Miracle-dz/UniFAR-FTUs huggingface dataset:
Transforming programs to allow Static Analysis to do better

During software development, bugs are costly and difficult to find and fix. There are many static and dynamic analyzers now available that analyze the source code to identify possible defects. For higher quality software, static analysis and dynamic analysis should be used in a complementary manner. We present a comparative study of various static analysis tools for JAVA and C and discuss the techniques used by them. We explore the concept of partitioning a program such that the partitions can be analyzed separately. With such partitioning, potentially different analysis techniques can be applied to different program partitions, with each analysis having to deal with reduced code which can increase its effectiveness. Our experiments show that partitioning can indeed increase the effectiveness of static analysis. Our initial experience in using testing data for partitioning and applying static analysis only on the complementary partition indicates that it results in detection of more errors which might have gone unnoticed otherwise. Finally we discuss the merits and demerits of using our approach on some of these tools.
A fuzzy-logic-based approach to qualitative modeling

This paper discusses a general approach to quali- tative modeling based on fuzzy logic. The method of qualitative modeling is divided into two parts: fuzzy modeling and linguistic approximation. It proposes to use a fuzzy clustering method (fuzzy c-means method) to identify the structure of a fuzzy model. To clarify the advantages of the proposed method, it also shows some examples of modeling, among them a model of a dynamical process and a model of a human operator's control action.
A Natural Experiment on Taste-Based Racial and Ethnic Discrimination in Elections

We exploit a natural experiment to identify discrimination in elections. In Illinois Republican presidential primaries, voters vote for delegates bound to presidential candidates, but delegatesâ€™ names convey information about their race and gender. We identify discrimination from variation in vote totals among delegates bound to the same presidential candidate and who face the same voters. We estimate nonwhite delegates receive 10 percent fewer votes, costing votersâ€™ preferred presidential candidates votes and reducing nonwhite representation. We find essentially no gender discrimination. Negligible incentives for statistical discrimination, the costs of discrimination to preferred candidates, and heterogeneity indicate tastes as the likely mechanism.
Effect of operators on straight line complexity

This paper concerns lower bounds on the straight line complexity of multi-variate polynomials. We obtain a conditional result showing that certain explicit linear operators must greatly increase the complexity of some polynomials. We do so by showing that if these operators roughly preserve the complexity of all polynomials then, co-NP is in AM. We show that certain explicit operators must vastly increase the straight line complexity of certain polynomials.
A Hybrid Search Engine Framework for the Internet of Things Based on Spatial-Temporal, Value-Based, and Keyword-Based Conditions

In recent years, the Internet of Things (IoT) has become a hot research issue and is changing the way how people live and work. IoT has a lot of benefits and meanwhile, it also brings about great challenges to the search engine community. In this paper, we analyze the challenges in the IoT search engine technology and propose a "Hybrid Search Engine Framework for the Internet of Things based on Spatial-Temporal, Value-based, and Keyword-based Conditions" ("IoT-SVK Search Engine" for short). The experimental results show that the IoT-SVK search engine has satisfactory performances in supporting multi-modal retrieval conditions, and provides a good solution for real-time retrieval of massive sensor sampling data in the Internet of Things.
Evaluation of Geometric Layout Analysis Techniques for Document Image Analysis

Document Image Analysis (DIA) is an interesting research area with a large variety of challenging applications. Document analysis is a component which decomposes a document image into several consistent items which represent coherent components of the document such as text-lines, photographs, graphics etc. without any knowledge of the specific format. A document image is composed of several blocks, each of which represents a coherent component of the document. One coherent component corresponds to a set of text lines with the same typeface and a consistent line spacing. The geometric structure means the geometric relationships between the blocks. This paper describes the current status of Document Image Analysis and Understanding techniques with particular attention to the evaluation of geometric layout analysis techniques. The textbased approach and A region-based approach are the two evaluation methods for page decomposition described in this paper.
ESTIMATES OF THE EXTERNAL AND SUSTAINABILITY COSTS OF CLIMATE CHANGE

Weak sustainability is typically defined as non-declining utility, or perhaps non-declining production capacity. Weak sustainability places human welfare at the core, and substituting one source of welfare for another is not an issue (e.g., Pearce and Turner, 1990; Perman et al., 1999). As climate change is unlikely to reverse economic growth (Fankhauser and Tol, 2001; Tol, 1998), it is compatible with weak sustainability. However, climate change does pose an efficiency problem, as greenhouse gas emissions are externalities, and perhaps large ones (Pearce et al., 1996; Smith et al., 2001). Therefore, our first approach to measuring climate change is to estimate the marginal external costs of carbon dioxide emissions.
Effects on transcription errors on supervised learning in speech recognition

Hidden Markov model-based speech recognition systems use supervised learning to train acoustic models. On difficult tasks such as conversational speech, there has been concern over the impact erroneous transcriptions have on the parameter estimation process. This work analyzes the effects of mislabeled data on recognition accuracy. Training is performed using manually corrupted transcriptions, and results are presented on three tasks: TIdigits, alphadigits and switchboard. For alphadigits, with 16% of the training data mislabeled, the performance of the system degrades by 12% relative to the baseline. On switchboard, at 16% mislabeled training data, the performance of the system degrades by 8.5% relative to the baseline. An analysis of these results revealed that the Gaussian mixture model contributes significantly to the robustness of the supervised learning training process.
Acoustic model training using feature vectors generated by manipulating speech parameters of real speakers

In this paper, we propose a robust speaker-independent acoustic model training method using generative training to generate many pseudo-speakers from a small number of real speakers. We focus on the difference between each speaker's vocal tract length, and manipulate it in order to create many different pseudo-speakers with a range of vocal tract lengths. This method employs frequency warping based on the inverted use Vocal Tract Length Normalization(VTLN). Another method for creating pseudo-speakers is to vary the speaking rate of the speakers. This can be achieved by a method called PICOLA; Pointer Interval Controlled OverLap and Add. In experiments, we train acoustic models using these generated pseudo-speakers in addition to the original speakers. Evaluation results show that generating pseudo-speakers by manipulating speaking rates did not result in a sufficient increase in performance, however, vocal tract length warping was effective.
A noise parameter configuration technique to mitigate detour inference attack on differential privacy

Nowadays, data has become more important as the core resource for the information society. However, with the development of data analysis techniques, the privacy violation such as leakage of sensitive data and personal identification exposure are also increasing. Differential privacy is the technique to satisfy the requirement that any additional information should not be disclosed except information from the database itself. It is well known for protecting the privacy from arbitrary attack. However, recent research argues that there is a several ways to infer sensitive information from data although the differential privacy is applied. One of this inference method is to use the correlation between the data. In this paper, we investigate the new privacy threats using attribute correlation which are not covered by traditional studies and propose a privacy preserving technique that configures the differential privacy's noise parameter to solve this new threat. In the experiment, we show the weaknesses of traditional differential privacy method and validate that the proposed noise parameter configuration method provide a sufficient privacy protection and maintain an accuracy of data utility.
Lessons learned in Web site architectures for public utilities

Web sites are complex systems. They evolve all the time and are hard to understand and be maintained. This paper discusses some issues and challenges related to Web site development and maintenance. An evolutionary three-tier Web site architecture is presented based on our experiences in building and maintaining Web-based public utility management systems. In the current public utility domain, the coexistence of regulated and deregulated markets and diverse business rules bring many challenges to software engineers to build extensible and maintainable Web-based systems. New technologies provide opportunities to meet these requirements. However, how to adopt new technologies appropriately becomes another issue. Some design and implementation tradeoffs, as well as some lessons learned are considered in this paper.
A Control System Based on WWW Technologies

Once used only in state-of-the-art accelerator and large experimental physics control systems, distributed computing has become part of our everyday life. In recent years we have witnessed an explosion of WWW applications and development tools. Therefore it makes sense to fully exploit the existing WWW-technologies provided conveniently by the computer industry as off-theshelf products. The control system of ANKA, a 2.5 GeV synchrotron radiation light source being built in Karlsruhe, Germany, uses WWW for both the graphical user interface and data transmission. All operator control is performed through a Web-browser with Java applets. Documentation and help are obtained as conventional html text, including JPEG images, Postscript documents, etc. An authentication system allows applets, which can only read data from the control system, to run on any Internet host in the world. Access to databases is possible through application/data servers in a three-tier architecture. Communication between applets and control system data servers is done through CORBA, RMI or bare sockets, if speed is the primary concern. This paper presents a running prototype, which includes applets ranging from those responsible for control of single elements such as power supplies to those that perform complex actions such as monitoring and logging. Extensive use of Java features such as multithreading, object serialization, networking etc. is employed to access fully asynchronous and distributed objects. Results of the prototype include an evaluation of several Java developing tools for PCs (J++, Visual Cafe and SuperCede) and benchmarks which clearly indicate that the interpreted nature of Java is not a problem, as computing power is not such an important issue in accelerator control.
Global Exponential Estimates of Stochastic Cohen-Grossberg Neural Networks with Time Delay

This paper is concerned with the exponential estimating problem for Cohen-Grossberg neural networks with time delay and stochastic disturbance. A sufficient condition, which does not only guarantee the global exponential stability but also provides more exact characterization on the decay rate and the coefficient, is established in terms of the Lyapunov-Krasovskii functional approach and the linear matrix inequality (LMI) technique. The estimates of the decay rate and the coefficient are obtained by solving a set of LMIs, which can be checked easily by effective algorithms. In addition, slack matrices are introduced to reduce the conservatism of the condition. A numerical example is provided to illustrate the effectiveness of the theoretical results.
Design of a Mechanism for Biaxial Rotation of a Wing for a Hovering Vehicle

This paper presents a novel mechanism to actuate the wings of a hovering micro air vehicle (MAV). The mechanism uses a single actuator, but each wing can rotate about two orthogonal axes. The goal of this work is to design a light-weight compact mechanism that flaps the wings, inspired from the wing motion of hummingbird and hovering insects, to generate enough lift for the vehicle to hover. This paper explains in detail the proposed mechanism and its working prototypes. Also, the paper presents a dynamic simulation of the mechanism. The proposed dynamic simulation is used to predict the theoretical lift of the ornithopter. Further, the theoretical model is supported by actual experimental data collected from the prototype. The experimental data shows that the vehicle has the potential to develop enough lift to hover
A hybrid algorithm combined color feature and keypoints for object detection

Object detecting methods based on local keypoints have shown stable and effective performance to detect object in clutter and occlusion environment, but these methods usually ignore color region information which is useful for object to distinguish it from background and other objects. To improve the veracity and celerity of object detection under clutter, we present a new approach for object detection with the basic idea of multi-features unity. Firstly, we define a novel color region descriptor called object dominant color set (ODCS). Then use an indirect approach combining ODCS region-based detection and scale invariant feature transform (SIFT) keypoint-based detection in an ordinal coarse-to-fine manner. At last, introduce a voting mechanism to decide the final detection result. The proposed approach has both the celerity of color region-based detector and veracity of keypoint-based detector concurrently, and is more effective to detect objects of few keypoints. The experimental data illuminate the potential of the proposed approach.
Graph Cuts for Image Segmentation

In computer vision, segmentation is the process of partitioning digital image into multiple regions (sets of pixels), according to some homogeneity criterion. The problem of segmentation is a well-studied one in literature and there are a wide variety of approaches that are used. Graph cuts has emerged as a preferred method to solve a class of energy minimization problems such as Image Segmentation in computer vision. Boykov et.al[3] have posed Image Segmentation problem as Energy Minimization in Markov Random Field and found approximately minimum solution using Graph cuts. Min-Cut/Max flow algorithms for Graph cuts include both push-relabel methods as well as augmenting paths methods. Boykov and Kolmogorov [2] have developed an efficient method for finding augmenting path. Though experimental comparison shows this algorithm efficient over other, worst case complexity of it is very high. In [1], Voronoi based Push Preflow method is suggested to find min-cut, which not only exploits the structural properties inherent in image based grid graphs but also combines the basic paradigms of max-flow theory in a novel way. Though Min-cut/Max-Flow based Graph cut methods can efficiently find partitions, those (partitions) may not be the desired ones. So, [7] have developed concept of Normalized cuts. Normalized cuts considers association within a cluster as well as the disassociation among clusters.
Virtual Homotopy

Two welded (respectively virtual) link diagrams are homotopic if one may be transformed into the other by a sequence of extended Reidemeister moves, classical Reidemeister moves, and self crossing changes. In this paper, we extend Milnor’s µ and ¯ µ invariants to welded and virtual links. We conclude this paper with several examples, and compute the µ invariants using the Magnus expansion and Polyak’s skein relation for the µ invariants.
OBSERVATION OF TeV GAMMA RAYS FROM THE FERMI BRIGHT GALACTIC SOURCES WITH THE TIBET AIR SHOWER ARRAY

Using the Tibet-III air shower array, we search for TeV γ-rays from 27 potential Galactic sources in the early list of bright sources obtained by the Fermi Large Area Telescope at energies above 100 MeV. Among them, we observe seven sources instead of the expected 0.61 sources at a significance of 2σ or more excess. The chance probability from Poisson statistics would be estimated to be 3.8 × 10−6. If the excess distribution observed by the Tibet-III array has a density gradient toward the Galactic plane, the expected number of sources may be enhanced in chance association. Then, the chance probability rises slightly, to 1.2 × 10−5, based on a simple Monte Carlo simulation. These low chance probabilities clearly show that the Fermi bright Galactic sources have statistically significant correlations with TeV γ-ray excesses. We also find that all seven sources are associated with pulsars, and six of them are coincident with sources detected by the Milagro experiment at a significance of 3σ or more at the representative energy of 35 TeV. The significance maps observed by the Tibet-III air shower array around the Fermi sources, which are coincident with the Milagro ⩾3σ sources, are consistent with the Milagro observations. This is the first result of the northern sky survey of the Fermi bright Galactic sources in the TeV region.
Pixel-based data fusion for a better object detection in automotive applications

The proposed technique addresses a fusion method of two imaging sensors on pixel-level. The fused image will provide a scene representation which is robust against illumination changes and different weather conditions. Thus, the combination of the advantages of each camera will extend the capabilities for many computer vision applications, such as video surveillance and automatic object recognition. The presented pixel-based fusion technique is examined on the images of two sensors, a far-infrared (FIR) light camera and a visible light camera which are built-in a vehicle. The sensor images are first decomposed using the Dyadic Wavelet Transform. The transformed data are combined in the wavelet domain controlled by a “goal-oriented” fusion rule. Finally, the fused wavelet representation image will be processed by a pedestrian detection system.
Federico Ravenna – J uha Seppälä Monetary policy , expected inflation and inflation risk premia Bank of Finland

Within a New Keynesian business cycle model, we study variables that are normally unobservable but are very important for the conduct of monetary policy, namely expected inflation and inflation risk premia. We solve the model using a third-order approximation that allows us to study time-varying risk premia. Our model is consistent with rejection of the expectations hypothesis and the businesscycle behaviour of nominal interest rates in US data. We find that inflation risk premia are very small and display little volatility. Hence, monetary policy authorities can use the difference between nominal and real interest rates from index-linked bonds as a proxy for inflation expectations. Moreover, for short maturities current inflation is a good predictor of inflation risk premia. We also find that short-term real interest rates and expected inflation are significantly negatively correlated and that short-term real interest rates display greater volatility than expected inflation. These results are consistent with empirical studies that use survey data and index-linked bonds to obtain measures of expected inflation and real interest rates. Finally, we show that our economy is consistent with the Mundell-Tobin effect: increases in inflation are associated with higher nominal interest rates, but lower real interest rates.
Hypoxia-Inducible Factor Activation Protects the Kidney from Gentamicin-Induced Acute Injury

Gentamicin nephrotoxicity is one of the most common causes of acute kidney injury (AKI). Hypoxia-inducible factor (HIF) is effective in protecting the kidney from ischemic and toxic injury. Increased expression of HIF-1α mRNA has been reported in rats with gentamicin-induced renal injury. We hypothesizd that we could study the role of HIF in gentamicin-induced AKI by modulating HIF activity. In this study, we investigated whether HIF activation had protective effects on gentamicin-induced renal tubule cell injury. Gentamicin-induced AKI was established in male Sprague-Dawley rats. Cobalt was continuously infused into the rats to activate HIF. HK-2 cells were pre-treated with cobalt or dimethyloxalylglycine (DMOG) to activate HIF and were then exposed to gentamicin. Cobalt or DMOG significantly increased HIF-1α expression in rat kidneys and HK-2 cells. In HK-2 cells, HIF inhibited gentamicin-induced reactive oxygen species (ROS) formation. HIF also protected these cells from apoptosis by reducing caspase-3 activity and the amount of cleaved caspase-3, and -9 proteins. Increased expression of HIF-1α reduced the number of gentamicin-induced apoptotic cells in rat kidneys and HK-2 cells. HIF activation improved the creatinine clearance and proteinuria in gentamicin-induced AKI. HIF activation also ameliorated the extent of histologic injury and reduced macrophage infiltration into the tubulointerstitium. In gentamicin-induced AKI, the activation of HIF by cobalt or DMOG attenuated renal dysfunction, proteinuria, and structural damage through a reduction of oxidative stress, inflammation, and apoptosis in renal tubular epithelial cells.
Specification and visualization of anisotropic interaction tensors in polypeptides and numerical simulations in biological solid-state NMR.

Software facilitating numerical simulation of solid-state NMR experiments on polypeptides is presented. The Tcl-controlled SIMMOL program reads in atomic coordinates in the PDB format from which it generates typical or user-defined parameters for the chemical shift, J coupling, quadrupolar coupling, and dipolar coupling tensors. The output is a spin system file for numerical simulations, e.g., using SIMPSON (Bak, Rasmussen, and Nielsen, J. Magn. Reson. 147, 296 (2000)), as well as a 3D visualization of the molecular structure, or selected parts of this, with user-controlled representation of relevant tensors, bonds, atoms, peptide planes, and coordinate systems. The combination of SIMPSON and SIMMOL allows straightforward simulation of the response of advanced solid-state NMR experiments on typical nuclear spin interactions present in polypeptides. Thus, SIMMOL may be considered a "sample changer" to the SIMPSON "computer spectrometer" and proves to be very useful for the design and optimization of pulse sequences for application on uniformly or extensively isotope-labeled peptides where multiple-spin interactions need to be considered. These aspects are demonstrated by optimization and simulation of novel DCP and C7 based 2D N(CO)CA, N(CA)CB, and N(CA)CX MAS correlation experiments for multiple-spin clusters in ubiquitin and by simulation of PISA wheels from PISEMA spectra of uniaxially oriented bacteriorhodopsin and rhodopsin under conditions of finite RF pulses and multiple spin interactions.