title: Scattering of electromagnetic waves by inhomogeneous dielectric gratings loaded with parallel perfectly conducting strips

abstract: In this paper, we have proposed a new method for the scattering of electromagnetic waves by inhomogeneous dielectric gratings loaded with parallel perfectly conducting strips using the combination of improved Fourier series expansion method and point matching method. Our approach also can treat periodic configurations having arbitrary combinations of dielectric, metallic, and perfectly conducting components. Numerical results are given for the transmitted scattered characteristics for the case of frequency loaded with parallel perfectly conducting strips for TE and TM waves.


Neuropsychological Pro fi les Differ among the Three Variants of Primary Progressive Aphasia


title: Moving Object Detection by Detecting Contiguous Outliers in the Low-Rank Representation

abstract: Object detection is a fundamental step for automated video analysis in many vision applications. Object detection in a video is usually performed by object detectors or background subtraction techniques. Often, an object detector requires manually labeled examples to train a binary classifier, while background subtraction needs a training sequence that contains no objects to build a background model. To automate the analysis, object detection without a separate training phase becomes a critical task. People have tried to tackle this task by using motion information. But existing motion-based methods are usually limited when coping with complex scenarios such as nonrigid motion and dynamic background. In this paper, we show that the above challenges can be addressed in a unified framework named DEtecting Contiguous Outliers in the LOw-rank Representation (DECOLOR). This formulation integrates object detection and background learning into a single process of optimization, which can be solved by an alternating algorithm efficiently. We explain the relations between DECOLOR and other sparsity-based methods. Experiments on both simulated data and real sequences demonstrate that DECOLOR outperforms the state-of-the-art approaches and it can work effectively on a wide range of complex scenarios.


title: A survey on ontologies for human behavior recognition

abstract: Describing user activity plays an essential role in ambient intelligence. In this work, we review different methods for human activity recognition, classified as data-driven and knowledge-based techniques. We focus on context ontologies whose ultimate goal is the tracking of human behavior. After studying upper and domain ontologies, both useful for human activity representation and inference, we establish an evaluation criterion to assess the suitability of the different candidate ontologies for this purpose. As a result, any missing features, which are relevant for modeling daily human behaviors, are identified as future challenges.


title: Restoring Degraded Face Images: A Case Study in Matching Faxed, Printed, and Scanned Photos

abstract: We study the problem of restoring severely degraded face images such as images scanned from passport photos or images subjected to fax compression, downscaling, and printing. The purpose of this paper is to illustrate the complexity of face recognition in such realistic scenarios and to provide a viable solution to it. The contributions of this work are two-fold. First, a database of face images is assembled and used to illustrate the challenges associated with matching severely degraded face images. Second, a preprocessing scheme with low computational complexity is developed in order to eliminate the noise present in degraded images and restore their quality. An extensive experimental study is performed to establish that the proposed restoration scheme improves the quality of the ensuing face images while simultaneously improving the performance of face matching.


title: Netbait: a Distributed Worm Detection Service

abstract: This paper presents Netbait, a planetary-scale service for distributed detection of Internet worms. Netbait allows users to pose queries that identify which machines on a given network have been compromised based on the collective view of a geographically distributed set of machines. It is based on a distributed query processing architecture that evaluates queries expressed using a subset of SQL against a single logical database table. This single logical table is realized using a distributed set of relational databases, each populated by local intrusion detection systems running on Netbait server nodes. For speed, queries in Netbait are processed in parallel by distributing them over dynamically constructed query processing trees built over Tapestry, a distributed object and location routing (DOLR) layer. For efficiency, query results are compressed using application-specific aggregation and compact encodings. We have implemented a prototype system based on a simplified version of the architecture and have deployed it on 90 nodes of the PlanetLab testbed at 42 sites spread across three continents. The system has been continuously running for over a month now and has been collecting probe information from machines compromised by both the Code Red and Nimda worms. Early results based on this data are promising. First, we observe that by having multiple machines sharing probe information from infected machines, we can identify a substantially larger set of infected hosts that would be possible otherwise. Second, we also observe that by having multiple viewpoints of the network, Netbait is able to identify compromised machines that otherwise would have been difficult to detect in cases where worms have an affinity to certain regions of the IP address space.


title: Beyond sliding windows: Object localization by efficient subwindow search

abstract: Most successful object recognition systems rely on binary classification, deciding only if an object is present or not, but not providing information on the actual object location. To perform localization, one can take a sliding window approach, but this strongly increases the computational cost, because the classifier function has to be evaluated over a large set of candidate subwindows. In this paper, we propose a simple yet powerful branch-and-bound scheme that allows efficient maximization of a large class of classifier functions over all possible subimages. It converges to a globally optimal solution typically in sublinear time. We show how our method is applicable to different object detection and retrieval scenarios. The achieved speedup allows the use of classifiers for localization that formerly were considered too slow for this task, such as SVMs with a spatial pyramid kernel or nearest neighbor classifiers based on the chi2-distance. We demonstrate state-of-the-art performance of the resulting systems on the UIUC Cars dataset, the PASCAL VOC 2006 dataset and in the PASCAL VOC 2007 competition.


A multidimensional trust formation model in B-to-C e-commerce: a conceptual framework and content analyses of academia/practitioner perspectives


title: Whole program paths

abstract: Whole program paths (WPP) are a new approach to capturing and representing a program's dynamic---actually executed---control flow. Unlike other path profiling techniques, which record intraprocedural or acyclic paths, WPPs produce a single, compact description of a program's entire control flow, including loop iteration and interprocedural paths.This paper explains how to collect and represent WPPs. It also shows how to use WPPs to find hot subpaths, which are the heavily executed sequences of code that should be the focus of performance tuning and compiler optimization.


title: Multi-column Deep Neural Networks for Image Classification

abstract: Traditional methods of computer vision and machine learning cannot match human performance on tasks such as the recognition of handwritten digits or traffic signs. Our biologically plausible, wide and deep artificial neural network architectures can. Small (often minimal) receptive fields of convolutional winner-take-all neurons yield large network depth, resulting in roughly as many sparsely connected neural layers as found in mammals between retina and visual cortex. Only winner neurons are trained. Several deep neural columns become experts on inputs preprocessed in different ways; their predictions are averaged. Graphics cards allow for fast training. On the very competitive MNIST handwriting benchmark, our method is the first to achieve near-human performance. On a traffic sign recognition benchmark it outperforms humans by a factor of two. We also improve the state-of-the-art on a plethora of common image classification benchmarks.


title: Continual Occlusions and Optical Flow Estimation

abstract: Two optical flow estimation problems are addressed: i) occlusion estimation and handling, and ii) estimation from image sequences longer than two frames. The proposed ContinualFlow method estimates occlusions before flow, avoiding the use of flow corrupted by occlusions for their estimation. We show that providing occlusion masks as an additional input to flow estimation improves the standard performance metric by more than 25\% on both KITTI and Sintel. As a second contribution, a novel method for incorporating information from past frames into flow estimation is introduced. The previous frame flow serves as an input to occlusion estimation and as a prior in occluded regions, i.e. those without visual correspondences. By continually using the previous frame flow, ContinualFlow performance improves further by 18\% on KITTI and 7\% on Sintel, achieving top performance on KITTI and Sintel.


title: Towards continuous control of flippers for a multi-terrain robot using deep reinforcement learning

abstract: In this paper we focus on developing a control algorithm for multi-terrain tracked robots with flippers using a reinforcement learning (RL) approach. The work is based on the deep deterministic policy gradient (DDPG) algorithm, proven to be very successful in simple simulation environments. The algorithm works in an end-to-end fashion in order to control the continuous position of the flippers. This end-to-end approach makes it easy to apply the controller to a wide array of circumstances, but the huge flexibility comes to the cost of an increased difficulty of solution. The complexity of the task is enlarged even more by the fact that real multi-terrain robots move in partially observable environments. Notwithstanding these complications, being able to smoothly control a multi-terrain robot can produce huge benefits in impaired people daily lives or in search and rescue situations.


title: Answer set programming at a glance

abstract: The motivation and key concepts behind answer set programming---a promising approach to declarative problem solving.


title: Detection of camera artifacts from camera images

abstract: Cameras are frequently used in state-of-the-art systems in order to get detailed information of the environment. However, when cameras are used outdoors they easily get dirty or scratches on the lens which leads to image artifacts that can deteriorate a system's performance. Little work has yet been done on how to detect and cope with such artifacts. Most of the previous work has concentrated on detecting a specific artifact like rain drops on the windshield of a car. In this paper, the authors show that on moving systems most artifacts can be detected by analyzing the frames in a stream of images from a camera for static image parts. Based on the observation that most artifacts are temporally stable in their position in the image the authors compute pixel-wise correlations between images. Since the system is moving the static artifacts will lead to a high correlation value while the pixels showing only scene elements will have a low correlation value. For testing this novel algorithm, the authors recorded some outdoor data with the three different artifacts: raindrops, dirt and scratches. The results of the authors new algorithm on this data show, that it reliably detects all three artifacts. Moreover, the authors show that the algorithm can be implemented efficiently by means of box-filters which allows it to be used as a self-checking routine running in background on low-power systems such as autonomous field robots or advanced driver assistant systems on a vehicle.


title: Dietary ketosis enhances memory in mild cognitive impairment

abstract: We randomly assigned 23 older adults with Mild Cognitive Impairment to either a high carbohydrate or very low carbohydrate diet. Following the six-week intervention period, we observed improved verbal memory performance for the low carbohydrate subjects (p = 0.01) as well as reductions in weight (p < 0.0001), waist circumference (p < 0.0001), fasting glucose (p = 0.009), and fasting insulin (p = 0.005). Level of depressive symptoms was not affected. Change in calorie intake, insulin level, and weight were not correlated with memory performance for the entire sample, although a trend toward a moderate relationship between insulin and memory was observed within the low carbohydrate group. Ketone levels were positively correlated with memory performance (p = 0.04). These findings indicate that very low carbohydrate consumption, even in the short-term, can improve memory function in older adults with increased risk for Alzheimer’s disease. While this effect may be attributable in part to correction of hyperinsulinemia, other mechanisms associated with ketosis such as reduced inflammation and enhanced energy metabolism also may have contributed to improved neurocognitive function. Further investigation of this intervention is warranted to evaluate its preventive potential and mechanisms of action in the context of early neurodegeneration.


title: Similarity and Locality Based Indexing for High Performance Data Deduplication

abstract: Data deduplication has gained increasing attention and popularity as a space-efficient approach in backup storage systems. One of the main challenges for centralized data deduplication is the scalability of fingerprint-index search. In this paper, we propose SiLo, a near-exact and scalable deduplication system that effectively and complementarily exploits similarity and locality of data streams to achieve high duplicate elimination, throughput, and well balanced load at extremely low RAM overhead. The main idea behind SiLo is to expose and exploit more similarity by grouping strongly correlated small files into a segment and segmenting large files, and to leverage the locality in the data stream by grouping contiguous segments into blocks to capture similar and duplicate data missed by the probabilistic similarity detection. SiLo also employs a locality based stateless routing algorithm to parallelize and distribute data blocks to multiple backup nodes. By judiciously enhancing similarity through the exploitation of locality and vice versa, SiLo is able to significantly reduce RAM usage for index-lookup, achieve the near-exact efficiency of duplicate elimination, maintain a high deduplication throughput, and obtain load balance among backup nodes.


title: Adaptive grid job scheduling with genetic algorithms

abstract: This paper proposes two models for predicting the completion time of jobs in a service Grid. The single service model predicts the completion time of a job in a Grid that provides only one type of service. The multiple services model predicts the completion time of a job that runs in a Grid which offers multiple types of services. We have developed two algorithms that use the predictive models to schedule jobs at both system level and application level. In application-level scheduling, genetic algorithms are used to minimize the average completion time of jobs through optimal job allocation on each node. The experimental results have shown that the scheduling system using the adaptive scheduling algorithms can allocate service jobs efficiently and effectively.


title: Surrogate Scoring Rules and a Dominant Truth Serum

abstract: Strictly proper scoring rules (SPSR) are incentive compatible for eliciting information about random variables from strategic agents when the principal can reward agents after the realization of the random variables. They also quantify the quality of elicited information, with more accurate predictions receiving higher score in expectation. In this paper, we extend such scoring rules to settings where a principal elicits private probabilistic beliefs but only has access to agents' reports. We name our solution \emph{Surrogate Scoring Rules} (SSR). SSR build on a bias correction step and an error rate estimation procedure for a reference answer defined using agents' reports. We show that, with one bit of information about the prior distribution of the random variables, SSR in a multi-task setting recover SPSR in expectation, as if having access to the ground truth. Therefore, a salient feature of SSR is that they quantify the quality of information despite the lack of ground truth, just as SPSR do for the {\em with} ground truth setting. As a by-product, SSR induce \emph{dominant truthfulness} in reporting. Our work complements the proper scoring rule literature via extending existing SPSR to operate when there is no clean ground truth verification. Because of the non-existence of verification, our setting falls into the classical information elicitation without verification (IEWV) domain, which has focused on eliciting discrete signals. Therefore our work also contributes to the peer prediction literature via providing a scoring rule that elicits continuous probabilistic beliefs, an approach that rewards accuracy instead of correlation, and a mechanism that achieves truthfulness in \emph{dominant strategy} in a multi-task setting. Our method is verified both theoretically and empirically using data collected from real human forecasters.


title: Adversarial vulnerability for any classifier

abstract: Despite achieving impressive performance, state-of-the-art classifiers remain highly vulnerable to small, imperceptible, adversarial perturbations. This vulnerability has proven empirically to be very intricate to address. In this paper, we study the phenomenon of adversarial perturbations under the assumption that the data is generated with a smooth generative model. We derive fundamental upper bounds on the robustness to perturbations of any classification function, and prove the existence of adversarial perturbations that transfer well across different classifiers with small risk. Our analysis of the robustness also provides insights onto key properties of generative models, such as their smoothness and dimensionality of latent space. We conclude with numerical experimental results showing that our bounds provide informative baselines to the maximal achievable robustness on several datasets.


title: Frequency invariant transformation of periodic signals (FIT-PS) for signal representation in NILM

abstract: It is the aim of non-intrusive load monitoring (NILM) to determine the individual energy consumption of multiple devices on the basis of the total energy consumption. The energy consumption of a single device is measured at a central point without the need of individual measuring instruments on the devices themselves. We present a new method for signal separation, decomposition of the signal into individual states and feature extraction. Frequency invariant transformation of periodic signals (FIT-PS) is based on a signal diagram similar to the concept of trajectories [1]–[3] utilizing the periodicity of voltage and current, and their correlation.


title: Boosting Dilated Convolutional Networks with Mixed Tensor Decompositions

abstract: The driving force behind deep networks is their ability to compactly represent rich classes of functions. The primary notion for formally reasoning about this phenomenon is expressive efficiency, which refers to a situation where one network must grow unfeasibly large in order to replicate functions of another. To date, expressive efficiency analyses focused on the architectural feature of depth, showing that deep networks are representationally superior to shallow ones. In this paper we study the expressive efficiency brought forth by connectivity, motivated by the observation that modern networks interconnect their layers in elaborate ways. We focus on dilated convolutional networks, a family of deep models delivering state of the art performance in sequence processing tasks. By introducing and analyzing the concept of mixed tensor decompositions, we prove that interconnecting dilated convolutional networks can lead to expressive efficiency. In particular, we show that even a single connection between intermediate layers can already lead to an almost quadratic gap, which in large-scale settings typically makes the difference between a model that is practical and one that is not. Empirical evaluation demonstrates how the expressive efficiency of connectivity, similarly to that of depth, translates into gains in accuracy. This leads us to believe that expressive efficiency may serve a key role in developing new tools for deep network design.


title: What Influences Travellers’ Adoption of a Location-based Social Media Service for Their Travel Planning?

abstract: Advances in location-acquisition and mobile communication technologies have empowered people to use location data with online social networks known as location-based social media. However, the technology is relatively new, and literature on the relevant factors determining location-based social media adoption and usage for the specific purpose of travel planning is sparse. Using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) as a base model, we extended the model by including users’ mobile Internet experience and the Information Adoption Model in the context of location-based social media adoption and use for travel planning. Data was collected from 200 Chinese respondents and partial least square was employed to test the research model. Our results showed that our model was able to explain about 64% of variance in intention and 48% in location-based social media use in travel planning. We found that besides variables from UTAUT2, argument strength, review rating, reviewer trustworthiness, reviewer expertise and review sidedness can influence users’ adoption of online reviews, which in turn will influence their use of location-based social media. The results of this study will be useful for location-based social media providers in formulating appropriate marketing strategies, as well as developing applications that will attract more users.


title: Detecting Vanishing Points Using Global Image Context in a Non-ManhattanWorld

abstract: We propose a novel method for detecting horizontal vanishing points and the zenith vanishing point in man-made environments. The dominant trend in existing methods is to first find candidate vanishing points, then remove outliers by enforcing mutual orthogonality. Our method reverses this process: we propose a set of horizon line candidates and score each based on the vanishing points it contains. A key element of our approach is the use of global image context, extracted with a deep convolutional network, to constrain the set of candidates under consideration. Our method does not make a Manhattan-world assumption and can operate effectively on scenes with only a single horizontal vanishing point. We evaluate our approach on three benchmark datasets and achieve state-of the-art performance on each. In addition, our approach is significantly faster than the previous best method.


title: Engaging by design: How engagement strategies in popular computer and video games can inform instructional design

abstract: Computer and video games are a prevalent form of entertainment in which the purpose of the design is to engage players. Game designers incorporate a number of strategies and tactics for engaging players in “gameplay.” These strategies and tactics may provide instructional designers with new methods for engaging learners. This investigation presents a review of game design strategies and the implications of appropriating these strategies for instructional design. Specifically, this study presents an overview of the trajectory of player positioning or point of view, the role of narrative, and methods of interactive design. A comparison of engagement strategies in popular games and characteristics of engaged learning is also presented to examine how strategies of game design might be integrated into the existing framework of engaged learning.


Bilateral malar milia en plaque as primary presentation of discoid lupus erythematosus


title: Safety verification of a fault tolerant reconfigurable autonomous goal-based robotic control system

abstract: Fault tolerance and safety verification of control systems are essential for the success of autonomous robotic systems. A control architecture called mission data system (MDS), developed at the Jet Propulsion Laboratory, takes a goal-based control approach. In this paper, a method for converting goal network control programs into linear hybrid systems is developed. The linear hybrid system can then be verified for safety in the presence of failures using existing symbolic model checkers. An example task is simulated in MDS and successfully verified using HyTech, a symbolic model checking software for linear hybrid systems.


title: Consistent , Two-Stage Sampled Distribution Regression via Mean Embedding

abstract: We focus on the distribution regression problem: regressing to a real-valued response from a probability distribution. Although there exist a large number of similarity measures between distributions, very little is known about their generalization performance in specific learning tasks. Learning problems formulated on distributions have an inherent two-stage sampled difficulty: in practice only samples from sampled distributions are observable, and one has to build an estimate on similarities computed between sets of points. To the best of our knowledge, the only existing method with consistency guarantees for distribution regression requires kernel density estimation as an intermediate step (which suffers from slow convergence issues in high dimensions), and the domain of the distributions to be compact Euclidean. In this paper, we provide theoretical guarantees for a remarkably simple algorithmic alternative to solve the distribution regression problem: embed the distributions to a reproducing kernel Hilbert space, and learn a ridge regressor from the embeddings to the outputs. Our main contribution is to prove the consistency of this technique in the two-stage sampled setting under mild conditions (on separable, topological domains endowed with kernels). For a given total number of observations, we derive convergence rates as an explicit function of the problem difficulty. As a special case, we answer a 15-year-old open question: we establish the consistency of the classical set kernel [Haussler, 1999; Gartner et. al, 2002] in regression, and cover more recent kernels on distributions, including those due to [Christmann and Steinwart, 2010].


title: www.elsevier.com/locate/ins Uncertainty measures for interval type-2 fuzzy sets

abstract: Ranking methods, similarity measures and uncertainty measures are very important concepts for interval type-2 fuzzy sets (IT2 FSs). So far, there is only one ranking method for such sets, whereas there are many similarity and uncertainty measures. A new ranking method and a new similarity measure for IT2 FSs are proposed in this paper. All these ranking methods, similarity measures and uncertainty measures are compared based on real survey data and then the most suitable ranking method, similarity measure and uncertainty measure that can be used in the computing with words paradigm are suggested. The results are useful in understanding the uncertainties associated with linguistic terms and hence how to use them effectively in survey design and linguistic information processing.


title: Modelling the PWM Zeta converter in discontinuous conduction mode

abstract: A full-order dynamic model of the pulse-width modulation (PWM) DC-to-DC Zeta converter in discontinuous conduction mode (DCM) is presented in this paper. The derivation procedure is based on the small-signal PWM switch model in DCM used in conjunction with a PWM derived Zeta topology. The small-signal characteristics of the PWM Zeta converter in DCM obtained in a similar way as the small-signal properties of a linear amplifier are presented in a symbolic form. Such a model can be directly used in conjunction with a computational environment as Matlab, MathCAD and Mathematica to analyze the small-signal low-frequency dynamics of the PWM Zeta converter in DCM.


title: Westminster Serious Games Platform (wmin-SGP) a tool for real-time authoring of roleplay simulations for learning

abstract: The use of serious games in education and their pedagogical benefit is being widely recognized. However, effective integration of serious games in education depends on addressing two big challenges: the successful incorporation of motivation and engagement that can lead to learning; and the highly specialised skills associated with customised development to meet the required pedagogical objectives. This paper presents the Westminster Serious Games Platform (wmin-SGP) an authoring tool that allows educators/domain experts without games design and development technical skills to create bespoke roleplay simulations in three dimensional scenes featuring fully embodied virtual humans capable of verbal and non-verbal interaction with users fit for specific educational objectives. The paper presents the wmin-SGP system architecture and it evaluates its effectiveness in fulfilling its purpose via the implementation of two roleplay simulations, one for Politics and one for Law. In addition, it presents the results of two types of evaluation that address how successfully the wmin-SGP combines usability principles and game core drives based on the Octalysis gamification framework that lead to motivating games experiences. The evaluation results shows that the wmin-SGP: provides an intuitive environment and tools that support users without advanced technical skills to create in real-time bespoke roleplay simulations in advanced graphical interfaces; satisfies most of the usability principles; and provides balanced simulations based on the Octalysis framework core drives. The paper concludes with a discussion of future extension of this real time authoring tool and directions for further development of the Octalysis framework to address learning.


title: Performance analysis of the ZigBee networks in 5G environment and the nearest access routing for improvement

abstract: The ZigBee network is widely studied and deployed recently because of its low cost and simplicity features. However, the power consumption issue needs a further improvement since the application requirements are not fully satisfied. The emerging 5G communication technology is characterized by the smarter devices and the native support for the M2M communication. On that basis, the 5G terminals are capable of joining the existing ZigBee networks and have the potential to improve the data transmission. In this paper, we investigate the performance of the ZigBee networks in the 5G environment for different scenarios. Then a nearest access routing (NAR) algorithm based on the physical depth is proposed for different communication types. To reduce the loads in ZigBee networks, the data flow in the neighborhood of 5G terminals is gathered and transmitted via the IP networks. The simulation results showed that NAR effectively share the communication in ZigBee networks. It leads to better performances with higher packet delivery ratio, less hop counts from ZigBee devices, lower latency, fewer packets sent by ZigBee nodes and zero routing overheads.


title: Analyzing and retrieving illicit drug-related posts from social media

abstract: Illicit drug use is a serious problem around the world. Social media has increasingly become an important tool for analyzing drug use patterns and monitoring emerging drug abuse trends. Accurately retrieving illicit drug-related social media posts is an important step in this research. Frequently, hashtags are used to identify and retrieve posts on a specific topic. However hashtags are highly ambiguous. Posts with the same hashtags are not always on the same topic. Moreover, hashtags are evolving, especially those related to illicit drugs. New street names are introduced constantly to avoid detection. In this paper, we employ topic modeling to disambiguate hashtags and track the changes of hashtags using semantic word embedding. Our preliminary evaluation shows the promise of these methods.


title: The Evolution of Business Incubators: Comparing demand and supply of business incubation services across different incubator generations

abstract: Business incubators (BIs) have been established around the world to stimulate new business creation. Whilst it is accepted that incubation models have evolved, little is known about whether existing incubators have adjusted their value proposition to incorporate recent incubation paradigms or have simply remained operating as originally founded. We present data collected within seven BIs and their tenants regarding service provision and selection criteria. Our findings show that whilst BIs of all generations offer similar support services, tenants in older generation BIs make less use of the BI's service portfolio. We suggest this is a consequence of slack selection criteria and the absence of clearly defined exit policies. These results imply that older generation BIs should update their service portfolio while simultaneously imposing stricter selection criteria and introducing exit policies. Finally, we discuss the wider implications this raises for BIs' managers, prospective tenants and policy makers.


title: Real-time flatness inspection of rolled products based on optical laser triangulation and three-dimensional surface reconstruction

abstract: Flatness is a major geometrical feature of rolled products specified by both production and quality needs. Real-time inspection of flatness is the basis of automatic flatness control. Industrial facilities where rolled products are manufactured have adverse environments that affect artificial vision systems. We present a low-cost flatness inspection system based on optical triangulation by means of a laser stripe emitter and a CMOS matrix camera, designed to be part of an online flatness control system. An accurate and robust method to extract a laser stripe in adverse conditions over rough surfaces is proposed and designed to be applied in real time. Laser extraction relies on a local and a global search. The global search is based on an adjustment of curve segments based on a split-and-merge technique. A real-time recording method of the input data of the flatness inspection system is proposed. It stores information about manufacturing conditions for an offline tuning of the laser stripe extraction method using real data. Flatness measurements carried out over steel strips are evaluated quantitatively and qualitatively. Moreover, the real-time performance of the proposed system is analyzed.


title: Safe Control under Uncertainty with Probabilistic Signal Temporal Logic

abstract: Safe control of dynamical systems that satisfy temporal invariants expressing various safety properties is a challenging problem that has drawn the attention of many researchers. However, making the assumption that such temporal properties are deterministic is far from the reality. For example, a robotic system might employ a camera sensor and a machine learned system to identify obstacles. Consequently, the safety properties the controller has to satisfy, will be a function of the sensor data and the associated classifier. We propose a framework for achieving safe control. At the heart of our approach is the new Probabilistic Signal Temporal Logic (PrSTL), an expressive language to define stochastic properties, and enforce probabilistic guarantees on them. We also present an efficient algorithm to reason about safe controllers given the constraints derived from the PrSTL specification. One of the key distinguishing features of PrSTL is that the encoded logic is adaptive and changes as the system encounters additional data and updates its beliefs about the latent random variables that define the safety properties. We demonstrate our approach by deriving safe control of quadrotors and autonomous vehicles in dynamic environments.


title: Scene Collages and Flexible Camera Arrays

abstract: This paper presents an automatic method for creating a collage from a collection of photos of a scene taken from different viewpoints. The collage is constructed by aligning the images (in terms of their positions, rotations and scales) using a least-squares formulation. We have developed a graph-based optimization algorithm for layering the images so as to minimize the fragmentation of the collage. A collage can be displayed with opaque layers, with transparent layers, or with blended image boundaries. A scene collage can be viewed as a piece-wise perspective representation of a scene with visible seams. This representation has not only aesthetic value but also conveys scene structure and camera motion in an intuitive way. To capture live-action collages of dynamic scenes we have developed camera arrays that can be physically flexed by the user to continuously vary the composition of the scene. The design of our camera arrays enables a user to reconfigure them in terms of the spatial arrangement of the cameras in a matter of minutes. We show several still and dynamic examples that demonstrate that scene collages provide a new and interesting way to experience scenes.


title: Low power HBC PHY baseband transceiver for IEEE 802.15.6 WBAN

abstract: The monitoring healthcare systems that can be used by patients wherever they are, has become very important for today efficient healthcare. Wireless body area network is one possible realization of these systems. Based on IEEE 802.15.6-2012 standard, this paper proposes a low power architecture of Human Body Communication transceiver for Wireless Body Area Network. A new efficient frame synchronization algorithm based on adaptive threshold is adopted. The proposed design is coded and simulated using MATLAB software. Then, the transceiver is implemented using Verilog and synthesized to 90nm CMOS technology. The implemented architecture meets all the standard requirements, consumes 0.63mW, and operates at a clock frequency of 42MHz.


Loxapine: a controlled evaluation in chronic schizophrenic patients.


title: Temporary overvoltage mitigation in symmetrical monopole VSC-MMC HVDC links

abstract: Symmetrical monopolar HVDC links, based on Modular Multilevel Converters (MMCs) represent the state of the art for submarine and underground cable interconnections over long distances. MMCs exhibit significant advantages if compared to other converter topologies, such as lower losses, footprint and harmonic distortion. However, due to the use of a symmetrical monopolar arrangement, severe temporary overvoltages on the dc side (dc-TOV) can arise, due to single pole to ground fault, endangering cable insulation. The paper presents a possible countermeasure for dc-TOV, using an innovative grounding device, which does not hinder the use of 3rd harmonic over modulation. The proposed method allow to reduce the dc-TOV below 1.4 p.u., thus allowing a safe operation of HVDC cables.


title: Standardization for evaluating software-defined networking controllers

abstract: Software-Defined Networking (SDN) is an emerging network architecture, centralized in the SDN controller entity, that decouples the control plane from the data plane. This controller-based solution allows programmability, and dynamic network reconfigurations, providing decision taking with global knowledge of the network. Currently, there are more than thirty SDN controllers with different features, such as communication protocol version, programming language, and architecture. Beyond that, there are also many studies about controller performance with the goal to identify the best one. However, some conclusions have been unjust because benchmark tests did not follow the same methodology, or controllers were not in the same category. Therefore, a standard benchmark methodology is essential to compare controllers fairly. The standardization can clarify and help us to understand the real behavior and weaknesses of an SDN controller. The main goal of this work-in-progress is to show existing benchmark methodologies, bringing a discussion about the need SDN controller benchmark standardization.


title: Buzz: An extensible programming language for heterogeneous swarm robotics

abstract: We present Buzz, a novel programming language for heterogeneous robot swarms. Buzz advocates a compositional approach, offering primitives to define swarm behaviors both from the perspective of the single robot and of the overall swarm. Single-robot primitives include robot-specific instructions and manipulation of neighborhood data. Swarm-based primitives allow for the dynamic management of robot teams, and for sharing information globally across the swarm. Self-organization stems from the completely decentralized mechanisms upon which the Buzz run-time platform is based. The language can be extended to add new primitives (thus supporting heterogeneous robot swarms), and its run-time platform is designed to be laid on top of other frameworks, such as the Robot Operating System. We showcase the capabilities of Buzz by providing code examples, and analyze scalability and robustness of the run-time platform through realistic simulated experiments with representative swarm algorithms.


title: Automatic Road Detection and Centerline Extraction via Cascaded End-to-End Convolutional Neural Network

abstract: Accurate road detection and centerline extraction from very high resolution (VHR) remote sensing imagery are of central importance in a wide range of applications. Due to the complex backgrounds and occlusions of trees and cars, most road detection methods bring in the heterogeneous segments; besides for the centerline extraction task, most current approaches fail to extract a wonderful centerline network that appears smooth, complete, as well as single-pixel width. To address the above-mentioned complex issues, we propose a novel deep model, i.e., a cascaded end-to-end convolutional neural network (CasNet), to simultaneously cope with the road detection and centerline extraction tasks. Specifically, CasNet consists of two networks. One aims at the road detection task, whose strong representation ability is well able to tackle the complex backgrounds and occlusions of trees and cars. The other is cascaded to the former one, making full use of the feature maps produced formerly, to obtain the good centerline extraction. Finally, a thinning algorithm is proposed to obtain smooth, complete, and single-pixel width road centerline network. Extensive experiments demonstrate that CasNet outperforms the state-of-the-art methods greatly in learning quality and learning speed. That is, CasNet exceeds the comparing methods by a large margin in quantitative performance, and it is nearly 25 times faster than the comparing methods. Moreover, as another contribution, a large and challenging road centerline data set for the VHR remote sensing image will be publicly available for further studies.


title: Speech-to-Singing Synthesis: Converting Speaking Voices to Singing Voices by Controlling Acoustic Features Unique to Singing Voices

abstract: This paper describes a speech-to-singing synthesis system that can synthesize a singing voice, given a speaking voice reading the lyrics of a song and its musical score. The system is based on the speech manipulation system STRAIGHT and comprises three models controlling three acoustic features unique to singing voices: the fundamental frequency (F0), phoneme duration, and spectrum. Given the musical score and its tempo, the F0 control model generates the F0 contour of the singing voice by controlling four types of F0 fluctuations: overshoot, vibrato, preparation, and fine fluctuation. The duration control model lengthens the duration of each phoneme in the speaking voice by considering the duration of its musical note. The spectral control model converts the spectral envelope of the speaking voice into that of the singing voice by controlling both the singing formant and the amplitude modulation of formants in synchronization with vibrato. Experimental results show that the proposed system can convert speaking voices into singing voices whose naturalness is almost the same as actual singing voices.


Odin: Team VictorTango's Entry in the DARPA Urban Challenge.


Mathematics Learning through Computational Thinking Activities: A Systematic Literature Review.


title: Solving the dynamic traveling salesman problem using a genetic algorithm with trajectory prediction

abstract: The paper addresses the synergies from combining a heuristic method with a predictive technique to solve the Dynamic Traveling Salesman Problem (DTSP). Particularly, we build a genetic algorithm that feeds on Newton?s motion equation to show how route optimization can be improved when targets are constantly moving. Our empirical evidence stems from the recovery of fish aggregating devices (FADs) by tuna vessels. Based on historical real data provided by GPS buoys attached to the FADs, we first estimate their trajectories to feed a genetic algorithm that searches for the best route considering their future locations. Our solution, which we name Genetic Algorithm based on Trajectory Prediction (GATP), shows that the distance traveled is significantly shorter than implementing other commonly used methods.


title: A General QoS Aware Flow-Balancing and Resource Management Scheme in Distributed Software-Defined Networks

abstract: Due to the limited service capabilities of centralized controllers, it is difficult to process high volume of flows within reasonable time. This particularly degrades the strict quality of service (QoS) requirements of interactive media applications, which is non-negligible factor. To alleviate this concern, distributed deployments of software-defined network (SDN) controllers are inevitable and have gained a predominant position. However, to maintain application specific QoS requirements, the number of resources used in network directly impacts the capital and operational expenditure. Hence, in distributed SDN architectures, issues such as flow arrival rate, resources required and operational cost have significant mutual dependencies on each other. Therefore, it is essential to research feasible methods to maintain QoS and minimize resources provisioning cost. Motivated from this, we propose a solution in a distributed SDN architectures that provides flow-balancing (with guaranteed QoS) in pro-active operations of SDN controllers, and attempts to optimize the use of instance resources provisioning costs. We validate our solution using the tools of queuing theory. Our studies indicate that with our solution, a network with minimum resources and affordable cost with guaranteed application QoS can be set-up.


title: Pedestrian Path Prediction with Recursive Bayesian Filters: A Comparative Study

abstract: In the context of intelligent vehicles, we perform a comparative study on recursive Bayesian filters for pedestrian path prediction at short time horizons (< 2s). We consider Extended Kalman Filters (EKF) based on single dynamical models and Interacting Multiple Models (IMM) combining several such basic models (constant velocity/acceleration/turn). These are applied to four typical pedestrian motion types (crossing, stopping, bending in, starting). Position measurements are provided by an external state-of-the-art stereo vision-based pedestrian detector. We investigate the accuracy of position estimation and path prediction, and the benefit of the IMMs vs. the simpler single dynamical models. Special care is given to the proper sensor modeling and parameter optimization. The dataset and evaluation framework are made public to facilitate benchmarking.


title: Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards

abstract: Ground vehicles equipped with monocular vision systems are a valuable source of high-resolution image data for precision agriculture applications in orchards. This paper presents an image processing framework for fruit detection and counting using orchard image data. A general-purpose image segmentation approach is used, including two feature learning algorithms; multiscale multilayered perceptrons (MLP) and convolutional neural networks (CNN). These networks were extended by including contextual information about how the image data was captured (metadata), which correlates with some of the appearance variations and/or class distributions observed in the data. The pixel-wise fruit segmentation output is processed using the watershed segmentation (WS) and circular Hough transform (CHT) algorithms to detect and count individual fruits. Experiments were conducted in a commercial apple orchard near Melbourne, Australia. The results show an improvement in fruit segmentation performance with the inclusion of metadata on the previously benchmarked MLP network. We extend this work with CNNs, bringing agrovision closer to the state-of-the-art in computer vision, where although metadata had negligible influence, the best pixel-wise F1-score of 0.791 was achieved. The WS algorithm produced the best apple detection and counting results, with a detection F1-score of 0.861. As a final step, image fruit counts were accumulated over multiple rows at the orchard and compared against the post-harvest fruit counts that were obtained from a grading and counting machine. The count estimates using CNN and WS resulted in the best performance for this data set, with a squared correlation coefficient of r2=0.826.


title: Multisensor data fusion: A review of the state-of-the-art

abstract: There has been an ever-increasing interest in multi-disciplinary research on multisensor data fusion technology, driven by its versatility and diverse areas of application. Therefore, there seems to be a real need for an analytical review of recent developments in the data fusion domain. This paper proposes a comprehensive review of the data fusion state of the art, exploring its conceptualizations, benefits, and challenging aspects, as well as existing methodologies. In addition, several future directions of research in the data fusion community are highlighted and described.


Molecular target agents in adrenocortical carcinoma: rationale and difficulties in trial design


title: Automatic rigging and animation of 3D characters

abstract: Animating an articulated 3D character currently requires manual rigging to specify its internal skeletal structure and to define how the input motion deforms its surface. We present a method for animating characters automatically. Given a static character mesh and a generic skeleton, our method adapts the skeleton to the character and attaches it to the surface, allowing skeletal motion data to animate the character. Because a single skeleton can be used with a wide range of characters, our method, in conjunction with a library of motions for a few skeletons, enables a user-friendly animation system for novices and children. Our prototype implementation, called Pinocchio, typically takes under a minute to rig a character on a modern midrange PC.


Strategic Management Accounting and Control


title: Evaluations of LTE Automatic Neighbor Relations

abstract: In recent years, there has been a strong focus on network management simplicity under the device self-organizing networks (SON). A number of SON use cases and features have been and are discussed for 3G Long Term Evolution (LTE). The main SON feature in the first LTE release is methods for automatic configuration of neighbor cell relations - Automatic Neighbor Relations (ANR). In this paper, we describe the neighbor relations management and ANR, and evaluate ANR in a pre-launch, commercially deployed network cluster. The results indicate that ANR configures discovered and needed neighbor relations such that handover can be performed in combination with the neighbor relation establishment without dropping the connection.


Gene Signature Is Associated with Early Stage Rectal Cancer Recurrence


title: A Cloud-Based Smart-Parking System Based on Internet-of-Things Technologies

abstract: This paper introduces a novel algorithm that increases the efficiency of the current cloud-based smart-parking system and develops a network architecture based on the Internet-of-Things technology. This paper proposed a system that helps users automatically find a free parking space at the least cost based on new performance metrics to calculate the user parking cost by considering the distance and the total number of free places in each car park. This cost will be used to offer a solution of finding an available parking space upon a request by the user and a solution of suggesting a new car park if the current car park is full. The simulation results show that the algorithm helps improve the probability of successful parking and minimizes the user waiting time. We also successfully implemented the proposed system in the real world.


Outside the walls of the asylum: the history of care in the community 1750–2000


title: Global anomaly detection in crowded scenes based on optical flow saliency

abstract: In this paper, an algorithm of global anomaly detection in crowded scenes using the saliency in optical flow field is proposed. Before the process of extracting the histogram of maximal optical flow projection (HMOFP), the scale invariant feature transforms (SIFT) method is utilized to get the saliency map of optical flow field. On the basis of the HMOFP feature of normal frames, the online dictionary learning algorithm is used to train an optimal dictionary with proper redundancy after a process of selecting the training samples, which is better than the dictionary simply composed by the HMOFP feature of the whole training frames. In order to detect whether a frame is normal or not, we use the l 1 -norm of the sparse reconstruction coefficients (i.e., the sparse reconstruction cost, SRC) to show the anomaly of the testing frame, which is simple but very effective. The experiment results on UMN dataset and the comparison to the state-of-the-art methods show that our algorithm is promising.


title: Using Monolingual Source-Language Data to Improve MT Performance *

abstract: Statistical machine translation systems are usually trained on large amounts of bilingual text and of monolingual text in the target language. In this paper, we will present a self-training approach which additionally explores the use of monolingual source text, namely the documents to be translated, to improve the system performance. An initial version of the translation system is used to translate the source text. Among the generated translations, target sentences of low quality are automatically identified and discarded. The reliable translations together with their sources are then used as a new bilingual corpus for training an additional phrase translation model. Thus, the translation system can be adapted to the new source data even if no bilingual data in this domain is available. Experimental evaluation was performed on a standard Chinese–English translation task. We focus on settings where the domain and/or the style of the test data is different from that of the training material. We will show a significant improvement in translation quality through the use of the adaptive phrase translation model. BLEU score rises up to 1.1 points, and mWER is reduced by up to 3.1% absolute.


title: Overview of fingerprint recognition system

abstract: This article is an overview of a current research based on fingerprint recognition system. In this paper we highlighted on the previous studies of fingerprint recognition system. This paper is a brief review in the conceptual and structure of fingerprint recognition. The basic fingerprint recognition system consists of four stages: firstly, the sensor which is used for enrolment & recognition to capture the biometric data. Secondly, the pre-processing stage which is used to remove unwanted data and increase the clarity of ridge structure by using enhancement technique. Thirdly, feature extraction stage which take the input from the output of the pre-processing stage to extract the fingerprint features. Fourthly, the matching stage is to compare the acquired feature with the template in the database. Finally, the database which stores the features for the matching stags. The aim of this paper is to review various recently work on fingerprint recognition system and explain fingerprint recognition stages step by step and give summaries of fingerprint databases with characteristics.


title: CAT2000: A Large Scale Fixation Dataset for Boosting Saliency Research

abstract: Saliency modeling has been an active research area in computer vision for about two decades. Existing state of the art models perform very well in predicting where people look in natural scenes. There is, however, the risk that these models may have been overfitting themselves to available small scale biased datasets, thus trapping the progress in a local minimum. To gain a deeper insight regarding current issues in saliency modeling and to better gauge progress, we recorded eye movements of 120 observers while they freely viewed a large number of naturalistic and artificial images. Our stimuli includes 4000 images; 200 from each of 20 categories covering different types of scenes such as Cartoons, Art, Objects, Low resolution images, Indoor, Outdoor, Jumbled, Random, and Line drawings. We analyze some basic properties of this dataset and compare some successful models. We believe that our dataset opens new challenges for the next generation of saliency models and helps conduct behavioral studies on bottom-up visual attention.


title: A comparison of forensic evidence recovery techniques for a windows mobile smart phone

abstract: Acquisition, decoding and presentation of information from mobile devices is complex and challenging. Device memory is usually integrated into the device, making isolation prior to recovery difficult. In addition, manufacturers have adopted a variety of file systems and formats complicating decoding and presentation. A variety of tools and methods have been developed (both commercially and in the open source community) to assist mobile forensics investigators. However, it is unclear to what extent these tools can present a complete view of the information held on a mobile device, or the extent the results produced by different tools are consistent. This paper investigates what information held on a Windows Mobile smart phone can be recovered using several different approaches to acquisition and decoding. The paper demonstrates that no one technique recovers all information of potential forensic interest from a Windows Mobile device; and that in some cases the information recovered is conflicting.


title: Analysis, design and implementation of an active clamp flyback converter

abstract: This paper presents the detailed circuit operation, mathematical analysis, and design example of the active clamp flyback converter. The auxiliary switch and clamp capacitor are used in the flyback converter to recycle the energy stored in the transformer leakage in order to minimize the spike voltage at the transformer primary side. Therefore the voltage stress of main switch can be reduced. The active clamped circuit can also help the main switch to turn on at ZVS using the switch output capacitor and transformer leakage inductance. First the circuit operation and mathematical analysis are provided. The design example of active clamp flyback converter is also presented. Finally the experimental results based on a 120 W prototype circuit are provided to verify the system performance


title: Photocatalytic disinfection using titanium dioxide: spectrum and mechanism of antimicrobial activity

abstract: The photocatalytic properties of titanium dioxide are well known and have many applications including the removal of organic contaminants and production of self-cleaning glass. There is an increasing interest in the application of the photocatalytic properties of TiO2 for disinfection of surfaces, air and water. Reviews of the applications of photocatalysis in disinfection (Gamage and Zhang 2010; Chong et al., Wat Res 44(10):2997–3027, 2010) and of modelling of TiO2 action have recently been published (Dalrymple et al. , Appl Catal B 98(1–2):27–38, 2010). In this review, we give an overview of the effects of photoactivated TiO2 on microorganisms. The activity has been shown to be capable of killing a wide range of Gram-negative and Gram-positive bacteria, filamentous and unicellular fungi, algae, protozoa, mammalian viruses and bacteriophage. Resting stages, particularly bacterial endospores, fungal spores and protozoan cysts, are generally more resistant than the vegetative forms, possibly due to the increased cell wall thickness. The killing mechanism involves degradation of the cell wall and cytoplasmic membrane due to the production of reactive oxygen species such as hydroxyl radicals and hydrogen peroxide. This initially leads to leakage of cellular contents then cell lysis and may be followed by complete mineralisation of the organism. Killing is most efficient when there is close contact between the organisms and the TiO2 catalyst. The killing activity is enhanced by the presence of other antimicrobial agents such as Cu and Ag.


title: Design of the Moses Decoder for Statistical Machine Translation

abstract: We present a description of the implementation of the open source decoder for statistical machine translation which has become popular with many researchers in SMT research. The goal of the project is to create an open, high quality phrase-based decoder which can reduce the time and barrier to entry for researchers wishing to do SMT research. We discuss the major design objective for the Moses decoder, its performance relative to other SMT decoders, and the steps we are taking to ensure that its success will continue.


title: Evaluating Experiences in Different Virtual Reality Setups

abstract: This paper describes the evaluation of three different scenarios in the fully immersive room-based virtual environment DAVE (Definitely Affordable Virtual Environment) and a head-mounted display, the Oculus Rift. The evaluation focuses on comparing the two immersive environments and three different scenarios (observation, emotion in a roller coaster, and interaction) in regards to typical virtual-reality characteristics, such as immersion, engagement, but also on cybersickness and the overall experience. First results indicate the DAVE environment better supports scenarios, which require the user to directly interact with the environment. The roller coaster scenario creates stronger immersion and a higher nausea-level, while the interactive task is more engaging in terms of fun.


Technology Addiction: How Social Network Sites Impact our Lives


title: Focus+Context Metro Maps

abstract: We introduce a focus+context method to visualize a complicated metro map of a modern city on a small displaying area. The context of our work is with regard the popularity of mobile devices. The best route to the destination, which can be obtained from the arrival time of trains, is highlighted. The stations on the route enjoy larger spaces, whereas the other stations are rendered smaller and closer to fit the whole map into a screen. To simplify the navigation and route planning for visitors, we formulate various map characteristics such as octilinear transportation lines and regular station distances into energy terms. We then solve for the optimal layout in a least squares sense. In addition, we label the names of stations that are on the route of a passenger according to human preferences, occlusions, and consistencies of label positions using the graph cuts method. Our system achieves real-time performance by being able to report instant information because of the carefully designed energy terms. We apply our method to layout a number of metro maps and show the results and timing statistics to demonstrate the feasibility of our technique.


A Localization Toolkit for SenticNet


title: Women on the Web: A critical appraisal of a sample reflecting the range and content of women’s sites on the Internet, with particular reference to the support of women’s interaction and participation

abstract: This paper investigates the availability of women’s sites on the Internet in the light of the research that has been conducted into women, communications and technology. The paper draws upon a systematic evaluation of ten women’s sites, covering a range of sectors including general sites, business, health and technology. The evaluation reveals that such sites offer useful sources of information on a range of subjects of potential interest to women and that they are stable and accessible resources. The sites tend to be freely accessible and apparently reputable, with little evidence of commercialism. However, there are concerns about the lack of security on the sites and a poor provision of the kind of information that is necessary to measure their authority and reliability. While all sites offer some form of interactivity, they tend to offer in each case only a small proportion of the interactive functions that might be enabled and demonstrate little awareness of the significance of these in helping women to network effectively.


title: Cogging Torque Definitions for Magnetic Gears and Magnetically Geared Electrical Machines

abstract: Although cogging torque in magnetic gears (MGs) has been studied, the additional degree of freedom inherent to MGs makes it unclear exactly what is defined as the cogging torque of a MG. Although MGs generally have relatively smooth torque transfer characteristics, this paper reveals that when the gear is used in an up-speed configuration, cogging effects may be amplified under startup conditions. This paper distinguishes between two kinds of cogging torque in MGs, termed the synchronous cogging torque and the true cogging torque . Calculation of the true cogging torque is more difficult than the synchronous cogging torque, but an approximation to the true cogging torque can be obtained using the synchronous cogging torque. The theory is verified experimentally and supported further by results from dynamic simulations. The impact on magnetically geared machines is also considered.


title: Optimizing the Block I/O Subsystem for Fast Storage Devices

abstract: Fast storage devices are an emerging solution to satisfy data-intensive applications. They provide high transaction rates for DBMS, low response times for Web servers, instant on-demand paging for applications with large memory footprints, and many similar advantages for performance-hungry applications. In spite of the benefits promised by fast hardware, modern operating systems are not yet structured to take advantage of the hardware’s full potential. The software overhead caused by an OS, negligible in the past, adversely impacts application performance, lessening the advantage of using such hardware. Our analysis demonstrates that the overheads from the traditional storage-stack design are significant and cannot easily be overcome without modifying the hardware interface and adding new capabilities to the operating system. ::: In this article, we propose six optimizations that enable an OS to fully exploit the performance characteristics of fast storage devices. With the support of new hardware interfaces, our optimizations minimize per-request latency by streamlining the I/O path and amortize per-request latency by maximizing parallelism inside the device. We demonstrate the impact on application performance through well-known storage benchmarks run against a Linux kernel with a customized SSD. We find that eliminating context switches in the I/O path decreases the software overhead of an I/O request from 20 microseconds to 5 microseconds and a new request merge scheme called Temporal Merge enables the OS to achieve 87% to 100% of peak device performance, regardless of request access patterns or types. Although the performance improvement by these optimizations on a standard SATA-based SSD is marginal (because of its limited interface and relatively high response times), our sensitivity analysis suggests that future SSDs with lower response times will benefit from these changes. The effectiveness of our optimizations encourages discussion between the OS community and storage vendors about future device interfaces for fast storage devices.


title: Adaptive Noise Immune Cluster Ensemble Using Affinity Propagation

abstract: Cluster ensemble is one of the main branches in the ensemble learning area which is an important research focus in recent years. The objective of cluster ensemble is to combine multiple clustering solutions in a suitable way to improve the quality of the clustering result. In this paper, we design a new noise immune cluster ensemble framework named as $AP^{2}CE$ to tackle the challenges raised by noisy datasets. $AP^{2}CE$ not only takes advantage of the affinity propagation algorithm (AP) and the normalized cut algorithm (Ncut), but also possesses the characteristics of cluster ensemble. Compared with traditional cluster ensemble approaches, $AP^{2}CE$ is characterized by several properties. ( $1$ ) It adopts multiple distance functions instead of a single Euclidean distance function to avoid the noise related to the distance function. ( $2$ ) $AP^{2}CE$ applies AP to prune noisy attributes and generate a set of new datasets in the subspaces consists of representative attributes obtained by AP. ( $3$ ) It avoids the explicit specification of the number of clusters. ( $4$ ) $AP^{2}CE$ adopts the normalized cut algorithm as the consensus function to partition the consensus matrix and obtain the final result. In order to improve the performance of $AP^{2}CE$ , the adaptive $AP^{2}CE$ is designed, which makes use of an adaptive process to optimize a newly designed objective function. The experiments on both synthetic and real datasets show that ( $1$ ) $AP^{2}CE$ works well on most of the datasets, in particular the noisy datasets; ( $2$ ) $AP^{2}CE$ is a better choice for most of the datasets when compared with other cluster ensemble approaches; ( $3$ ) $AP^{2}CE$ has the capability to provide more accurate, stable and robust results.


title: Hierarchical Neural Language Models for Joint Representation of Streaming Documents and their Content

abstract: We consider the problem of learning distributed representations for documents in data streams. The documents are represented as low-dimensional vectors and are jointly learned with distributed vector representations of word tokens using a hierarchical framework with two embedded neural language models. In particular, we exploit the context of documents in streams and use one of the language models to model the document sequences, and the other to model word sequences within them. The models learn continuous vector representations for both word tokens and documents such that semantically similar documents and words are close in a common vector space. We discuss extensions to our model, which can be applied to personalized recommendation and social relationship mining by adding further user layers to the hierarchy, thus learning user-specific vectors to represent individual preferences. We validated the learned representations on a public movie rating data set from MovieLens, as well as on a large-scale Yahoo News data comprising three months of user activity logs collected on Yahoo servers. The results indicate that the proposed model can learn useful representations of both documents and word tokens, outperforming the current state-of-the-art by a large margin.


title: Parallel skeletons for structured composition

abstract: In this paper, we propose a straightforward solution to the problems of compositional parallel programming by using skeletons as the uniform mechanism for structured composition. In our approach parallel programs are constructed by composing procedures in a conventional base language using a set of high-level, pre-defined, functional, parallel computational forms known as skeletons . The ability to compose skeletons provides us with the essential tools for building further and more complex application-oriented skeletons specifying important aspects of parallel computation. Compared with the process network based composition approach, such as PCN, the skeleton approach abstracts away the fine details of connecting communication ports to the higher level mechanism of making data distributions conform, thus avoiding the complexity of using lower level ports as the means of interaction. Thus, the framework provides a natural integration of the compositional programming approach with the data parallel programming paradigm.


title: A 0.035-pJ/bit/dB 20-Gb/s Adaptive Linear Equalizer With an Adaptation Time of 2.68 $\mu\text{s}$

abstract: A 20-Gb/s adaptive linear equalizer with a coefficient fast-converging method is presented. By using the asynchronous sampling technique, the power dissipation of the circuits, realizing the adaptation method, can be reduced. However, the equalization coefficients require a considerable amount of time to be determined. To shorten the asynchronous sampling time, the high-frequency gain of the linear equalizer is calibrated prior to the low-frequency one. With a 20-Gb/s pseudorandom binary sequence of <inline-formula><tex-math notation="LaTeX">$2^{7}-1$</tex-math></inline-formula>, the measured bit-error-rates are all less than 10<sup>−12</sup> for channel loss from −7.98 to −18.3 dB. Moreover, the equalization coefficients are determined within 2.68 <inline-formula><tex-math notation="LaTeX">$\mu\text{s}$</tex-math></inline-formula>. Fabricated in a 40-nm CMOS technology, this equalizer totally consumes 12.8 mW from a 1.1-V supply, of which only 4.9 mW dissipates in the circuits, realizing the proposed method. The calculated figure-of-merit is 0.035-pJ/bit/dB.


Assessing Nurses Knowledge of Glasgow Coma Scale in Emergency and Outpatient Department


title: Processing of Multi-dimensional Sensorimotor Information in the Spinal and Cerebellar Neuronal Circuitry: A New Hypothesis

abstract: Why are sensory signals and motor command signals combined in the neurons of origin of the spinocerebellar pathways and why are the granule cells that receive this input thresholded with respect to their spike output? In this paper, we synthesize a number of findings into a new hypothesis for how the spinocerebellar systems and the cerebellar cortex can interact to support coordination of our multi-segmented limbs and bodies. A central idea is that recombination of the signals available to the spinocerebellar neurons can be used to approximate a wide array of functions including the spatial and temporal dependencies between limb segments, i.e. information that is necessary in order to achieve coordination. We find that random recombination of sensory and motor signals is not a good strategy since, surprisingly, the number of granule cells severely limits the number of recombinations that can be represented within the cerebellum. Instead, we propose that the spinal circuitry provides useful recombinations, which can be described as linear projections through aspects of the multi-dimensional sensorimotor input space. Granule cells, potentially with the aid of differentiated thresholding from Golgi cells, enhance the utility of these projections by allowing the Purkinje cell to establish piecewise-linear approximations of non-linear functions. Our hypothesis provides a novel view on the function of the spinal circuitry and cerebellar granule layer, illustrating how the coordinating functions of the cerebellum can be crucially supported by the recombinations performed by the neurons of the spinocerebellar systems.


title: A novel approach for constructing basis functions in approximate dynamic programming for feedback control

abstract: This paper presents a novel approach for constructing basis functions in approximate dynamic programming (ADP) through the locally linear embedding (LLE) process. It considers the experience (sample) data as a high-dimensional space and the basis functions to be solved as a low-dimensional space. Through mapping the high-dimensional data into a single global coordinate system of lower dimensionality, the solved basis functions in low-dimensional space have the property that nearby experience data in the high dimensional space remain nearby and similarly co-located with respect to one in the low dimensional space. Thus, the obtained basis functions can precisely approximate the real value/action-value function. The simulation results show that the basis functions obtained by LLE can represent the final policy with a higher precision.


title: A Compact S-Box Design for SMS4 Block Cipher

abstract: This paper proposes a compact design of SMS4 S-box using combinational logic which is suitable for the implementation in area constraint environments like smart cards. The inversion algorithm of the proposed S-box is based on composite field GF(((22)2)2) using normal basis at all levels. In our approach, we examined all possible normal basis combinations having trace equal to one at each subfield level. There are 16 such possible combinations with normal basis and we have compared the S-box designs based on each case in terms of logic gates it uses for implementation. The isomorphism mapping and inverse mapping bit matrices are fully optimized using greedy algorithm. We prove that our best case reduces the complexity upon the SMS4 S-box design with existing inversion algorithm based on polynomial basis by 15% XOR and 42% AND gates.


title: A network analysis of tourism research

abstract: This paper uses network analysis to identify the pioneering scholars and seminal works which have influenced recent papers in leading journals. The analysis extends beyond rankings of scholars by using co-citation networks to visualize the relationships between the most influential scholars and works and to uncover the disciplinary contributions which have supported the emergence of tourism as a field of academic study. The networks of scholars and works illuminate invisible colleges, tribes and territories in tourism research and indicate that while the social sciences have been most influential, business-related citations are increasing. The findings contribute to the discourse about the epistemology of tourism research by using bibliometric techniques to offer insights into the interdisciplinary structure of tourism research.


title: Landmark image classification using 3D point clouds

abstract: Most of the existing approaches for landmark image classification utilize either holistic features or interest of points in the whole image to train the classification model, which may lead to unsatisfactory result due to involvement of much information non-located on the landmark in the training process. In this paper, we propose a novel approach to improve landmark image classification result via a process of 2D to 3D reconstruction and 3D to 2D projection of iconic landmark images. Particularly, we first select iconic images from labeled landmark image collections to reconstruct a 3D landmark represented in point clouds. Then, 3D point clouds are projected back onto the same iconic images to obtain the landmark-region of each iconic image and subsequently extract SIFT features from the landmark-region to construct a k-dimensional tree (kd-tree) for each landmark. This process is able to filter out noise points corresponding to clutter background and non-landmark objects in the iconic images. Finally, the unlabeled images can be classified into predefined landmark categories based on the amount of matched feature points between the image features and the kd-trees. The experimental result and comparison with the state-of-the-art demonstrate the effectiveness of our approach.


title: BRAINIAC: Bringing reliable accuracy into neurally-implemented approximate computing

abstract: Applications with large amounts of data, real-time constraints, ultra-low power requirements, and heavy computational complexity present significant challenges for modern computing systems, and often fall within the category of high performance computing (HPC). As such, computer architects have looked to high performance single instruction multiple data (SIMD) architectures, such as accelerator-rich platforms, for handling these workloads. However, since the results of these applications do not always require exact precision, approximate computing may also be leveraged. In this work, we introduce BRAINIAC, a heterogeneous platform that combines precise accelerators with neural-network-based approximate accelerators. These reconfigurable accelerators are leveraged in a multi-stage flow that begins with simple approximations and resorts to more complex ones as needed. We employ high-level, application-specific light-weight checks (LWCs) to throttle this multi-stage acceleration flow and reliably ensure user-specified accuracy at runtime. Evaluation of the performance and energy of our heterogeneous platform for error tolerance thresholds of 5%–25% demonstrates an average of 3× gain over computation that only includes precise acceleration, and 15×-35× gain over software-based computation.


Attentional inhibitory surrounds in orentation space


Field-Aware Factorization Machines


title: Intelligent Big Data Analysis Architecture Based on Automatic Service Composition

abstract: Big Data contains massive information, which are generating from heterogeneous, autonomous sources with distributed and anonymous platforms. Since, it raises extreme challenge to organizations to store and process these data. Conventional pathway of store and process is happening as collection of manual steps and it is consuming various resources. An automated real-time and online analytical process is the most cognitive solution. Therefore it needs state of the art approach to overcome barriers and concerns currently facing by the Big Data industry. In this paper we proposed a novel architecture to automate data analytics process using Nested Automatic Service Composition (NASC) and CRoss Industry Standard Platform for Data Mining (CRISP-DM) as main based technologies of the solution. NASC is well defined scalable technology to automate multi-disciplined problems domains. Since CRISP-DM also a well-known data science process which can be used as innovative accumulator of multi-dimensional data sets. CRISP-DM will be mapped with Big Data analytical process and NASC will automate the CRISP-DM process in an intelligent and innovative way.


title: Wideband CPW-Fed Flexible Bow-Tie Slot Antenna for WLAN/WiMax Systems

abstract: A bow-tie antenna based on a slot configuration in a single metal sheet on top of a very thin flexible substrate is introduced. The antenna is constructed from two slotted right-angle triangles fed by a coplanar waveguide transmission line. The topology is very simple and extremely easy to tune in order to reach the proper characteristics after mounting on a supporting structure. A prototype is designed, fabricated, and characterized experimentally. The tunability is proven by considering both the version in free space and the version for use on a brick wall. Measurements demonstrate good agreement with simulations. Both versions cover the wireless local area network (2.4 and 3.65 GHz) and WiMax (2.3, 2.5, and 3.5 GHz) spectra, with an overall impedance bandwidth of 1.79 GHz (57.7%) and 1.46 GHz (49.7%), respectively. The radiation of the antenna is bidirectional with maximum gains of 6.30 and 5.09 dBi for the free space and brick wall versions, respectively.


title: SONYC: A System for the Monitoring, Analysis and Mitigation of Urban Noise Pollution

abstract: We present the Sounds of New York City (SONYC) project, a smart cities initiative focused on developing a cyber-physical system for the monitoring, analysis and mitigation of urban noise pollution. Noise pollution is one of the topmost quality of life issues for urban residents in the U.S. with proven effects on health, education, the economy, and the environment. Yet, most cities lack the resources to continuously monitor noise and understand the contribution of individual sources, the tools to analyze patterns of noise pollution at city-scale, and the means to empower city agencies to take effective, data-driven action for noise mitigation. The SONYC project advances novel technological and socio-technical solutions that help address these needs. SONYC includes a distributed network of both sensors and people for large-scale noise monitoring. The sensors use low-cost, low-power technology, and cutting-edge machine listening techniques, to produce calibrated acoustic measurements and recognize individual sound sources in real time. Citizen science methods are used to help urban residents connect to city agencies and each other, understand their noise footprint, and facilitate reporting and self-regulation. Crucially, SONYC utilizes big data solutions to analyze, retrieve and visualize information from sensors and citizens, creating a comprehensive acoustic model of the city that can be used to identify significant patterns of noise pollution. These data can be used to drive the strategic application of noise code enforcement by city agencies to optimize the reduction of noise pollution. The entire system, integrating cyber, physical and social infrastructure, forms a closed loop of continuous sensing, analysis and actuation on the environment. SONYC provides a blueprint for the mitigation of noise pollution that can potentially be applied to other cities in the US and abroad.


title: Learning Spatially Regularized Correlation Filters for Visual Tracking

abstract: Robust and accurate visual tracking is one of the most challenging computer vision problems. Due to the inherent lack of training data, a robust approach for constructing a target appearance model is crucial. Recently, discriminatively learned correlation filters (DCF) have been successfully applied to address this problem for tracking. These methods utilize a periodic assumption of the training samples to efficiently learn a classifier on all patches in the target neighborhood. However, the periodic assumption also introduces unwanted boundary effects, which severely degrade the quality of the tracking model. We propose Spatially Regularized Discriminative Correlation Filters (SRDCF) for tracking. A spatial regularization component is introduced in the learning to penalize correlation filter coefficients depending on their spatial location. Our SRDCF formulation allows the correlation filters to be learned on a significantly larger set of negative training samples, without corrupting the positive samples. We further propose an optimization strategy, based on the iterative Gauss-Seidel method, for efficient online learning of our SRDCF. Experiments are performed on four benchmark datasets: OTB-2013, ALOV++, OTB-2015, and VOT2014. Our approach achieves state-of-the-art results on all four datasets. On OTB-2013 and OTB-2015, we obtain an absolute gain of 8.0% and 8.2% respectively, in mean overlap precision, compared to the best existing trackers.


title: Task and user effects on reading patterns in information search

abstract: We report on an investigation into people’s behaviors on information search tasks, specifically the relation between eye movement patterns and task characteristics. We conducted two independent user studies (n = 32 and n = 40), one with journalism tasks and the other with genomics tasks. The tasks were constructed to represent information needs of these two different users groups and to vary in several dimensions according to a task classification scheme. For each participant we classified eye gaze data to construct models of their reading patterns. The reading models were analyzed with respect to the effect of task types and Web page types on reading eye movement patterns. We report on relationships between tasks and individual reading behaviors at the task and page level. Specifically we show that transitions between scanning and reading behavior in eye movement patterns and the amount of text processed may be an implicit indicator of the current task type facets. This may be useful in building user and task models that can be useful in personalization of information systems and so address design demands driven by increasingly complex user actions with information systems. One of the contributions of this research is a new methodology to model information search behavior and investigate information acquisition and cognitive processing in interactive information tasks.


title: An Introduction to Concept-Level Sentiment Analysis

abstract: The ways people express their opinions and sentiments have radically changed in the past few years thanks to the advent of social networks, web communities, blogs, wikis, and other online collaborative media. The distillation of knowledge from the huge amount of unstructured information on the Web can be a key factor for marketers who want to create an image or identity in the minds of their customers for their product or brand. These online social data, however, remain hardly accessible to computers, as they are specifically meant for human consumption. The automatic analysis of online opinions, in fact, involves a deep understanding of natural language text by machines, from which we are still very far. To this end, concept-level sentiment analysis aims to go beyond a mere word-level analysis of text and provide novel approaches to opinion mining and sentiment analysis that enable a more efficient passage from (unstructured) textual information to (structured) machine-processable data, in potentially any domain.


title: A Case Study on Formal Verification of Self-Adaptive Behaviors in a Decentralized System

abstract: Self-adaptation is a promising approach to manage the complexity of modern software systems. A self-adaptive system is able to adapt autonomously to internal dynamics and changing conditions in the environment to achieve particular quality goals. Our particular interest is in decentralized selfadaptive systems, in which central control of adaptation is not an option. One important challenge in self-adaptive systems, in particular those with decentralized control of adaptation, is to provide guarantees about the intended runtime qualities. In this paper, we present a case study in which we use model checking to verify behavioral properties of a decentralized self-adaptive system. Concretely, we contribute with a formalized architecture model of a decentralized traffic monitoring system and prove a number of self-adaptation properties for flexibility and robustness. To model the main processes in the system we use timed automata, and for the specification of the required properties we use timed computation tree logic. We use the Uppaal tool to specify the system and verify the flexibility and robustness properties.


title: A breakthrough in Speech emotion recognition using Deep Retinal Convolution Neural Networks

abstract: Speech emotion recognition (SER) is to study the formation and change of speaker's emotional state from the speech signal perspective, so as to make the interaction between human and computer more intelligent. SER is a challenging task that has encountered the problem of less training data and low prediction accuracy. Here we propose a data augmentation algorithm based on the imaging principle of the retina and convex lens, to acquire the different sizes of spectrogram and increase the amount of training data by changing the distance between the spectrogram and the convex lens. Meanwhile, with the help of deep learning to get the high-level features, we propose the Deep Retinal Convolution Neural Networks (DRCNNs) for SER and achieve the average accuracy over 99%. The experimental results indicate that DRCNNs outperforms the previous studies in terms of both the number of emotions and the accuracy of recognition. Predictably, our results will dramatically improve human-computer interaction.


title: Accurate Camera Calibration from Multi-View Stereo and Bundle Adjustment

abstract: The advent of high-resolution digital cameras and sophisticated multi-view stereo algorithms offers the promise of unprecedented geometric fidelity in image-based modeling tasks, but it also puts unprecedented demands on camera calibration to fulfill these promises. This paper presents a novel approach to camera calibration where top-down information from rough camera parameter estimates and the output of a multi-view-stereo system on scaled-down input images is used to effectively guide the search for additional image correspondences and significantly improve camera calibration parameters using a standard bundle adjustment algorithm (Lourakis and Argyros 2008). The proposed method has been tested on six real datasets including objects without salient features for which image correspondences cannot be found in a purely bottom-up fashion, and objects with high curvature and thin structures that are lost in visual hull construction even with small errors in camera parameters. Three different methods have been used to qualitatively assess the improvements of the camera parameters. The implementation of the proposed algorithm is publicly available at Furukawa and Ponce (2008b).


title: Participation of three lymphoid cell types in the in vitro activation of cell-mediated immunity to a syngeneic gross virus-induced lymphoma in rats

abstract: Abstract W/Fu rats inoculated with the syngeneic Gross-virus induced lymphoma, (C58NT)D, had transient lymphocyte-mediated specific cytotoxicity against the tumor cells at 7–15 days after tumor injection. Spleen cells 40 days after immunization (spleen 40) were unreactive by a direct 4-hr 51 Cr release assay, but activity appeared after in vitro culture of these spleen eclls by themselves for 18–24 hr. The nature of the cells involved in the activation of cytotoxicity and the characteristics of the effector cells themselves were studied. Significant differences were seen in the cell types involved in the activation phase and the effector phase. Activation appeared to require the cooperation of three cell types. Induction of activity was lost by treatment of cells with ATS plus complement, by passage over an EAC-column, or by treatment with carbonyl iron. Thus, T cells, CRL and macrophages were necessary for full activation of cytotoxicity in spleen 40. In contrast, after activation, only CRL seemed to be required for cytotoxicity, and treatment wih ATS or carbonyl iron had little effect. The effector cell detected after in vitro activation was quite distinct from that seen in the direct cytotoxicity assay with spleen cells at 10 days after tumor cell inoculation. The early, direct cytotoxic reactivity was dependent on T cells, being eliminated by treatment with ATS and complement but not by EAC columns or carbonyl iron. It appears therefore that the in vitro activation is a separate mechanism for cytotoxicity against tumor cells, rather than a simple recovery of T cells from in vivo inhibition.


title: Striving for semantic convergence with fuzzy cognitive maps and graph databases

abstract: The exponentially rising amounts of urban data demand new conceptual and technical methods for their management and storage. The era of the Semantic Web requires a convergence toward commonly shared meanings. The combination of fuzzy cognitive maps with graph databases is a first approach to a solution. This article determines the basic requirements for the storing of fuzzy cognitive maps to test current graph databases for their structural suitability. Six out of 47 graph databases fulfill the requirements and are thus recommended for further research purposes. As a proof of concept, OrientDB is used to present how a semantic convergence can be reached through the combination of fuzzy cognitive maps and graph databases in a cognitive city by tackling fuzziness.


title: Radical Analysis Network for Zero-Shot Learning in Printed Chinese Character Recognition

abstract: Chinese characters have a huge set of character categories, more than 20, 000 and the number is still increasing as more and more novel characters continue being created. However, the enormous characters can be decomposed into a compact set of about 500 fundamental and structural radicals. This paper introduces a novel radical analysis network (RAN) to recognize printed Chinese characters by identifying radicals and analyzing two-dimensional spatial structures among them. The proposed RAN first extracts visual features from input by employing convolutional neural networks as an encoder. Then a decoder based on recurrent neural networks is employed, aiming at generating captions of Chinese characters by detecting radicals and two-dimensional structures through a spatial attention mechanism. The manner of treating a Chinese character as a composition of radicals rather than a single character class largely reduces the size of vocabulary and enables RAN to possess the ability of recognizing unseen Chinese character classes, namely zero-shot learning.