Miracle-dz/UniFAR-FTUs huggingface dataset:
Minimum-latency gossiping in multi-hop wireless networks

We studied the minimum-latency gossiping (all-to-all broadcast) problem in multi-hop wireless networks defined as follows. Each node in the network is initially given a message and the objective is to design a minimum-latency schedule such that each node distributes its message to all other nodes. We considered the unit-size message model, in which different messages cannot be combined as one message, and the unit disk graph model, in which a link exists between two nodes if and only if their Euclidean distance is less than 1. This problem is known to be NP-hard in such models. In this work we designed a gossiping scheme that significantly improved all current gossiping algorithms in terms of approximation ratio. Our work has approximation ratio 27, a great improvement of the current state-of-the-art algorithm (which has ratio 1000+).
A Peer-to-Peer Massive Battle Observing System to Support Game Live

Gaming services are attractive for Internet users. How to broadcast live gaming services to large-scale Internet users is still a big problem. Traditional schemes, based on Peer-to-Peer (P2P) live-streaming and based on TV shows, have poor user experiences (good graphic quality with high resolution) and need big bandwidth or TV set support. In order to avoid network congestion, a small number of solutions propose an alternative approach based on game data content instead of video content to provide game live services. At present, all of them who use client/server framework do not emphasize the design of system architecture, so the number of users is constrained and the system is hard to expand. In this paper, we introduce PKTV, a P2P game battle observing system, which is now optimized specially for War craft 3. PKTV system broadcasts game data content to reduce bandwidth overhead, maintains a P2P overlay for each gaming channel and uses a reliable UDP protocol to break the limitation of the TCP connections, decentralizes servers to enhance system's availability and scalability. Furthermore, tests and simulations show that PKTV also have robust functionality and good performance: low-latency, low bandwidth consumption and very considerable bandwidth savings for servers.
Encoding for the Blackwell Channel with Reinforced Belief Propagation

A key idea in coding for the broadcast channel (BC) is binning, in which the transmitter encode information by selecting a codeword from an appropriate bin (the messages are thus the bin indexes). This selection is normally done by solving an appropriate (possibly difficult) combinatorial problem. Recently it has been shown that binning for the Blackwell channel -a particular BC- can be done by iterative schemes based on Survey Propagation (SP). This method uses decimation for SP and suffers a complexity of O(n2). In this paper we propose a new variation of the Belief Propagation (BP) algorithm, named Reinforced BP algorithm, that turns BP into a solver. Our simulations show that this new algorithm has complexity O(n log n). Using this new algorithm together with a non-linear coding scheme, we can efficiently achieve rates close to the border of the capacity region of the Blackwell channel.
Making Syntax of Sense: Number Agreement

things (substances, events, abstractions); Corbett (2000, chap. 3) reviewed the relevant evidence regarding linguistic number. Because less imageable referents have less discernible internal boundaries, they may tend to be construed as units even when they are potentially ambiguous in notional number (Eberhard, 1999). Ambiguities in notional number result from conflicts in valuation that can come about when a change in perspective changes the number inferred (Humphreys & Bock, in press). A related source of notional variability is likely to be found in the conceptual operations that integrate over the notional numbers of complex referents (Jackendoff, 1991; Solomon & Pearlmutter, 2004). We hypothesize that the features involved in number construal, the products of construal, and the construal processes themselves do not differ with respect to verb and pronoun number or, more accurately, with respect to the controllers of verb and pronoun number. A unitary notion should have the same representation regardless of whether it is the source of verb number, pronoun number, or both. So, the notional number of a referent should be the same regardless of whether the referent is expressed as a subject noun phrase that controls verb number, as a bound pronoun with a coreferential same-clause antecedent, or as a free (unbound) pronoun. This commits us to a single notional value, or to the same notional number ambiguities, behind different manifestations of verb and pronoun number.
TASS 2013 - A Second Step in Reputation Analysis in Spanish

TASS 2013 is the second edition of the experimental evaluation workshop within the SEPLN annual Conference focused on reputation analysis in Spanish language. The main objective is to foster the research on advanced algorithms and techniques for performing sentiment analysis and automatic text categorization on opinions extracted from short social media messages in Spanish. This paper fully describes, as compared to the previous edition, the proposed tasks, the contents, format and main figures of the generated corpus, the participant groups and their different approaches, and, finally, the overall results achieved and lessons learned in these two years.
PROGRESSIVE IMAGE TRANSMISSION USING LEVELS OF DETAIL AND REGIONS OF INTEREST

This paper presents a method for progressive image transmission, which consistently handles the problem of levels of detail and regions of interest. We propose a combined method, that allows the specification of the desired degree of detail for overlapping regions of interest in an image, transmits the necessary image data using a modified zerotree algorithm and supports the refinement of image parts by transmitting only differential data. As an application for small displays, we present the technique of rectangular fisheye views, which takes advantage of the properties of the wavelet representation.
Towards Semantic Image Annotation With Keyword Disambiguation Using Semantic And Visual Knowledge

This paper deals with the semantic enrichment of automatic annotations of images. Since it partially tackles theSemantic Gap Problem, semantic image annotation has received a large attention in the recent years. Nevertheless, the results of existing image annotation approaches are still not sufficient. We propose an original approach combininga priori knowledge (in our case, the WordNet lexical resource) and visual knowledge to build sense-tagged keywords-based annotation. First, a graph-based approach assigns a bag-ofkeywords to a query image. Then, we propose to adapt a word sense disambiguation algorithm named SSI (Structural Semantic Interconnections), initially dedicated to text. We make two adaptations. First the grammar used in the SSI is modified to reflect the preponderance of semantic relations in image databases. Then, visual knowledge, including co-occurrence statistics in the visual domain and visual cues , is integrated. At last, a method to evaluate our approach is proposed.
Knowledge discovery in multimedia repositories: the role of metadata

Data mining has recently emerged as an important issue of multimedia research. New trends are focused on knowledge discovery in large multimedia repositories, however, traditional data mining approaches do not seem to bear fruits. There is a need to use new techniques to mine knowledge from multimedia, link content top context and more importantly, to exploit them in real life applications. In this communication we review the state of the art in multimedia data mining, discuss the role of metadata standards and present future trends in this research area.
Life-cycle Asset Allocation and Optimal Consumption Using Stochastic Linear Programming

We consider optimal consumption and (strategic) asset allocation of an investor with uncertain lifetime in the context of time-varying investment opportunities. To solve this problem we use a multi-stage stochastic linear programming (SLP) model. We consider aspects of the application of the SLP approach which arise in the context of life-cycle asset allocation, but are also relevant for other problems of similar structure. The objective is to maximize the expected utility of consumption over the lifetime and of bequest at the time of death of the investor. Since we maximize utility (rather than other objectives which can be implemented more easily) we provide a new approach to optimize the breakpoints required for the linearization of the utility function. Asset returns and state variables follow a vector autoregression and the associated uncertainty is described by discrete scenario trees. To deal with the long time intervals involved in life-cycle problems we consider a few short-term decisions (which reflect return predictability), and incorporate a closed-form solution for the long, subsequent steady-state period. In our numerical examples we first show that available closed-form solutions can be accurately replicated with the SLP-based approach. Second, we add elements to the problem specification which are usually beyond the scope of closed-form solutions. We find that the asset allocation remains independent of age even if asset returns (and state variables) follow a vector autoregression, and short-sale constraints or transaction costs are included.
Anaphora resolution by antecedent identification followed by anaphoricity determination

We propose a machine learning-based approach to noun-phrase anaphora resolution that combines the advantages of previous learning-based models while overcoming their drawbacks. Our anaphora resolution process reverses the order of the steps in the classification-then-search model proposed by Ng and Cardie [2002b], inheriting all the advantages of that model. We conducted experiments on resolving noun-phrase anaphora in Japanese. The results show that with the selection-then-classification-based modifications, our proposed model outperforms earlier learning-based approaches.
Covering the Syrian conflict: How Middle East reporters deal with challenging situations

Reporters covering the Middle East are often confronted with situations where information is notoriously hard to verify and where confrontations with witnesses’ harsh realities can be extraordinarily intense. How does one deal with claims that there are no chemical weapons in Syria, for instance, if no foreign visitors are allowed to enter the neighbourhoods where the attacks allegedly took place? And how far does one go in adopting or contextualizing the story of a crying little girl blaming ‘terrorists’ for destroying her life if you are taken to her by a regime official, who considers every form of opposition an act of terror? Under such conditions, reporters can hardly rely upon seemingly self-evident routines, nor can they simply revert to general values such as impartiality or bearing witness without much further ado. Instead, they find themselves forced to make judgements on particular situations time and time again. Based on 14 in-depth interviews with Dutch and Flemish reporters covering Syria, this article sets out to identify, first, the challenging situations with which these journalists have been confronted, and second, how they have responded to these challenges through the use of particular professional strategies. To explore these challenges and strategies, the article develops a theoretical and methodological approach centred around situated value judgements.
Non-invasive Metabolomic Analysis of Breath Using Differential Mobility Spectrometry in Patients with Chronic Obstructive Pulmonary Disease and Healthy Smokers

The rapid, accurate and non-invasive diagnosis of respiratory disease represents a challenge to clinicians, and the development of new treatments can be confounded by insufficient knowledge of lung disease phenotypes. Exhaled breath contains a complex mixture of volatile organic compounds (VOCs), some of which could potentially represent biomarkers for lung diseases. We have developed an adaptive sampling methodology for collecting concentrated samples of exhaled air from participants with impaired respiratory function, against which we employed two-stage thermal desorption gas chromatography-differential mobility spectrometry (GC-DMS) analysis, and showed that it was possible to discriminate between participants with and without chronic obstructive pulmonary disease (COPD). A 2.5 dm 3 volume of end tidal breath was collected onto adsorbent traps (Tenax TA/Carbotrap), from participants with severe COPD and healthy volunteers. Samples were thermally desorbed and analysed by GC-DMS, and the chromatograms analysed by univariate and multivariate analyses. Kruskal–Wallis ANOVA indicated several discriminatory (p < 0.01) signals, with good classification performance (receiver operator characteristic area up to 0.82). Partial least squares discriminant analysis using the full DMS chromatograms also gave excellent discrimination between groups (a ¼ 19% and b ¼ 12.4%).
SLA-Aware Provisioning and Scheduling of Cloud Resources for Big Data Analytics

The stunning growth in data has immensely impacted organizations. Their infrastructure and traditional data management system could not keep up to scale of Big Data. They have to either invest heavily on their infrastructure or move their Big Data analytics to Cloud where they can benefit from both on-demand scalability and contemporary data management techniques. However, to make Cloud hosted Big Data analytics available to wider range of enterprises, we have to carefully capture their preferences in terms of budget and service level objectives. Therefore, this study aims at proposing a SLA and cost-aware resource provisioning and task scheduling approach tailored for Big Data applications in the Cloud. Current approaches assume that data is pre-stored in cluster nodes prior to deployment of Big Data applications. In addition, their focus is purely on task scheduling, and not virtual machine provisioning. We argue that in the Cloud computing context this is not applicable, because the nodes are provisioned dynamically (data cannot be pre-stored) and leaving provisioning to user may lead to under or over provisioning that can both lead to SLA or budget constraint violations. Therefore,in this study we first model the user request, which consist of Big Data analytics jobs with budget and deadline. Then, we model infrastructures as a list of data centers, virtual machines (offered in a pay-as-you-go model), data sources, and network throughputs. After that, to address the aforementioned issues, we propose and compare cost-aware and SLA-based algorithms which provision cloud resources and schedule analytics tasks.
Theoretical Basis for the Measurement of Small Differences in the Length of the Cell Cycle between Two Cell Populations

The length of the cell cycle (TC) is a tight regulated process and is important for proper development and homeostasis. Although several methods are available for estimating the duration of the cell cycle, it is difficult to determinate small differences of TC between two different cell populations due to biological and/or experimental variability. A novel strategy based in co-cultivation of two cell strains followed by a series of dilution and propagation of the culture will allow the quantification of very small differences in the length of two cell populations at resolution levels not possible at present with current methods. This is achieved by a separation of the endpoint variable measured to compare between two cell populations. The theoretical basis of this approach is discussed in the context of published experimental data and simulation of idealized experiments using virtual strains of different cell cycle length.
Aligning Brain Activity and Sketch in Multi-Modal CAD Interface

This paper investigates the proper synchronization of sketch data and cognitive states in a multi-modal CAD interface. In a series of experiments, 5 subjects were instructed to watch and then explain 6 mechanical mechanisms by sketching them on a touch based screen. Simultaneously, subject’s brain waves were recorded in terms of electroencephalogram (EEG) signals from 9 locations on the scalp. EEG signals were analyzed and translated into mental workload and cognitive state. A dynamic time window was then constructed to align these features with sketch features such that the combination of two modalities maximizes the classification of gesture from non-gesture strokes. Quadratic Discriminant Analysis (QDA) was used as classification method. Our experimental results show that the best temporal alignment for workload and sketch analysis starts from 30% time lag with previous stroke and ends before 30% time lag with next stroke.Copyright © 2014 by ASME
Matriochka symmetric Boolean functions

We present the properties of a new class of Boolean functions defined as the sum of m symmetric functions with decreasing number of variables and degrees. The choice of this construction is justified by the possibility to study these functions by using tools existing for symmetric functions. On the one hand we show that the synthesis is well understood and give an upper bound on the gate complexity. On the other hand, we investigate the Walsh spectrum of the sum of two functions and get explicit formulae for the case of degree at most three.
Wyner-Ziv coding of image using compressed sensing

Compressed sensing (CS) is a technique for acquiring and reconstructing a signal utilizing the prior knowledge that it is sparse or compressible. In this paper, we present a Wyner-Ziv image coding framework, which combines the traditional image coding and compressed sensing. In our method, the image separate into two parts according to pixel location: the down-sampling image is encoded by traditional intra-frame coding and the rest part is encoded through CS method. In order to obtain a better recover performance, the whole image is jointly reconstructed using the two part data based on image sparse representation. The experimental results are reported to demonstrate the effectiveness of the proposed coding method.
Is there an independent effect of polycystic ovary syndrome (PCOS) and menopause on the prevalence of subclinical atherosclerosis in middle aged women?

Polycystic ovary syndrome (PCOS), a common reproductive endocrine condition manifests at puberty, and is characterized by hyperandrogenism, chronic anovulation, and obesity. PCOS cases exhibit an adverse coronary heart disease (CHD) profile at an early age, including insulin resistance, dyslipidemia and increased central adiposity. It can be hypothesized that the menopausal transition, whether natural or surgical, may provide an additional “insult”, resulting in greater cumulative risk to their vasculature. Coronary artery calcification (CAC), a measure of subclinical atherosclerosis (SCA), was measured by electron beam tomography in 149 PCOS cases and 166 controls (mean age 47.3 and 49.4 respectively). Cases had a higher prevalence of CAC (63.1%) compared to controls (41.0%), (p = 0.037) after adjustment for age and BMI. A total of 22 cases and 39 controls had undergone natural menopause, 12 cases and 26 controls underwent surgical menopause (with biochemical confirmation) and 115 cases and 101 controls reported being currently premenopausal. There was a significant difference in CAC values between cases and controls in all three-menopause categories including pre-menopausal, surgically induced and natural menopause (p < 0.001). Duration since menopause (years) and use of hormone replacement therapy were not different between cases and controls for the two menopause groups. Logistic regression was carried out with CAC ( ≤10 vs >10) as the dependent variable, and independent variables: PCOS status, current age, BMI, and menopausal status, (pre-menopause, surgical and natural menopause) and selected CHD risk factors. The data indicate that women with PCOS exhibit significantly increased CAC compared to controls after adjustment for age and BMI and menopausal status. PCOS status and fasting glucose were significant risk factors for CAC (p < 0.05). Both natural and surgical menopause were independent risk factors for CAC as well (p < 0.01). HDLT was of borderline significance, p < 0.10. Further follow-up of this cohort will be valuable in determining whether PCOS status continues to affect cardiovascular risk as they undergo the menopausal transition.
DIRECTIONAL VARIATIONS OF THE NON-GAUSSIANITY PARAMETER fNL

We investigate local variations of the primordial non-Gaussianity parameter fNL in the WMAP data, looking for possible influence of foreground contamination in the full-sky estimate of fNL. We first improve the needlet bispectrum estimate in Rudjord et al. on the full sky to fNL = 73 ± 31 using the KQ75 mask on the co-added V + W channel. We find no particular values of fNL estimates close to the galactic plane and conclude that foregrounds are unlikely to affect the estimate of fNL in the V and W bands even for the smaller KQ85 mask. In the Q band, however, we find unexpectedly high values of fNL in local estimates close to the galactic mask, as well as significant discrepancies between Q-band estimates and V/W-band estimates. We therefore conclude that the Q band is too contaminated to be used for non-Gaussianity studies even with the larger KQ75 mask. We further note that the local fNL estimates on the V + W channel are positive on all equatorial bands from the north to the south pole. The probability for this to happen in a universe with fNL = 0 is less than 1%.
Photo assessment based on computational visual attention model

It is difficult to be satisfied for automatic photo assessment using only low level visual features such as brightness, lighting, hue, contrast, color distribution and so on. Instead of using low level visual features, we present a novel computational visual attention model to assess photos. Firstly, a face-sensitive saliency map analysis is deployed to estimate attention distribution. Then, a Rate of Focused Attention (RFA) measurement is proposed to quantify photo quality. By integrating top-down supervision into the visual attention model, we further achieve personalized photo assessment to take user preference into quality evaluation, which can be extended into object or semantic oriented photo assessment scenarios. Experiments on personal photo albums with comparison to ground-truth user evaluations demonstrate the effeteness of the proposed method.
GOAL SEEKING OF MOBILE ROBOTS USING DYNAMIC FUZZY Q-LEARNING

In this paper, goal seeking of mobile robots using the Dynamic Fuzzy Q-Learning method is presented. The proposed method allows a mobile robot to avoid obstacles and reach the goal efficiently. The salient characteristics of the proposed methods are: 1) Selforganizing fuzzy inference to calculate continuous valued actions and Q-functions; 2) Automatic generation of fuzzy rules and online recruitment of appropriate rules; 3) Eligibility trace method to learn faster and to alleviate the experimentation-sensitive problem caused by an arbitrarily selected bad training policy; 4) A better alternative to random search for initial state-space exploration especially when reward-giving states are sparse. Experimental results demonstrate that the robot is able to learn a near optimal policy within a few trials.