Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust visual tracking using dynamic feature weighting based on multiple dictionary learning

Using multiple features in appearance modeling has shown to be effective for visual tracking. In this paper, we dynamically measured the importance of different features and proposed a robust tracker with the weighted features. By doing this, the dictionaries are improved in both reconstructive and discriminative way. We extracted multiple features of the target, and obtained multiple sparse representations, which plays an essential role in the classification issue. After learning independent dictionaries for each feature, we then implement weights to each feature dynamically, with which we select the best candidate by a weighted joint decision measure. Experiments have shown that our method outperforms several recently proposed trackers.
Performance Analysis of LXC for HPC Environments

Despite of Cloud infrastructures can be used as High Performance Computing (HPC) platforms, many issues from virtualization overhead had kept them unrelated. However, with advent of container-based virtualizers, this scenario acquires new perspectives because this technique promises to decrease the virtualization overhead, achieving a near-native performance. In this work, we analyzed the performance of a container-based virtualization solution - Linux Container (LXC) - against a hyper visor-based virtualization solution - KVM - under HPC activities. For our experiments, we considered CPU and communication (network and inter-process communication), and results showed the virtualizer type can impact distinctly in performance according to resource used by application.
Applying Scaling to Search

This paper proposes a method for characterising and comparing search algorithms. To illustrate the method we assess relaxation search and rule-based search on randomlygenerated k-SAT problems. The performance of relaxation search is investigated by using an energy-function formulation of k-SAT problems and combining the methods of stochastic gradient descent and simulated annealing. Rule-based-search performance is studied by applying a version of the Davis-Putnam algorithm. For both search approaches, a performance analysis is done by varying the constraint density (i.e., the level of difficulty) and monitoring its effect on the search cost. At a critical value of the constraint density ac, a phase transition occurs from satisfiable to unsatisfiable instances. Focusing on the satisfiable instances we compare search costs of both approaches over a wide range of a-values: we define a scaling relation that maps both search curves onto a single rescaled search-cost curve. Our method allows for a quantitative comparison of search algorithms as a function of problem difficulty. The results are conjectured to be generalisable to other k-SAT problems which belong to the same “universality class” of optimisation problems. We conclude that our method allows for a quantitative characterisation and comparison of search algorithms in terms of a single scaling parameter.
CONTROLLER EVALUATION AND REDESIGN USING RELAY EXPERIMENTS

Abstract In this paper it is presented a procedure for closed loop controller redesign using a relay based experiment. The present closed loop is evaluated in relation to a symmetrical optimum design. The loop gain is estimated at a few frequency points using an excitation derived from a relay experiment. The controller is redesigned to yield some stability margin using a controller procedure derived from the symmetrical optimum. Simulation examples illustrate the properties of the design scheme.
Two-stage speaker adaptation of hybrid tied-posterior acoustic models

In this paper, strategies are explored to adapt hybrid neural network/HMM systems based on the tied-posterior paradigm. We investigate the retraining of selected important parts of the neural network and a gradient based adaptation strategy for the HMM mixture coefficients based on maximizing the scaled likelihood. The paper presents the following innovations: first, it introduces one of the first adaptation methods for hybrid systems where the HMM component contributes significantly to the adaptation success; second, it presents a novel approach to the neural network's adaptation, based on the selection of suitable neurons for adaptation. Results on the WSJ speaker adaptation test show the capability of our methods to adapt to new speakers, especially in the case of adapting the neural net, and that both methods can be combined to achieve additional improvement of the word error rate in most cases.
The case study of an Australian crime dataset

Analysis of crime data is crucial for prevention and assessment of illegal activity. This paper is one of the first case studies of a crime dataset collected in New South Wales, Australia. We apply methods from complex network analysis to identify key aspects of criminal activity in the state of New South Wales. We further detect groups of local government areas and examine their dynamics over time. We represent our results by using various different visualisation techniques.
Distilling Reverse-Mode Automatic Differentiation (DrMAD) for Optimizing Hyperparameters of Deep Neural Networks

The performance of deep neural networks is sensitive to the setting of their hyperparameters (e.g. L2-norm panelties). Recent advances in reverse-mode automatic differentiation have made it possible to optimize hyperparameters with gradients. The standard way of computing these gradients involves a forward and backward pass, which is similar to its cousin, back-propagation, used for training weights of neural networks. However, the backward pass usually needs to exactly reverse a training procedure, starting from the trained parameters and working back to the initial random ones. This incurs unaffordable memory consumption as it needs to store all the intermediate variables. Here we propose to distill the knowledge of the forward pass into an shortcut path, through which we approximately reverse the training trajectory. Experiments carried out on MNIST dataset show that our approach reduces memory consumption by orders of magnitude without sacrificing its effectiveness. Our method makes it feasible, for the first time, to automatically tune hundreds of thousands of hyperparameters of deep neural networks in practice.
QOOK: enhancing information revisitation for active reading with a paper book

Revisiting information on previously accessed pages is a common activity during active reading. Both physical and digital books have their own benefits in supporting such activity according to their manipulation natures. In this paper, we introduce QOOK, a paper-book based interactive reading system, which integrates the advanced technology of digital books with the affordances of physical books to facilitate people's information revisiting process. The design goals of QOOK are derived from the literature survey and our field study on physical and digital books respectively. QOOK allows page flipping just like on a real book and enables people to use electronic functions such as keyword searching, highlighting and bookmarking. A user study is conducted and the study results demonstrate that QOOK brings faster information revisiting and better reading experience to readers.
Utilizing Jini Features to Implement a Multiagent Framework for Performance-based Resource Allocation in Grid Environment

Jini is able to support applications in heterogeneous, dynamic computing environments and is also being used to develop agent-based applications. This paper presents the design of a multiagent framework for resource brokering and management in a Grid environment and implementation of a part of it utilizing Jini features. The multiagent framework provides services like resource brokering and allocation, analysis of performance-monitoring data, local tuning of applications and also rescheduling in case of any performance problem on a specific resource provider. Part of the framework makes use of mobile agents for scheduling and rescheduling purposes.
Monitoring of basic daily activities with inertial sensors and wireless modules

This paper is concerned with monitoring of basic daily activities using inertial sensors and foot pressure sensors. Daily activities are classified into eight states: : sitting, walking, walking up, walking down, running, running up, running down, and standing. A hidden Markov model filter is used to estate activity states. Also when the activity states are walking or running, walking distance is also computed using inertial navigation algorithms. The location where activities are taking place is determined using wireless modules.
A Pattern Identification System Using Linear Decision Functions

This paper is concerned with application of linear decision functions to the pattern identification problem and describes an experimental pattern recognition system for the magnetic ink character font now used in the banking industry. The system is based on a linear decision function determined by means of a variant of an "adaptive training" technique due to Rosenblatt. The system has been partially implemented (in part, through simulation with aid of a digital computer and, in part, by hardware) and experimental results in using the system are reported.
Should the science communication community play a role in political activism

2016 was a difficult year for science. Political voices distrusting the scientific method and evidence-based policy seemed to grow louder and more incessant throughout the year. The role of science in society has evolved and, not for the first time, its future is veiled in uncertainty. Natural philosophy was once a privilege of the upper classes in ancient and medieval times before the age of enlightenment laid the foundations for modern science to become integral to society [Grant, 2007]. Has 2016 signalled a sea change in the relationship between science and society? Will science once again be regarded with suspicion by a society that feels excluded from its practice and is apathetic about its benefits?
Hippocampal BDNF mediates the efficacy of exercise on synaptic plasticity and cognition

We found that a short exercise period enhanced cognitive function on the Morris water maze (MWM), such that exercised animals were significantly better than sedentary controls at learning and recalling the location of the platform. The finding that exercise increased brain‐derived neurotrophic factor (BDNF), a molecule important for synaptic plasticity and learning and memory, impelled us to examine whether a BDNF‐mediated mechanism subserves the capacity of exercise to improve hippocampal‐dependent learning. A specific immunoadhesin chimera (TrkB‐IgG), that mimics the BDNF receptor, TrkB, to selectively bind BDNF molecules, was used to block BDNF in the hippocampus during a 1‐week voluntary exercise period. After this, a 2‐trial‐per‐day MWM was performed for 5 consecutive days, succeeded by a probe trial 2 days later. By inhibiting BDNF action we blocked the benefit of exercise on cognitive function, such that the learning and recall abilities of exercising animals receiving the BDNF blocker were reduced to sedentary control levels. Inhibiting BDNF action also blocked the effect of exercise on downstream systems regulated by BDNF and important for synaptic plasticity, cAMP response‐element‐binding protein (CREB) and synapsin I. Specific to exercise, we found an association between CREB and BDNF expression and cognitive function, such that animals who were the fastest learners and had the best recall showed the highest expression of BDNF and associated CREB mRNA levels. These findings suggest a functional role for CREB under the control of BDNF in mediating the exercise‐induced enhancement in learning and memory. Our results indicate that synapsin I might also contribute to this BDNF‐mediated mechanism.
Knowing a Good Show When You See One

Social media has become an integral part of the web, and its popularity continues to provide an outlet for people's opinions and discussion about any topic of interest. In this paper we examine the interest around a number of television series that are broadcast on a weekly basis. Our aim is to show that by observing solely the initial interactions of fans or users of a web forum, we can extrapolate the longer-term interest in particular episodes. We do so by observing the temporal dynamics of the conversation, and performing a clustering so as to judge how much time is required before reasonable conclusions can be drawn about the level of interest surrounding an episode. We find that early interaction trends have strong similarities with the overall conversation patterns.
The Radical Consequences of Realistic Satellite Orbits for the Heating and Implied Merger Histories of Galactic Disks

Previous models of galactic disk heating in interactions invoke restrictive assumptions not necessarily valid in modern ΛCDM contexts: that satellites are rigid and orbits are circular, with slow decay over many orbital periods from dynamical friction. This leads to a linear scaling of disk heating with satellite mass: disk heights and velocity dispersions scale ∝ Msat/Mdisk. In turn, observed disk thicknesses present strong constraints on merger histories: the implication for the Milky Way is that <5% of its mass could come from mergers since z ∼ 2, in conflict with cosmological predictions. More realistically, satellites merge on nearly radial orbits, and once near the disk, resonant interactions efficiently remove angular momentum while tidal stripping removes mass, leading to rapid merger/destruction in a couple of free-fall plunges. Under these conditions the proper heating efficiency is nonlinear in mass ratio, ∝ (Msat/Mdisk)2. We derive the scaling of disk scale heights and velocity dispersions as a function of mass ratio and disk gas content in this regime, and show that this accurately describes the results of simulations with appropriate “live” halos and disks. Under realistic circumstances, we show that disk heating in minor mergers is suppressed by an order of magnitude relative to the expectations of previous analyses. We show that the Milky Way disk could have experienced ~5-10 independent 1:10 mass ratio mergers since z ∼ 2, in agreement with cosmological models. Because the realistic heating rates are nonlinear in mass, the predicted heating is dominated by the more stochastic, rare low mass ratio mergers, and the existence of populations with little or no thick disk does not require fundamental modifications to the cosmology. This also leads to important differences in the predicted isophotal shapes of bulge-disk systems along the Hubble sequence.
A Reliability-Oriented Web Service Discovery Scheme with Cross-Layer Design in MANET

Web service technologies are playing an increasingly important role in service-oriented architecture design and application convergence over Mobile ad hoc networks (MANET). Due to the decentralized administration and dynamic wireless connectivity problems, accomplishing reliable service discovery in MANET faces a large number of challenges. In order to relieve the communication inefficiency among service providers and clients caused mainly by the unpredictable node mobility, this paper proposes a cross-layer service discovery scheme which enables improved network efficiency and reduced resource consumption. Firstly a network-layer based underlay framework is presented. It specifically establishes a reliability-oriented source routing mechanism which is equipped with a novel reliability-maximized path selection metric and a backup path support fast route recovery strategy. The cross-layer design is prudentially realized by piggybacking service discovery procedures on the reliability enhanced underlay routing mechanism. Simulation analysis verifies that the proposed scheme improves service discovery reliability by achieving low rediscovery frequency, and guarantees high network efficiency by providing reduced service discovery delay and control overhead.
Con: Renal denervation for all resistant hypertensive patients: the Emperor's new clothes

Alexandre Persu, Yu Jin, Jean-Philippe Lengelé, Lotte Jacobs, Jean Renkin and Jan A. Staessen Pole of Cardiovascular Research, Institut de Recherche Expérimentale et Clinique, Université Catholique de Louvain, Brussels, Belgium, Division of Cardiology, Cliniques Universitaires Saint-Luc, Université Catholique de Louvain, Brussels, Belgium, Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, University of Leuven, Leuven, Belgium, Nephrology
Prediction of a large diffusion/perfusion mismatch by the presence of a small diffusion abnormality in the setting of a distal ICA or proximal MCA occlusion

Poster: "ECR 2010 / C-2613 / Prediction of a large diffusion/perfusion mismatch by the presence of a small diffusion abnormality in the setting of a distal ICA or proximal MCA occlusion" by: "R. Hakimelahi, A. J. Yoo, N. S. Rost, L. H. Schwamm, M. H. Lev, P. W. Schaefer, R. G. Gonzalez; Boston, MA/US"
Redshift-Distance Survey of Early-Type Galaxies: Circular-Aperture Photometry

We present R-band CCD photometry for 1332 early-type galaxies, observed as part of the ENEAR survey of peculiar motions using early-type galaxies in the nearby universe. Circular apertures are used to trace the surface brightness profiles, which are then fitted by a two-component bulge-disk model. From the fits, we obtain the structural parameters required to estimate galaxy distances using the Dn-σ and fundamental plane relations. We find that about 12% of the galaxies are well represented by a pure r1/4 law, while 87% are best fitted by a two-component model. There are 356 repeated observations of 257 galaxies obtained during different runs that are used to derive statistical corrections and bring the data to a common system. We also use these repeated observations to estimate our internal errors. The accuracy of our measurements are tested by the comparison of 354 galaxies in common with other authors. Typical errors in our measurements are 0.011 dex for log Dn, 0.064 dex for log re, 0.086 mag arcsec-2 for ⟨μe⟩, and 0.09 for m, comparable to those estimated by other authors. The photometric data reported here represent one of the largest high-quality and uniform all-sky samples currently available for early-type galaxies in the nearby universe, especially suitable for peculiar motion studies.
Application of Epipolar Line Rectification to the Stereovision-Based Measurement of Workpieces

A general epipolar line constraint equation for binocolar stereo vision is first established for the measurement of workpiece surface, in which the intrinsic parameters of two cameras and their extrinsic structural parameters with respect to a reference base are included. To overcome the drawback of the relative distortion between left image and right image, An epipolar line rectification technique is used for the corresponding point detection in the stereovision-based measurement system. Instead of traditional rectangular window, a circular window sliding on epipolar lines is used to compute the correlation coefficient between the regions surrounding a selected point in the left image and its corresponding point in the right image. The stereovision-based measurement of a T-shaped circular pipe (as an example of workpieces) is carried out using the rectified epipolar lines and the workpiece surface is reconstructed with its acquired three-dimensional coordinates. Experimental results show that the relative distortion of left and right images can be eliminated and the measurement accuracy can be greatly improved after the application of epipolar line rectification, and the workpiece surface is reconstructed with less errors.
ROBUST FEATURES AND SYSTEM FUSION FOR REVERBERATION-ROBUST SPEECH RECOGNITION

Reverberation in speech degrades the performance of speech recognition systems, leading to higher word error rates. Human listeners can often ignore reverberation, indicating that the auditory system somehow compensates for reverberation degradations. In this work, we present robust acoustic features motivated by the knowledge gained from human speech perception and production, and we demonstrate that these features provide reasonable robustness to reverberation effects compared to traditional melfilterbank-based features. Using a single-feature system trained with the data distributed through the REVERB 2014 challenge on automatic speech recognition, we show a modest 12% and 0.2% relative reduction in word error rate (WER) compared to the melscale-feature-based baseline system for simulated and real reverberation conditions. The reduction is more pronounced when three systems are combined, resulting in a relative 20% reduction in WER for the simulated reverberation condition and 11.7% for the real reverberation condition compared to the mel-scale-featurebased baseline system. The WER was found to reduce even further with addition of more systems trained with robust acoustic features. HLDA transform of features and MLLR adaptation of speaker clusters were also explored in this study and both of them were found to improve the recognition performance under reverberant conditions. Index Terms— feature combination, robust speech recognition, reverberation robustness, robust features.
Towards a paradigm change in computer science and software engineering: a synthesis

In this paper, we identify and analyse a set of characteristics that increasingly distinguish today's complex software systems from ‘‘traditional” ones. Several examples in different areas show that these characteristics are not limited to a few application domains but are widespread. Then, we discuss how these characteristics are likely to impact dramatically on the very way software systems are modelled and engineered. In particular, we appear to be on the edge of a radical shift of paradigm, which is about to change our very attitudes in software systems modelling and engineering.
Trust based D2D communications for accessing services in Internet of Things

Internet of Things (IoT) is a vision of connecting everything for providing better services efficiently. IoT consists of enormous number of heterogeneous computing devices with different capabilities to provide a diverse range of services situated around the globe. Increasing number of IoT devices and availability of different services in the edge of the networks makes it inevitable to interact among devices. As a result, this open, non-homogeneous and distributed environment also breaches the integrity of secure and reliable device to device (D2D) communication. Traditional access control mechanisms are not prolific to the itinerant, decentralized and dynamic scenarios in the IoT. Trust management is a proven technology for applications like P2P, Grid, and ad hoc network. Hence, this technology can also be used to increase the user reliability in IoT. In this paper, we propose a trust based D2D communication mechanism for accessing different services to meet the growing transactions and successful operations of IoT. We also analyze the effect of adaptive trust parameters for IoT device to device communication in order to access the services.