Miracle-dz/UniFAR-FTUs huggingface dataset:
PEEP- Privacy Enforcement in Email Project

Breaching information privacy is a critical problem where legal remedies intervene only after the fact rather than prevent it. This paper presents an organizational privacy compliance engine that monitors outgoing emails to detect breaches of a privacy policy in an organization. The PEEP system employs email content analysis techniques to extract information and their ownership. Access to the extracted information is verified by privacy rules assisted by an ontology-based model to represent information disclosure privileges. This paper addresses the issues of, first, the information extraction techniques from email, and second, the implementation of an ontology-based model of multilevel disclosure privileges to represent privacy rules. We experiment with the PEEP system on real life emails in an academic environment to detect breaches of privacy in emails. Our results report an F-score of 71.7% of privacy violations detection.
Fast Depth-Image Meshing and Warping

In this paper we present a novel and efficient depthimage warping technique based on a piece-wise linear approximation of the depth-image as a textured and simplified triangle mesh. We describe the application of a hierarchical triangulation method to generate triangulated depthmeshes efficiently from depth-images, discuss alternative depth-mesh segmentation methods to avoid occlusion artifacts, and propose a new technique to reduce exposure artifacts that exploits hardware accelerated warping and weighted blending of multiple depth-images in real-time. Applications of our technique include image-based object representations, large scale walk-through visualization systems, as well as terrain rendering.
Removal of Gaussian Noise Using Rao-Blackwellised Particle Filters

In this paper, we propose an efficient Monte Carlo particle filter for restoring images using probabilistic dynamic model. The evolution of the discrete and continuous states are described by the probabilistic dynamic model. Usually, the continuous states are assumed to be Gaussian distributed. This algorithm exploits some of the analytical structure of the model. Using this algorithm the values of the discrete states are found, and possible to compute the distribution of the continuous states exactly. The importance of the Rao-Blackwellized particle filter is to improve the learning stage by estimating a posterior. The proposed work is to identify the discrete state of operation using the continuous measurements corrupted by Gaussian noise. This algorithm also finds some of the analytical structure of the model. The Rao-Blackwellized particle filter is the combination of particle filter (PF) and states with a bank of Kalman filters.
Detection of motion-defined form using night vision goggles

Perception of motion-defined form is important in operational tasks such as search and rescue and camouflage breaking. Previously, we used synthetic Aviator Night Vision Imaging System (ANVIS-9) imagery to demonstrate that the capacity to detect motion-defined form was degraded at low levels of illumination (see Macuda et al., 2004; Thomas et al., 2004). To validate our simulated NVG results, the current study evaluated observer’s ability to detect motion-defined form through a real ANVIS-9 system. The image sequences consisted of a target (square) that moved at a different speed than the background, or only depicted the moving background. For each trial, subjects were shown a pair of image sequences and required to indicate which sequence contained the target stimulus. Mean illumination and hence image noise level was varied by means of Neutral Density (ND) filters placed in front of the NVG objectives. At each noise level, we tested subjects at a series of target speeds. With both real and simulated NVG imagery, subjects had increased difficulty detecting the target with increased noise levels, at both slower and higher target speeds. These degradations in performance should be considered in operational planning. Further research is necessary to expand our understanding of the impact of NVG-produced noise on visual mechanisms.
Responsive fingers — capacitive sensing during object manipulation

We present a novel approach of active object categorization based on an iterative Bayesian method using capacitive sensing during object manipulation. The approach uses a novel type of capacitive sensor, which can measure internal properties of materials that are inaccessible to vision or tactile sensing. The electrodes of this capacitive sensor are sufficiently flexible and thin to be attached to various types of robot tools, including humanoid fingers and palms. They are mechanically robust and wear resistant. In comparison to earlier capacitive sensors systems, we perform single ended and differential measurements over an array of electrodes, and coordinate measurement with robot manipulation to extract information not available from static measurements. We demonstrate the capability of our active object categorization system in the James robot bartender system. This system can manipulate objects and measure continuously in order to categorize empty and non-empty bottles. The principle of capacitive sensing during manipulation can be applied to more general object manipulation tasks in robotics and also in other fields of industrial automation.
Branching of calyceal afferents during postnatal development in the rat auditory brainstem

Cells in the anteroventral cochlear nucleus (aVCN) send out calyceal axons that form large excitatory somatic terminals, the calyces of Held, onto principal cells of the contralateral medial nucleus of the trapezoid body (MNTB). It is unclear which fraction of these axons might form more than one calyx and whether this fraction changes during development. We combined in vitro anterograde tracing, stereological cell counts, analysis of apoptosis, and immunohistochemistry to study the development of calyceal afferents in rats of different postnatal ages. We found that some principal cells were contacted by multiple large axosomatic inputs, but these invariably originated from the same axon. Conversely, at least 18% of traced afferents branched to form multiple calyces, independently of age. Calyces from the same axon generally innervated nearby principal cells, and most of these branch points were <50 μm away from the synaptic terminals. Our results show that the projection from the aVCN to the MNTB is divergent, both when calyces have just been formed and in the adult. Cell counts did not provide evidence for principal cell loss during development, although analysis of apoptosis showed a large increase in nonneuronal cell death around the onset of hearing. Our data suggest that, once a calyceal synapse forms in the MNTB, it stays. J. Comp. Neurol. 496:214–228, 2006. © 2006 Wiley‐Liss, Inc.
A New Technique for LBP Method to Improve Face Recognition

-LBP is a very powerful method to describe the texture and shape of a digital image. Therefore it appeared to be suitable for feature extraction in face recognition systems. A face image is first divided into small regions from which LBP histograms are extracted and then concatenated into a single feature vector. This feature vector forms an efficient representation of the face and can be used to measure similarities between images.
Myosin light chain kinase-regulated endothelial cell contraction: the relationship between isometric tension, actin polymerization, and myosin phosphorylation

The phosphorylation of regulatory myosin light chains by the Ca2+/calmodulin-dependent enzyme myosin light chain kinase (MLCK) has been shown to be essential and sufficient for initiation of endothelial cell retraction in saponin permeabilized monolayers (Wysolmerski, R. B. and D. Lagunoff. 1990. Proc. Natl. Acad. Sci. USA. 87:16-20). We now report the effects of thrombin stimulation on human umbilical vein endothelial cell (HUVE) actin, myosin II and the functional correlate of the activated actomyosin based contractile system, isometric tension development. Using a newly designed isometric tension apparatus, we recorded quantitative changes in isometric tension from paired monolayers. Thrombin stimulation results in a rapid sustained isometric contraction that increases 2- to 2.5-fold within 5 min and remains elevated for at least 60 min. The phosphorylatable myosin light chains from HUVE were found to exist as two isoforms, differing in their molecular weights and isoelectric points. Resting isometric tension is associated with a basal phosphorylation of 0.54 mol PO4/mol myosin light chain. After thrombin treatment, phosphorylation rapidly increases to 1.61 mol PO4/mol myosin light chain within 60 s and remains elevated for the duration of the experiment. Myosin light chain phosphorylation precedes the development of isometric tension and maximal phosphorylation is maintained during the sustained phase of isometric contraction. Tryptic phosphopeptide maps from both control and thrombin-stimulated cultures resolve both monophosphorylated Ser-19 and diphosphorylated Ser-19/Thr-18 peptides indicative of MLCK activation. Changes in the polymerization of actin and association of myosin II correlate temporally with the phosphorylation of myosin II and development of isometric tension. Activation results in a 57% increase in F-actin content within 90 s and 90% of the soluble myosin II associates with the reorganizing F-actin. Furthermore, the disposition of actin and myosin II undergoes striking reorganization. F- actin initially forms a fine network of filaments that fills the cytoplasm and then reorganizes into prominent stress fibers. Myosin II rapidly forms discrete aggregates associated with the actin network and by 2.5 min assumes a distinct periodic distribution along the stress fibers.
A Dynamic Grouping-Based Trust Model for Mobile P2P Networks

With the rapid proliferation of wireless networks and mobile devices, Mobile Peer-to-Peer (MP2P) networks have attracted a huge amount of users for resource sharing. However, in an MP2P network, peers frequently join and leave the network, which makes the network topology dynamically change. Thus, it is difficult to establish long term and effective trust relationship among peers, making trust management become a challenging task. In this paper, we propose a dynamic grouping-based trust model DGTM, to classify peers. A group is formed according to the peers' interests. The experiments illustrate that our proposed dynamic grouping-based trust model DGTM always achieves the highest successful transaction rate under different circumstances.
Semantic access control in grid computing

Grid computing provides a computational infrastructure supporting flexible, secure, coordinated resource sharing among dynamic collections of individuals, institutions, and resources. Which means the grid should offer access for users and systems from various places. In order to protect the information and resource from abuse, access control in grid computing is therefore necessary and important. In this paper, a new approach is presented to authorize and administrate grid access requests, in which the requests are obliged to negotiate with a policy enforcement system in order to gain access to the target grid resources. This new method is called semantic access control as it exploits semantic Web technology, and use machine reasoning about the messages and policies at a semantic level.
Nonparametric bottom-up saliency detection using hypercomplex spectral contrast

Saliency detection is an useful technique for image semantic analysis such as auto image segmentation, image retargeting, advertising design and image compression. Inspired by two existing saliency detection algorithms, named spectral residual (SR) and phase spectrum of quaternion Fourier transform (PQFT), we propose a new bottom-up saliency detection method which is featured with the introduction of hypercomplex spectral contrast (HSC) in saliency detection. The proposed HSC algorithm introduces the HSV color image vector space in hypercomplex number, and is better comprehensive to consider amplitude spectral contrast into saliency model as well as phase spectral contrast. Meanwhile, we also incorporate the human vision nonuniform sampling into our model, which is a common phenomenon that directs visual attention to the logarithmic center of image in natural scenes. Experimental results on two public saliency detection datasets show that our approach performs better than four state-of-the art approaches remarkably.
A component-based architecture for power-efficient media access control in wireless sensor networks

The diverse requirements of wireless sensor network applications necessitate the development of multiple media access control (MAC) protocols to meet their varying throughput, latency, and network lifetime needs. Building new MAC protocols has proven to be extremely difficult, however, given the monolithic nature of existing protocol implementations as well as their dependence on a particular radio or processor platform. To address these issues, we propose the MAC Layer Architecture (MLA), a component-based architecture for power-efficient MAC protocol development in wireless sensor networks. MLA consists of optimized, reusable components that implement a common set of features shared by existing MAC protocols, as well as abstractions that encapsulate the intricacies of the hardware platforms they run on. Through an instantiation of MLA in TinyOS 2.0. 1, we have implemented five representative MAC protocols. Empirical results show that MLA results in significant code reuse among different protocols, while achieving comparative performance and memory footprints to monolithic implementations of the same protocols.
An Evaluation of the Selectivity Characteristics of Different Juvenile Fish Escape Panel Designs for the Demersal Trap Fishery of Abu Dhabi, United Arab Emirates

The selectivity characteristics of 4 juvenile fish escape panel designs and their utility for the regulation of a multi-species demersal trap fishery were evaluated using a suite of objective socio-economic and biological criteria. The panel designs consisted of a control (type A) which had a hexagonal mesh size which was the same as that of the body of the trap (3.5 cm), a rectangular mesh (type B) which was representative of the current regulation (5.0 x 7.6 cm) and 2 escape panels with square meshes of 7.5 x 7.5 cm (type C) and 10.0 x 10.0 cm (type D). The results demonstrated that there was only a limited reduction in the proportion of juvenile fish and by-catch retained for the existing juvenile escape panel design (type B). Furthermore, as the selectivity characteristics for the key species (Epinephelus coioides and Diagramma pictum) were similar to the control type, the predicted increases in yields, revenues and spawning stock biomass were small by comparison. The escape panel with the largest mesh size (type D) retained the least un-utilized and discarded by-catch. Whilst simulations predicted the highest spawner biomass per recruit, long term yields and revenues for the key species, its use was associated with a dramatic short-term decline in revenues which were 23.3% of the value of the control type. Traps fitted with the type C which had a square mesh of 7.5 x 7.5 cm had the lowest juvenile retention and the highest overall score for all the assessment criteria combined. The study provides an empirical basis for gear regulations for the demersal trap fishery of the Emirate of Abu Dhabi and the wider Arabian Gulf region.
Towards Path-Sensitive Points-to Analysis

Points-to analysis is a static program analysis aiming at analyzing the reference structure of dynamically allocated objects at compile-time. It constitutes the basis for many analyses and optimizations in software engineering and compiler construction. Sparse program representations, such as Whole Program Points-to Graph (WPP2G) and Points-to SSA (P2SSA), represent only dataflow that is directly relevant for points-to analysis. They have proved to be practical in terms of analysis precision and efficiency. However, intra-procedural control flow information is removed from these representations, which sacrifices analysis precision to improve analysis performance. We show an approach for keeping control flow related information even in sparse program representations by representing control flow effects as operations on the data transferred, i.e., as dataflow information. These operations affect distinct paths of the program differently, thus yielding a certain degree of path-sensitivity. Our approach works with both WPP2G and P2SSA representations. We apply the approach to P2SSA-based and flow-sensitive points-to analysis and evaluate a context-insensitive and a context-sensitive variant. We assess our approach using abstract precision metrics. Moreover, we investigate the precision improvements and performance penalties when used as an input to three source-code-level analyses: dead code, cast safety, and null pointer analysis.
Main 96: an Interactive Software for Density Modiications, Model Building, Structure Reenement and Analysis

MAIN is a molecular graphics program, which provides outstanding graphical presentation of molecular and map images, designed to deal interactively with complex tasks of macromolecular crystallography. With MAIN it is possible to perform density modiications, model building, structure reenement and various types of molecular structure analysis. The prompt availability of various analytical tools and immediate visualisation of molecular and map objects allows a user to eeciently reduce number errors. It is a single executable program that intensively uses computer hardware resources, with a general premise that combined use of computational methods and molecular graphics signiicantly simpliies and speeds up a structure determination process.
Robust adaptive dynamic surface control for metal cutting system with hysteresis input

In this paper, a new robust adaptive dynamic surface control is proposed for the tuning metal cutting system with PI hysteresis input. The main advantages of our control scheme are that: the control precision can be arbitrarily prespecified by a tracking error performance function which guaranteed the system tracking performance; the computational burden can be greatly reduced and the explosion of complicity problem inherent in the backstepping control can be eliminated. It is proved that our new scheme can guarantee all the signals in the tuning metal cutting system can be uniformly ultimately bounded. Simulation results show its effectiveness to the tuning metal cutting system.
Motion analysis: model selection and motion segmentation

A new model selection criterion based on physical characteristics of underlying motion models is proposed. The proposed criterion is then incorporated in a robust motion segmentation scheme, which is based upon robust least K-th order statistical model fitting. The proposed model criterion has been compared with many other competing techniques and is shown to be more suitable for the motion segmentation task. The motion segmentation algorithm has been tested (and shown to be successful) on a number of synthetic and real image sequences.
Fencing Training Decision Support System Based on Bayesian Network

In order to coordinate with and promote the scientific process of the National Fencing Team, we developed the decision support system for training. In fencing training, we established a two-way reasoning model based on Bayesian Network and found the relationship between training process and physiological indicators. Combined with experienced knowledge and sample data, we did research on knowledge representation, learning methods and reasoning functions of network in this model and ultimately got the formation of a complete set of training process management and analysis system. We took the women's epee team as experimental data and did performance comparison with the BP neural network method. The experiments showed that the model can provide effective decision support for coaches. Keywords-sports mathematical modeling; decision support system; Bayesian Network; fencing training optimization; physiological indicator
Multidimensional Optimality of Microbial Metabolism

Metabolic Networking Understanding complex biological networks, such as those underlying cellular metabolism, requires evaluation not only of the network connections but also the flux through the various biochemical pathways. Schuetz et al. (p. 601) explored the evolutionary constraints that appear to be most critical for the metabolic network in the bacteria Escherichia coli using a combination of experimental tests of reaction flux under various conditions along with mathematical modeling. As a pathway evolves, there are likely to be competing objectives that must be satisfied. Key objectives for the bacterium were strong performance under a given environmental condition, balanced by a requirement for adaptability—minimizing the adjustments required to respond to changed conditions. A key design principle of bacterial metabolic networks is optimal performance, but not at the expense of adaptability. Although the network topology of metabolism is well known, understanding the principles that govern the distribution of fluxes through metabolism lags behind. Experimentally, these fluxes can be measured by 13C-flux analysis, and there has been a long-standing interest in understanding this functional network operation from an evolutionary perspective. On the basis of 13C-determined fluxes from nine bacteria and multi-objective optimization theory, we show that metabolism operates close to the Pareto-optimal surface of a three-dimensional space defined by competing objectives. Consistent with flux data from evolved Escherichia coli, we propose that flux states evolve under the trade-off between two principles: optimality under one given condition and minimal adjustment between conditions. These principles form the forces by which evolution shapes metabolic fluxes in microorganisms’ environmental context.
Automatic Test Data Synthesis using UML Sequence Diagrams

Model based testing techniques are used to generate test scenarios from a behavioral description of system under tests. For a large and complex system, there are usually a large number of scenarios and hence a large number of test paths also called test speci¯cations. To automate test execution, each test speci¯cation should be aug- mented with appropriate test input data. In this paper, we propose an approach of synthesizing test data from the information embedded in model elements such as class diagrams, sequence diagrams and OCL constraints. In our approach, we enrich a se- quence diagram with attribute and constraint information derived from class diagram and OCL constraints and map it onto a structured composite graph called SCG. The test speci¯cations are then generated from SCG. For each test speci¯cation, we follow a constraint solving system to generate test data.
HOS Based Goodness-of-Fit Testing Signal Detection

Detection performance of cognitive radios can be significantly improved by utilizing Higher Order Statistics (HOS) in the spectrum sensing process. The additional usage of goodness-of-fit tests on the inspected frequency spectrum can further improve the signal detection. This letter proposes a novel detection method that uses goodness-of-fit tests on the skewness and kurtosis of the FFT power spectrum (empirically derived) to evaluate the signal existence. Testbed evaluations prove that the method adapts to the specific hardware noise characteristics. The result is powerful detection performance, i.e. the capability of detecting various signals even 32dB below the noise floor.