Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Multitask Multiview Tracking in Videos

Various sparse-representation-based methods have been proposed to solve tracking problems, and most of them employ least squares (LSs) criteria to learn the sparse representation. In many tracking scenarios, traditional LS-based methods may not perform well owing to the presence of heavy-tailed noise. In this paper, we present a tracking approach using an approximate least absolute deviation (LAD)-based multitask multiview sparse learning method to enjoy robustness of LAD and take advantage of multiple types of visual features, such as intensity, color, and texture. The proposed method is integrated in a particle filter framework, where learning the sparse representation for each view of the single particle is regarded as an individual task. The underlying relationship between tasks across different views and different particles is jointly exploited in a unified robust multitask formulation based on LAD. In addition, to capture the frequently emerging outlier tasks, we decompose the representation matrix to two collaborative components that enable a more robust and accurate approximation. We show that the proposed formulation can be effectively approximated by Nesterov's smoothing method and efficiently solved using the accelerated proximal gradient method. The presented tracker is implemented using four types of features and is tested on numerous synthetic sequences and real-world video sequences, including the CVPR2013 tracking benchmark and ALOV++ data set. Both the qualitative and quantitative results demonstrate the superior performance of the proposed approach compared with several state-of-the-art trackers.
From A Social Influence Perspective: The Impact Of Social Media On Movie Sales

In this study, we draw upon literature on social influence to investigate the relationships between comments generated from online social media and product sales. Using a natural language processor, we measure the favourability (comments sentiments) as well as the visibility (volume of comments) of social media comments on the list of cinematic movie released for a year beginning in late 2010. Specifically, we study how favourability and visibility of these comments have an impact on daily product sales. We also explore if different types of social media platforms have differing impacts on product sales. The types of social media studied include expert reviews, blogs, forums, microblogs (e.g. Twitter) and network-based blogs (e.g. Facebook).
A multidimensional QoE monitoring system for audiovisual and haptic interactive IP communications

This paper deals with audiovisual and haptic interactive IP communications and designs a multidimensional QoE monitoring system with the QoE estimation in real time. For the estimation, we carry out regression analysis between QoE and application-level QoS and obtain equations for the estimation. We measured the application-level QoS and made a subjective experiment by assessing QoE multidimensionally with the SD method. To monitor QoE, we derive estimates for three QoE metrics. We apply linear and non-linear regression analysis to the experimental results. As a result, we find that the obtained equations can estimate QoE with high accuracy. By using the equations, we have built the QoE monitoring system.
Data61-CSIRO systems at the CLPsych 2016 Shared Task

This paper describes the Data61-CSIRO text classification systems submitted as part of the CLPsych 2016 shared task. The aim of the shared task is to develop automated systems that can help mental health professionals with the process of triaging posts with ideations of depression and/or self-harm. We structured our participation in the CLPsych 2016 shared task in order to focus on different facets of modelling online forum discussions: (i) vector space representations; (ii) different text granularities; and (iii) fine- versus coarse-grained labels indicating concern. We achieved an F1score of 0.42 using an ensemble classification approach that predicts fine-grained labels of concern. This was the best score obtained by any submitted system in the 2016 shared task.
FOR THE EURO : A MORE DETAILED ANALYSIS

This paper brings together the literature on determination of home bias in equity holdings and the portfolio balance model of exchange rates to consider whether the dollar might be affected by a change in transactions costs that alters international portfolio allocations. Our empirical findings lend support to the view that transactions costs have a significant influence on US portfolio holdings, even after accounting for float market share. In addition, new survey evidence on the equity holdings of European firms indicates home bias for European investors, and points to a reduction in the magnitude of this home bias since 1997. This paper was produced as part of the International Financial Stability Programme at the Centre for Economic P erformance, London School of Economics and Political Science. An abridged version of this paper entitled ‘Home Bias, Transactions Costs, and Prospects for the Euro’ is forthcoming in Asset Price Bubbles: The Implications for Monetary, Regulatory, and International Policies , W.C. Hunter, ed., MIT Press. The authors thank Frank Warnock for comments on an earlier draft and Josh Catlin for research assistance.
Global Analysis of the Small RNA Transcriptome in Different Ploidies and Genomic Combinations of a Vertebrate Complex – The Squalius alburnoides

The Squalius alburnoides complex (Steindachner) is one of the most intricate hybrid polyploid systems known in vertebrates. In this complex, the constant switch of the genome composition in consecutive generations, very frequently involving a change on the ploidy level, promotes repetitive situations of potential genomic shock. Previously in this complex, it was showed that in response to the increase in genome dosage, triploids hybrids could regulate gene expression to a diploid state. In this work we compared the small RNA profiles in the different genomic compositions interacting in the complex in order to explore the miRNA involvement in gene expression regulation of triploids. Using high-throughput arrays and sequencing technologies we were able to verify that diploid and triploid hybrids shared most of their sequences and their miRNA expression profiles were high correlated. However, an overall view indicates an up-regulation of several miRNAs in triploids and a global miRNA expression in triploids higher than the predicted from an additive model. Those results point to a participation of miRNAs in the cellular functional stability needed when the ploidy change.
How to describe speech emotion more completely - An investigation on Chinese broadcast news speech

A multi-perspective describing method for emotional speech is proposed. Three perspectives - cognitive, psychological, physiological ones and seven phonetic evaluation features are involved, with which it is expected to describe speech emotion more completely and to bridge the gap between emotions and acoustic signals. A series of experiments including cognition tests and corpus annotation are implemented. Although preliminarily, the results still reveal the effectiveness of the method.
Fine structure of the postpygidial gland in Aenictus army ants

Army ants of the genus Aenictus are characterized by the presence of a conspicuous postpygidial gland, which is the source of the trail pheromone. The paired gland at each side consists of a reservoir sac into which the secretory cells open through their accompanying duct cells. The secretory cells are characterized by a well developed Golgi apparatus, numerous mitochondria and strands of smooth endoplasmic reticulum. The reservoir opens near the abdomen tip, which facilitates deposition of the secretory products onto the substrate. The large reservoir of the postpygidial gland may enable the incessant trail laying of at least one of the investigated species.
Mixed context networks for semantic segmentation

Semantic segmentation is challenging as it requires both object-level information and pixel-level accuracy. Recently, FCN-based systems gained great improvement in this area. Unlike classification networks, combining features of different layers plays an important role in these dense prediction models, as these features contains information of different levels. A number of models have been proposed to show how to use these features. However, what is the best architecture to make use of features of different layers is still a question. In this paper, we propose a module, called mixed context network, and show that our presented system outperforms most existing semantic segmentation systems by making use of this module.
An unsupervised approach to language identification

This paper presents an unsupervised approach to automatic language identification (ALI) based on vowel system modeling. Each language vowel system is modeled by a Gaussian mixture model (GMM) trained with automatically detected vowels. Since this detection is unsupervised and language independent, no labeled data are required. GMMs are initialized using an efficient data-driven variant of the LBG algorithm: the LBG-Rissanen (1983) algorithm. With 5 languages from the OGI MLTS corpus and in a close set identification task, we reach 79% of correct identification using only the vowel segments detected in 45 second duration utterances for the male speakers.
An entice resistant automatic phishing detection

Phishing is turning into a hotbed for vast fraudulency over the Internet; therefore it's one of the most challenges toward Internet security. Utilizing a centralized list of Website is a common solution; as the most of the browsers and commercial anti-phishing products utilize it. Nevertheless, this solution is helpless against zero-day phishing attacks. So, many researches study and suggest methods based on machine learning techniques. Most of the features involved in these methods can be easily enticed. This paper introduces a novel method with high precision and also resistant to enticement. This method was tested against common legitimate and phishing websites, and produced high detection accuracy.
Normalisation by Evaluation for System F using Staged Outermost Reduction

We give a strikingly simple presentation of Normalisation by Evaluation (NbE) for System F. It is formulated using straightforward type theory in the form of System F, a purpose-defined two-level version of System F. One level of System F contains copies of all the System F terms while the other level contains copies of all the long β(η)-normal forms of System F. Overlap is allowed only at variable type. We derive the NbE algorithm as the canonical coercers of terms between the levels in System F, up-to outermost-only β-normalisation (a.k.a. evaluation) in just the one level. In doing so, we prove the NbE algorithm correct: for any System F term, NbE computes its long β(η)-normal form.
Vector quantization and density estimation

The connection between compression and the estimation of probability distributions has long been known for the case of discrete alphabet sources and lossless coding. A universal lossless code which does a good job of compressing must implicitly also do a good job of modeling. In particular, with a collection of codebooks, one for each possible class or model, if codewords are chosen from among the ensemble of codebooks so as to minimize bit rate, then the codebook selected provides an implicit estimate of the underlying class. Less is known about the corresponding connections between lossy compression and continuous sources. We consider aspects of estimating conditional and unconditional densities in conjunction with Bayes-risk weighted vector quantization for joint compression and classification.
PhishCage: reproduction of fraudulent websites in the emulated internet

This paper introduces PhishCage, an experimental infrastructure for phishing detection systems. Due to the short life time of phishing sites, comparative study of effectiveness, especially universality, among the detection systems is difficult. Our key idea is developing a testbed in which preserved phishing sites can be browsed as if they existed in the wild. According to our survey for phishing detection systems, this paper defines the requirements for the testbed, and designs PhishCage to meet with the requirements. The experiment of PhishCage demonstrates our mapping algorithm for 121 phishing sites into the emulated Japanese Internet topology. We confirm that phishing detection systems can obtain the realistic IP address and autonomous system number of the phishing sites in PhishCage, and few modifications enable the systems to work as if they are in the real Internet. With regard to the experimental results, we analyze the limitation of PhishCage, and finally discuss the feasibility of our emulation technique.
Interaction Between Leg Muscle Performance and Sprint Acceleration Kinematics

Abstract This study investigated relationships between 10 m sprint acceleration, step kinematics (step length and frequency, contact and flight time), and leg muscle performance (power, stiffness, strength). Twenty-eight field sport athletes completed 10 m sprints that were timed and filmed. Velocity and step kinematics were measured for the 0-5, 5-10, and 0-10 m intervals to assess acceleration. Leg power was measured via countermovement jumps (CMJ), a fivebound test (5BT), and the reactive strength index (RSI) defined by 40 cm drop jumps. Leg stiffness was measured by bilateral and unilateral hopping. A three-repetition maximum squat determined strength. Pearson’s correlations and stepwise regression (p ≤ 0.05) determined velocity, step kinematics, and leg muscle performance relationships. CMJ height correlated with and predicted velocity in all intervals (r = 0.40-0.54). The 5BT (5-10 and 0-10 m intervals) and RSI (5-10 m interval) also related to velocity (r = 0.37-0.47). Leg stiffness did not correlate with acceleration kinematics. Greater leg strength related to and predicted lower 0-5 m flight times (r = -0.46 to -0.51), and a longer 0-10 m step length (r = 0.38). Although results supported research emphasizing the value of leg power and strength for acceleration, the correlations and predictive relationships (r2 = 0.14-0.29) tended to be low, which highlights the complex interaction between sprint technique and leg muscle performance. Nonetheless, given the established relationships between speed, leg power and strength, strength and conditioning coaches should ensure these qualities are expressed during acceleration in field sport athletes.
Depth from defocus by changing camera aperture: a spatial domain approach

The application of a new spatial-domain convolution/deconvolution transform (S transform) for determining the distance of objects using image defocus is described. The method, known as STMAP, involves simple local operations on only two images taken with different aperture diameters and can be easily implemented in parallel. Both images can be arbitrarily blurred, and neither of them needs to be a focused image taken with a pin-hole camera. STMAP is implemented on an actual camera system named the Stonybrook Passive Autofocusing and Ranging Camera System (SPARCS). Experimental results on real-world planar objects are presented. The results indicate that STMAP is useful in practical applications. The utility of the method is demonstrated for rapid autofocusing of electronic cameras. STMAP is computationally more efficient than other depth-from-focus methods. The results are comparable to a Fourier-transform-based approach. <<ETX>>
2 Detection of Gaussian Signals in White Gaussian Noise

In this chapter we consider the problem of detecting a sample function from a Gaussian random process in the presence of additive white Gaussian noise. This problem is a special case of the general Gaussian problem described in Chapter 1. It is characterized by the property that on both hypotheses, the received waveform contains an additive noise component w(t), which is a sample function from a zero-mean white Gaussian process with spectral height N,/2. When HI is true, the received waveform also contains a signal s(t), which is a sample function from a Gaussian random process whose mean and covariance function are known. Thus,
Surveying Learners' Attitudes Toward a Saudi E-learning System

The purpose of this study was to analyse learners' perceptions of a Saudi E-learning System to support and enhance brick and mortar classroom. Accordingly, Data were collected from 25 female undergraduate students enrolled at the computer and Information Science department, in the college for women at Prince Sultan University (PSU). The survey item analysis revealed that all of the participant students regard the features of JUSUR to be helpful and enhance the understanding of course materials, and hope that this system will be adopted for other courses in the future. Results also indicate that most of the available system features were favored by the students. Nevertheless, the online chat sessions were the most preferable in this study because it seems to be convenient for female students to discuss online group project with their peers.
Traditional and patient-centred outcomes with three classes of asthma medication

Lung function is commonly used as the primary endpoint in asthma clinical trials, but it may not reflect changes which are important to patients. The present study compared changes in, and relationships between, traditional and patient-centred end-points during treatment with three classes of asthma medication. Subjects with mild-to-moderate asthma were randomised to double-blind, double-dummy crossover treatment with eformoterol 12 µg b.i. d. or montelukast 10 mg q.d. , then single-blind treatment with fluticasone 250 µg b.i. d. /placebo capsules, with 6-week treatment periods and 1-week washouts. Individual “traditional” end-points (symptoms, reliever use, forced expiratory volume in one second per cent predicted, morning peak expiratory flow, airway hyperresponsiveness) and “patient-centred” end-points (asthma control questionnaire, quality of life, patient global assessments) were assessed. Principal component analysis and linear modelling were used to explore overall rank orders for treatment, and relationships between outcomes. A total of 58 subjects were randomised. The rank order of benefit from eformoterol and fluticasone differed for three factors derived from principal component analysis (eformoterol>fluticasone for symptom/reliever use factor, fluticasone>eformoterol for lung function factor, eformoterol = fluticasone for patient-centred factor). Montelukast was ranked third for all three factors. A significant relationship between patient-based variables and lung function was found only for montelukast treatment. In asthma treatment, traditional end-points do not fully capture patient-centred benefits, and the relationship between end-points differs with medication class.
An approach to fixture systems management in machining processes

Starting from the premise that the management of fixture systems in the area of machining has been somewhat neglected from the standpoint of its technological development, the work reported on herein focuses on the development of a methodology for the management of these systems. Based on the findings of a survey carried out at companies operating in this sector and on a review of the literature on the subject, both of which confirm this lack, hypotheses are drawn up and supporting data compiled to underpin the development of a systematic, extensive and flexible model. The model, founded on principles of standardization and modularity, basically consists of four main stages: the company's definition/characterization of fixtures management, selection of the type of fixture suitable for the company, rationalization and organization of the area, and the construction of planning and control architectures within the company. We foresee that the formalization of the scientifically-based management support model proposed here will contribute significantly to productive and R&D sectors, computerizing the fixtures management methods used by companies and integrating their managerial philosophies and procedures for machining tools and fixtures.
Fast implementation for semidefinite programs with positive matrix completion

Solving semidefinite programs (SDPs) in a short time is the key to managing various mathematical optimization problems. The matrix-completion primal–dual interior-point method (MC-PDIPM) extracts a sparse structure of input SDP by factorizing the variable matrices. In this paper, we propose a new factorization based on the inverse of the variable matrix to enhance the performance of MC-PDIPM. We also use multithreaded parallel computing to deal with the major bottlenecks in MC-PDIPM. Numerical results show that the new factorization and multithreaded computing reduce the computation time for SDPs that have structural sparsity.
GENIUS : a new tool for gene networks visualization

GENIUS is a graphical software tool for visualizing genetic networks composed by a high number of genes. It accepts as input a matrix summarizing gene relationships inferred by means of reverse engineering methods. GENIUS offers two visualization modalities (the Agorà style and the TouchGraph style), that can be exploited in a complementary way. It is thus possible to obtain a clear and easily customizable view of one or more networks of interest which allows to simplify the exploration of the inferred relationships. GENIUS is in a constant development: the algorithms on which the network visualization is based are in fact very flexible, thus offering the possibility to extend the features provided by the tool.
A Bidirectional Brain-Machine Interface Algorithm That Approximates Arbitrary Force-Fields

We examine bidirectional brain-machine interfaces that control external devices in a closed loop by decoding motor cortical activity to command the device and by encoding the state of the device by delivering electrical stimuli to sensory areas. Although it is possible to design this artificial sensory-motor interaction while maintaining two independent channels of communication, here we propose a rule that closes the loop between flows of sensory and motor information in a way that approximates a desired dynamical policy expressed as a field of forces acting upon the controlled external device. We previously developed a first implementation of this approach based on linear decoding of neural activity recorded from the motor cortex into a set of forces (a force field) applied to a point mass, and on encoding of position of the point mass into patterns of electrical stimuli delivered to somatosensory areas. However, this previous algorithm had the limitation that it only worked in situations when the position-to-force map to be implemented is invertible. Here we overcome this limitation by developing a new non-linear form of the bidirectional interface that can approximate a virtually unlimited family of continuous fields. The new algorithm bases both the encoding of position information and the decoding of motor cortical activity on an explicit map between spike trains and the state space of the device computed with Multi-Dimensional-Scaling. We present a detailed computational analysis of the performance of the interface and a validation of its robustness by using synthetic neural responses in a simulated sensory-motor loop.