Miracle-dz/UniFAR-FTUs huggingface dataset:
Linguistic Features for Quality Estimation

This paper describes a study on the contribution of linguistically-informed features to the task of quality estimation for machine translation at sentence level. A standard regression algorithm is used to build models using a combination of linguistic and non-linguistic features extracted from the input text and its machine translation. Experiments with English-Spanish translations show that linguistic features, although informative on their own, are not yet able to outperform shallower features based on statistics from the input text, its translation and additional corpora. However, further analysis suggests that linguistic information is actually useful but needs to be carefully combined with other features in order to produce better results.
Eurographics/ Ieee-vgtc Symposium on Visualization 2010 Hypermoval: Interactive Visual Validation of Regression Models for Real-time Simulation

During the development of car engines, regression models that are based on machine learning techniques are increasingly important for tasks which require a prediction of results in real-time. While the validation of a model is a key part of its identification process, existing computation-or visualization-based techniques do not adequately support all aspects of model validation. The main contribution of this paper is an interactive approach called HyperMoVal that is designed to support multiple tasks related to model validation: 1) comparing known and predicted results, 2) analyzing regions with a bad fit, 3) assessing the physical plausibility of models also outside regions covered by validation data, and 4) comparing multiple models. The key idea is to visually relate one or more n-dimensional scalar functions to known validation data within a combined visualization. HyperMoVal lays out multiple 2D and 3D sub-projections of the n-dimensional function space around a focal point. We describe how linking HyperMoVal to other views further extends the possibilities for model validation. Based on this integration, we discuss steps towards supporting the entire workflow of identifying regression models. An evaluation illustrates a typical workflow in the application context of car-engine design and reports general feedback of domain experts and users of our approach. These results indicate that our approach significantly accelerates the identification of regression models and increases the confidence in the overall engineering process.
LIA @ MediaEval 2013 Spoken Web Search Task: An I-Vector based Approach

In this paper, we describe the LIA system proposed for the MediaEval 2013 Spoken Web Search task. This multi-language task involves searching for an audio content query, in a database, with no training resources available. The participants must then find locations of each given query term within a large database of untranscribed audio files. For this task, we propose to build a language-independent audio search system using an i-vector based approach [2].
BUSNet: Model and Usage of Regular Traffic Patterns in Mobile Ad Hoc Networks for Inter-Vehicular Communications

This paper looks into the possibility of using the regular traffic patterns provided by public buses to improve the performance of Mobile Ad-hoc Networks (MANET) for Inter-vehicular communications systems (IVCS). MANET is an adhoc network with mobile nodes that are random and unpredictable. IVCS provide drivers and passengers with a range of services, and implementation of IVCS is possible using MANET. However, certain differences in the properties of nodes affect the performance. This performance degradation would be discussed in this paper through the results that are obtained from NS-2 simulations in a suggested Metropolitan GRID (M-GRID) scenario that attempts to simulate the physical traffic situation in a small part of any typical metropolitan environment. In view of this, a novel approach named BUSNet is introduced. This approach utilizes the deterministic nature of bus routes or any other public transport system to incorporate a mobile backbone infrastructure that improves the performance of IVCS using MANET.
Transparency for Item Highlighting

In this paper, we introduce a system for reducing the visibility of infrequently used user interface items by increasing their transparency, thus increasing the visibility of frequently used items. We believe this system to be a superior alternative to the common method of completely removing infrequently used items from view. We discuss applications of this system to menus, toolboxes and desktop items, and issues associated with its implementation.
Autism spectrum disorder detection using projection based learning meta-cognitive RBF network

In this paper, we present an approach for the diagnosis of Autism Spectrum Disorder (ASD) from Magnetic Resonance Imaging (MRI) scans with Voxel-Based Morphometry (VBM) detected features using Projection Based Learning (PBL) algorithm for a Meta-cognitive Radial Basis Function Network (McRBFN) classifier. McRBFN emulates human-like meta-cognitive learning principles. As each sample is presented to the network, the McRBFN uses the estimated class label, the maximum hinge error and class-wise significance to address the self-regulating principles of what-to-learn, when-to-learn and how-to-learn in a meta-cognitive framework. Initially, McRBFN begins with zero hidden neurons and adds required number of neurons to approximate the decision surface. When a neuron is added, its parameters are initialized based on the sample overlapping conditions. The output weights are updated using a PBL algorithm such that the network finds the minimum point of an energy function defined by the hinge-loss error. Moreover, as samples with similar information are deleted, over-training is avoided. The PBL algorithm helps to reduce the computational effort used in training. For simulation studies, we have used MR images from the Autism Brain Imaging Data Exchange (ABIDE) data set. The performance of the PBL-McRBFN classifier is evaluated on complete morphometric features set obtained from the VBM analysis. The performance evaluation study clearly indicates the superior performance of PBL-McRBFN classifier over other classification algorithms.
Cross-Modal Interaction between Vision and Touch: The Role of Synesthetic Correspondence

At each moment, we experience a melange of information arriving at several senses, and often we focus on inputs from one modality and ‘reject’ inputs from another. Does input from a rejected sensory modality modulate one's ability to make decisions about information from a selected one? When the modalities are vision and hearing, the answer is “yes”, suggesting that vision and hearing interact. In the present study, we asked whether similar interactions characterize vision and touch. As with vision and hearing, results obtained in a selective attention task show cross-modal interactions between vision and touch that depend on the synesthetic relationship between the stimulus combinations. These results imply that similar mechanisms may govern cross-modal interactions across sensory modalities.
Effects of turbulent diffusion on the chemistry of diffuse clouds

Aims. We probe the effect of turbulent diffusion on the chemistry at the interface between a cold neutral medium (CNM) cloudlet and the warm neutral medium (WNM). Methods. We perform moving grid, multifluid, 1D, hydrodynamical simulations with chemistry including thermal and chemical diffusion. The diffusion coefficients are enhanced to account for turbulent diffusion. We post-process the steady-states of our simulations with a crude model of radiative transfer to compute line profiles. Results. Turbulent diffusion spreads out the transition region between the CNM and the WNM. We find that the CNM slightly expands and heats up: its CH and H2 content decreases due to the lower density. The change of physical conditions and diffusive transport increase the H + content in the CNM which results in increased OH and H2O. Diffusion transports some CO out of the CNM. It also brings H2 into contact with the warm gas with enhanced production of CH + ,H + ,O H and H 2O at the interface. O lines are sensitive to the spread of the thermal profile in the intermediate region between the CNM and the WNM. Enhanced molecular content at the interface of the cloud broadens the molecular line profiles and helps exciting transitions of intermediate energy. The relative molecular yields are found higher for bigger clouds. Conclusions. Turbulent diffusion can be the source of additional molecular production and should be included in chemical models of the interstellar medium (ISM). It also is a good candidate for the interpretation of observational problems such as warm H2 ,C H + formation and presence of H + .
Hybrid cooperative positioning with reference stations and distance dependent range corrections

Hybrid cooperative positioning fuses pseudoranges and ranging among peer nodes to figure out node positions. However, multipath, undetected direct path and various propagation delays often result in positive biased ranging and pseudorange measurements, which are the limiting factor in improving position precision. In this work, we propose a message passing algorithm for hybrid cooperative positioning, named hybrid sum-product algorithm over a wireless network with reference stations (HSPAWN-R). The proposed algorithm not only models and corrects the bias of ranging and pseudorange, but also explores ranging with references stations that have “known” positions in the message passing iteration. Extensive simulations are conducted, the results shows that the proposed HSPAWN-R significantly outperforms existing algorithms such as HSPAWN.
The Role of Neoadjuvant Chemoradiation Therapy in Patients With Borderline Resectable Pancreatic Cancer With Isolated Venous Vascular Involvement

Abstract The rationale for neoadjuvant chemoradiation therapy (Neo-CRT) and the definition of borderline resectable pancreatic cancer (BRPC) are still controversial. In particular, surgical treatment of BRPC with isolated venous vascular involvement (IVVI) is debatable. From January 2000 to December 2013, 84 patients diagnosed with BRPC according to NCCN guidelines were identified, and 70 patients were found to have BRPC with IVVI. We divided all 70 patients into 3 groups: surgery first without Neo-CRT (Group 1); pancreatectomy following Neo-CRT (Group 2); and no operation following Neo-CRT (Group 3). Patient characteristics including oncologic outcomes were analyzed for each of the 3 patients groups. Thirty-seven patients were female and 33 were male, with a mean age of 61.7 ± 9.74 years. Among the 70 BRPC patients with IVVI, 28 patients (40%) belonged to Group 1, 30 patients (42.9%) belonged to Group 2, and 12 patients (17.1%) belonged to Group 3. Pathological tumor size (P < 0.001), pT stage (P = 0.001), pTNM stage (P = 0.002), combined vascular resection (P = 0.003), completeness of adjuvant therapy (P = 0.004) were found to be statistically significantly different between Groups 1 and 2. In addition, disease-free survival (P = 0.055) and disease-specific survival (DSS) (P = 0.006) were improved in Group 2. Interestingly, when comparing DSS, there was no statistically significant difference between Groups 1 and 3 (P = 0.991). The clinical practice of pancreatectomy following Neo-CRT in BRPC with IVVI provided favorable oncologic outcomes. The effect of Neo-CRT in BRPC with IVVI may be multifactorial, providing proper patient selection, complete adjuvant chemotherapy, and potential therapeutic (downstaging) effect.
Reduced sampling and incomplete sensitivity for low‐complexity robust parametric optimization

The paper considers robust parametric optimization problems using multipoint formulations and makes the link with momentum‐based formulations. Optimal sampling issues are discussed, and a procedure is proposed to quantify the confidence level on the robustness of the design. We also discuss incomplete sensitivity evaluations to take into account the computational complexity constraint. This permits to take advantage of what was previously developed for efficient monopoint design where the cost of the optimization is comparable with one state evaluations. The proposed algorithm is fully parallel and the time‐to‐solution is comparable with monopoint situations. Concepts are introduced through simple examples, and the paper ends with the design of the shape of an aircraft robust over a range of transverse winds.Copyright © 2013 John Wiley & Sons, Ltd.
Wire space estimation and routability analysis

This paper presents a new stochastic model for two-dimensional layouts of large size. Two problems are addressed. (1) Under the condition that the number of wires emanating from a block is Poisson distributed, determine the distribution of channel width, thus estimating the average channel width. (2) Given T tracks for each channel, determine the success probability of routing an M/spl times/M cell array chip. For both problems, closed formulas are obtained. The experiment demonstrates the effectiveness of our approach. The results can be applied to gate array, field-programmable gate array design, etc.
Systemic optimization of an active vibration micro-isolator: An interval computation and constraint propagation based approach

This paper is concerned with the optimization of an active vibration micro-isolator. The process is applied to the whole system including the passive structure, the piezoelectric transducers, the electrical interfaces and the controller. An efficient optimization algorithm based on an interval computation method is used. The authors' general objective is to include and develop an early optimization step in the systemic design process of a smart structure. For this purpose, an analytic mathematical model is built, which describes the analytic equations of motion of the main controlled natural mode shape of the micro-isolator. Basically, the method is applicable to a large scale of systems if an analytic description is developed. As case study, the controller and the passive structure are simultaneously optimized so as to obtain a compromise between transmissibility, control energy level and beam strength. The first results obtained are relevant.
Integrating the Trend of Research Interest for Reviewer Assignment

Reviewer assignment problem in the research field usually refers to invite experts for comments on the quality of papers, projects, etc. Different factors in conventional approaches are reckoned to choose appropriate reviewers, such as the relevance between reviewer candidates and submissions, the diversity of candidates. However, many studies ignore the temporal changes of reviewer interest and the stability of reviewers' interest trend. Accordingly, in this research, three indispensable aspects are analyzed, including the relevance between reviewer candidates and submissions, the interest trend of candidates as well as the authority of candidates. Next, with extracted aspects, the reviewer assignment is formulated as an integer linear programming problem. Finally, categories of comparative experiments are conducted with two large datasets that are built from WANFANG and ArnetMiner, which shows the availability of the proposed approach in modeling the temporal changes of reviewers' research interest. Also, it demonstrates the effectiveness of the proposed approach for the reviewer assignment problem.
Molecular Epidemiology of Invasive Listeriosis due to Listeria monocytogenes in a Spanish Hospital over a Nine-Year Study Period, 2006–2014

We investigated the pathogenicity, invasiveness, and genetic relatedness of 17 clinical Listeria monocytogenes stains isolated over a period of nine years (2006–2014). All isolates were phenotypically characterised and growth patterns were determined. The antimicrobial susceptibility of L. monocytogenes isolates was determined in E-tests. Invasion assays were performed with epithelial HeLa cells. Finally, L. monocytogenes isolates were subtyped by PFGE and MLST. All isolates had similar phenotypic characteristics (β-haemolysis and lecithinase activity), and three types of growth curve were observed. Bacterial recovery rates after invasion assays ranged from 0.09% to 7.26% (1.62 ± 0.46). MLST identified 11 sequence types (STs), and 14 PFGE profiles were obtained, indicating a high degree of genetic diversity. Genetic studies unequivocally revealed the occurrence of one outbreak of listeriosis in humans that had not previously been reported. This outbreak occurred in October 2009 and affected three patients from neighbouring towns. In conclusion, the molecular epidemiological analysis clearly revealed a cluster (three human cases, all ST1) of not previously reported listeriosis cases in northwestern Spain. Our findings indicate that molecular subtyping, in combination with epidemiological case analysis, is essential and should be implemented in routine diagnosis, to improve the tracing of the sources of outbreaks.
Privacy-preserving matrix factorization

Recommender systems typically require users to reveal their ratings to a recommender service, which subsequently uses them to provide relevant recommendations. Revealing ratings has been shown to make users susceptible to a broad set of inference attacks, allowing the recommender to learn private user attributes, such as gender, age, etc. In this work, we show that a recommender can profile items without ever learning the ratings users provide, or even which items they have rated. We show this by designing a system that performs matrix factorization, a popular method used in a variety of modern recommendation systems, through a cryptographic technique known as garbled circuits. Our design uses oblivious sorting networks in a novel way to leverage sparsity in the data. This yields an efficient implementation, whose running time is O(Mlog^2M) in the number of ratings M. Crucially, our design is also highly parallelizable, giving a linear speedup with the number of available processors. We further fully implement our system, and demonstrate that even on commodity hardware with 16 cores, our privacy-preserving implementation can factorize a matrix with 10K ratings within a few hours.
Experimental Performance Evaluation of a Pro-Active Ad-hoc Routing Protocol in Out- and Indoor Scenarios

In this paper, we present a general methodology to assess the performance of an ad-hoc network equipped with a proactive routing protocol, namely the open link state routing protocol. We are interested in identifying the parameters which strongly affect the overall performance. To this aim, and contrary to the common use of computing means and variances only, we use a hypothesis test-based toolkit, because of the large number of factors which interact with our testbed. We discovered that there is a hop-count threshold, after which the performance deteriorates. For instance, the statistical analysis has shown that after the hop-count threshold the variability of our data increases, i.e. a marked oscillatory behaviour arises. The results of this work confirm the need of adaptive selection of the routing protocol parameters.
A Novel Explicit Pose Estimation Algorithm based on Euclidean Geometry

In this paper, a novel explicit pose estimation algorithm using Euclidean geometry is presented. Based on feature point extraction using stereo cameras, the pose of arbitrary moving rigid body in 3D space is estimated in real-time. The merit of the algorithm proposed lies on the fact that, instead of estimating the entries of rotational matrix, the axis around which the rotation is made is estimated, which removes the constraints of other algorithms that estimated rotational matrix must be a special orthogonal one. Once the axis is obtained rotation angles and translation vectors can be solved straight forward. The algorithm is simple enough for realtime applications and can be directly applied for tracking and visual servoing. Simulation included shows the effectiveness of the algorithm.
Estimation of eye and mouth corner point positions in a knowledge-based coding system

Automatic extraction of facial feature points is one of the main problems for semantic coding of videophone sequences at very low bit rates In this contribution, an approach for estimation of the eye and mouth corner point positions is presented. For this proposition, the location informations of the face model are exploited to define search areas for estimation of the eye and mouth corner point positions. Then, the eye and mouth corner point positions are estimated based on a template matching technique with eye and mouth corner templates. Finally, in order to verify these estimated corner point positions, some geometric conditions between the corner point positions and the center point positions of the eyes and the mouth are exploited. The proposed algorithm has been applied to test sequences Claire and Miss America with a spatial resolution corresponding to CIF and a frame rate of 10 Hz.
Automatic Fatigue Detection of Drivers through Pupil Detection and Yawning Analysis

This paper presents a non-intrusive fatigue detection system based on the video analysis of drivers. The system relies on multiple visual cues to characterize the level of alertness of the driver. The parameters used for detecting fatigue are: eye closure duration measured through eye state information and yawning analyzed through mouth state information. Initially, the face is located through ViolaJones face detection method to ensure the presence of driver in video frame. Then, a mouth window is extracted from the face region, in which lips are searched through spatial fuzzy c-means (s-FCM) clustering. Simultaneously, the pupils are also detected in the upper part of the face window on the basis of radii, inter-pupil distance and angle. The monitored information of eyes and mouth are further passed to SVM (Support Vector Machines) that classify the true state of the driver. The system has been tested using real data, with different sequences recorded in day and night driving conditions, and with users belonging to different race and gender.
Improved Survival with Therapeutic Hypothermia after Cardiac Arrest with Cold Saline and Surfacing Cooling: Keep It Simple

Aim. To evaluate whether the introduction of a therapeutic hypothermia (TH) protocol consisting of cold saline infusion and surface cooling would be effective in targeting mild therapeutic hypothermia (32–34°C). Additionally, to evaluate if TH would improve survival after cardiac arrest. Design. Before-after design. Setting. General Intensive Care Unit (ICU) at an urban general hospital with 470 beds. Patients and Methods. Patients admitted in the ICU after cardiac arrest between 2004 and 2009 were included. Effectiveness of the TH protocol to achieve the targeted temperature was evaluated. Hospital mortality was compared before (October 2004–March 2006) and after (April 2006–September 2009) the protocol implementation. Results. Hundred and thirty patients were included, 75 patients were not submitted to TH (before TH group), and 55 were submitted to TH (TH group). There were no significant differences concerning baseline, ICU, and cardiac arrest characteristics between both groups. There was a significant reduction in hospital mortality from 61% (n = 46) in the before TH group to 40% (n = 22) in the TH group. Conclusion. Our protocol consisting of cold saline infusion and surface cooling might be effective in inducing and maintaining mild therapeutic hypothermia. TH achieved with this protocol was associated with a significant reduction in hospital mortality.