Miracle-dz/UniFAR-FTUs huggingface dataset:
JSPChecker: Static Detection of Context-Sensitive Cross-Site Scripting Flaws in Legacy Web Applications

JSPChecker is a static analysis tool that detects context-sensitive cross-site scripting vulnerabilities in legacy web applications. While cross-site scripting flaws can be mitigated through sanitisation, a process that removes dangerous characters from input values, proper sanitisation requires knowledge about the output context of input values. Indeed, web pages are built using a mix of different languages (e.g. HTML, CSS, JavaScript and others) that call for different sanitisation routines. Context-sensitive cross-site scripting vulnerabilities occur when there is a mismatch between sanitisation routines and output contexts. JSPChecker uses data-flow analysis to track the sanitisation routines that are applied to an input value, a combination of string analysis and fault-tolerant parsing to approximate the output context of sanitised values, and uses this information to detect context-sensitive cross-site scripting vulnerabilities. We demonstrate the effectiveness of our approach by analysing five open-source applications and showing how JSPChecker can identify several context-sensitive XSS flaws in real world applications with a precision ranging from 96% to 100%.
Reserve Option Mechanism : Does it Work as an Automatic Stabilizer?

Central Bank of the Republic of Turkey (CBRT) designed and implemented a new scheme since end-2011, called reserve options mechanism (ROM) in order to alleviate the adverse impact of capital flow volatility on the domestic economy. Although there are numerous studies on the mechanics of ROM, there has been no attempt to investigate the determinants of the ROM utilization in practice. In this paper, we aim to fill this gap by using bank-level data to assess the behavioral aspects of ROM. Our results suggest that the relative cost of Turkish lira funding to foreign currency funding as well as the reserve option coefficients set by the CBRT largely explains the variations in the ROM utilization. We find that the most relevant proxy for the cost of Turkish lira funding for banks is overnight money market interest rates and the weighted average cost of CBRT funding. Moreover, foreign currency liquidity does not seem to be a significant parameter in driving the utilization of ROM. We argue that the systematic policy induced movements in the short term domestic interest rates—higher during outflows, lower during inflows—may undermine the automatic stabilizer feature of ROM. And we propose an adjustment in the remuneration of reserve requirements to strengthen the automatic stabilizer effect of ROM.
Remote point-of-gaze estimation requiring a single-point calibration for applications with infants

This paper describes a method for remote, non-contact point-of-gaze estimation that tolerates free head movements and requires a simple calibration procedure in which the subject has to fixate only on a single point. This method uses the centers of the pupil and at least two corneal reflections that are estimated from eye images captured by at least two cameras. Experimental results obtained with three adult subjects exhibited RMS point-of-gaze estimation errors ranging from 7 to 12 mm (equivalent to about 0.6 -- 1° of visual angle) for head movements in a volume of about 1 dm3. Preliminary results with two infants demonstrated the ability of a system that requires a single-point calibration procedure to estimate infants' point-of-gaze. The ability to record infants' visual scanning behavior can be used for the study of visual development, the determination of attention allocation and the assessment of visual function in preverbal infants.
The Face of Need 779

Vol. XLVI (December 2009), 777–787 777 © 2009, American Marketing Association ISSN: 0022-2437 (print), 1547-7193 (electronic) *Deborah A. Small is Wroe Alderson Term Assistant Professor of Marketing (e-mail: deborahs@wharton.upenn.edu), and Nicole M. Verrochi is a doctoral candidate in Marketing (e-mail: verrochn@wharton.upenn.edu), the Wharton School, University of Pennsylvania. The authors thank David Bell, Eric Bradlow, Uri Simonsohn, and the two anonymous JMR reviewers for helpful comments; the Wharton Behavioral Laboratory for assistance in collecting the data; and Christine M. Verrochi for assistance in collecting the children’s photographs. Jeff Inman served as associate editor for this article. DEBORAH A. SMALL and NICOLE M. VERROCHI*
Competing over Networks

Recent advances in information technology have allowed firms to gather vast amounts of data regarding consumers’ preferences and the structure and intensity of their social interactions. This paper examines a game-theoretic model of competition between firms which can target their marketing budgets to individuals embedded in a social network. We provide a sharp characterization of the optimal targeted marketing strategies and highlight their dependence on the underlying social network structure. Furthermore, we identify network structures for which the returns to targeting are maximized and we provide conditions under which it is optimal for the firms to asymmetrically target a subset of the individuals. Finally, we provide a lower bound on the extent of asymmetry in these asymmetric equilibria and therefore shed light on the effect of the network structure on the outcome of marketing competition between the firms.
Evolution-Based Clustering Technique for Data Streams with Uncertainty

The evolution-based stream clustering method supports the monitoring and change detection of clustering structures. This paper presented HUE-Stream which extends E-Stream and E-Stream++ by introducing a distance function, cluster representation and histogram management for the different types of clustering structure evolution. Compared with UMicro and LuMicro, HUE-Stream produces higher clustering quality and is more robust over highly uncertain data streams; however, it requires longer processing time due to the fact that HUE-Stream detects change in the clustering structure evolution too frequently (in every round). To improve the processing time, proper periods of clustering structure evolution change detection were determined. With these proper periods, the processing time was greatly improved, while retaining the clustering quality. Compared to actual class of data in the KDDCup 1999 network intrusion detection dataset, a comparable number of clusters was obtained in all stream progressions.
Immune Activation, Immunosenescence, and Osteoprotegerin as Markers of Endothelial Dysfunction in Subclinical HIV-Associated Atherosclerosis

HIV-infected patients have a significantly greater risk of cardiovascular disease. Several markers including osteoprotegerin have been shown to be involved in the development and progression of atherosclerosis. We investigated the relationship between T-cell phenotype, osteoprotegerin, and atherosclerosis evaluated by carotid intima-media thickness (c-IMT) in 94 HIV+ patients on suppressive antiretroviral therapy with Framingham score <10%. As for the control group, 24 HIV-negative subjects were enrolled. c-IMT was assessed by ultrasound. CD4+/CD8+ T-cell activation (CD38+ HLADR+) and senescence (CD57+ CD28−) were measured by flow cytometry. IL-6 and OPG levels were measured by ELISA kit. c-IMT was higher in HIV+ than in controls. Among HIV+ patients, 44.7% had pathological c-IMT (≥0.9 mm). CD8+ T-cell activation and senescence and OPG plasma levels were higher in HIV+ patients than in controls. Subjects with pathological c-IMT exhibited higher CD8+ immune activation and immunosenescence and OPG levels than subjects with normal c-IMT. Multivariate analysis showed that age, CD8+ CD38+ HLADR+, and CD8+ CD28− CD57+ were independently associated with pathological c-IMT. Several factors have been implicated in the pathogenesis of atherosclerosis in HIV patients. Immune activation and immunosenescence of CD8+ T cell together with OPG plasma levels might be associated with the development and progression of early atherosclerosis, even in the case of viral suppression.
A genetic approach for personalized healthcare

Remote Health monitoring involves continuous monitoring of vital signs and transmission of alert signals to the physician when vital sign values fluctuate above or below the threshold. Existing healthcare systems obtain the vital data of a patient periodically and require the intervention of a doctor to detect the severity of abnormality which is time consuming. Hence, there is a need for an intelligent, personalized and efficient healthcare system to detect the abnormality. In a multipatient environment when several patients have abnormalities, existing scheduling schemes do not consider the degree of severity in order to schedule the most critical patient who has to be served first. To overcome these issues, this paper proposes a Genetic Algorithm (GA) based Personalized Healthcare System (GAPHS). This system represents the abnormality levels of the vital parameters of the patient as a chromosome and determines the Severity Index of the chromosome to identify the severity. The proposed system outperforms in terms of speed and accuracy when compared to traditional GA in dynamic scenarios.
Frame correlation based autoregressive GMM method for voice conversion

In this paper, we present a frame correlation based autoregressive GMM method for voice conversion. In our system, the cross-frame correlation of the source feature is modeled with augmented delta features, and the cross-frame correlation of target feature is modeled by autoregressive models. The expectation maximization (EM) algorithm is used for the model training, and a maximum likelihood parameter conversion algorithm is then employed to convert the feature of a source speaker into the one of a target speaker frame by frame. This method is consistent in training and conversion by using target feature's cross-frame correlation explicitly at both stage. The experimental results show that the proposed method has excellent performance. The test set log probability of it is higher than the GMM-DYN (GMM with dynamic features) method, and the subjective evaluation results of it are also comparable to the GMM-DYN method. Furthermore, it is much more suitable for low-latency application.
Increased Oxidative Stress in Scavenger Receptor BI Knockout Mice With Dysfunctional HDL

Objective—In the current study the effect of disruption of SR-BI, a prominent regulator of HDL metabolism, on the activity of the HDL-associated antioxidant enzymes PON1 and PAF-AH as well as in vivo oxidative stress were investigated. Methods and Results—SR-BI deficiency resulted in 1.4-fold (P<0.001) and 1.6-fold (P<0.01) lower serum paraoxonase and arylesterase activity of PON1, respectively. Furthermore, a trend to slightly lower PAF-AH activity was observed. In vivo oxidative stress was evaluated by measuring isoprostane F2&agr;-VI (iPF2&agr;-VI) and protein carbonyls. Compared with wild-type animals, SR-BI knockouts had 1.4-fold (P<0.05) higher levels of plasma iPF2&agr;-VI, whereas urinary excretion was increased 2-fold (P<0.0001). Plasma carbonyls were 1.5-fold (P<0.05) higher in SR-BI knockout animals. Furthermore, iPF2&agr;-VI and carbonyl levels were 2.1-fold (P<0.01) and 1.4-fold (P<0.01), respectively, increased in livers of SR-BI knockout mice, and in reaction to the increased oxidative stress the expression of several endogenous antioxidant systems was upregulated. On challenging the SR-BI knockout mice with an atherogenic Western-type diet, a further increase in oxidative stress in these animals was observed. Conclusion—SR-BI deficiency results in a reduced activity of the antioxidant enzyme PON1 and a significant increase in oxidative stress, potentially contributing to the proatherogenic effect of SR-BI deficiency.
Continuous humanoid locomotion over uneven terrain using stereo fusion

For humanoid robots to fulfill their mobility potential they must demonstrate reliable and efficient locomotion over rugged and irregular terrain. In this paper we present the perception and planning algorithms which have allowed a humanoid robot to use only passive stereo imagery (as opposed to actuating a laser range sensor) to safely plan footsteps to continuously walk over rough and uneven surfaces without stopping. The perception system continuously integrates stereo imagery to build a consistent 3D model of the terrain which is then used by our footstep planner which reasons about obstacle avoidance, kinematic reachability and foot rotation through mixed-integer quadratic optimization to plan the required step positions. We illustrate that our stereo imagery fusion approach can measure the walking terrain with sufficient accuracy that it matches the quality of terrain estimates from LIDAR. To our knowledge this is the first such demonstration of the use of computer vision to carry out general purpose terrain estimation on a locomoting robot - and additionally to do so in continuous motion. A particular integration challenge was ensuring that these two computationally intensive systems operate with minimal latency (below 1 second) to allow re-planning while walking. The results of extensive experimentation and quantitative analysis are also presented. Our results indicate that a laser range sensor is not necessary to achieve locomotion in these challenging situations.
Defending Secret-Key Based Authentication Protocols against the Stolen-Secret Attack

The security of a two-party authentication protocol relies on the stored secrets of each entity are not easily compromised by adversaries. However, in the real world, hackers can always divulge the stored secrets. In this paper, we introduce the concept of the stolen-secret attack and point out that all existing secret-key based authentication protocols and password based authentication protocols suffer from this attack. We also propose two methods that defend against the stolen-secret attack. Security proof and implementation analysis are given for both methods to illustrate their soundness and usefulness.
Piceatannol exhibits anti‐inflammatory effects on macrophages interacting with adipocytes

Abstract Piceatannol (PIC), a natural analog of resveratrol (RES), is a phytochemical found in passion fruit seeds. To clarify the effects of PIC on obesity‐induced inflammation in adipose tissue, we investigated the anti‐inflammatory activity of PIC‐related compounds (PIC, RES, and metabolites from PIC) in culture models of obese adipose tissue. Lipopolysaccharide (LPS) and conditioned medium from 3T3‐L1 adipocytes (3T3‐L1‐CM) enhanced proinflammatory gene expression and synthesis of nitric oxide (NO), tumor necrosis factor‐α (TNF‐α), and interleukin‐6 (IL‐6) in RAW264.7 macrophages. Although each compound inhibited the mRNA expression of iNOS (inducible NO synthase), TNF‐α, and IL‐6, PIC potently inhibited them, and 30 μmol/L PIC suppressed the LPS‐ and 3T3‐L1‐CM‐induced mRNA expression of iNOS (70.4% and 69.2% suppression, respectively), TNF‐α (42.6% and 47.0% suppression), and IL‐6 (27.3% and 42.1% suppression). PIC also significantly suppressed production of NO (80.3% suppression) and inflammatory cytokines (TNF‐α; 33.7% suppression, IL‐6; 66.5% suppression). Furthermore, PIC was found to rescue the uncoupling protein 1 mRNA expression induced by isoproterenol in 10T1/2 adipocytes, which was suppressed by LPS‐activated macrophages. These results suggest that PIC may attenuate the pathologic inflammation triggered by adipose tissues.
Non-rigid objects detection and segmentation in video sequence using 3D mean shift analysis

A new technique for robust detection and segmentation of non-rigid objects in video sequence is proposed in this paper. In our approach, spatio-temporal mean shift analysis (MSA) is employed to convert raw video/object data to their corresponding 3D/2D region feature spaces (RFS) respectively. The distance metric of RFS can be defined based on its spatio-temporal continuous property. Within the MSA derived RFS, any selected or given objects can be detected and segmented automatically in successive frames by local motion estimation. Experiments on various sequences show our method is robust to clutter, partial occlusion, object's out-of-plane rotation and significant relative movement among targets, scene and camera.
Modeling User Activities on the Web using Paragraph Vector

Modeling user activities on the Web is a key problem for various Web services, such as news article recommendation and ad click prediction. In this paper, we propose an approach that summarizes each sequence of user activities using the Paragraph Vector, considering users and activities as paragraphs and words, respectively. The learned user representations are used among the user-related prediction tasks in common. We evaluate this approach on two data sets based on logs from Web services of Yahoo! JAPAN. Experimental results demonstrate the effectiveness of our proposed methods.
Targeting and Readout Strategies for Fast Optical Neural Control In Vitro and In Vivo

Major obstacles faced by neuroscientists in attempting to unravel the complexity of brain function include both the heterogeneity of brain tissue (with a multitude of cell types present in vivo) and the high speed of brain information processing (with behaviorally relevant millisecond-scale electrical activity patterns). To address different aspects of these technical constraints, genetically targetable neural modulation tools have been developed by a number of groups (Zemelman et al., 2002; Banghart et al., 2004; Karpova et al., 2005; Lima and Miesenbock, 2005; Thompson et al., 2005; Chambers et al., 2006; Tan et al., 2006; Gorostiza et al., 2007; Lerchner et al., 2007; Szobota et al., 2007). One approach recently brought to neurobiology, combining both high speed and genetic targeting, is based on a family of fast light-responsive microbial opsins including halorhodopsins (e.g., NpHR) and channelrhodopsins (e.g., ChR2) (for review, see Zhang et al., 2007b). These microbial opsins are single-component transmembrane conductance regulators encompassing light sensitivity and fast membrane potential control within a single open reading frame, which can be used to achieve fast bidirectional control of specific cell types even in freely moving animals (Adamantidis et al., 2007; Zhang et al., 2007a). Although the basic functioning of these tools has been reviewed previously (Zhang et al., 2007b), here we describe a collection of targeting and readout strategies designed for rapid and flexible application of the microbial opsin system, and provide pointers to the relevant literature. Combinations of these multiple levels of targeting and readout define an evolving toolbox that may open up new possibilities for basic neuroscience investigation.
M A Context-Based Adaptation In Mobile Learning

— Recent developments on mobile devices and wireless technologies enable new technical capabilities for the learning domain. Nowadays, learners are able to learn anywhere and at any time. The dynamic and continually changing learning setting in learner's mobile environment gives rise to many different learning contexts. The challenge in context-aware mobile learning is to develop an approach building the best learning content according to dynamic learning situations. This paper aims to develop an adaptive system based on the semantic modeling of the learning content and the learning context. The behavioral part of this approach is made up of rules and metaheuristics to optimize the combination of pieces of learning content according to learner's context.
Real-time 3D body reconstruction for immersive TV

In this work, a novel and fast algorithm for real-time 3D body reconstruction from stereo sequences is proposed. The main contributions of this work consist of a novel approach for a statistically guided stereo processing and a data parallel iteration scheme for 3D estimation that includes temporal predecessors from a local spatial neighborhood. A purely GPU based implementation is provided that exhibits a nearly linear scaling of the runtime with respect to the number of GPUs. This leads to an inherent sub-pixel processing due to the availability of hardware supported texture lookups. Our implementation is able to process 4K (UHD) stereo streams on a 4×4 grid with 30 fps on a single state-of-the-art consumer graphics card. The algorithmic performance of our approach is demonstrated in the context of an immersive TV application.
An improved localization algorithm in wireless sensor network

An improved localization algorithm W-Ecolocation in wireless sensor network is proposed. When there is a high erroneous constraints rate, the Ecolocation algorithm which only considers the locations with maximum computing number of matched constraints cannot obtain the optimal location estimate. The W-Ecolocation makes use of more locations with great computing number of matched constraints and takes the weighted average as the location estimate of the unknown node. The simulation results show that W-Ecolocation performs better than Ecolocation in terms of location error and location precision. A new approach to compute the number of matched constraints is also presented. It reduces both the time complexity and space complexity.
Transport behaviors in graphene field effect transistors on boron nitride substrate

We model the transport behavior of a top-gated graphene field-effect transistor where boron nitride is used as substrate and gate insulator material. Our simulation model is based on the non-equilibrium Green's function approach to solving a tight-binding Hamiltonian for graphene, self-consistently coupled with Poisson's equation. The analysis emphasizes the effects of the chiral character of carriers in graphene in the different transport regimes including Klein and band-to-band tunneling processes. We predict the possible emergence of negative differential conductance and investigate its dependence on the temperature and the BN-induced bandgap. Short-channel effects are evaluated from the analysis of transfer characteristics as a function of gate length and gate insulator thickness. They manifest through the shift of the Dirac point and the appearance of current oscillations at short gate length.
Towards a novel no-hair theorem for black holes

We provide strong numerical evidence for a new no-scalar-hair theorem for black holes in general relativity, which rules out spherical scalar hair of static four dimensional black holes if the scalar field theory, when coupled to gravity, satisfies the Positive Energy Theorem. This sheds light on the no-scalar-hair conjecture for Calabi-Yau compactifications of string theory, where the effective potential typically has negative regions but where supersymmetry ensures the total energy is always positive. In theories where the scalar tends to a negative local maximum of the potential at infinity, we find the no-scalar-hair theorem holds provided the asymptotic conditions are invariant under the full anti-de Sitter symmetry group.
Robustness of noisy consensus dynamics with directed communication

In this paper we study robustness of consensus in networks of coupled single integrators driven by white noise. Robustness is quantified as the H2 norm of the closed-loop system. In particular we investigate how robustness depends on the properties of the underlying (directed) communication graph. To this end several classes of directed and undirected communication topologies are analyzed and compared. The trade-off between speed of convergence and robustness to noise is also investigated.