusts the clusters during the windows displacement in order to better adapt the classifier to any new local modification of the colors. For digitized documents, we propose to define several different clusters in the color feature space for the same logical class. We also reintroduce the user into the initialization step who must define the different samples of colors for each class and the number of classes. This algorithm has been tested successfully on ancient color manuscripts having heavy defects, showing lighting variation and transparency. Nevertheless, the proposed algorithm is generic enough to be applied on a large variety of images using other features for different purposes like color image segmentation as well as image binarization.
Catalogue of averaged stellar effective magnetic fields - I. Chemically peculiar A and B type stars

This paper presents the catalogue and the method of determination of averaged quadratic effective magnetic fields $\langle B_{\rm e} \rangle$ for 596 main sequence and giant stars. The catalogue is based on measurements of the stellar effective (or mean longitudinal) magnetic field strengths B e , which were compiled from the existing literature. We analysed the properties of 352 chemically peculiar A and B stars in the catalogue, including Am, ApSi, He-weak, He-rich, HgMn, ApSrCrEu, and all ApSr type stars. We have found that the number distribution of all chemically peculiar (CP) stars vs. averaged magnetic field strength is described by a decreasing exponential function. Relations of this type hold also for stars of all the analysed subclasses of chemical peculiarity. The exponential form of the above distribution function can break down below about 100 G, the latter value representing approximately the resolution of our analysis for A type stars.
Rumination as a Mediator of Attachment Anxiety and Symptoms of Depression and Anxiety Among Women

The objective of this study was to examine whether rumination served as a mediator of attachment anxiety and psychological distress among university women. Two overarching mediational models were examined: (a) Rumination as a mediator of the association between attachment anxiety and depressive symptoms and (b) Rumination as a mediator of the link between attachment anxiety and anxiety symptoms. In the second model, two sub-models were examined: one predicting trait anxiety (a more stable and enduring personality characteristic) as an outcome, and the second predicting state anxiety (momentary, situationally-based experiences of anxiety). Each variable was assessed using quantitative self-report measures completed by female students (n = 123) at three universities in the United States. The Baron and Kenny (1986) method of testing mediation was used. Results showed that rumination fully mediated the relation between attachment anxiety and two of the outcome variables (depressive symptoms, trait anxiety symptoms) and partially mediated the relation between attachment anxiety and state anxiety. Tests of reverse mediational models were analyzed to rule out the possibility that symptoms were causing brooding rumination, which in turn was influencing attachment anxiety. Tests of these models, however, suggested that it was not possible to rule out reverse mediation. Conclusions and implications for clinical work and future research
Harmony search based wrapper feature selection method for 1-nearest neighbour classifier

The high-dimensional feature vectors often impose a high computational cost when classification is performed. Feature selection plays major role as a pre-processing technique in reducing the dimensionality of the datasets in data analysis and data mining. This process reduces the number of features by removing irrelevant and redundant data and hence resulting in acceptable classification accuracy. Filter and wrapper are the two kinds of feature selection methods. Experimental results have proved that the wrapper methods can yield better performance, although they have the disadvantage of high computational cost. This paper presents a Harmony Search based novel optimization algorithm for wrapper feature selection. 1-NN classifier method has been used to evaluate the quality of the solutions. The performance of the proposed approach has been analysed by experiments with various real-world data sets. The proposed method, HS-1-NN, produced better performance than other state-of-the-art methods in terms of classification accuracy and convergence rate.
Audio-visual emotion recognition using an emotion space concept

In this paper, we present novel methods for estimating spontaneously expressed emotions using audio-visual information. Emotions are described with three continuous-valued emotion primitives, namely valence, activation, and dominance in a 3D emotion space. We used prosodic and spectral features to represent the audio characteristics of the emotional speech. For the extraction of visual features, the 2-dimensional Discrete Cosine Transform (2D-DCT) was applied to blocks of a predefined size in facial images. Support Vector Machines (SVM) are used in their application for regression (Support Vector Regression, SVR) to estimate these 3 emotion primitives. The result showed that the emotion primitives activation and dominance can be best estimated with acoustic features, whereas the estimation of valence yields the best result when visual features are used. Both monomodal emotion estimations were subsequently fused at a decision level by a weighted linear combination. The average estimation error of the fused result was 17.6% and 12.7% below the individual error of the acoustic and visual emotion recognition, respectively. The correlation between the emotion estimates and the manual reference was increased by 12.3% and 9.0%, respectively.