Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting Cross-Site Scripting Vulnerabilities through Automated Unit Testing

The best practice to prevent Cross Site Scripting (XSS) attacks is to apply encoders to sanitize untrusted data. To balance security and functionality, encoders should be applied to match the web page context, such as HTML body, JavaScript, and style sheets. A common programming error is the use of a wrong encoder to sanitize untrusted data, leaving the application vulnerable. We present a security unit testing approach to detect XSS vulnerabilities caused by improper encoding of untrusted data. Unit tests for the XSS vulnerability are automatically constructed out of each web page and then evaluated by a unit test execution framework. A grammar-based attack generator is used to automatically generate test inputs. We evaluate our approach on a large open source medical records application, demonstrating that we can detect many 0-day XSS vulnerabilities with very low false positives, and that the grammar-based attack generator has better test coverage than industry best practices.
A Novel 3D-Printed and Miniaturized Periodic Counter Current Chromatography System for Continuous Purification of Monoclonal Antibodies

Continuous chromatography has emerged as one of the most attractive methods for protein purification. Establishing such systems involves installing several chromatographic units in series to enable continuous separation processes and reduce the cost of the production of expensive proteins and biopharmaceuticals (such as monoclonal antibodies). However, most of the established systems are bulky and plagued by high dead volume, which requires further optimization for improved separation procedures. In this article, we present a miniaturized periodic counter-current chromatography (PCCC) system, which is characterized by substantially reduced dead volume when compared to traditional chromatography setups. The PCCC device was fabricated by 3D printing, allowing for flexible design adjustments and rapid prototyping, and has great potential to be used for the screening of optimized chromatography conditions and protocols. The functionality of the 3D-printed device was demonstrated with respect to the capture and polishing steps during a monoclonal antibody purification process. Furthermore, this novel miniaturized system was successfully used for two different chromatography techniques (affinity and ion-exchange chromatography) and two different types of chromatographic units (columns and membrane adsorbers). This demonstrated versability underscores the flexibility of this kind of system and its potential for utilization in various chromatography applications, such as direct product capture from perfusion cell cultures.
Retrofitting software safety in an implantable medical device

Regulatory agencies are requiring companies to systematically demonstrate software safety in their implantable devices, even those already developed. The authors devised a rigorous software safety analysis technique and applied it to a nearly-completed cardiac rhythm management device. They were able to identify safety faults, modify the software to mitigate them, and verify overall safety. <<ETX>>
High-Order Super-Block GLRT for Non-Coherent Grassmann Codes in MIMO-OFDM Systems

We investigate non-coherent multi-input multi-output (MIMO) signal processing which requires no channel state information (CSI) at either the transmitter or the receiver. With non-coherent codes on Grassmann manifold, a receiver employing generalized likelihood ratio test (GLRT) algorithm offers the maximum-likelihood performance even without CSI. However, the conventional GLRT suffers from a severe performance degradation when the channel changes fast within a coding block duration. We propose an improved GLRT algorithm referred to as high-order super-block techniques. The super-block scheme makes effective use of correlated channels for adjacent blocks in slow fading, whereas the high-order scheme can overcome the channel fluctuation during a block in fast fading. We demonstrate that the proposed scheme significantly improves performance for MIMO-OFDM with non-coherent Grassmann space-frequency block codes (SFBC).
Group delay features for emotion detection

This paper focuses on speech based emotion classification utilizing acoustic data. The most commonly used acoustic features are pitch and energy, along with prosodic information like the rate of speech. We propose the use of a novel feature based on the phase response of an all-pole model of the vocal tract obtained from linear predictive coefficients (LPC), in addition to the aforementioned features. We compare this feature to other commonly used acoustic features based on classification accuracy. The back-end of our system employs a probabilistic neural network based classifier. Evaluations conducted on the LDC Emotional Prosody speech corpus indicate the proposed features are well suited to the task of emotion classification. The proposed features are able to provide a relative increase in classification accuracy of about 14% over established features when combined with them to form a larger feature vector.
Adaptive fuzzy backstepping control for a class of switched nonlinear systems with actuator faults

ABSTRACT This paper investigates the problem of fault-tolerant control (FTC) for a class of switched nonlinear systems. These systems are under arbitrary switchings and are subject to both lock-in-place and loss-of-effectiveness actuator faults. In the control design, fuzzy logic systems are used to identify the unknown switched nonlinear systems. Under the framework of the backstepping control design, FTC, fuzzy adaptive control and common Lyapunov function stability theory, an adaptive fuzzy control approach is developed. It is proved that the proposed control approach can guarantee that all the signals in the closed-loop switched system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error remains an adjustable neighbourhood of the origin. Two simulation examples are provided to illustrate the effectiveness of the proposed approach.
An adaptively balancing workload protocol on query trees for maximizing lifetime in sensor networks

Since querying trees in wireless sensor networks are normally built in an ad-hoc manner, equal workload among sensors cannot be guaranteed. A sensor with more descendants has to relay more data that cause its energy exhausted more quickly. To prolong the lifetime of such a sensor and its network we propose an adaptive balancing workload protocol consisting of two phases. First, it builds a balanced-query tree that allows nodes close to a sink to share their workload. Second, it adaptively switches the workload from an exhausted node to non-exhausted nodes. The simulation was carried out to validate the proposed protocol and compare it with previous work. The simulation shows that after running 2,100 rounds, the node by our proposed scheme still remains 20% of initial energy while the one by previous schemes runs out of energy fully exhausted.
Confidence-based Link/Attribute Inference based on Friendship Circles

In this paper we discuss how to determine the appropriate order when recommending new friends or new interests based on the confidence scores generated by using SVM on a social network. The main idea is analyzing the ego's friendship circles to find interests or friends which are not currently included in their profile, and then the confidence scores obtained by SVM are used to decide which interests or friends should be recommended in every turn, based on the focal individual's network structure, also called an ego network. The novel factor in this approach is that it does not make recommendations using a fixed order. This is because a fixed order would make some improper recommendations, then mislead next recommendation, and obtain increasingly less accuracy results. In our experiments, it is shown that the accuracy achieved when using our confidence score order is better than that with a fixed order, even when there is very little information contained in a person's profile.
The Par Lab Perspective on Computer Architecture

Given the multicore microprocessor revolution, we argue that the architecture research community needs a dramatic increase in simulation capacity. We believe FPGA Architecture Model Execution (FAME) simulators can increase the number of useful architecture research experiments per day by two orders of magnitude over Software Architecture Model Execution (SAME) simulators. To clear up misconceptions about FPGA-based simulation methodologies, we propose a FAME taxonomy to distinguish the cost-performance of variations on these ideas. We demonstrate our simulation speedup claim with a case study wherein we employ a prototype FAME simulator, RAMP Gold, to research the interaction between hardware partitioning mechanisms and operating system scheduling policy. The study demonstrates FAME’s capabilities: we run a modern parallel benchmark suite on a research operating system, simulate 64-core target architectures with multi-level memory hierarchy timing models, and add experimental hardware mechanisms to the target machine. The simulation speedup achieved by our adoption of FAME—250×—enables experiments with more realistic time scales and data set sizes than are possible with SAME.
Dynamic model behavior analysis of small groups based on particle video

Video surveillance is becoming more and more significant in the detection of abnormal events for public security. As a usual kind of crowd activity mode, the research on the motion analysis of small groups is under increasing attention. This paper presents a particle video-based abnormal behavior detection method of small groups. First, use a particle tracking algorithm to obtain the video stream of particles. Since the particle stream contains too much redundant information in the same group, therefore propose to use the longest common subsequence algorithm to make trajectory clustering in order to obtain main information. Then use location information to build effective particle dynamic model, which means changes in the state space. Finally, Hidden Markov Model is used for small groups of abnormal behavior detection.
UOSPR: UnObservable secure proactive routing protocol for fast and secure transmission using B.A.T.M.A.N

USOR deals with the concept of unlinkability, unobservability and anonymity by combining group signature and ID-based encryption for route discovery. Apart from the security, there is another requirement in transmission is effective and efficient delivery of data. USOR uses ordinary distance calculations to get the knowledge about neighbour nodes. At the same time we can't incorporate the entire information about the network into single node or every node. We know that nodes in ad-hoc network have limited storage and battery power. Already half of the power vested in security, remaining power has to be used for calculating routes and data transmission. For that we are merging the above USOR with Better Approach To Mobile Ad-hoc Networking, or B.A.T.M.A.N. which is a routing protocol that has the knowledge of its best route alone - no single node has all the data. This technique eliminates extending the information to the concerning network changes to every node in the network. Each node only maintains the best hop details not the entire network topology. From this we are achieving fast transmission of data in a secure way with minimum node failure rate.
Network anticorrelations, global regression, and phase‐shifted soft tissue correction

Synchronized low‐frequency BOLD fluctuations are observed in dissociable large‐scale, distributed networks with functional specialization. Two such networks, referred to as the task‐positive network (TPN) and the task‐negative network (TNN) because they tend to be active or inactive during cognitively demanding tasks, show reproducible anticorrelation of resting BOLD fluctuations after removal of the global brain signal. Because global signal regression mandates that anticorrelated regions to a given seed region must exist, it is unclear whether such anticorrelations are an artifact of global regression or an intrinsic property of neural activity. In this study, we demonstrate from simulated data that spurious anticorrelations are introduced during global regression for any two networks as a linear function of their size. Using actual resting state data, we also show that both the TPN and TNN become anticorrelated with the orbits when soft tissues are included in the global regression algorithm. Finally, we propose a technique using phase‐shifted soft tissue regression (PSTCor) that allows improved correction of global physiological artifacts without global regression that shows improved anatomic specificity to global regression but does not show significant network anticorrelations. These results imply that observed anticorrelations between TNN and TPN may be largely or entirely artifactual in the resting state. These results also imply that differences in network anticorrelations attributed to pathophysiological or behavioral states may be due to differences in network size or recruitment rather than actual anticorrelations. Hum Brain Mapp, 2011. © 2010 Wiley‐Liss, Inc.
Perceptual illusions for Multisensory Displays

Human perceptual properties have been applied for designing multisensory display technologies. This paper overviews the sensory-illusion-based approach we have used to create a force display that elicits illusory continuous force sensation by presenting asymmetric vibrations and a self-motion display based on a cross-modal effect between visual and tactile motion.
Well-Posedness for the Euler-Nordstrom System with Cosmological Constant

In this paper the author considers the motion of a relativistic perfect fluid with self-interaction mediated by Nordstrom's scalar theory of gravity. The evolution of the fluid is determined by a quasilinear hyperbolic system of PDEs, and a cosmological constant is introduced in order to ensure the existence of non-zero constant solutions. Accordingly, the initial value problem for a compact perturbation of an infinitely extended quiet fluid is studied. Although the system is neither symmetric hyperbolic nor strictly hyperbolic, Christodoulou's constructive results on the existence of energy currents for equations derivable from a Lagrangian can be adapted to provide energy currents that can be used in place of the standard energy principle available for first-order symmetric hyperbolic systems. After providing such energy currents, the author uses them to prove that the Euler-Nordstrom system with a cosmological constant is well-posed in a suitable Sobolev space.
Provably safe robot navigation with obstacle uncertainty

As drones and autonomous cars become more widespread, it is becoming increasingly important that robots can operate safely under realistic conditions. The noisy information fed into real systems means that robots must use estimates of the environment to plan navigation. Efficiently guaranteeing that the resulting motion plans are safe under these circumstances has proved difficult. We examine how to guarantee that a trajectory or policy has at most ϵ collision probability ( ϵ -safe) with only imperfect observations of the environment. We examine the implications of various mathematical formalisms of safety and arrive at a mathematical notion of safety of a long-term execution, even when conditioned on observational information. We explore the idea of shadows that generalize the notion of a confidence set to estimated shapes and present a theorem that allows us to understand the relationship between shadows and their classical statistical equivalents such as confidence and credible sets. We present efficient algorithms that use shadows to prove that trajectories or policies are safe with much tighter bounds than in previous work. Notably, the complexity of the environment does not affect our method’s ability to evaluate whether a trajectory or policy is safe. We then use these safety-checking methods to design a safe variant of the rapidly exploring random tree (RRT) planning algorithm.
Unit commitment by enhanced adaptive Lagrangian relaxation

This paper proposes an enhanced adaptive Lagrangian relaxation (ELR) for a unit commitment (UC) problem. ELR consists of adaptive LR (ALR) and heuristic search. The ALR algorithm is enhanced by new on/off decision criterion, new initialization of Lagrangian multipliers, unit classification, identical marginal unit decommitment, and adaptive adjustment of Lagrangian multipliers. After the ALR best feasible solution reached is obtained, the heuristic search consisting of unit substitution and unit decommitment is used to fine tune the solution. The proposed ELR is tested and compared to conventional Lagrangian relaxation (LR), genetic algorithm (GA), evolutionary programming (EP), Lagrangian relaxation and genetic algorithm (LRGA), and genetic algorithm based on unit characteristic classification (GAUC) on the systems with the number of generating units in the range of 10 to 100. ELR total system production costs are less expensive than the others especially for the large number of generating units. Furthermore, the computational times of ELR are much less than the others and increase linearly with the system size, which is favorable for large-scale implementation.
Automatic video background replacement using shape-based probabilistic spatio-temporal object segmentation

While video background replacement has an extensive range of applications, the chroma-keying process is still the most widely applied despite it having to maintain specific lighting conditions, a blue (or green) background, and the requirement of painstaking manual handling. This paper addresses these issues by presenting a novel fully automatic video background replacement technique that does not require any specific recording constraints. This paper employs a generic shape-based probabilistic spatio-temporal (PST) video object segmentation algorithm employing a Gaussian mixture model (GMM) to achieve a long-desired outcome towards fully automated video background replacement techniques. Experimental results using a number of standard video test sequences reveal the merits of the proposed technique.
Low temperature tolerance for Artemisia tridentata seedlings over an elevation gradient

Twenty Artemisia tridentata seedlings weire collected from sites at 2575, 2175, and 1775 m in the eastern Sierra Nevada. Freezing tolerance for the seedlings from the three altitudes was assessed using excised leaves and subjecting them to low temperature treatments ranging between 20 °C and -20 °C. LT50, as determined by F^/Fn, measurements was approximately -15 °C for leaves from plants from all three altitudes. LT50, as determined by conductivity measurements was slightly lower at approximately -13.7 °C for leaves from plants from all three sites. Shifting the environmental chamber from 25/15 °C to 15/5 °C day/night air temperatures only initiated a small acclimation response by plants from all three altitudes. Although slight, individuals from the highest site responded greatest. Microclimate temperature data indicated that low temperature extremes and averages at the three sites were within the seedling's range of cold torerancd. Snow cover ameliorated temperatures near the soil surface and could play a role in seedling survival at higher colder altitudes. Seedling tolerances to abiotic factors, like cold, influence successful seedling establishment. Future climate changes will cause many species to migrate as they track
Damage to the Locus Coeruleus Alters the Expression of Key Proteins in Limbic Neurodegeneration

The present investigation was designed based on the evidence that, in neurodegenerative disorders, such as Alzheimer’s dementia (AD) and Parkinson’s disease (PD), damage to the locus coeruleus (LC) arising norepinephrine (NE) axons (LC-NE) is documented and hypothesized to foster the onset and progression of neurodegeneration within target regions. Specifically, the present experiments were designed to assess whether selective damage to LC-NE axons may alter key proteins involved in neurodegeneration within specific limbic regions, such as the hippocampus and piriform cortex, compared with the dorsal striatum. To achieve this, a loss of LC-NE axons was induced by the neurotoxin N-(2-chloroethyl)-N-ethyl-2-bromobenzylamine (DSP4) in C57 Black mice, as assessed by a loss of NE and dopamine-beta-hydroxylase within target regions. In these experimental conditions, the amount of alpha-synuclein (alpha-syn) protein levels were increased along with alpha-syn expressing neurons within the hippocampus and piriform cortex. Similar findings were obtained concerning phospho-Tau immunoblotting. In contrast, a decrease in inducible HSP70-expressing neurons and a loss of sequestosome (p62)-expressing cells, along with a loss of these proteins at immunoblotting, were reported. The present data provide further evidence to understand why a loss of LC-NE axons may foster limbic neurodegeneration in AD and limbic engagement during PD.
Context-aware location recommendations with tensor factorization

Location-based social networks (LBSNs) enable users not only to record where and when they go using check-ins but also to publish their ratings and comments of these locations. All information can be utilized to infer users' interests and to generate personalized location recommendations for different individuals. In this process, temporal context plays a significant role, because life patterns of users vary greatly and different locations have different proper visiting time. Therefore, in this paper, we propose a top-k location recommendation scheme, considering the influence of temporal context. To be specific, we first model user-location-time relations with a three dimensional tensor. Then, we extract a user-user similarity matrix and a location feature matrix to relieve data sparsity problem and to increase the prediction accuracy of estimated ratings during the tensor decomposition process. Through a context-aware tensor decomposition approach, we can obtain any user's rating for any location in any time slot. Moreover, we partition a large area into several small ones and adopt the Threshold Algorithm (TA) to improve efficiency of online recommendations. We evaluate our method in a real-world dataset and the results demonstrate the effectiveness of our method.
Intelligence on the Web

This paper describes a courseware system delivered over the World Wide Web for transforming existing video-taped courses. The on-line courses take advantage of the interactivity and the individualized instruction of intelligent tutoring systems. They use synchronized audio and HTML slides to present material to the student. An overlay student model records a student’s ability on each topic in the system, determined by which slides the student has seen and quizzes he has taken. This model is then used to help guide the student through the material, to provide interactive and adaptive quizzes, and to prefetch course material before it is explicitly requested, reducing the delays seen by the student. The first goal is accomplished by using adaptive hypertext techniques including adaptive navigation support and adaptive presentation. The second goal is achieved by dynamically constructing quizzes at the correct level of difficulty for the student. The third goal is accomplished by predicting the next actions the student will take, and preparing for them ahead of time. In this paper we discuss initial efforts to implement a student model for the kinds of web-based courses supported in the system. We also discuss how the student model is used to accomplish the three goals. We conclude by providing a brief description of the first student encounters with the system.