Miracle-dz/UniFAR-FTUs huggingface dataset:
Good or Bad Committers? A Case Study of Committers' Cautiousness and the Consequences on the Bug Fixing Process in the Eclipse Project

There are many roles to play in the bug fixing process in open source software development. A developer called ``Committer'', who has a permission to submit a patch into software repository, plays a major role in this process and hold a key to the successfulness of the project. In this work, we have observed each committer activities from the Eclipse-Platform bug tracking system and version archives. Despite the importance of committer's activities, we suspected that sometimes committers can make mistakes, which have negative consequences to the bug fixing process. Our research focus on studying the consequences of each committer's activities to this process. We collected each committer's historical data and evaluated each of them by comparing the more cautiousness to less cautiousness committers. Then we looked deeper into each committer's characteristics to see the reasons why some committers tend to make mistakes more than the others. From our results, we would like to make a humbly suggestion to the OSS's committers to be aware of their importance to the projects and be cautious before doing their jobs.
Context-aware vehicular cyber-physical systems with cloud support: architecture, challenges, and solutions

The advances in wireless communication techniques, mobile cloud computing, and context- aware technologies boost a growing interest in the design, development, and deployment of vehicular networks for emerging applications. This leads to an increasing evolutionary tendency to change from vehicular networks toward cloud-assisted context-aware vehicular cyberphysical systems. In this article, we first propose a multi-layered context-aware architecture and introduce two crucial service components, vehicular social networks and context-aware vehicular security. Then we propose an application scenario regarding the context-aware dynamic parking services by illuminating the cloud-assisted architecture and logic flow. Finally, we investigate the challenges and possible solutions, including context-aware safety hazard prediction, context-aware dynamic vehicle routing, and context-aware vehicular clouds.
H-AND P-ADAPTIVE INCOMPRESSIBLE FLOW SOLUTIONS ON CARTESIAN GRIDS USING LEAST SQUARES SPECTRAL ELEMENT METHOD

Use of numerical solutions to flow phenomena has become increasingly common among non-engineering disciplines such as medical sciences. This increasing interest can be promoted by the ability of solvers to obtain accurate numerical solutions without the need for expertise in some specific subjects such as grid generation or automatic grid adaptation. In this work, an incompressible flow solver is developed with emphasis on generating the initial computational grid automatically and obtaining the solution accurately, fast and efficiently with minimum user intervention. For this purpose, a non-conforming Cartesian grid (NCG) structure is used. The 2-dimensional, laminar, incompressible Navier-Stokes equations are solved by a spectral element method based on least squares principles, which offers significant advantages over traditionally employed Galerkin formulations for flow problems. Convergence to the solution is automated by a p-type adaptive refinement strategy based on least squares functional error estimate. Two solution procedures that are employed in least squares spectral element method (LSSEM) literature, matrix-free method and Schur complement decomposition are compared qualitatively based on CPU and memory performance. The solver is tested by solving various benchmark problems and comparing the results with past studies. It is shown that mass conservation performance of LSSEM can be improved effectively by using p-type adaptive refinement.
Detection of foot-and-mouth disease virus and identification of serotypes in East Azerbaijan province of Iran

Three serotypes of foot-and-mouth disease virus (FMDV) including A, Asia1 and O, have been detected in Iran. Following the mass vaccination program which was implemented in all parts of the country in 2002, the number of outbreaks has been significantly reduced. Therefore, rapid detection of FMDV and its serotypes in clinical samples is essential for control of new outbreaks. In this study a reverse transcription-polymerase chain reaction (RT-PCR) was used for detection and serotyping of FMDV in clinical samples. Twelve tissue samples were collected from suspected outbreaks from East Azerbaijan province and tested by this method. Of 12 samples, 10 were found to be positive for FMDV. These samples were also tested in a multiplex-PCR for serotype identification. Four samples showed to be serotype O, 4 samples identified as serotype A and 2 samples detected to be serotype Asia 1. Multiplex-PCR products were sequenced and specificity of results was confirmed. Results indicate that in order to practice a good control measure, the RT-PCR and multiplex-PCR could be successfully used as a robust diagnostic method.
Effects of Using Monensin and Different Levels of Crude Protein on Milk Production, Blood Metabolites and Digestion of Dairy Cows

Twenty-four Holstein dairy cows were used to evaluate the single and combined effects of different levels of crude protein (CP) and monensin treatment during early lactation on blood metabolites, milk yield and digestion of dairy cows. The experiment was designed as a completely randomized block with a factorial arrangement of treatments. The factors were three concentrations of CP supplement (19.5, 21.4, and 23.4% of dry matter) and two levels of monensin (0 and 350 mg per cow per day). The experiment consisted of three phases and each phase was 3 wk in length. Monensin did not affect milk yield, lactose, solids-non-fat (SNF), blood glucose, triglyceride and DMI, but increased blood cholesterol, blood urea nitrogen (BUN), insulin and reduced blood -hydroxybutyrate (BHBA), milk fat and protein percentage. Monensin premix significantly decreased rumen ammonia, but rumen pH and microbial protein synthesis were not affected by monensin treatment. Increasing dietary CP improved milk and protein production, but did not alter the other components of milk. Digestibility of NDF, ADF, CP were improved by increasing dietary CP. Increasing dietary CP from 19.5 to 21.4% had no significant effect on ruminal ammonia, but increasing CP to 23.4% significantly increased ruminal ammonia. There was a linear relationship between level of crude protein in the diet and volume of urine excretion. Microbial protein synthesis was affected by increasing CP level; in this way maximum protein synthesis was achieved at 23.4% CP.
A Keck Spectroscopic Survey of MS 1054–03 (z = 0.83): Forming the Red Sequence

Using a magnitude-limited, spectroscopic survey of the X-ray-luminous galaxy cluster MS 1054-03, we isolate 153 cluster galaxies and measure MS 1054's redshift and velocity dispersion to be z = 0.8307 ± 0.0004 and σz = 1156 ± 82 km s-1. The absorption-line, poststarburst ("E+A"), and emission-line galaxies, respectively, make up 63% ± 7%, 15% ± 4%, and 23% ± 4% of the cluster population. With photometry from HST ACS, we find that the absorption-line members define an exceptionally tight red sequence over a span of ~3.5 mag in i775: their intrinsic scatter in (V606 - i775) color is only 0.048 ± 0.008, corresponding to a (U - B)z scatter of 0.041. Their color scatter is comparable to that of the ellipticals (σVi = 0.055 ± 0.008), but measurably smaller than that of the combined E+S0 sample (σVi = 0.072 ± 0.010). The color scatter of MS 1054's absorption-line population is approximately twice that of the ellipticals in Coma; this difference is consistent with passive evolution where most of the absorption-line members (>75%) formed by z ~ 2, and all of them by z ~ 1.2. For red members, we find a trend (>95% confidence) of weakening Hδ absorption with redder colors that we conclude is due to age: in MS 1054, the color scatter on the red sequence is driven by differences in mean stellar age of up to ~1.5 Gyr. We also generate composite spectra and estimate that the average S0 in MS 1054 is ~0.5-1 Gyr younger than the average elliptical; this difference in mean stellar age is mainly due to a number of S0's that are blue (18%) and/or are poststarburst systems (21%).
Player Salaries, Player Mobility and the Invariance Principle: Evidence from the National Hockey League

This paper develops a bargaining model of wages and contract length as applied to a professional sports league. Using data based on the recent Collective Bargaining Agreement between players and owners in the National Hockey League, I examine the effect of restrictions on player contracting on both the mobility and salaries of players over the period 2005-2010. Using a variety of estimators both for player mobility and salary determination, I find some evidence that a player’s free agency status effects both mobility and player salaries, but significantly less than might be expected. A key result is that mobility needs to be taken into account when developing models of wage determination in professional sports.
Human movement performance in relation to path constraint - the law of steering in locomotion

We examine the law of steering - a quantitative model of human movement time in relation to path width and length previously established in hand drawing movement - in a VR locomotion paradigm. Participants drove a simulated vehicle in a virtual environment on paths whose shape and width were manipulated Results showed that the law of steering also applies to locomotion. Participants' mean trial completion times linearly correlated (r/sup 2/ between 0.985 and 0.999) with an index of difficulty quantified as path distance to width ratio for the straight and circular paths used in this experiment. Their average mean and maximum speed was linearly proportional to path width. Such human performance regularity provides a quantitative tool for 3D human machine interface design and evaluation.
Differential proteomics analysis of liver failure in peripheral blood mononuclear cells using isobaric tags for relative and absolute quantitation

The aim of the present study was to examine differentially expressed proteome profiles for candidate biomarkers in peripheral blood mononuclear cells (PBMCs) of liver failure (LF) patients. Ten patients were diagnosed as LF and 10 age- and gender-matched subjects were recruited as healthy controls. Isobaric tags for relative and absolute quantitation (iTRAQ)-based quantitative proteomic technology is efficiently applicable for identification and relative quantitation of the proteomes of PBMCs. Eight-plex iTRAQ coupled with strong cation exchange chromatography, and liquid chromatography coupled with tandem mass spectrometry were used to analyze total proteins in LF patients and healthy control subjects. Molecular variations were detected using the iTRAQ method, and western blotting was used to verify the results. LF is a complex type of medical emergency that evolves following a catastrophic insult to the liver, and its outcome remains the most ominous of all gastroenterologic diseases. Serious complications tend to occur during the course of the disease and further exacerbate the problems. Using the iTRAQ method, differentially expressed proteome profiles of LF patients were determined. In the present study, 627 proteins with different expression levels were identified in LF patients compared with the control subjects; with 409 proteins upregulated and 218 proteins downregulated. Among them, four proteins were significantly differentially expressed; acylaminoacyl-peptide hydrolase and WW domain binding protein 2 were upregulated, and resistin and tubulin β 2A class IIa were downregulated. These proteins demonstrated differences in their expression levels compared with other proteins with normal expression levels and the significant positive correlation with LF. The western blot results were consistent with the results from iTRAQ. Thus, investigation of the molecular mechanism of the proteins involved in LF may facilitate an improved understanding of the pathogenesis of LF and elucidation of novel biomarker candidates.
DADODICE: An interactive installation to support learning

In smart technological solutions, interactive installations, multi-touch surfaces are all around us nowadays. In particular, pupils, who are digital native, are keen on these devices. So the adoption of these smart technologies could be an interesting approach to capture their attention. DADODICE (DD), our latest interactive installation, takes advantage of this trend. DD, in fact, is especially thought for pupils, the main target of Sardegna in Miniatura, an amusement park located in Sardinia. Pupils visit the park in small groups so we developed a collaborative installation to allow visitors to interact and benefit from the various contents simultaneously. DD aims to combine both learning (detailed explanations concerning the different renewable energies investigated and used in our region) and amusing contents, utilizing as much as possible invisible technological solutions. The installation is powered by xPlaces, the software framework we develop. We report here some preliminary results of an ongoing research field, which seem to suggest that there are peculiar behavioural patterns in interacting with the installation, various interaction modalities and different perceptions of the installation (toy-like versus highly technological) depending on the age (adults, adolescents and children).
Infrared Human Face Auto Locating Based on SVM and A Smart Thermal Biometrics System

The SARS epidemic resulted in the introduction of IR thermal camera for non-voluntary screening on human face for fever symptoms. However, two major problems emerged. One is the temperature measurement precision, which is not adequate enough. The other is the poor automatic ability. This paper analyses the importance and deals with the second automation problem. We discuss the inherent consistency of SVM with the pattern recognition in infrared image. A smart biometrics system that automatically detects human face in infrared video and performs temperature measurement is implemented. The potential for illumination invariant face recognition using thermal IR imagery is fully utilized. This potentiality is approved by the result of infrared human face detection experiment based on C-SVM
EVO-EXPLORER: A Tool for Designing High-Performance Digital Systems

In this paper we describe a tool for designing high-performance digital systems. The system is described behaviorally in VHDL and then translated into data flow graph (DTG) structure which captures all control and data flow dependencies of the given behavioral description. Then the design process goes through architecture selection, scheduling, pipelining and component selection processes. As it enters each step, it is explored by a local exploration scheme. The set of developed algorithms tested with standard benchmarks.
Test Planning for Core-based 3D Stacked ICs under Power Constraints

Test planning for core-based 3D stacked ICs under power constraint is different from test planning for non-stacked ICs as the same test schedule cannot be applied both at wafer sort and package test. In this paper, we assume a test flow where each chip is tested individually at wafer sort and jointly at package test. We define cost functions and test planning optimization algorithms for non-stacked ICs, 3D SICs with two chips and 3D SICs with an arbitrary number of chips. We motivate the problem by demostrating the trade-off between test time and hardware, within a power constraint, while arriving at the minimal cost.
Bayesian contact state segmentation for programming by human demonstration in compliant motion tasks

This paper presents a contribution to programming by human demonstration [3], [14], in the context of compliant motion [4] tasks in which an object held by a manipulator moves in contact with an environmental object, as shown in Fig. 1. Major challenges in the automatic translation from a human compliant motion demonstration into an executable compliant motion robot program are: (i) to recognize the contact formation (CF) to which the human demonstration is currently subjected, (ii) to estimate the geometric parameters of that contact formation, and (iii) to detect when exactly the human demonstration execution changes between two CFs. Initial research on the identification of CFs focused mainly on two different approaches: (i) ad hoc identification strategies that exploit geometric knowledge of the contacting objects, but that have a very poor stochastic foundation (e.g. [1], [7]) and (ii) Hidden Markov Model based (hence stochastic) solutions to assembly problems that can recognize CF transitions very fast but only with limited allowed uncertainty (e.g. [6], [12]). Slaets et al. [13] presented some results in the field of compliant motion that go a bit beyond the scope of this paper: they build a geometric model of an unknown environment from a given number of primitives, while this paper starts from a known parameterization of a geometric model. However, their approach is based on the Non-Minimal State Kalman Filter [9], [11], and is only valid if the estimation has converged to a unimodal Gaussian before a contact transition. They only allow new contact constraints to be added gradually, and no contact constraints to be removed. Recently Gadeyne et al. [8] developed a particle filter [5] approach to estimate, simultaneously, geometric parameters of a known geometric model and to recognize contact transitions. Their approach is able to estimate (continuous) geometric parameters with a large uncertainty, and simultaneously recognize (discrete) contact transitions in an experiment consisting of six possible and initially known CFs. This paper generalizes and scales the approach of Gadeyne et al. to allow all possible contacts between two polyhedral objects. To cope with this increased complexity, an improved prediction step is used, based on the topological information contained in a contact state graph, [15], [16], and the relative pose of the contacting objects. This paper also presents efficient algorithms for the pose and consistency measurement equations [2], [10], that reduce the numerical cost and allow the estimators to discriminate in realtime between 245 different CFs, in an uncertain environment.
A Semi-supervised Algorithm for Indonesian Named Entity Recognition

Named Entity Recognition or NER is one of the sub-research field of Information Extraction which can be used for machine translation, question answering, semantic web, etc. One of the biggest challenge of NER is the adversity to construct a manually labeled training data. In this work, we present a semi-supervised approach for Indonesian language NER which is capable of creating high quality training data automatically. Semi-supervised approach works by utilizing unlabeled data made from Wikipedia and DBPedia to form high accuracy and non-redundant additional training data for each iteration of semi-supervised process. We show that our system manages to generate new training data and gain an increasing F1 score as the iteration of semi-supervised process goes.
A Method for Predicting Marker Tracking Error

Many augmented reality (AR) applications use marker-based vision tracking systems to recover camera pose by detecting one or more planar landmarks. However, most of these systems do not interactively quantify the accuracy of the pose they calculate. Instead, the accuracy of these systems is either ignored, assumed to be a fixed value, or determined using error tables (constructed in an off-line ground-truthed process) along with a run-time interpolation scheme. The validity of these approaches are questionable as errors are strongly dependent on the intrinsic and extrinsic camera parameters and scene geometry. In this paper we present an algorithm for predicting the statistics of marker tracker error in real-time. Based on the scaled spherical simplex unscented transform (SSSUT), the algorithm is applied to the augmented reality toolkit plus (ARToolKitPlus). The results are validated using precision off-line photogrammetric techniques.
Optimal trajectory generation for generalization of discrete movements with boundary conditions

Trajectory generation methods play an important role in robotics since they are essential for the execution of actions. In this paper we present a novel trajectory generation method for generalization of accurate movements with boundary conditions. Our approach originates from optimal control theory and is based on a second order dynamic system. We evaluate our method and compare it to state-of-the-art movement generation methods in both simulations and a real robot experiment. We show that the new method is very compact in its representation and can reproduce demonstrated trajectories with zero error. Moreover, it has most of the properties of the state-of-the-art trajectory generation methods such as robustness to perturbations and generalisation to new boundary position and velocity conditions. We believe that, due to these features, our method has great potential for various robotic applications, especially, where high accuracy is required, for example, in industrial and medical robotics.
A Compensated Numerical Method for Solving Stochastic Differential Equations with Variable Delays and Random Jump Magnitudes

Stochastic differential equations with jumps are of a wide application area especially in mathematical finance. In general, it is hard to obtain their analytical solutions and the construction of some numerical solutions with good performance is therefore an important task in practice. In this study, a compensated split-step method is proposed to numerically solve the stochastic differential equations with variable delays and random jump magnitudes. It is proved that the numerical solutions converge to the analytical solutions in mean-square with the approximate rate of 1/2. Furthermore, the mean-square stability of the exact solutions and the numerical solutions are investigated via a linear test equation and the results show that the proposed numerical method shares both the mean-square stability and the so-called A-stability.
Upper bounds for individual queue length distribution in GPS with LRD traffic input

We analyze the arrival process of long range dependent (LRD) traffic and demonstrate that it is a Weibull bounded burstiness (WBB) process. By decomposing a generalized processor sharing (GPS) system into isolated queues and servers, we then obtain two upper bounds on the individual session queue length, which are useful for quality of service (QoS) control. We also demonstrate that some parameters in determining the upper bound on an individual session queue, such as the index, the asymptotic constant and the decay rate may be affected by other flows existing in the GPS system. However, under certain conditions, by carefully choosing the GPS weight parameters, an individual session with LRD traffic input can be well isolated from other flows.