Miracle-dz/UniFAR-FTUs huggingface dataset:
Coexecutability : How To Automatically Verify Loops

Verification of web applications is a very important problem, and verifying loops is necessary to achieve that goal. However, loop verification is a long studied and very difficult problem. We find that interdependence of iterations is a major cause of this difficulty. We present coexecution a way to model a loop that avoids the problem of iteration interdependence. We introduce the coexecutability condition that implies that coexecution is a correct model. Through experiments, we demonstrate that coexecution reduces the number of inconclusive verification results by three times, and in 43% of cases increases performance of verification by at least an order of magnitude. Keywords—Verification, Loops
Search for optimal measure for discriminating spike trains with different randomness

We wish to discriminate spike sequences based on the degree of irregularity. For this purpose, we search for a rational expressions of quadratic functions of consecutive interspike intervals that efficiently measures spiking irregularity. Under natural assumptions, the functional form of the coefficient can be parameterized by a single parameter. The parameter is determined so as to maximize the mutual information between the distributions of coefficients computed for spike sequences derived from different renewal point processes. We find that the local variation of interspike intervals, LV (Neural Comput. Vol. 15, pp. 2823-42, 2003), is nearly optimal for whose intrinsic irregularity is close to that of experimental data.
Modeling Adaptive Degraded Document Image Binarization and Optical Character System

This paper presents an enhanced system for degraded old document. The developed system is able to deal with degradations which occur due to shadows, non-uniform illumination, low contrast and noise. The developed system is able to separate the two regions of the document. Different filtering techniques are used in the de-noising step for the purpose of de-noising and a rough estimation of foreground region and background region. Binarization step is applied by computing an approximate background surface of an original image. Final threshold step is performed by combining the calculated background surface with the preprocessed original image, using a threshold parameter for predefined local window of specific size. Different interpolation techniques are used in the final step to achieve better quality binary image which yield to elimination noises, improve the quality of the text regions and preserve stroke connectivity by filling possible breaks, gaps or holes. The second part of this research deals with optical character recognition,OCR. The result obtained after preprocessing step (typewritten or printed text, usually captured by scanner) is converted into machine-editable text. In this phase, we initially trained the system (on the known samples of each character) in order to to read a specific font "Intelligent system", then we performed the testing step (converting the image into editable text). The adaptive image will pass through several steps: image analyses for characters, detecting individual symbols, line and character boundary detection, resize character, feature extraction, output computation and finally displaying character representation of the Unicode output (on microsoft office word application). The proposed system is implemented and tested on actual degraded images. The proposed technique offers good output quality and quite fast.
Adaptive Kernel Density Approximation and Its Applications to Real-Time Computer Vision

Title of dissertation: ADAPTIVE KERNEL DENSITY APPROXIMATION AND ITS APPLICATIONS TO REAL-TIME COMPUTER VISION Bohyung Han, Doctor of Philosophy, 2005 Dissertation directed by: Professor Larry S. Davis Department of Computer Science Density-based modeling of visual features is very common in computer vision research due to the uncertainty of observed data; so accurate and simple density representation is essential to improve the quality of overall systems. Even though various methods, either parametric or non-parametric, are proposed for density modeling, there is a significant trade-off between flexibility and computational complexity. Therefore, a new compact and flexible density representation is necessary, and the dissertation provides a solution to alleviate the problems as follows. First, we describe a compact and flexible representation of probability density functions using a mixture of Gaussians which is called Kernel Density Approximation (KDA). In this framework, the number of Gaussians components as well as the weight, mean, and covariance of each Gaussian component are determined automatically by mean-shift mode-finding procedure and curvature fitting. An original density function estimated by kernel density estimation is simplified into a compact mixture of Gaussians by the proposed method; memory requirements are dramatically reduced while incurring only a small amount of error. In order to adapt to variations of visual features, sequential kernel density approximation is proposed in which a sequential update of the density function is performed in linear time. Second, kernel density approximation is incorporated into a Bayesian filtering framework, and we design a Kernel-based Bayesian Filter (KBF). Particle filters have inherent limitations such as degeneracy or loss of diversity which are mainly caused by sampling from discrete proposal distribution. In kernel-based Bayesian filtering, every relevant probability density function is continuous and the posterior is simplified by kernel density approximation so as to propagate a compact form of the density function from step to step. Since the proposal distribution is continuous in this framework, the problems in conventional particle filters are alleviated. The sequential kernel density approximation technique is naturally applied to background modeling, and target appearance modeling for object tracking. Also, the kernel-based Bayesian filtering framework is applied to object tracking, which shows improved performance with a smaller number of samples. We demonstrate the performance of kernel density approximation and its application through various simulations and experiments with real videos. ADAPTIVE KERNEL DENSITY APPROXIMATION AND ITS APPLICATIONS TO REAL-TIME COMPUTER VISION
Gradient Sparsification for Communication-Efficient Distributed Optimization

Modern large scale machine learning applications require stochastic optimization algorithms to be implemented on distributed computational architectures. A key bottleneck is the communication overhead for exchanging information such as stochastic gradients among different workers. In this paper, to reduce the communication cost we propose a convex optimization formulation to minimize the coding length of stochastic gradients. To solve the optimal sparsification efficiently, several simple and fast algorithms are proposed for approximate solution, with theoretical guaranteed for sparseness. Experiments on $\ell_2$ regularized logistic regression, support vector machines, and convolutional neural networks validate our sparsification approaches.
Flicker Regularity Is Crucial for Entrainment of Alpha Oscillations

Previous studies have shown that alpha oscillations (8–13 Hz) in human electroencephalogram (EEG) modulate perception via phase-dependent inhibition. If entrained to an external driving force, inhibition maxima and minima of the oscillation appear more distinct in time and make potential phase-dependent perception predictable. There is an ongoing debate about whether visual stimulation is suitable to entrain alpha oscillations. On the one hand, it has been argued that a series of light flashes results in transient event-related responses (ERPs) superimposed on the ongoing EEG. On the other hand, it has been demonstrated that alpha oscillations become entrained to a series of light flashes if they are presented at a certain temporal regularity. This raises the question under which circumstances a sequence of light flashes causes entrainment, i.e., whether an arrhythmic stream of light flashes would also result in entrainment. Here, we measured detection rates in response to visual targets at two opposing stimulation phases during rhythmic and arrhythmic light stimulation. We introduce a new measure called “behavioral modulation depth” to determine differences in perception. This measure is capable of correcting for inevitable artifacts that occur in visual detection tasks during visual stimulation. The physical concept of entrainment predicts that increased stimulation intensity should produce stronger entrainment. Thus, two experiments with medium (Experiment 1) and high (Experiment 2) stimulation intensity were performed. Data from the first experiment show that the behavioral modulation depth (alpha phase-dependent differences in detection threshold) increases with increasing entrainment of alpha oscillations. Furthermore, individual alpha phase delays of entrained alpha oscillations determine the behavioral modulation depth: the largest behavioral modulation depth can be found if targets presented during the minimum of the entrained oscillation are compared to those presented during the maximum. In the second experiment stimulation with higher light intensity during both rhythmic and arrhythmic stimulation lead to an increased behavioral modulation depth, supposedly as a consequence of stronger entrainment during rhythmic stimulation. Altogether, our results reveal evidence for rhythmic and arrhythmic visual stimulation to induce fundamentally different processes in the brain: we suggest that rhythmic but not arrhythmic stimulation interacts with ongoing alpha oscillations via entrainment.
Probabilistic evaluation of oscillatory stability margin with large-scale wind generation

This paper presents a Monte Carlo based probabilistic methodology of evaluating the oscillatory stability margin of power system with large-scale wind generation. The approach considers not only the stochastic characteristics of wind speeds which are treated as random variables with Weibull distributions, while also the wind speed spatial correlations which are modeling by Nataf transformation. A case study is carried out on IEEE New England system and the probabilistic indices for OSM are computed from the statistical processing of the obtained results. The results indicate that the OSM obtained by deterministic study cannot guarantee the system being surely oscillatory stable, but stable with a given risk of oscillatory instability. It is shown that, for the cases studied, wind penetration and wind speed correlation detrimentally affects the OSM.
Data analytics development of FDR (Flight Data Recorder) data for airline maintenance operations

In this article, we propose a data analytics development to detect unusual patterns of flights from a vast amounts of FDR (flight data recorder) data for supporting airline maintenance operations. A fundamental rationale behind this development is that if there are potential issues on mechanical parts of an aircraft during a flight, evidences for these issues are most likely included in the FDR data. Therefore, the data analysis of FDR data enables us to detect the potential issues in the aircraft before they occur. To this end, in a data pre-processing step, a data filtering, a data sampling, and a data transformation are sequentially performed. And then, in this analysis, all time series data in the FDR are classified into three types: a continuous signal, a discrete signal, and a warning signal. For each type of signal, a high-dimensional vector by arranging the time series data is chosen as features. In the feature section process, a correlation analysis, a correlation relaxation, and a dimension reduction are sequentially conducted. Finally, a type of k-nearest neighbor approach is applied to automatically identify the FDR data in which the unusual flight patterns are recorded from a large amount of FDR data. The proposed method is tested with using a realistic FDR data from the NASA's open database.
5.1 Introduction 5.1.1 Why Whole Genome Alignment?

People believe that many widely divergent organisms are decended from a common ancestor through a process called evolution. Evolution is a process that results in inheritable changes in the properties of populations of organisms that spread over many generations. Since the changes are inheritable, scientists conclude that there must be some changes in the genome of these organisms that correspond to each evolutionary change. Knowing this fact, biologists start comparing the genomes of two organisms in order to understand the genetic relationship and evolutionary linkage between the two organisms.
Can taking the perspective of an expert debias human decisions? The case of risky and delayed gains

In several previously reported studies, participants increased their normative correctness after being instructed to think hypothetically, specifically taking the perspective of an expert or researcher (Beatty and Thompson, 2012; Morsanyi and Handley, 2012). The goal of this paper was to investigate how this manipulation affects risky or delayed payoffs. In two studies, participants (n = 193) were tested online (in exchange for money) using the adjusting procedure. Individuals produced certain/immediate equivalents for risky/delayed gains. Participants in the control group were solving the problem from their own perspective, while participants in the experimental group were asked to imagine “what would a reliable and honest advisor advise them to do. ” Study 1 showed that when taking the perspective of an expert, participants in experimental group became more risk aversive compared to participants in the control group. Additionally, their certain equivalents diverged from the expected value to a greater extent. The results obtained from the experimental group in Study 2 suggest that participants became less impulsive, which means they tried to inhibit their preferences. This favors the explanation, which suggests that the perspective shift forced individuals to override their intuitions with the social norms. Individuals expect to be blamed for impatience or risk taking thus expected an expert to advise them to be more patient and risk aversive.
Role of tumor associated macrophages in tumor angiogenesis and lymphangiogenesis

Tumor angiogenesis is an essential process for supplying rapidly growing malignant tissues with essential nutrients and oxygen. An angiogenic switch allows tumor cells to survive and grow, and provides them access to vasculature resulting in metastatic disease. Monocyte-derived macrophages recruited and reprogrammed by tumor cells serve as a major source of angiogenic factors boosting the angiogenic switch. Tumor endothelium releases angiopoietin-2 and further facilitates recruitment of TIE2 receptor expressing monocytes (TEM) into tumor sites. Tumor-associated macrophages (TAM) sense hypoxia in avascular areas of tumors, and react by production of angiogenic factors such as VEGFA. VEGFA stimulates chemotaxis of endothelial cells (EC) and macrophages. In some tumors, TAM appeared to be a major source of MMP9. Elevated expression of MMP9 by TAM mediates extracellular matrix (ECM) degradation and the release of bioactive VEGFA. Other angiogenic factors released by TAM include basic fibroblast growth factor (bFGF), thymidine phosphorylase (TP), urokinase-type plasminogen activator (uPA), and adrenomedullin (ADM). The same factors used by macrophages for the induction of angiogenesis [like vascular endothelial growth factor A (VEGF-A) and MMP9] support lymphangiogenesis. TAM can express LYVE-1, one of the established markers of lymphatic endothelium. TAM support tumor lymphangiogenesis not only by secretion of pro-lymphangiogenic factors but also by trans-differentiation into lymphatic EC. New pro-angiogenic factor YKL-40 belongs to a family of mammalian chitinase-like proteins (CLP) that act as cytokines or growth factors. Human CLP family comprises YKL-40, YKL-39, and SI-CLP. Production of all three CLP in macrophages is antagonistically regulated by cytokines. It was recently established that YKL-40 induces angiogenesis in vitro and in animal tumor models. YKL-40-neutralizing monoclonal antibody blocks tumor angiogenesis and progression. The role of YKL-39 and SI-CLP in tumor angiogenesis and lymphangiogenesis remains to be investigated.
P 3 P Deployment on Websites

We studied the deployment of computer-readable privacy policies encoded using the standard W3C Platform for Privacy Preferences (P3P) format to inform questions about P3P’s usefulness to end users and researchers. We found that P3P adoption is increasing overall and that P3P adoption rates greatly vary across industries. We found that P3P had been deployed on 10% of the sites returned in the top-20 results of typical searches, and on 21% of the sites returned in the top-20 results of e-commerce searches. We examined a set of over 5,000 web sites in both 2003 and 2006 and found that P3P deployment among these sites increased over that time period, although we observed decreases in some sectors. In the Fall of 2007 we observed 470 new P3P policies created over a two month period. We found high rates of syntax errors among P3P policies, but much lower rates of critical errors that prevent a P3P user agent from interpreting them. We also found that most P3P policies have discrepancies with their natural language counterparts. Some of these discrepancies can be attributed to ambiguities, while others cause the two policies to have completely different meanings. Finally, we show that the privacy policies of P3P-enabled popular websites are similar to the privacy policies of popular websites that do not use P3P.
Image factorization and feature fusion for enhancing robot vision in human face recognition

Illumination variation has been a challenging problem for face recognition in robot vision. To reduce the effect caused by illumination variation, a lot of studies have been explored. The Total Variation (TV) method is particular used to factorize images into a low frequency component and a high frequency one. However, the low frequency component still contains significant intrinsic features resulting in failure in face recognition in some cases. In this paper, we propose to further extract illumination invariant features from face images under uncontrolled varying lighting conditions. The Nonsampled Contourlet Transform (NSCT) method is employed to enhance the extraction of intrinsic feature. The combined factorization model is very effective in the experiment on the Yale database.
${\cal H}_{\infty}$ Model Reduction of Takagi–Sugeno Fuzzy Stochastic Systems

This paper is concerned with the problem of H∞ model reduction for Takagi-Sugeno (T-S) fuzzy stochastic systems. For a given mean-square stable T-S fuzzy stochastic system, our attention is focused on the construction of a reduced-order model, which not only approximates the original system well with an H∞ performance but also translates it into a linear lower dimensional system. Then, the model reduction is converted into a convex optimization problem by using a linearization procedure, and a projection approach is also presented, which casts the model reduction into a sequential minimization problem subject to linear matrix inequality constraints by employing the cone complementary linearization algorithm. Finally, two numerical examples are provided to illustrate the effectiveness of the proposed methods.
Web Document Clustering with Multi-view Information Bottleneck

Clustering is an important way to organize the large amount of information on the Web. In this paper, we study how to incorporate many information of Web document, such as content, anchor, URL etc, to improve the performance of clustering. We propose a novel algorithm: multi-view information bottleneck (MVIB), to cluster Web documents with multi-type features. In this algorithm, the compatible constraint maximizing the agreement between clustering hypotheses on different views is imposed on the individual views to cluster instances. Based on the compatible constraints, the set of clustering hypotheses revealing lots of information about correct one is obtained. The final hypothesis can be deduced from these hypotheses. We study the performance of MVIB in different views setting. Experiments on two real datasets indicate that MVIB with 3-view setting based on content, anchor text and URL can improve the quality of clusters more effectively.
Automated delineation of roof planes from LIDAR data

In this paper, we describe an algorithm for roof line delineation from LIDAR data which aims at achieving models of a high level of detail. Roof planes are initially extracted by segmentation based on the local homogeneity of surface normal vectors of a digital surface model (DSM). A case analysis then reveals which of these roof planes intersect and which of them are separated by a step edge. The positions of the step edges are determined precisely by a new algorithm taking into account domain specific information. Finally, all step edges and intersection lines are combined to form consistent polyhedral models. In all phases of this workflow, decision making is based upon statistical reasoning about geometrical relations between neighbouring entities in order to reduce the number of control parameters and to increase the robustness of the method.
Determinants of Employee Punctuality

Abstract Although researchers have studied employee lateness empirically (e.g., S. Adler & J. Golan, 1981; C. W. Clegg, 1983), few have attempted to describe the punctual employee. In the present study, results of a discriminant analysis on employees in Israel indicated that a personality characteristic, time urgency, a subcomponent of Type A behavioral pattern, distinguished between punctual and late employees. Organizational commitment and age of employee's youngest child also distinguished between the groups.
The effect of blog peer review and teacher feedback on the revisions of EFL writers

This exploratory study investigated ELF adult learners' reactions to once highly acclaimed writing pedagogic techniques: peer review and teacher feedback in EFL composition class. Both the peer review and teacher feedback were given and transmitted via the web to learners' blog. The author examined 7 first drafts, 32 peer reviews, and 8 revised drafts (one student made two revisions), trying to find out what effects online peer review have on the revisions of low-intermediate EFL writers. Then, teacher feedback given to these seven students was also reviewed to see whether teacher feedback made tangible impacts on their revised drafts. Finally, another 7 articles written by the same group of students which received little or no peer review and teacher feedback were compared with their final drafts to see whether EFL learners can do a substantive revision without peer review and teacher feedback. As expected, while teacher feedback appeared to lead to both positive and negative revisions, depending on learners' attitude and English proficiency, a significant proportion of the peer review did not serve a linguistic function to give meaningful and constructive comments but serve a pragmatic function to give complimentary praise or blessings.
Gossypiboma as an incidentaloma.

A 72-year-old woman with known rectal carcinoma had an abdominal perineal resection. Within 11 months, the serum CEA was rising. PET revealed a lesion in the left mid pelvis and an unusual rim of enhanced activity within the left mid abdomen. Magnetic resonant imaging (MRI) localized an enlarged left internal iliac lymph node and ovoid abdominal mass. Computed tomography (CT) identified a serpiginous area compatible with an opaque sponge marker near the colostomy site. The left internal iliac lymph node was moderately differentiated necrotizing adenocarcinoma consistent with metastatic colon primary.