Miracle-dz/UniFAR-FTUs huggingface dataset:
Web Information Extraction Based on Hybrid Conditional Model

The traditional Hidden Markov Model for web information extraction is sensitive to the initial model parameters and easy to lead to a sub-optimal model in practice. A hybrid conditional model to combine maximum entropy and maximum entropy Markov model is put forward for Web information extraction. With this approach, the input Web page is parsed to build an HTML tree, data regions are located in each HTML sub-tree node by estimating the entropy, which allows observations to be represented as arbitrary overlapping features (such as vocabulary, capitalization, HTML tags, and semantics), and defines the conditional probability of state sequences given to observation sequences for Web information extraction. Experimental results show that the new approach improves the performance in precision and recall over traditional hidden Markov model and maximum entropy Markov model.
The Exact Feasibility of Randomized Solutions of Uncertain Convex Programs

Many optimization problems are naturally delivered in an uncertain framework, and one would like to exercise prudence against the uncertainty elements present in the problem. In previous contributions, it has been shown that solutions to uncertain convex programs that bear a high probability to satisfy uncertain constraints can be obtained at low computational cost through constraint randomization. In this paper, we establish new feasibility results for randomized algorithms. Specifically, the exact feasibility for the class of the so-called fully-supported problems is obtained. It turns out that all fully-supported problems share the same feasibility properties, revealing a deep kinship among problems of this class. It is further proven that the feasibility of the randomized solutions for all other convex programs can be bounded based on the feasibility for the prototype class of fully-supported problems. The feasibility result of this paper outperforms previous bounds and is not improvable because it is exact for fully-supported problems.
ANALYSIS OF SAFETY MEASURES AND QUALITY ROUTING IN VANETS

Vehicle to vehicle communication (V2V) systems, where vehicles exchange information's with each other, become an important need in order to decrease traffic accidents and improve capacity of highways traffic and also road side infrastructure (V2I) communication from transportation operation center. The security could indeed be increased if the communication between the vehicles is established and maintained, this enables the driver of a car to be aware at an early time of the emergency breaking of the preceding vehicle and so eventually avoid a collision. Two efficient algorithms are proposed. Distributed-fair power adjustment for vehicular environments (D-FPAV) is a transmit power control approach for periodic messages based on a strict fairness criterion that can maximize the minimum value over all transmission power levels assigned to nodes that form the vehicular network under a given constraint on the Maximum Beaconing Load. Emergency message dissemination for vehicular environments (EMDV), for fast and effective multihop information dissemination of event-driven messages with respect to both probability of reception and latency. Synergy is gained when using both protocols together because D-FPAV can ensure the channel load is kept at a level where EMDV can successfully operate with the help of nearby base station. Analysis and Simulation is carried using NS-2.
Identification of a Serum-Induced Transcriptional Signature Associated With Type 1 Diabetes in the BioBreeding Rat

OBJECTIVE Inflammatory mediators associated with type 1 diabetes are dilute and difficult to measure in the periphery, necessitating development of more sensitive and informative biomarkers for studying diabetogenic mechanisms, assessing preonset risk, and monitoring therapeutic interventions. RESEARCH DESIGN AND METHODS We previously utilized a novel bioassay in which human type 1 diabetes sera were used to induce a disease-specific transcriptional signature in unrelated, healthy peripheral blood mononuclear cells (PBMCs). Here, we apply this strategy to investigate the inflammatory state associated with type 1 diabetes in biobreeding (BB) rats. RESULTS Consistent with their common susceptibility, sera of both spontaneously diabetic BB DRlyp/lyp and diabetes inducible BB DR+/+ rats induced transcription of cytokines, immune receptors, and signaling molecules in PBMCs of healthy donor rats compared with control sera. Like the human type 1 diabetes signature, the DRlyp/lyp signature, which is associated with progression to diabetes, was differentiated from that of the DR+/+ by induction of many interleukin (IL)-1–regulated genes. Supplementing cultures with an IL-1 receptor antagonist (IL-1Ra) modulated the DRlyp/lyp signature (P < 10−6), while administration of IL-1Ra to DRlyp/lyp rats delayed onset (P = 0.007), and sera of treated animals did not induce the characteristic signature. Consistent with the presence of immunoregulatory cells in DR+/+ rats was induction of a signature possessing negative regulators of transcription and inflammation. CONCLUSIONS Paralleling our human studies, serum signatures in BB rats reflect processes associated with progression to type 1 diabetes. Furthermore, these studies support the potential utility of this approach to detect changes in the inflammatory state during therapeutic intervention.
Assisting developers of Big Data Analytics Applications when deploying on Hadoop clouds

Big data analytics is the process of examining large amounts of data (big data) in an effort to uncover hidden patterns or unknown correlations. Big Data Analytics Applications (BDA Apps) are a new type of software applications, which analyze big data using massive parallel processing frameworks (e.g., Hadoop). Developers of such applications typically develop them using a small sample of data in a pseudo-cloud environment. Afterwards, they deploy the applications in a large-scale cloud environment with considerably more processing power and larger input data (reminiscent of the mainframe days). Working with BDA App developers in industry over the past three years, we noticed that the runtime analysis and debugging of such applications in the deployment phase cannot be easily addressed by traditional monitoring and debugging approaches. In this paper, as a first step in assisting developers of BDA Apps for cloud deployments, we propose a lightweight approach for uncovering differences between pseudo and large-scale cloud deployments. Our approach makes use of the readily-available yet rarely used execution logs from these platforms. Our approach abstracts the execution logs, recovers the execution sequences, and compares the sequences between the pseudo and cloud deployments. Through a case study on three representative Hadoop-based BDA Apps, we show that our approach can rapidly direct the attention of BDA App developers to the major differences between the two deployments. Knowledge of such differences is essential in verifying BDA Apps when analyzing big data in the cloud. Using injected deployment faults, we show that our approach not only significantly reduces the deployment verification effort, but also provides very few false positives when identifying deployment failures.
Neural network adaptive inversion control law design for a supermaneuverable aircraft

A discrete time neural network adaptive inversion controller design for a supermaneuverable aircraft nonlinear model is presented. The singular perturbation theory is used to separate the nonlinear dynamics into fast and slow sub-systems; The dynamic inversion is applied to design the control laws for the two sub-systems separately; The neural network adaptive control is based on the dynamic inversion, and the inversion error of system is compensated by the off-line neural network. To evaluate the control performance, the maneuver generator is designed to simulate the pilot inputs. The simulation results show that the designed control law can ensure the stability of the closed-loop system, and tracks the inputs well. The design takes into account the multi input multi output nature of the model. Nonlinear simulation results are given to prove the effectiveness of the controller.
Empirical pricing kernels August 1999

This paper investigates the empirical characteristics of investor risk aversion over equity return states by estimating a time-varying semi-parametric pricing kernel, which is referred to as the empirical pricing kernel (EPK). The two key features of this estimator are: (1) the functional form of the pricing kernel is estimated semi-parametrically, instead of being prespecified and (2) the pricing kernel is re-estimated on a monthly basis, allowing measurement of time-variation in risk-aversion over equity return states. Important findings of the paper are as follows. First, a constant relative risk aversion (power utility) pricing kernel over S&P500 return states is rejected in favor of an empirical pricing kernel which incorporates stochastic risk aversion and higher value for payoffs in large negative return states. Second, empirical risk aversion over equity return states is found to be closely linked to business conditions; the level of risk aversion is positively correlated with indicators of recession and negatively correlated with indicators of expansion. An option hedging methodology is developed as a test of the predictive information in the empirical pricing kernel and its associated state probability model. The results of hedging performance tests for out-of-the-money S&P500 index put options indicate that time-varying risk aversion over equity return states is an significant factor affecting option prices.
Real-Time Hand Posture Recognition for Human-Robot Interaction Tasks

In this work, we present a multiclass hand posture classifier useful for human-robot interaction tasks. The proposed system is based exclusively on visual sensors, and it achieves a real-time performance, whilst detecting and recognizing an alphabet of four hand postures. The proposed approach is based on the real-time deformable detector, a boosting trained classifier. We describe a methodology to design the ensemble of real-time deformable detectors (one for each hand posture that can be classified). Given the lack of standard procedures for performance evaluation, we also propose the use of full image evaluation for this purpose. Such an evaluation methodology provides us with a more realistic estimation of the performance of the method. We have measured the performance of the proposed system and compared it to the one obtained by using only the sampled window approach. We present detailed results of such tests using a benchmark dataset. Our results show that the system can operate in real time at about a 10-fps frame rate.
Bayesian Analysis of Comparative Survey Data

Bayesian hierarchical models provide a useful way of analyzing multilevel survey data. The Bayesian estimates have good statistical properties, make good predictions, and realistically account for clustering in the data. Still the Bayesian estimates can be biased in the presence of omitted variables and fixed effect models might sometimes be preferable. Bayesian statistics for model comparison and evaluation—posterior predictive checks and the Deviance Information Criterion—assist an empirical approach to distinguishing between hierarchical models and their alternatives. These ideas are illustrated with an analysis of migration data from 22 villages in the Nang Rong district of Thailand. Bayesian statistics can make a special contribution to comparative and historical social science. Comparative data are often not generated by a welldefined probability mechanism, so a researcher’s uncertainty may be better described by a degree-of-belief than the frequency behavior of sample statistics (Berk et al. 1994). Comparative and historical researchers also unearth rich qualitative information about particular countries, regions, and historical periods. In a classicial analysis, this nonsample information generally provides informal guides to model choice or the post hoc interpretation of results. Bayesian prior distributions explicitly incorporate non-sample information that often influences data analysis in a more informal way (Western and Jackman 1994). Prior information can have a large effect in comparative analysis because data sets can be small and collinear. Under these conditions, the final results may also depend closely on the choice of models. Bayesian statistics can incorporate uncertainty about the model specification, pushing inference in a more conservative direction (Western 1995). Finally, a key message of comparative social science is that social and political processes vary across countries, regions, and time periods. Bayesian hierarchical models help us analyze these kinds of heterogeneity (Western 1998; Western and Kleykamp 2004). This symposium on the analysis of multilevel survey data provides another opportunity to apply Bayesian methods to the special methodological problems of comparative research. Multilevel survey data are collected from, say, a dozen or more countries, perhaps at several points in time. This data structure shares some features with the pooled time series familiar to comparative researchers—observations are clustered by country and there is likely causal heterogeneity across countries. But unlike other comparative data, multilevel survey data provide enough information about each country to
Footstep Planning for Slippery and Slanted Terrain Using Human-Inspired Models

Energy efficiency and robustness of locomotion to different terrain conditions are important problems for humanoid robots deployed in the real world. In this paper, we propose a footstep-planning algorithm for humanoids that is applicable to flat, slanted, and slippery terrain, which uses simple principles and representations gathered from human gait literature. The planner optimizes a center-of-mass (COM) mechanical work model subject to motion feasibility and ground friction constraints using a hybrid A* search and optimization approach. Footstep placements and orientations are discrete states searched with an A* algorithm, while other relevant parameters are computed through continuous optimization on state transitions. These parameters are also inspired by human gait literature and include footstep timing (double-support and swing time) and parameterized COM motion using knee flexion angle keypoints. The planner relies on work, the required coefficient of friction (RCOF), and feasibility models that we estimate in a physics simulation. We show through simulation experiments that the proposed planner leads to both low electrical energy consumption and human-like motion on a variety of scenarios. Using the planner, the robot automatically opts between avoiding or (slowly) traversing slippery patches depending on their size and friction, and it chooses energy-optimal stairs and climbing angles in slopes. The obtained motion is also consistent with observations found in human gait literature, such as human-like changes in RCOF, step length and double-support time on slippery terrain, and human-like curved walking on steep slopes. Finally, we compare COM work minimization with other choices of the objective function.
Sensitivity and specificity of five abundance estimators for high-density oligonucleotide microarrays

MOTIVATION A number of algorithms have been proposed for the processing of feature-level data from high-density oligonucleotide microarrays to give estimates of transcript abundance. Performance in the common task of detecting differential expression between samples can be quantified by the statistical concepts of sensitivity and specificity, and represented by the use of receiver operating characteristic curves. These have been previously presented for small numbers of genes known to be differentially present in spiked-in samples. We present here a study of performance over a large number (thousands) of transcripts for which there is strong evidence of differential expression, with corresponding false positive rates controlled by comparisons between replicates. RESULTS The straight-line regression analysis of a mixture series with replicates by five estimation algorithms produces a consensus set of 4462 transcripts with differential expression of agreed direction and high significance (p < 0.01) according to all algorithms. The more difficult task of two-sample tests between adjacent mixture levels produces performance curves of fraction true positive detected against significance level. Performance varies significantly between algorithms: at the p < 0.01 level, the detection rate varies between 41 and 66%. A control using comparisons between replicates at the same levels indicates that the tests produce empirical false positive rates closely matching the nominal p-values.
EGFR mutation testing in blood for guiding EGFR tyrosine kinase inhibitor treatment in patients with nonsmall cell lung cancer

Background: Epidermal growth factor receptor (EGFR) mutation testing in tumor tissue is now a common practice in selecting non-small cell lung cancer (NSCLC) patients for EGFR tyrosine kinase inhibitor (TKI) treatment. However, tumor tissues are often absent or insufficient for the testing. Blood is a potential substitute providing a noninvasive, easily accessible and repeatedly measureable source of genotypic information. However which is the best blood EGFR mutation testing method remains unclear. We undertake this study to investigate the best blood EGFR mutation testing method for selecting EGFR TKI treatment in patients with NSCLC. Methods: This study was registered in PROSPERO (CRD42017055263). PubMed, EMBASE, Cochrane library, and NIHR Health Technology Assessment program will be searched. Studies fulfill the following criteria will be eligible: (1) randomized controlled trials or cohort studies; (2) included patients with NSCLC; (3) reported response, progression-free survival, or overall survival for EGFR TKI by the EGFR mutation status in blood sample. Diagnostic accuracy of blood EGFR mutation tests for predicting response to TKI will be pooled. Tumor response, progression-free survival, and overall survival according to different blood EGFR mutation testing methods will be evaluated and compared. Results: Based published data and combined analysis, this study will quantitatively compare the blood EGFR mutation testing methods according to their accuracy for predicting treatment response and relationship with clinical outcome in NSCLC patients treated with EGFR TKIs. Conclusion: This protocol will determine the best blood EGFR mutation testing method.
Evaluation of Aligning Methods for Landmark-Based Maps in Visual SLAM

Building maps is one of the most fundamental tasks for an autonomous robot. This robot should be able to construct a map of the environment and, at the same time, localize itself in it. This problem, known as Simultaneous Localization and Mapping (SLAM), has received great interest in the last years (Leonard & Durrant-Whyte, 1991). In our particular case, the robots build their maps using the FastSLAM algorithm (Montemerlo et al., 2002). The main idea of the FastSLAM algorithm is to separate the two fundamental aspects of the SLAM problem: the estimate of the robot’s pose and the estimate of the map. This algorithm uses a particle set that represents the uncertainty of the robot’s pose (localization problem) meanwhile each particle has its own associated map (several individual estimates of the landmarks conditioned to the robot’s path). The solution to the SLAM problem is performed by means of a sampling and particle generation process, in which the particles whose current observations do not fit with their associated map are eliminated. The FastSLAM algorithm has proved to be robust to false data association and it is able to represent models of non-linear movements in a reliable way (Howard, 2006). In relation to the sensors used to build the maps, many authors use range sensors such as sonar (Kwak et al., 2008; Wijk & Christensen, 2000) or laser (Thrun, 2004; Triebel & Burgard, 2005). Nevertheless, there is an increasing interest on using cameras as sensors. This approach is denoted as visual SLAM (Valls Miro et al., 2006). These devices obtain a higher amount of information from the environment and they are less expensive than laser as well. Furthermore, 3D information can be obtained when using stereo cameras. These are the sensors used in this work. Most visual SLAM approaches are landmark-based. These landmarks consist of a set of distinctive points which are referred to a global reference system. The main advantage of landmark-based maps is the compactness of their representation. By contrast, this kind of maps requires the existence of structures or objects that are distinguishable enough. The map building problem can be solved by a single robot (Moutalier & Chatila, 1989), but it will be more efficiently managed if there is a team of robots, which collaborates in the construction of the map (Howard, 2006). In this case, the space can be divided so that the distances traversed are shorter and thus the odometry errors will be smaller. In this work, 7
Deep Belief Networks for Image Denoising

Deep Belief Networks which are hierarchical generative models are effective tools for feature representation and extraction. Furthermore, DBNs can be used in numerous aspects of Machine Learning such as image denoising. In this paper, we propose a novel method for image denoising which relies on the DBNs' ability in feature representation. This work is based upon learning of the noise behavior. Generally, features which are extracted using DBNs are presented as the values of the last layer nodes. We train a DBN a way that the network totally distinguishes between nodes presenting noise and nodes presenting image content in the last later of DBN, i.e. the nodes in the last layer of trained DBN are divided into two distinct groups of nodes. After detecting the nodes which are presenting the noise, we are able to make the noise nodes inactive and reconstruct a noiseless image. In section 4 we explore the results of applying this method on the MNIST dataset of handwritten digits which is corrupted with additive white Gaussian noise (AWGN). A reduction of 65.9% in average mean square error (MSE) was achieved when the proposed method was used for the reconstruction of the noisy images.
Are you a trustworthy partner in a cross-cultural virtual environment?: behavioral cultural intelligence and receptivity-based trust in virtual collaboration

Globally distributed work has been prevalent in organizations. However, cultural issues in distributed work are still challenging team performance. Cultural intelligence, defined as individuals' capability to perform in cross-cultural settings, has great potential in untangling these issues. The present study examines three individual capabilities (behavioral cultural intelligence, language proficiency and technical skills) and their effects on partners' receptivity-based trust and satisfaction in a cross-cultural virtual environment. We develop a theoretical model based on the extended adaptive structuration theory (EAST) and verify the model in a cross-border experiment. The result suggests that focal members' behavioral cultural intelligence strongly influences their remote partners' receptivity/trust. This effect is moderated by language proficiency; 57% of the variance of partners' satisfaction is predicted by receptivity/trust and the focal members' technical skills.