title: Polyunsaturated fatty acids, inflammation and immunity

abstract: The fatty acid composition of inflammatory and immune cells is sensitive to change according to the fatty acid composition of the diet. In particular, the proportion of different types of polyunsaturated fatty acids (PUFA) in these cells is readily changed, and this provides a link between dietary PUFA intake, inflammation, and immunity. The n-6 PUFA arachidonic acid (AA) is the precursor of prostaglandins, leukotrienes, and related compounds, which have important roles in inflammation and in the regulation of immunity. Fish oil contains the n-3 PUFA eicosapentaenoic acid (EPA). Feeding fish oil results in partial replacement of AA in cell membranes by EPA. This leads to decreased production of AA-derived mediators. In addition, EPA is a substrate for cyclooxygenase and lipoxygenase and gives rise to mediators that often have different biological actions or potencies than those formed from AA. Animal studies have shown that dietary fish oil results in altered lymphocyte function and in suppressed production of proinflammatory cytokines by macrophages. Supplementation of the diet of healthy human volunteers with fish oil-derived n-3 PUFA results in decreased monocyte and neutrophil chemotaxis and decreased production of proinflammatory cytokines. Fish oil feeding has been shown to ameliorate the symptoms of some animal models of autoimmune disease. Clinical studies have reported that fish oil supplementation has beneficial effects in rheumatoid arthritis, inflammatory bowel disease, and among some asthmatics, supporting the idea that the n-3 PUFA in fish oil are anti-inflammatory and immunomodulatory.


The Network Paradigm in Organizational Research: A Review and Typology


title: On minimizing budget and time in influence propagation over social networks

abstract: In recent years, study of influence propagation in social networks has gained tremendous attention. In this context, we can identify three orthogonal dimensions—the number of seed nodes activated at the beginning (known as budget), the expected number of activated nodes at the end of the propagation (known as expected spread or coverage), and the time taken for the propagation. We can constrain one or two of these and try to optimize the third. In their seminal paper, Kempe et al. constrained the budget, left time unconstrained, and maximized the coverage: this problem is known as Influence Maximization (or MAXINF for short). In this paper, we study alternative optimization problems which are naturally motivated by resource and time constraints on viral marketing campaigns. In the first problem, termed minimum target set selection (or MINTSS for short), a coverage threshold η is given and the task is to find the minimum size seed set such that by activating it, at least η nodes are eventually activated in the expected sense. This naturally captures the problem of deploying a viral campaign on a budget. In the second problem, termed MINTIME, the goal is to minimize the time in which a predefined coverage is achieved. More precisely, in MINTIME, a coverage threshold η and a budget threshold k are given, and the task is to find a seed set of size at most k such that by activating it, at least η nodes are activated in the expected sense, in the minimum possible time. This problem addresses the issue of timing when deploying viral campaigns. Both these problems are NP-hard, which motivates our interest in their approximation. For MINTSS, we develop a simple greedy algorithm and show that it provides a bicriteria approximation. We also establish a generic hardness result suggesting that improving this bicriteria approximation is likely to be hard. For MINTIME, we show that even bicriteria and tricriteria approximations are hard under several conditions. We show, however, that if we allow the budget for number of seeds k to be boosted by a logarithmic factor and allow the coverage to fall short, then the problem can be solved exactly in PTIME, i.e., we can achieve the required coverage within the time achieved by the optimal solution to MINTIME with budget k and coverage threshold η. Finally, we establish the value of the approximation algorithms, by conducting an experimental evaluation, comparing their quality against that achieved by various heuristics.


Evaluating Human Cognition of Containing Relations with Physical Simulation.


title: Applications of JAVA programming language to database management

abstract: The Java programming language [1,3] from its inception has been publicized as a web programming language. Many programmers have developed simple applications such as games, clocks, news tickers and stock tickers in order to create informative, innovative web sites. However, it is important to note that the Java programming language possesses much more capability. The language components and constructs originally designed to enhance the functionality of Java as a web-based programming language can be utilized in a broader extent. Java provides a developer with the tools allowing for the creation of innovative network, database, and Graphical User Interface (GUI) applications. In fact, Java and its associated technologies such as JDBC API [11,5], JDBC drivers [2,12], threading [10], and AWT provide the programmer with the much-needed assistance for the development of platform-independent database-independent interfaces. Thus, it is possible to build a graphical database interface capable of connecting and querying distributed databases [13,14]. Here are components that are important for building the database interface we have in mind.


title: Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling

abstract: We present new techniques for automatically constructing probabilistic programs for data analysis, interpretation, and prediction. These techniques work with probabilistic domain-specific data modeling languages that capture key properties of a broad class of data generating processes, using Bayesian inference to synthesize probabilistic programs in these modeling languages given observed data. We provide a precise formulation of Bayesian synthesis for automatic data modeling that identifies sufficient conditions for the resulting synthesis procedure to be sound. We also derive a general class of synthesis algorithms for domain-specific languages specified by probabilistic context-free grammars and establish the soundness of our approach for these languages. We apply the techniques to automatically synthesize probabilistic programs for time series data and multivariate tabular data. We show how to analyze the structure of the synthesized programs to compute, for key qualitative properties of interest, the probability that the underlying data generating process exhibits each of these properties. Second, we translate probabilistic programs in the domain-specific language into probabilistic programs in Venture, a general-purpose probabilistic programming system. The translated Venture programs are then executed to obtain predictions of new time series data and new multivariate data records. Experimental results show that our techniques can accurately infer qualitative structure in multiple real-world data sets and outperform standard data analysis methods in forecasting and predicting new data.


title: Mining roles with semantic meanings

abstract: With the growing adoption of role-based access control (RBAC) in commercial security and identity management products, how to facilitate the process of migrating a non-RBAC system to an RBAC system has become a problem with significant business impact. Researchers have proposed to use data mining techniques to discover roles to complement the costly top-down approaches for RBAC system construction. A key problem that has not been adequately addressed by existing role mining approaches is how to discover roles with semantic meanings. In this paper, we study the problem in two settings with different information availability. When the only information is user-permission relation, we propose to discover roles whose semantic meaning is based on formal concept lattices. We argue that the theory of formal concept analysis provides a solid theoretical foundation for mining roles from userpermission relation. When user-attribute information is also available, we propose to create roles that can be explained by expressions of user-attributes. Since an expression of attributes describes a real-world concept, the corresponding role represents a real-world concept as well. Furthermore, the algorithms we proposed balance the semantic guarantee of roles with system complexity. Our experimental results demonstrate the effectiveness of our approaches.


A Review of Biomedical Centrifugal Microfluidic Platforms


title: A functional adhesive robot skin with integrated micro rubber suction cups

abstract: The goal of this research is to develop a functional adhesive robot skin with micro suction cups, which realizes two new functions: adaptive adhesion to rough/curved surfaces and anisotropic adhesion. Both functions are realized by integration of asymmetric micro cups. This skin can be applied to various robot mechanisms such as robot hands, wall-climbing robot feet and so on as a kind of robot skins. This paper especially reports the concept of this adhesive robot skin and its fundamental characteristics. The experiments show the developed skin realizes novel characteristics, high adhesion even on rough surface and anisotropic adhesion.


title: Shape from Shading: A Survey

abstract: Since the first shape-from-shading (SFS) technique was developed by Horn in the early 1970s, many different approaches have emerged. In this paper, six well-known SFS algorithms are implemented and compared. The performance of the algorithms was analyzed on synthetic images using mean and standard deviation of depth (Z) error, mean of surface gradient (p, q) error, and CPU timing. Each algorithm works well for certain images, but performs poorly for others. In general, minimization approaches are more robust, while the other approaches are faster.


title: 3D Face Reconstruction by Learning from Synthetic Data

abstract: Fast and robust three-dimensional reconstruction of facial geometric structure from a single image is a challenging task with numerous applications. Here, we introduce a learning-based approach for reconstructing a three-dimensional face from a single image. Recent face recovery methods rely on accurate localization of key characteristic points. In contrast, the proposed approach is based on a Convolutional-Neural-Network (CNN) which extracts the face geometry directly from its image. Although such deep architectures outperform other models in complex computer vision problems, training them properly requires a large dataset of annotated examples. In the case of three-dimensional faces, currently, there are no large volume data sets, while acquiring such big-data is a tedious task. As an alternative, we propose to generate random, yet nearly photo-realistic, facial images for which the geometric form is known. The suggested model successfully recovers facial shapes from real images, even for faces with extreme expressions and under various lighting conditions.


title: Learning Permutations with Sinkhorn Policy Gradient

abstract: Many problems at the intersection of combinatorics and computer science require solving for a permutation that optimally matches, ranks, or sorts some data. These problems usually have a task-specific, often non-differentiable objective function that data-driven algorithms can use as a learning signal. In this paper, we propose the Sinkhorn Policy Gradient (SPG) algorithm for learning policies on permutation matrices. The actor-critic neural network architecture we introduce for SPG uniquely decouples representation learning of the state space from the highly-structured action space of permutations with a temperature-controlled Sinkhorn layer. The Sinkhorn layer produces continuous relaxations of permutation matrices so that the actor-critic architecture can be trained end-to-end. Our empirical results show that agents trained with SPG can perform competitively on sorting, the Euclidean TSP, and matching tasks. We also observe that SPG is significantly more data efficient at the matching task than the baseline methods, which indicates that SPG is conducive to learning representations that are useful for reasoning about permutations.


title: Performance measurements for the Microsoft Kinect skeleton

abstract: The Microsoft Kinect for Xbox 360 (“Kinect”) provides a convenient and inexpensive depth sensor and, with the Microsoft software development kit, a skeleton tracker (Figure 2). These have great potential to be useful as virtual environment (VE) control interfaces for avatars or for viewpoint control. In order to determine its suitability for our applications, we devised and conducted tests to measure standard performance specifications for tracking systems. We evaluated the noise, accuracy, resolution, and latency of the skeleton tracking software. We also measured the range in which the person being tracked must be in order to achieve these values.


Attitudes -1 The Construction of Attitudes


title: A theoretical analysis of camera response functions in image deblurring

abstract: Motion deblurring is a long standing problem in computer vision and image processing. In most previous approaches, the blurred image is modeled as the convolution of a latent intensity image with a blur kernel. However, for images captured by a real camera, the blur convolution should be applied to scene irradiance instead of image intensity and the blurred results need to be mapped back to image intensity via the camera's response function (CRF). In this paper, we present a comprehensive study to analyze the effects of CRFs on motion deblurring. We prove that the intensity-based model closely approximates the irradiance model at low frequency regions. However, at high frequency regions such as edges, the intensity-based approximation introduces large errors and directly applying deconvolution on the intensity image will produce strong ringing artifacts even if the blur kernel is invertible. Based on the approximation error analysis, we further develop a dual-image based solution that captures a pair of sharp/blurred images for both CRF estimation and motion deblurring. Experiments on synthetic and real images validate our theories and demonstrate the robustness and accuracy of our approach.


title: Calibration of a parallel robot using multiple kinematic closed loops

abstract: A method is presented for autonomous kinematic calibration of a 3-DOF redundant parallel robot. Multiple closed loops are used in a least squares optimization method. Ill-conditioning, column scaling of the gradient matrix, and observability indices for the best pose set of robot calibration configurations are discussed. Experimental results are presented and compared with the results using an external calibration device. >


title: Emergence of social networks via direct and indirect reciprocity

abstract: Many models of social network formation implicitly assume that network properties are static in steady-state. In contrast, actual social networks are highly dynamic: allegiances and collaborations expire and may or may not be renewed at a later date. Moreover, empirical studies show that human social networks are dynamic at the individual level but static at the global level: individuals’ degree rankings change considerably over time, whereas network-level metrics such as network diameter and clustering coefficient are relatively stable. There have been some attempts to explain these properties of empirical social networks using agent-based models in which agents play social dilemma games with their immediate neighbours, but can also manipulate their network connections to strategic advantage. However, such models cannot straightforwardly account for reciprocal behaviour based on reputation scores (“indirect reciprocity”), which is known to play an important role in many economic interactions. In order to account for indirect reciprocity, we model the network in a bottom-up fashion: the network emerges from the low-level interactions between agents. By so doing we are able to simultaneously account for the effect of both direct reciprocity (e.g. “tit-for-tat”) as well as indirect reciprocity (helping strangers in order to increase one’s reputation). This leads to a strategic equilibrium in the frequencies with which strategies are adopted in the population as a whole, but intermittent cycling over different strategies at the level of individual agents, which in turn gives rise to social networks which are dynamic at the individual level but stable at the network level.


title: Hierarchical regression analysis in structural equation modeling

abstract: In a hierarchical or fixed-order regression analysis, the independent variables are entered into the regression equation in a prespecified order. Such an analysis is often performed when the extra amount of variance accounted for in a dependent variable by a specific independent variable is the main focus of interest (e.g., Cohen & Cohen, 1983). For example, in the area of reading achievement, there is a general interest in the specific abilities that predict reading development. Because these specific abilities are often correlated with more general abilities, such as verbal intelligence, the latter abilities are controlled for first (e.g., Wagner, Torgesen, & Rashotte, 1994). An additional reason for performing a hierarchical regression analysis is that, in these research applications, as well as in many others, the independent variables are often highly correlated. When correlated independent variables are included simultaneously in the regression model, multicollinearity arises (Cohen & Cohen, 1983). Though regularly used with observed variables, hierarchical regression analysis has not been performed with latent variables. In most applications of structural equation modeling (SEM), the latent predictors have been entered simultaneously into the regression model, although in several cases hierarchical regression analysis would have been the more appropriate approach (e.g., Guthrie et al., 1998; Normandeau & Guay, 1998; Wagner et al., 1994; Wagner et al., 1997). In this article we describe how a hierarchical regression analysis may be conducted in SEM. The main procedure proposed is to perform a Cholesky or triangular decomposition of the intercorrelations among the latent predictors (Harman, 1976; Loehlin, 1996). First the procedure is described and then an example of a hierarchical regression analysis with latent variables is given. Copyright © 1999, Lawrence Erlbaum Associates, Inc.


Survey on DoS Attack Challenges in Software Defined Networking


A Comparative Study of Software Bugs in Clone and Non-Clone Code


title: Automated face swapping and its detection

abstract: Face has been used as one of the mainstream manners for user identification. However, with the popularity of face-swapping apps, it takes only a few seconds to change the faces between two facial images. Such swapped results, when using improperly or carelessly, might create some security issues in certain applications. This paper is the first work to address the importance of this issue and discusses the feasibility to achieve an automated face swapping detection through machine learning. Several approaches are tested on a face swapping database derived from a face benchmarking repository. The best solution in the experiments achieved a detection accuracy of over 92%.


title: Information structures and workflows in health care informatics

abstract: Patient data in health care have traditionally been used to support direct patient care. Although there is great potential in combining such data with genetic information from patients to improve d ...


title: Persuasive Design for Behaviour Change Apps: Issues for Designers

abstract: The main aim of this position paper is to identify and briefly discuss design-related issues commonly encountered with the implementation of both behaviour change techniques and persuasive design principles in physical activity smartphone applications. These overlapping issues highlight a disconnect in the perspectives held between health scientists' focus on the application of behaviour change theories and components of interventions, and the information systems designers' focus on the application of persuasive design principles as software design features intended to motivate, facilitate and support individuals through the behaviour change process. A review of the current status and some examples of these different perspectives is presented, leading to the identification of the main issues associated with this disconnection. The main behaviour change technique issues identified are concerned with: the fragmented integration of techniques, hindrances in successful use, diversity of user needs and preferences, and the informational flow and presentation. The main persuasive design issues identified are associated with: the fragmented application of persuasive design principles, hindrances in successful usage, diversity of user needs and preferences, informational flow and presentation, the lack of pragmatic guidance for application designers, and the maintenance of immersive user interactions and engagements. Given the common overlap across four of the identified issues, it is concluded that a methodological approach for integrating these two perspectives, and their associated issues, into a consolidated framework is necessary to address the apparent disconnect between these two independently-established, yet complementary fields.


Affordances: Clarifying and Evolving a Concept


title: I tweet honestly , I tweet passionately : Twitter users , context collapse , and the imagined audience

abstract: Social media technologies collapse multiple audiences into single contexts, making it difficult for people to use the same techniques online that they do to handle multiplicity in face-to-face conversation. This article investigates how content producers navigate ‘imagined audiences’ on Twitter. We talked with participants who have different types of followings to understand their techniques, including targeting different audiences, concealing subjects, and maintaining authenticity. Some techniques of audience management resemble the practices of ‘micro-celebrity’ and personal branding, both strategic self-commodification. Our model of the networked audience assumes a many-to-many communication through which individuals conceptualize an imagined audience evoked through their tweets.


title: The Cummins Model: A Framework for Teaching Nursing Students for Whom English is a Second Language

abstract: The health care system requires nurses with the language ability and the cultural knowledge to meet the health care needs of ethnic minority immigrants. The recruitment, admission, retention, and graduation of English as a Second Language (ESL) students are essential to provide the workforce to meet the demands of the multicultural community. Yet, ESL students possess language difficulties that affect their academic achievement in nursing programs. The application of the Cummins Model of language proficiency is discussed. The Cummins Model provides a framework for nursing faculty to develop educational support that meets the learning needs of ESL students.


title: The Pleiades: the celestial herd of ancient timekeepers

abstract: In the ancient Egypt seven goddesses, represented by seven cows, composed the celestial herd that provides the nourishment to her worshippers. This herd is observed in the sky as a group of stars, the Pleiades, close to Aldebaran, the main star in the Taurus constellation. For many ancient populations, Pleiades were relevant stars and their rising was marked as a special time of the year. In this paper, we will discuss the presence of these stars in ancient cultures. Moreover, we will report some results of archeoastronomy on the role for timekeeping of these stars, results which show that for hunter-gatherers at Palaeolithic times, they were linked to the seasonal cycles of aurochs.


title: A Dependency Graph Approach for Fault Detection and Localization Towards Secure Smart Grid

abstract: Fault diagnosis in power grids is known to be challenging, due to the massive scale and spatial coupling therein. In this study, we explore multiscale network inference for fault detection and localization. Specifically, we model the phasor angles across the buses as a Markov random field (MRF), where the conditional correlation coefficients of the MRF are quantified in terms of the physical parameters of power systems. Based on the MRF model, we then study decentralized network inference for fault diagnosis, through change detection and localization in the conditional correlation matrix of the MRF. Particularly, based on the hierarchical topology of practical power systems, we devise a multiscale network inference algorithm that carries out fault detection and localization in a decentralized manner. Simulation results are used to demonstrate the effectiveness of the proposed approach.


title: Gender violence: transgender experiences with violence and discrimination.

abstract: There is a pervasive pattern of discrimination and prejudice against transgendered people within society. Both economic discrimination and experiencing violence could be the result of a larger social climate that severely sanctions people for not conforming to society's norms concerning gender; as such, both would be strongly associated with each other. Questionnaires were distributed to people either through events or through volunteers, and made available upon the World Wide Web. A sample of 402 cases was collected over the span of 12 months (April 1996-April 1997). We found that over half the people within this sample experienced some form of harassment or violence within their lifetime, with a quarter experiencing a violent incident. Further investigation found that experiencing economic discrimination because one is transgendered had the strongest association with experiencing a transgender related violent incident. Economic discrimination was related to transgendered people's experience wit...


title: A MULTIPLE SCALE MODEL FOR TUMOR GROWTH

abstract: We present a physiologically structured lattice model for vascular tumor growth which accounts for blood flow and structural adaptation of the vasculature, transport of oxygen, interaction between cancerous and normal tissue, cell division, apoptosis, vascular endothelial growth factor release, and the coupling between these processes. Simulations of the model are used to investigate the effects of nutrient heterogeneity, growth and invasion of cancerous tissue, and emergent growth laws.


title: Delta oscillation and short-term plasticity in the rat medial prefrontal cortex: modelling NMDA hypofunction of schizophrenia

abstract: Dysfunction of the prefrontal cortex (PFC) is considered to be an important factor contributing to a decrease in cognitive performance of schizophrenia patients. The medial PFC (mPFC) is innervated by the hippocampus/subiculum, and the subiculum–mPFC pathway is known to be involved in various cognitive processes. Glutamate-containing subicular axons innervate cortical pyramidal neurons and interneurons where AMPA and NMDA receptors are implicated in synaptic transmission. In our experiments, properties of subiculum–mPFC interactions were studied using pathway stimulation and local field potential (LFP) recordings of the mPFC in urethane-anaesthetized rats. Changes in paired-pulse facilitation (PPF) and LFP oscillations, effects of the NMDA receptor antagonist MK-801, and the AMPAkine LY451395 were evaluated. Effects of disruption of the thalamo-cortical loop with local microinjection of lidocaine into the mediodorsal thalamic nucleus (MD) were also studied. Our findings demonstrate that both systemic administration of MK-801 and local MD lidocaine microinjection produce similar changes in LFP oscillations and reduction in PPF. Specifically, it was observed that MK-801 (0.05 mg/kg i.v.) and intra-thalamic lidocaine changed regular, 2 Hz delta oscillation to a less regular 0.5–1.5 Hz delta rhythm. Concurrently, PPF in response to electrical stimulation of the subiculum was significantly attenuated. Administration of the AMPAkine LY451395 (0.01 mg/kg i.v.) reversed the MK-801- and lidocaine-induced changes, and was itself blocked by the AMPA receptor antagonist CP-465022. Analysis of our findings suggests a critical role of cortical interneurons in NMDA/AMPA receptor-mediated changes in thalamo-cortical oscillations and PPF, and contributes to our understanding of the NMDA hypofunction model of schizophrenia.


title: EEG-based decoding of error-related brain activity in a real-world driving task.

abstract: Objectives. Recent studies have started to explore the implementation of brain–computer interfaces (BCI) as part of driving assistant systems. The current study presents an EEG-based BCI that decodes error-related brain activity. Such information can be used, e.g., to predict driver's intended turning direction before reaching road intersections. Approach. We executed experiments in a car simulator (N = 22) and a real car (N = 8). While subject was driving, a directional cue was shown before reaching an intersection, and we classified the presence or not of an error-related potentials from EEG to infer whether the cued direction coincided with the subject's intention. In this protocol, the directional cue can correspond to an estimation of the driving direction provided by a driving assistance system. We analyzed ERPs elicited during normal driving and evaluated the classification performance in both offline and online tests. Results. An average classification accuracy of 0.698 ± 0.065 was obtained in offline experiments in the car simulator, while tests in the real car yielded a performance of 0.682 ± 0.059. The results were significantly higher than chance level for all cases. Online experiments led to equivalent performances in both simulated and real car driving experiments. These results support the feasibility of decoding these signals to help estimating whether the driver's intention coincides with the advice provided by the driving assistant in a real car. Significance. The study demonstrates a BCI system in real-world driving, extending the work from previous simulated studies. As far as we know, this is the first online study in real car decoding driver's error-related brain activity. Given the encouraging results, the paradigm could be further improved by using more sophisticated machine learning approaches and possibly be combined with applications in intelligent vehicles.


title: Cost effective digitization of home appliances for home automation with low-power WiFi devices

abstract: The notion of a smart home with integrated sensors, actuators, wireless network and a graphical user interface is very enticing. This paper presents the design and implementation of reliable, flexible, secure and economical sensor network for transforming traditional home into a smart home. The heterogeneous sensor and actuator nodes based on wireless networking technologies are deployed in the home environment. These nodes communicate to a middleware which runs home automation server and which operates as broker to facilitate the MQTT connectivity protocol. The middleware facilitates controlling of wireless nodes over local as well as remote network. The proposed system is designed to be low cost and scalable to accommodate variety of devices to be controlled.


title: Local Spectral Clustering for Overlapping Community Detection

abstract: Large graphs arise in a number of contexts and understanding their structure and extracting information from them is an important research area. Early algorithms for mining communities have focused on global graph structure, and often run in time proportional to the size of the entire graph. As we explore networks with millions of vertices and find communities of size in the hundreds, it becomes important to shift our attention from macroscopic structure to microscopic structure in large networks. A growing body of work has been adopting local expansion methods in order to identify communities from a few exemplary seed members. ::: ::: In this article, we propose a novel approach for finding overlapping communities called Lemon (Local Expansion via Minimum One Norm). Provided with a few known seeds, the algorithm finds the community by performing a local spectral diffusion. The core idea of Lemon is to use short random walks to approximate an invariant subspace near a seed set, which we refer to as local spectra. Local spectra can be viewed as the low-dimensional embedding that captures the nodes’ closeness in the local network structure. We show that Lemon’s performance in detecting communities is competitive with state-of-the-art methods. Moreover, the running time scales with the size of the community rather than that of the entire graph. The algorithm is easy to implement and is highly parallelizable. We further provide theoretical analysis of the local spectral properties, bounding the measure of tightness of extracted community using the eigenvalues of graph Laplacian. ::: ::: We thoroughly evaluate our approach using both synthetic and real-world datasets across different domains, and analyze the empirical variations when applying our method to inherently different networks in practice. In addition, the heuristics on how the seed set quality and quantity would affect the performance are provided.


title: Review of XY Routing Algorithm for Network-on-Chip Architecture

abstract: The Network-on-Chip (NoC) is Network-version of Systemon-Chip (SoC) means that on-chip communication is done through packet based networks. In NOC topology, routing algorithm and switching are main terminology .The routing algorithm is one of the key factor in NOC architecture. The routing algorithm ,which defines as the path taken by a packet between the source and the destination .As XY routing algorithm mainly used in NOC because of its simplicity. This paper basically review of XY routing algorithm in which we study a different type of XY routing algorithm. The classification of XY routing algorithm is totally depend upon the environment and requirement. Such that IX/Y routing algorithm is for less collision in network, for deadlock-free and livelock-free DyXY is used, for fault-tolerant XYX routing algorithm is proposed and Adaptive XY routing algorithm is used for fully utilization of network resource.


title: Foster b-trees

abstract: Foster B-trees are a new variant of B-trees that combines advantages of prior B-tree variants optimized for many-core processors and modern memory hierarchies with flash storage and nonvolatile memory. Specific goals include: (i) minimal concurrency control requirements for the data structure, (ii) efficient migration of nodes to new storage locations, and (iii) support for continuous and comprehensive self-testing. Like Blink-trees, Foster B-trees optimize latching without imposing restrictions or specific designs on transactional locking, for example, key range locking. Like write-optimized B-trees, and unlike Blink-trees, Foster B-trees enable large writes on RAID and flash devices as well as wear leveling and efficient defragmentation. Finally, they support continuous and inexpensive yet comprehensive verification of all invariants, including all cross-node invariants of the B-tree structure. An implementation and a performance evaluation show that the Foster B-tree supports high concurrency and high update rates without compromising consistency, correctness, or read performance.


title: Improving malware classification: bridging the static/dynamic gap

abstract: Malware classification systems have typically used some machine learning algorithm in conjunction with either static or dynamic features collected from the binary. Recently, more advanced malware has introduced mechanisms to avoid detection in these views by using obfuscation techniques to avoid static detection and execution-stalling techniques to avoid dynamic detection. In this paper we construct a classification framework that is able to incorporate both static and dynamic views into a unified framework in the hopes that, while a malicious executable can disguise itself in some views, disguising itself in every view while maintaining malicious intent will prove to be substantially more difficult. Our method uses kernels to place a similarity metric on each distinct view and then employs multiple kernel learning to find a weighted combination of the data sources which yields the best classification accuracy in a support vector machine classifier. Our approach opens up new avenues of malware research which will allow the research community to elegantly look at multiple facets of malware simultaneously, and which can easily be extended to integrate any new data sources that may become popular in the future.


title: Vertex Stimulation as a Control Site for Transcranial Magnetic Stimulation: A Concurrent TMS/fMRI Study

abstract: Abstract Background A common control condition for transcranial magnetic stimulation (TMS) studies is to apply stimulation at the vertex. An assumption of vertex stimulation is that it has relatively little influence over on-going brain processes involved in most experimental tasks, however there has been little attempt to measure neural changes linked to vertex TMS. Here we directly test this assumption by using a concurrent TMS/fMRI paradigm in which we investigate fMRI blood-oxygenation-level-dependent (BOLD) signal changes across the whole brain linked to vertex stimulation. Methods Thirty-two healthy participants to part in this study. Twenty-one were stimulated at the vertex, at 120% of resting motor threshold (RMT), with short bursts of 1 Hz TMS, while functional magnetic resonance imaging (fMRI) BOLD images were acquired. As a control condition, we delivered TMS pulses over the left primary motor cortex using identical parameters to 11 other participants. Results Vertex stimulation did not evoke increased BOLD activation at the stimulated site. By contrast we observed widespread BOLD deactivations across the brain, including regions within the default mode network (DMN). To examine the effects of vertex stimulation a functional connectivity analysis was conducted. Conclusion The results demonstrated that stimulating the vertex with suprathreshold TMS reduced neural activity in brain regions related to the DMN but did not influence the functional connectivity of this network. Our findings provide brain imaging evidence in support of the use of vertex simulation as a control condition in TMS but confirm that vertex TMS induces regional widespread decreases in BOLD activation.


title: Opinion mining from noisy text data

abstract: The proliferation of Internet has not only generated huge volumes of unstructured information in the form of web documents, but a large amount of text is also generated in the form of emails, blogs, and feedbacks etc. The data generated from online communication acts as potential gold mines for discovering knowledge. Text analytics has matured and is being successfully employed to mine important information from unstructured text documents. Most of these techniques use Natural Language Processing techniques which assume that the underlying text is clean and correct. Statistical techniques, though not as accurate as linguistic mechanisms, are also employed for the purpose to overcome the dependence on clean text. The chief bottleneck for designing statistical mechanisms is however its dependence on appropriately annotated training data. None of these methodologies are suitable for mining information from online communication text data due to the fact that they are often noisy. These texts are informally written. They suffer from spelling mistakes, grammatical errors, improper punctuation and irrational capitalization. This paper focuses on opinion extraction from noisy text data. It is aimed at extracting and consolidating opinions of customers from blogs and feedbacks, at multiple levels of granularity. Ours is a hybrid approach, in which we initially employ a semi-supervised method to learn domain knowledge from a training repository which contains both noisy and clean text. Thereafter we employ localized linguistic techniques to extract opinion expressions from noisy text. We have developed a system based on this approach, which provides the user with a platform to analyze opinion expressions extracted from a repository.


title: The Future of Industrial Communication: Automation Networks in the Era of the Internet of Things and Industry 4.0

abstract: With the introduction of the Internet of Things (IoT) and cyberphysical system (CPS) concepts in industrial application scenarios, industrial automation is undergoing a tremendous change. This is made possible in part by recent advances in technology that allow interconnection on a wider and more fine-grained scale. The purpose of this article is to review technological trends and the impact they may have on industrial communication. We will review the impact of IoT and CPSs on industrial automation from an industry 4.0 perspective, give a survey of the current state of work on Ethernet time-sensitive networking (TSN), and shed light on the role of fifth-generation (5G) telecom networks in automation. Moreover, we will point out the need for harmonization beyond networking.


title: 3D-Subspace-Based Auto-Paired Azimuth Angle, Elevation Angle, and Range Estimation for 24G FMCW Radar with an L-Shaped Array

abstract: In this paper, a three-dimensional (3D)-subspace-based azimuth angle, elevation angle, and range estimation method with auto-pairing is proposed for frequency-modulated continuous waveform (FMCW) radar with an L-shaped array. The proposed method is designed to exploit the 3D shift-invariant structure of the stacked Hankel snapshot matrix for auto-paired azimuth angle, elevation angle, and range estimation. The effectiveness of the proposed method is verified through a variety of experiments conducted in a chamber. For the realization of the proposed method, K-band FMCW radar is implemented with an L-shaped antenna.


title: Try Walking in My Shoes, if You Can: Accurate Gait Recognition Through Deep Learning

abstract: Human gait seamless continuous authentication, based on wearable accelerometers, is a novel biometric instrument which can be exploited to identify the user of mobile and wearable devices. In this paper, we present a study on recognition of user identity, by analysis of gait data, collected through body inertial sensors from 175 different users. The mechanism used for identity recognition is based on deep learning machinery, specifically on a convolutional network, trained with readings from different sensors, and on filtering and buffering mechanism to increase the accuracy. Results show a very high accuracy in both recognizing known and unknown identities.


title: Improving Neural Language Models with a Continuous Cache

abstract: We propose an extension to neural network language models to adapt their prediction to the recent history. Our model is a simplified version of memory augmented networks, which stores past hidden activations as memory and accesses them through a dot product with the current hidden activation. This mechanism is very efficient and scales to very large memory sizes. We also draw a link between the use of external memory in neural network and cache models used with count based language models. We demonstrate on several language model datasets that our approach performs significantly better than recent memory augmented networks.


title: Compact Tensor Pooling for Visual Question Answering

abstract: Performing high level cognitive tasks requires the integration of feature maps with drastically different structure. In Visual Question Answering (VQA) image descriptors have spatial structures, while lexical inputs inherently follow a temporal sequence. The recently proposed Multimodal Compact Bilinear pooling (MCB) forms the outer products, via count-sketch approximation, of the visual and textual representation at each spatial location. While this procedure preserves spatial information locally, outer-products are taken independently for each fiber of the activation tensor, and therefore do not include spatial context. In this work, we introduce multi-dimensional sketch ({MD-sketch}), a novel extension of count-sketch to tensors. Using this new formulation, we propose Multimodal Compact Tensor Pooling (MCT) to fully exploit the global spatial context during bilinear pooling operations. Contrarily to MCB, our approach preserves spatial context by directly convolving the MD-sketch from the visual tensor features with the text vector feature using higher order FFT. Furthermore we apply MCT incrementally at each step of the question embedding and accumulate the multi-modal vectors with a second LSTM layer before the final answer is chosen.


title: Changes in Expression of Prolactin- and Cortisol-receptor Genes during Early-life Stages of Euryhaline Tilapia (Oreochromis mossambicus) in Fresh Water and Seawater

abstract: Abstract Expression of prolactin receptor (PRLR) and cortisol receptor (CR) mRNAs was examined during early-life stages of euryhaline Mozambique tilapia (Oreochromis mossambicus) by competitive reverse transcription-polymerase chain reaction (cRT-PCR). Concentration of prolactin receptor mRNA was higher in the gills of mature fish reared in fresh water (FW) than in those reared in seawater (SW), whereas no difference was seen in CR mRNA. Whole eggs just after fertilization contained the receptor mRNAs for both prolactin and cortisol. The concentration of PRLR mRNA increased gradually as the embryo grows both in FW and in SW. On the other hand, the concentration of CR mRNA was highest in the egg just after fertilization, decreased rapidly toward hatching, and increased slightly thereafter. When embryos 3 days before hatching were transferred to SW, the levels of PRLR mRNA were significantly lower at the time of hatching and also 3 days after hatching than in the embryo and larvae maintained in FW. Environm...


title: Multiuser quantum communication networks

abstract: We study a quantum state transfer between spins interacting with an arbitrary network of spins coupled by uniform XX interactions. It is shown that in such a system under fairly general conditions, we can expect a nearly perfect transfer of states. Then we analyze a generalization of this model to the case of many network users, where the sender can choose which party he wants to communicate with by appropriately tuning his local magnetic field. We also remark that a similar idea can be used to create an entanglement between several spins coupled to the network.


title: Comparison of Relational Database with Document-Oriented Database (MongoDB) for Big Data Applications

abstract: Database can accommodate a very large number of users on an on-demand basis. The main limitations with conventional relational database management systems (RDBMS) are that they are hard to scale with Data warehousing, Grid, Web 2.0 and Cloud applications, have non-linear query execution time, have unstable query plans and have static schema. Even though RDBMS's have provided database users with the best mix of simplicity, robustness, flexibility, performance, scalability and compatibility but they are not able to satisfy the present day users and applications for the reasons mentioned above. The next generation NonSQL (NoSQL) databases are mostly non-relational, distributed and horizontally scalable and are able to satisfy most of the needs of the present day applications. The main characteristics of these databases are schema-free, no join, non-relational, easy replication support, simple API and eventually consistent. The aim of this paper is to illustrate how a problem being solved using MySQL will perform when MongoDB is used on a Big data dataset. The results are encouraging and clearly showcase the comparisons made. Queries are executed on a big data airlines database using both MongoDB and MySQL. Select, update, delete and insert queries are executed and performance is evaluated.


title: Solid texture synthesis from 2D exemplars

abstract: We present a novel method for synthesizing solid textures from 2D texture exemplars. First, we extend 2D texture optimization techniques to synthesize 3D texture solids. Next, the non-parametric texture optimization approach is integrated with histogram matching, which forces the global statistics of the synthesized solid to match those of the exemplar. This improves the convergence of the synthesis process and enables using smaller neighborhoods. In addition to producing compelling texture mapped surfaces, our method also effectively models the material in the interior of solid objects. We also demonstrate that our method is well-suited for synthesizing textures with a large number of channels per texel.


title: Spatio-temporal (S-T) similarity model for constructing WIFI-based RSSI fingerprinting map for indoor localization

abstract: WIFI-based received signal strength indicator (RSSI) fingerprinting is widely used for indoor localization due to desirable features such as universal availability, privacy protection, and low deployment cost. The key of RSSI fingerprinting is to construct a trustworthy RSSI map, which contains the measurements of received access point (AP) signal strengths at different calibration points. Location can be estimated by matching live RSSIs with the RSSI map. However, a fine-grained map requires much labor and time. This calls for developing efficient interpolation and approximation methods. Besides, due to environmental changes, the RSSI map requires periodical updates to guarantee localization accuracy. In this paper, we propose a spatio-temporal (S-T) similarity model which uses the S-T correlation to construct a fine-grained and up-to-date RSSI map. Five S-T correlation metrics are proposed, i.e., the spatial distance, signal similarity, similarity likelihood, RSSI vector distance, and the S-T reliability. This model is evaluated based on experiments in our indoor WIFI positioning system test bed. Results show improvements in both the interpolation accuracy (up to 7%) and localization accuracy (up to 32%), compared to four commonly used RSSI map construction methods, namely, linear interpolation, cubic interpolation, nearest neighbor interpolation, and compressive sensing.


title: Spectral feature selection for supervised and unsupervised learning

abstract: Feature selection aims to reduce dimensionality for building comprehensible learning models with good generalization performance. Feature selection algorithms are largely studied separately according to the type of learning: supervised or unsupervised. This work exploits intrinsic properties underlying supervised and unsupervised feature selection algorithms, and proposes a unified framework for feature selection based on spectral graph theory. The proposed framework is able to generate families of algorithms for both supervised and unsupervised feature selection. And we show that existing powerful algorithms such as ReliefF (supervised) and Laplacian Score (unsupervised) are special cases of the proposed framework. To the best of our knowledge, this work is the first attempt to unify supervised and unsupervised feature selection, and enable their joint study under a general framework. Experiments demonstrated the efficacy of the novel algorithms derived from the framework.


title: Why Artificial Intelligence Needs a Task Theory --- And What It Might Look Like

abstract: The concept of “task” is at the core of artificial intelligence (AI): Tasks are used for training and evaluating AI systems, which are built in order to perform and automatize tasks we deem useful. In other fields of engineering theoretical foundations allow thorough evaluation of designs by methodical manipulation of well understood parameters with a known role and importance; this allows an aeronautics engineer, for instance, to systematically assess the effects of wind speed on an airplane’s performance and stability. No framework exists in AI that allows this kind of methodical manipulation: Performance results on the few tasks in current use (cf. board games, question-answering) cannot be easily compared, however similar or different. The issue is even more acute with respect to artificial general intelligence systems, which must handle unanticipated tasks whose specifics cannot be known beforehand. A task theory would enable addressing tasks at the class level, bypassing their specifics, providing the appropriate formalization and classification of tasks, environments, and their parameters, resulting in more rigorous ways of measuring, comparing, and evaluating intelligent behavior. Even modest improvements in this direction would surpass the current ad-hoc nature of machine learning and AI evaluation. Here we discuss the main elements of the argument for a task theory and present an outline of what it might look like for physical tasks.


Freescale Semiconductor Application Note AN 3059 Rev . 0 , 1 / 2006 Channel Estimation in OFDM Systems


title: Face attribute prediction using off-the-shelf CNN features

abstract: Predicting attributes from face images in the wild is a challenging computer vision problem. To automatically describe face attributes from face containing images, traditionally one needs to cascade three technical blocks --- face localization, facial descriptor construction, and attribute classification --- in a pipeline. As a typical classification problem, face attribute prediction has been addressed using deep learning. Current state-of-the-art performance was achieved by using two cascaded Convolutional Neural Networks (CNNs), which were specifically trained to learn face localization and attribute description. In this paper, we experiment with an alternative way of employing the power of deep representations from CNNs. Combining with conventional face localization techniques, we use off-the-shelf architectures trained for face recognition to build facial descriptors. Recognizing that the describable face attributes are diverse, our face descriptors are constructed from different levels of the CNNs for different attributes to best facilitate face attribute prediction. Experiments on two large datasets, LFWA and CelebA, show that our approach is entirely comparable to the state-of-the-art. Our findings not only demonstrate an efficient face attribute prediction approach, but also raise an important question: how to leverage the power of off-the-shelf CNN representations for novel tasks.


title: VirtualPower: coordinated power management in virtualized enterprise systems

abstract: Power management has become increasingly necessary in large-scale datacenters to address costs and limitations in cooling or power delivery. This paper explores how to integrate power management mechanisms and policies with the virtualization technologies being actively deployed in these environments. The goals of the proposed VirtualPower approach to online power management are (i) to support the isolated and independent operation assumed by guest virtual machines (VMs) running on virtualized platforms and (ii) to make it possible to control and globally coordinate the effects of the diverse power management policies applied by these VMs to virtualized resources. To attain these goals, VirtualPower extends to guest VMs `soft' versions of the hardware power states for which their policies are designed. The resulting technical challenge is to appropriately map VM-level updates made to soft power states to actual changes in the states or in the allocation of underlying virtualized hardware. An implementation of VirtualPower Management (VPM) for the Xen hypervisor addresses this challenge by provision of multiple system-level abstractions including VPM states, channels, mechanisms, and rules. Experimental evaluations on modern multicore platforms highlight resulting improvements in online power management capabilities, including minimization of power consumption with little or no performance penalties and the ability to throttle power consumption while still meeting application requirements. Finally, coordination of online methods for server consolidation with VPM management techniques in heterogeneous server systems is shown to provide up to 34% improvements in power consumption.


title: Online detection of abnormal events using incremental coding length

abstract: We present an unsupervised approach for abnormal event detection in videos. We propose, given a dictionary of features learned from local spatiotemporal cuboids using the sparse coding objective, the abnormality of an event depends jointly on two factors: the frequency of each feature in reconstructing all events (or, rarity of a feature) and the strength by which it is used in reconstructing the current event (or, the absolute coefficient). The Incremental Coding Length (ICL) of a feature is a measure of its entropy gain. Given a dictionary, the ICL computation does not involve any parameter, is computationally efficient and has been used for saliency detection in images with impressive results. In this paper, the rarity of a dictionary feature is learned online as its average energy, a function of its ICL. The proposed approach is applicable to real world streaming videos. Experiments on three benchmark datasets and evaluations in comparison with a number of mainstream algorithms show that the approach is comparable to the state-of-the-art.


title: Active subgroup mining: a case study in coronary

abstract: This paper presents an approach to active mining of patient records aimed at discovering patient groups at high risk for coronary heart disease (CHD). The approach proposes active expert involvement in the following steps of the knowledge discovery process: data gathering, cleaning and transformation, subgroup discovery, statistical characterization of induced subgroups, their interpretation, and the evaluation of results. As in the discovery and characterization of risk subgroups, the main risk factors are made explicit, the proposed methodology has high potential for patient screening and early detection of patient groups at risk for CHD.


title: Verifiable Cpd Paper: An audit improves the quality of water within the dental unit water lines of three separate facilities of a United Kingdom NHS Trust

abstract: OBJECTIVE ::: To improve the quality of water emanating from dental unit waterlines (DUWLs). ::: ::: ::: DESIGN ::: A prospective clinical audit. ::: ::: ::: SETTING ::: Three geographically separate district dental facilities of a United Kingdom NHS Trust, involving two community clinics and one hospital orthodontic department, which were evaluated between 2002 and 2004. ::: ::: ::: METHODS ::: Samples of water discharged from the DUWLs were collected prior to the start and midway through a morning session. These were tested microbiologically at a United Kingdom Accreditation Service testing laboratory within six hours of sampling. ::: ::: ::: INTERVENTIONS ::: One of the clinics followed the contemporaneous BDA advice of flushing water through its DUWLs while the other two clinics used separate intermittent disinfection purging regimes instead. One of them used a two stage protocol of Ethylene Diamine Tetra-Acetic acid followed by hydrogen peroxide, while the other used Bio 2000 as a single agent, which was subsequently superseded by the continuous use of super-oxidised water (Sterilox). ::: ::: ::: MAIN OUTCOME MEASURE ::: To assess whether the samples either met the American Dental Association's guideline on the quality of DUWL water, or the more stringent European Union standards for potable (drinking) water. ::: ::: ::: RESULTS ::: The two units which used a disinfection regime both complied with the ADA guideline and the EU potable water standard. However, the unit which only flushed through its DUWLs without using a disinfectant failed to comply with either of them. After all three dental facilities subsequently standardised their DUWL disinfection regimes by using Bio 2000, the colony counts from all of the water samples thereafter remained well below the EU recommended level. The unit which progressed to using Sterilox as a continuous disinfectant achieved and maintained zero readings from its water samples. ::: ::: ::: CONCLUSIONS ::: Clinical audit can result in the improvement of the quality of water that is discharged through DUWLs, thereby minimising both the risk of cross infection to vulnerable patients, as well as to dental staff chronically exposed to contaminated aerosols.


title: An Understanding of the Impact of Gamification on Purchase Intentions

abstract: Gamification is a diverse application of game elements implemented by firms in various circumstances to motivate customers for frequent and loyal patronage. Due to the advancement of information and telecommunication technologies, more dynamic and interactive gamified experiences have been developed as strategies to retain customers and encourage repeat purchasing. As the increasing application of social and mobile games plays important trends in culture today, enterprises need to understand the value of adding the elements of games into customer’s every encounter. This study consolidates the different gamification design elements and demographics to explore purchase intentions of customers of different types. We reviewed the literature about game design and identified seven motivational elements that have been incorporated in the design of gamification. Using case analysis, we verified these elements and proposed hypotheses about gamification design for customers of different attributes. Further study is planned to test the hypotheses by collecting responses from 500 participants regarding their perceptions of the elements of the gamification activities. It’s hoped that the findings can provide a basis for enterprises to leverage the emerging strategy properly.


SIDE EFFECTS OF VIRTUAL REALITY EXPOSURE THERAPY ( VRET )


title: Extracting Aspect-Evaluation and Aspect-Of Relations in Opinion Mining

abstract: The technology of opinion extraction allows users to retrieve and analyze people’s opinions scattered over Web documents. We define an opinion unit as a quadruple consisting of the opinion holder, the subject being evaluated, the part or the attribute in which the subject is evaluated, and the value of the evaluation that expresses a positive or negative assessment. We use this definition as the basis for our opinion extraction task. We focus on two important subtasks of opinion extraction: (a) extracting aspect-evaluation relations, and (b) extracting aspect-of relations, and we approach each task using methods which combine contextual and statistical clues. Our experiments on Japanese weblog posts show that the use of contextual clues improve the performance for both tasks.


title: Unbiased online active learning in data streams

abstract: Unlabeled samples can be intelligently selected for labeling to minimize classification error. In many real-world applications, a large number of unlabeled samples arrive in a streaming manner, making it impossible to maintain all the data in a candidate pool. In this work, we focus on binary classification problems and study selective labeling in data streams where a decision is required on each sample sequentially. We consider the unbiasedness property in the sampling process, and design optimal instrumental distributions to minimize the variance in the stochastic process. Meanwhile, Bayesian linear classifiers with weighted maximum likelihood are optimized online to estimate parameters. In empirical evaluation, we collect a data stream of user-generated comments on a commercial news portal in 30 consecutive days, and carry out offline evaluation to compare various sampling strategies, including unbiased active learning, biased variants, and random sampling. Experimental results verify the usefulness of online active learning, especially in the non-stationary situation with concept drift.


title: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers

abstract: In this paper, we investigate two new strategies to detect objects accurately and efficiently using deep convolutional neural network: 1) scale-dependent pooling and 2) layerwise cascaded rejection classifiers. The scale-dependent pooling (SDP) improves detection accuracy by exploiting appropriate convolutional features depending on the scale of candidate object proposals. The cascaded rejection classifiers (CRC) effectively utilize convolutional features and eliminate negative object proposals in a cascaded manner, which greatly speeds up the detection while maintaining high accuracy. In combination of the two, our method achieves significantly better accuracy compared to other state-of-the-arts in three challenging datasets, PASCAL object detection challenge, KITTI object detection benchmark and newly collected Inner-city dataset, while being more efficient.


title: Robust facial landmark tracking via cascade regression

abstract: Recently, tremendous improvements have been achieved for facial landmark localization on static images. However, detecting and tracking facial shapes in sequential images is still challenging due to the large appearance variations in unconstrained videos. To address this issue, we present a robust facial landmark tracking system via cascade regression, which is able to deal well with some challenges emerging in the sequential images. Specially, our system employs a pose-based cascade shape regression model to predict the facial landmark locations. Pose-based cascade shape regression model decreases the shape variances in the model learning stage, making the learned regression model more robust to the large pose variances. In addition, we explore a pose tracking model to enhance the temporal consecutiveness between the adjacent frames, and leverage the Kalman filter to make the predicted shape more smooth and stable. Finally, we incorporate a re-initialization mechanism with the facial landmarks as the position priors into the system, which is able to effectively and accurately locate the face when it is misaligned or lost. Experiments on the LFPW, Helen, 300W and 300VW datasets illustrate the superiority of proposed system over the state-of-the-art approaches, and it is worthy emphasizing that our method has won the 300VW competition in the category one. HighlightsA robust facial landmark tracking system via cascade regression has been proposed.Our method achieves competing results on the LFPW, Helen, 300-W and 300-VW datasets.Our method won the 300-VW competition in category one.


Decision Trees


title: Radar and vision data fusion for hybrid adaptive cruise control on highways

abstract: Abstract. A system for hybrid adaptive cruise control (HACC) on high-speed roads designed as a combination of a radar-based ACC and visual perception is presented. The system is conceived to run on different performance levels depending on the actual perception capabilities. The advantages of a combination of the two different types of sensors are discussed in comparison to the shortcomings of each single sensor. A description of the visual lane detection and tracking procedure is given, followed by an overview of the vehicle detection, hypothesis generation, and tracking procedure. Enhanced robustness is achieved by cooperative estimation of egomotion and the dynamics of other vehicles using the lane-coordinate system as a common reference. Afterwards, the assignment of vehicles to lanes and the determination of the relevant vehicle for the longitudinal controller is described.


title: Inflammation and α-Synuclein’s Prion-like Behavior in Parkinson's Disease—Is There a Link?

abstract: Parkinson’s disease patients exhibit progressive spreading of aggregated α-synuclein in the nervous system. This slow process follows a specific pattern in an inflamed tissue environment. Recent research suggests that prion-like mechanisms contribute to the propagation of α-synuclein pathology. Little is known about factors that might affect the prion-like behavior of misfolded α-synuclein. In this review, we suggest that neuroinflammation plays an important role. We discuss causes of inflammation in the olfactory bulb and gastrointestinal tract and how this may promote the initial misfolding and aggregation of α-synuclein, which might set in motion events that lead to Parkinson's disease neuropathology. We propose that neuroinflammation promotes the prion-like behavior of α-synuclein and that novel anti-inflammatory therapies targeting this mechanism could slow disease progression.


title: Array-Antenna Decoupling Surface

abstract: Massive multiple-input multiple-output (M-MIMO) technology is considered to be a key enabling technology for future wireless communication systems. One of the challenges in effectively implementing an advanced precoding scheme to a large-scale array antenna is how to reduce the mutual coupling among antenna elements. In this paper, a new concept that is called array-antenna decoupling surface (ADS) for reducing the mutual coupling between antenna elements in a large-scale array antenna is proposed for the first time. An ADS is a thin surface that is composed of a plurality of electrical small metal patches and is placed in front of the array antenna. The partially diffracted waves from the ADS can be controlled to cancel the unwanted coupled waves. Two practical design examples are given to illustrate the design process and considerations, and to demonstrate the usefulness of ADS for the applications of phased array antennas and M-MIMO systems when commonly used precoding schemes are applied. The attractive features of ADS include its applicability to a large-scale array antenna; suitability for a wide range of antenna forms; wide decoupling bandwidth; and simplicity in implementation.


title: Classification Techniques for Intrusion Detection – An Overview

abstract: Security is becoming a critical part of organizational information systems and security of a computer system or network is compromised when an intrusion takes place. In the field of computer networks security, the detection of threats or attacks is nowadays a critical problem to solve. Intrusion Detection Systems (IDS) have become a standard component in network security infrastructures and is an essential mechanism to protect computer systems from many attacks. In recent years, intrusion detection using data mining have attracted researchers more and more interests. Different researchers propose a different algorithm in different categories. Classifier construction is another research challenge to build an efficient intrusion detection system. KDDCup 1999 intrusion detection dataset plays a key role in fine tuning intrusion detection system and is most widely used by the researchers working in the field of intrusion detection. This paper presents an overview of intrusion detection, KDDCup’99 dataset and detailed analysis of classification techniques used in intrusion detection.


title: Design and use paradigms for Gazebo, an open-source multi-robot simulator

abstract: Simulators have played a critical role in robotics research as tools for quick and efficient testing of new concepts, strategies, and algorithms. To date, most simulators have been restricted to 2D worlds, and few have matured to the point where they are both highly capable and easily adaptable. Gazebo is designed to fill this niche by creating a 3D dynamic multi-robot environment capable of recreating the complex worlds that would be encountered by the next generation of mobile robots. Its open source status, fine grained control, and high fidelity place Gazebo in a unique position to become more than just a stepping stone between the drawing board and real hardware: data visualization, simulation of remote environments, and even reverse engineering of blackbox systems are all possible applications. Gazebo is developed in cooperation with the Player and Stage projects (Gerkey, B. P., et al., July 2003), (Gerkey, B. P., et al., May 2001), (Vaughan, R. T., et al., Oct. 2003), and is available from http://playerstage.sourceforge.net/gazebo/ gazebo.html.


title: BRAND: A robust appearance and depth descriptor for RGB-D images

abstract: This work introduces a novel descriptor called Binary Robust Appearance and Normals Descriptor (BRAND), that efficiently combines appearance and geometric shape information from RGB-D images, and is largely invariant to rotation and scale transform. The proposed approach encodes point information as a binary string providing a descriptor that is suitable for applications that demand speed performance and low memory consumption. Results of several experiments demonstrate that as far as precision and robustness are con- cerned, BRAND achieves improved results when compared to state of the art descriptors based on texture, geometry and combination of both information. We also demonstrate that our descriptor is robust and provides reliable results in a registration task even when a sparsely textured and poorly illuminated scene is used.


title: TreeSketch: interactive procedural modeling of trees on a tablet

abstract: TreeSketch is a system for modeling complex trees that look natural yet are creatively designed. The system integrates procedural tree generation with a multi-touch tablet interface that provides detailed control of tree form. The procedural component is based on the concept of tree self-organization and simulates competition of branches for space and light as the tree develops from a seedling. The modeler can control this process by directing growth with a procedural brush, changing parameters as the tree grows, interleaving controlled and autonomous growth, and editing generated forms. Complex trees can be created in a matter of seconds.


title: Towards Developing a Tool to Detect Phishing URLs: A Machine Learning Approach

abstract: Despite efforts to curb online fraud, there continues to be a significant proliferation of fraud in the online space. In the same vein, Phishing attacks are a significant and growing problem for users, and carrying out certain actions such as mouse hovering, clicking, etc., on malicious URLs may cause unwary users to unwittingly fall victim to identity theft and problems. In this paper, we propose a methodology that could be used towards developing an anti-phishing URL tool to thwart a phishing attack by either masking the potentially phishing URL or by alerting the user about the potential threat.


title: Authentication handover and privacy protection in 5G hetnets using software-defined networking

abstract: Recently, densified small cell deployment with overlay coverage through coexisting heterogeneous networks has emerged as a viable solution for 5G mobile networks. However, this multi-tier architecture along with stringent latency requirements in 5G brings new challenges in security provisioning due to the potential frequent handovers and authentications in 5G small cells and HetNets. In this article, we review related studies and introduce SDN into 5G as a platform to enable efficient authentication hand-over and privacy protection. Our objective is to simplify authentication handover by global management of 5G HetNets through sharing of userdependent security context information among related access points. We demonstrate that SDN-enabled security solutions are highly efficient through its centralized control capability, which is essential for delay-constrained 5G communications.


title: Suicide of a man with known allergy to fish protein by ingesting tinned fish

abstract: On a day in November, the body of a 31-year-old man was found near a swimming lake with two open and partly emptied fish tins lying next to him. Further investigations showed that the man had been allergic to fish protein and suffered from severe depression and drug psychosis. Already some days before the suicide, he had repeatedly asked for fish to kill himself. Although the results of the chemical and toxicological examinations were negative, the autopsy findings and histological tests suggest that death was caused by an anaphylactic reaction.


title: Design of low-cost 4W & 6W MMIC high power amplifiers for Ka-band modules

abstract: Two Ka-band power amplifier MMICs, 4W and 6W, with high power density and gain are presented. Each amplifier was designed using a 5-stage topology to demonstrate over 30dB of gain. The 4W design exhibited a peak saturated output power of 37.2dBm and a chip output power density of 532 mW/mm/sup 2/. This is the highest recorded power density for a Ka-band power amplifier design to date. The high gain and power density make them ideal for low-cost Ka-band transmit systems.


title: Factors influencing students’ acceptance of m-learning: An investigation in higher education

abstract: M-learning will play an increasingly significant role in the development of teaching and learning methods for higher education. However, the successful implementation of m-learning in higher education will be based on users’ acceptance of this technology. Thus, the purpose of this paper is to study the factors that affect university students’ intentions to accept m-learning. Based on the unified theory of acceptance and use of technology (UTAUT) (Venkatesh et al., 2003), this study proposes a model to identify the factors that influence the acceptance of m-learning in higher education and to investigate if prior experience of mobile devices affects the acceptance of m-learning. A structural equation model was used to analyse the data collected from 174 participants. The results indicate that performance expectancy, effort expectancy, influence of lecturers, quality of service, and personal innovativeness were all significant factors that affect behavioural intention to use m-learning. Prior experience of mobile devices was also found to moderate the effect of these constructs on behavioural intention. The results of this research extend the UTAUT in the context of m-learning acceptance by adding quality of service and personal innovativeness to the structure of UTAUT and provide practitioners and educators with useful guidelines for designing a successful m-learning system.


title: 348-GHz endfire slotline antennas on thin dielectric membranes

abstract: Tapered slotline endfire antennas, of the broken linearly tapered type, have been fabricated on 1.7- mu m-thin SiO/sub 2/-Si/sub 3/N/sub 4/ ( epsilon /sub r/=4.5) membranes. Antenna patterns in the E-, H-, D-, and D-cross planes have been measured at the design frequency of 348 GHz, with bismuth microbolometer detectors. The antennas have approximately 12-dB directivity, and the -10-dB beam widths are 50 degrees and 56 degrees in the E- and H-planes, respectively. The 348-GHz measurements have been compared with model measurements at 45 GHz, and show good agreement. >


title: Internet of Plants Application for Smart Agriculture

abstract: Nowadays, Internet of Things (IoT) is receiving a great attention due to its potential strength and ability to be integrated into any complex system. The IoT provides the acquired data from the environment to the Internet through the service providers. This further helps users to view the numerical or plotted data. In addition, it also allows objects which are located in long distances to be sensed and controlled remotely through embedded devices which are important in agriculture domain. Developing such a system for the IoT is a very complex task due to the diverse variety of devices, link layer technologies, and services. This paper proposes a practical approach to acquiring data of temperature, humidity and soil moisture of plants. In order to accomplish this, we developed a prototype device and an android application which acquires physical data and sends it to cloud. Moreover, in the subsequent part of current research work, we have focused towards a temperature forecasting application. Forecasting metrological parameters have a profound influence on crop growth, development and yields of agriculture. In response to this fact, an application is developed for 10 days ahead maximum and minimum temperatures forecasting using a type of recurrent neural network.


title: When governments hack opponents: a look at actors and technology

abstract: Repressive nation-states have long monitored telecommunications to keep tabs on political dissent. The Internet and online social networks, however, pose novel technical challenges to this practice, even as they open up new domains for surveillance. We analyze an extensive collection of suspicious files and links targeting activists, opposition members, and nongovernmental organizations in the Middle East over the past several years. We find that these artifacts reflect efforts to attack targets' devices for the purposes of eavesdropping, stealing information, and/or unmasking anonymous users. We describe attack campaigns we have observed in Bahrain, Syria, and the United Arab Emirates, investigating attackers, tools, and techniques. In addition to off-the-shelf remote access trojans and the use of third-party IP-tracking services, we identify commercial spyware marketed exclusively to governments, including Gamma's FinSpy and Hacking Team's Remote Control System (RCS). We describe their use in Bahrain and the UAE, and map out the potential broader scope of this activity by conducting global scans of the corresponding command-and-control (C&C) servers. Finally, we frame the real-world consequences of these campaigns via strong circumstantial evidence linking hacking to arrests, interrogations, and imprisonment.


title: Secure routing for structured peer-to-peer overlay networks

abstract: Structured peer-to-peer overlay networks provide a substrate for the construction of large-scale, decentralized applications, including distributed storage, group communication, and content distribution. These overlays are highly resilient; they can route messages correctly even when a large fraction of the nodes crash or the network partitions. But current overlays are not secure; even a small fraction of malicious nodes can prevent correct message delivery throughout the overlay. This problem is particularly serious in open peer-to-peer systems, where many diverse, autonomous parties without preexisting trust relationships wish to pool their resources. This paper studies attacks aimed at preventing correct message delivery in structured peer-to-peer overlays and presents defenses to these attacks. We describe and evaluate techniques that allow nodes to join the overlay, to maintain routing state, and to forward messages securely in the presence of malicious nodes.


title: The Optimization of LRU Algorithm Based on Pre-Selection and Cache Prefetching of Files in Hybrid Cloud

abstract: In recent years, the research on caching in cloud environment has become an important research topic, and it has profound meaning to research the cache replacement algorithm in hybrid Cloud. There aren't enough considerations on some aspects, such as the selection of pending cache files, the prefetching of pending cache files among different clouds and the cost of recovery of files. Considering those shortages, this paper proposes an optimized LRU algorithm based on pre-selection and cache prefetching of files. This algorithm determines whether the file is to meet the pre-selection and cache prefetching conditions before adding a cache file, and it implements the LRU cache replacement algorithm which is based on priority. The algorithm divides the cache into multiple priority queues, and uses the LRU cache replacement algorithm to select the replacement file in each queue. Then select the files in each priority and put them together, select the file to perform replacement operation which has minimum probability of being accessed again. Compared with three typical cache replacement algorithm GD-Size, LRU, LFU, experimental results show that the cache replacement algorithm in this paper not only effectively save cost, but also greatly enhance the byte hit rate, delay savings rate and cache hit rate.


title: "Split-ADC" Digital Background Correction of Open-Loop Residue Amplifier Nonlinearity Errors in a 14b Pipeline ADC

abstract: The "split ADC" architecture concept is applied to correction of errors due to nonlinearity of an open-loop residue amplifier in a pipeline ADC. Determination of calibration parameters and correction of errors takes place entirely in the background in the digital domain; no interaction with analog circuitry is required. An algorithm exhibiting convergence of calibration parameters in fewer than 100 000 conversions is presented.


title: Hydrodynamic modeling of an undulating fin for robotic fish design

abstract: Motivated by the interests to develop an agile, high-efficient robotic fish for underwater applications where safe environment for data-acquisition without disturbing the surrounding during exploration is of particular concern, this paper presents computational and experimental results of a biologically-inspired mechanical undulating-fin. The findings offer intuitive insights for optimizing the design of a fin-based robotic fish which has potentials to offer several advantages including low underwater acoustic noise, great maneuverability and better propulsion efficiency at low speeds. Specifically, this paper begins with the design of a robotic fish developed for experimental investigation and for validating computational hydrodynamic models of an undulating fin. A relatively complete computational model describing the hydrodynamics of an undulating fin is given. To analyze the effect of propagating wave motions on the forces acting on the fin surface, the three-dimensional unsteady fluid flow around the undulating fin is numerically solved using computational fluid dynamics (CFD) method. The pressure and velocity distributions acting on the undulating fin have been numerically simulated providing a basis to compute the forces acting on the undulating fin. The computational model has been experimentally validated by comparing the computed thrust coefficient against measured data based on a prototype flexible-fin mechanism.


title: ABS: a system for scalable approximate queries with accuracy guarantees

abstract: Approximate Query Processing (AQP) based on sampling is critical for supporting timely and cost-effective analytics over big data. To be applied successfully, AQP must be accompanied by reliable estimates on the quality of sample-produced approximate answers; the two main techniques used in the past for this purpose are (i) closed-form analytic error estimation, and (ii) the bootstrap method. Approach (i) is extremely efficient but lacks generality, whereas (ii) is general but suffers from high computational overhead. Our recently introduced Analytical Bootstrap method combines the strengths of both approaches and provides the basis for our ABS system, which will be demonstrated at the conference. The ABS system models bootstrap by a probabilistic relational model, and extends relational algebra with operations on probabilistic relations to predict the distributions of the AQP results. Thus, ABS entails a very fast computation of bootstrap-based quality measures for a general class of SQL queries, which is several orders of magnitude faster than the standard simulation-based bootstrap. In this demo, we will demonstrate the generality, automaticity, and ease of use of the ABS system, and its superior performance over the traditional approaches described above.


title: Multistage generation and network expansion planning in distribution systems considering uncertainty and reliability

abstract: This paper describes the incorporation of uncertainty and reliability in the dynamic expansion planning of distribution network assets and distributed generation. Several alternatives for the installation of feeders, transformers, and distributed generation are considered. Thus, the optimal expansion plan identifies the best alternative, location, and installation time for the candidate assets under the uncertainty related to demand and renewable energy sources. To that end, an iterative algorithm is devised to yield a pool of high-quality candidate solutions in terms of total investment and operational costs. Each candidate solution results from a stochastic-programming-based model driven by the minimization of the expected investment and operational costs. The associated scenario-based deterministic equivalent is formulated as a mixed-integer linear program for which finite convergence to optimality is guaranteed and efficient off-the-shelf software is available. Standard metrics are subsequently applied to each candidate solution to characterize its reliability so that valuable information is provided to the distribution system planner. Numerical results illustrate the effective performance of the proposed approach.


title: Danmaku vs. Forum Comments: Understanding User Participation and Knowledge Sharing in Online Videos

abstract: Danmaku is a new video comment feature that is gaining popularity. Unlike typical forum comments that are displayed with user names below videos, danmaku comments are overlaid on the screen of videos without showing users' information. Prior work studied forum comments and danmaku separately, and little work compared how these two features were used. We collected 38,399 danmaku comments and 16,414 forum comments posted in 2017 on 30 popular videos on Bilibili.com. We examined the usage of these two features in terms of user participation, language used, and ways of sharing knowledge. We found that more users posted danmaku comments, and they also posted these more frequently than forum comments. Even though, in total, more negative language was used in danmaku comments than in forum comments, active users appeared to post more positive comments in danmaku. There was no such correlation in forum comments. It is interesting to find that danmaku and forum comments enabled knowledge sharing in a complementary manner, where danmaku comments involved more explicit knowledge sharing and forum comments exhibited more tacit knowledge sharing. We discuss design implications to promote social interactions for online video systems.


title: Exploring the Trade-off Between Accuracy and Observational Latency in Action Recognition

abstract: An important aspect in designing interactive, action-based interfaces is reliably recognizing actions with minimal latency. High latency causes the system's feedback to lag behind user actions and thus significantly degrades the interactivity of the user experience. This paper presents algorithms for reducing latency when recognizing actions. We use a latency-aware learning formulation to train a logistic regression-based classifier that automatically determines distinctive canonical poses from data and uses these to robustly recognize actions in the presence of ambiguous poses. We introduce a novel (publicly released) dataset for the purpose of our experiments. Comparisons of our method against both a Bag of Words and a Conditional Random Field (CRF) classifier show improved recognition performance for both pre-segmented and online classification tasks. Additionally, we employ GentleBoost to reduce our feature set and further improve our results. We then present experiments that explore the accuracy/latency trade-off over a varying number of actions. Finally, we evaluate our algorithm on two existing datasets.


title: Why so serious? On the Relation of Serious Games and Learning

abstract: Serious games have become a key segment in the games market as well as in academic research. Although the number of games that identify themselves as belonging to this category as well as the research done on their effects has been rapidly growing, there has thus far been no attempt to define all of the various opportunities that digital games provide for learning. To address this issue we look at existing definitions of serious games and their potential for learning. We identify the shortcomings of existing definitions and typologies. We discuss opportunities for an educational use of serious games which have been marginalized so far and develop a more flexible classification system for serious games in order to include commercial-off-the-shelf (COTS) games for learning purposes and description options for future developments of gaming technology. This classification system for digital and serious games uses labels and tags as a preferable solution instead of fixed genre categories. The aim of this paper is to move the focus from what serious games and their uses for learning currently are to what they can be.


title: Image attributes and quality for evaluation of tone mapping operators

abstract: The problem of reproducing high dynamic range images on devices with restricted dynamic range has gained a lot of interest in the computer graphics community. There exist various approaches to this issue, which span several research areas including computer graphics, image processing, color science, physiology, neurology, psychology, etc. These approaches assume a thorough knowledge of both the objective and subjective attributes of an image. However, no comprehensive overview and analysis of such attributes has been published so far. In this paper, we present an overview of image quality attributes of different tone mapping methods. Furthermore, we propose a scheme of relationships between these attributes, leading to the definition of an overall image quality measure. We present results of subjective psychophysical tests that we have performed to prove the proposed relationship scheme. We also present the evaluation of existing tone mapping methods with regard to these attributes. Our effort is not just useful to get into the tone mapping field or when implementing a tone mapping operator, but it also sets the stage for well-founded quality comparisons between tone mapping operators. By providing good definitions of the different attributes, user-driven or fully automatic comparisons are made possible at all.


title: Moderate ethanol consumption increases hippocampal cell proliferation and neurogenesis in the adult mouse

abstract: Alcoholism is a lifelong disease often associated with emotional disturbances and a high risk of relapse even years after detoxification. To explore if cell proliferation in the dentate gyrus of the hippocampus might be important for alcohol-induced brain adaptation, we analysed hippocampal neurogenesis and gliogenesis in adult C57Bl/6 mice that consumed moderate levels of ethanol (∼6 g/kg.d) in a two-bottle free-choice model during ∼10 wk. The mice developed a 53% preference for ethanol vs. water and displayed a blood ethanol concentration of 0.24‰ at the time of sacrifice. Bromo-deoxy-uridine (BrdU) was administered in different regimes to analyse proliferation, survival, cell distribution and differentiation of new cells in the dentate gyrus. Moderate ethanol consumption increased the proliferation of cells, which survived and developed a neural phenotype. Ethanol consumption did not induce apoptosis, neither did it change differentiation or the distribution patterns of the newly formed cells. The cell proliferation rate in the dentate gyrus returned to basal levels 3 d after ethanol withdrawal. We conclude that voluntary ethanol intake by mice can change the rate of cell proliferation in the dentate gyrus. These observations add to the emerging picture of dentate gyrus neurogenesis as a highly regulated process. Since there was no increase in apoptosis concomitant with the ethanol-induced increase in neurogenesis, it is possible that the new cells in the dentate gyrus may contribute to the long-lasting changes of brain function after ethanol consumption.


title: 25-Hydroxycholesterol-3-sulfate regulates macrophage lipid metabolism via the LXR/SREBP-1 signaling pathway

abstract: The oxysterol receptor LXR is a key transcriptional regulator of lipid metabolism. LXR increases expression of SREBP-1, which in turn regulates at least 32 genes involved in lipid synthesis and transport. We recently identified 25-hydroxycholesterol-3-sulfate (25HC3S) as an important regulatory molecule in the liver. We have now studied the effects of 25HC3S and its precursor, 25-hydroxycholesterol (25HC), on lipid metabolism as mediated by the LXR/SREBP-1 signaling in macrophages. Addition of 25HC3S to human THP-1-derived macrophages markedly decreased nuclear LXR protein levels. 25HC3S administration was followed by dose- and time-dependent decreases in SREBP-1 mature protein and mRNA levels. 25HC3S decreased the expression of SREBP-1-responsive genes, acetyl-CoA carboxylase-1, and fatty acid synthase (FAS) as well as HMGR and LDLR, which are key proteins involved in lipid metabolism. Subsequently, 25HC3S decreased intracellular lipids and increased cell proliferation. In contrast to 25HC3S, 25HC acted as an LXR ligand, increasing ABCA1, ABCG1, SREBP-1, and FAS mRNA levels. In the presence of 25HC3S, 25HC, and LXR agonist T0901317, stimulation of LXR targeting gene expression was repressed. We conclude that 25HC3S acts in macrophages as a cholesterol satiety signal, downregulating cholesterol and fatty acid synthetic pathways via inhibition of LXR/SREBP signaling. A possible role of oxysterol sulfation is proposed.


title: Reflectance-based surface saliency

abstract: In this paper, we propose an original methodology allowing the computation of the saliency maps for high dimensional RTI data (Reflectance Transformation Imaging). Unlike most of the classical methods, our approach aims at devising an intrinsic visual saliency of the surface, independent of the sensor (image) and the geometry of the scene (light-object-camera). From RTI data, we use the DMD (Discrete Modal Decomposition) technique for the angular reflectance reconstruction, which we extend by a new transformation on the modal basis enabling a rotation-invariant representation of reconstructed reflectances. This orientation-invariance of the resulting reflectance shapes fosters a robust estimation of saliency maps linked to the local visual appearance behaviour of surfaces on the scene. The proposed methodology has been tested and validated on real surfaces with controlled singularities, and the results demonstrated its efficiency since the estimated saliency maps show strong correlation with sensorial visual assessments.