title: Detecting Opinion Leaders and Trends in Online Communities

abstract: Today, online communities in the World Wide Web become increasingly interactive and networked. Web 2.0 technologies provide a multitude of platforms, such as blogs, wikis, and forums where for example consumers can disseminate data about products and manufacturers. This data provides an abundance of information on personal experiences and opinions which are extremely relevant for companies and sales organizations. Subjects of postings can be partly retrieved by state of the art text mining techniques. A much more challenging task is to detect factors influencing the evolvement of opinions within the social network. For such a kind of trend scouting you have to take into account the relationships among the community members. Social network analysis helps to explain social behavior of linked persons by providing quantitative measures of social interactions. A new approach based on social network analysis is presented, which allows detecting opinion leaders and opinion trends. This leads a better understanding of opinion formation. The overall concept based on text mining and social network analysis is introduced. An example is given which illustrates the analysis process.


title: Altered Resting Brain Function and Structure in Professional Badminton Players

abstract: Neuroimaging studies of professional athletic or musical training have demonstrated considerable practice-dependent plasticity in various brain structures, which may reflect distinct training demands. In the present study, structural and functional brain alterations were examined in professional badminton players and compared with healthy controls using magnetic resonance imaging (MRI) and resting-state functional MRI. Gray matter concentration (GMC) was assessed using voxel-based morphometry (VBM), and resting-brain functions were measured by amplitude of low-frequency fluctuation (ALFF) and seed-based functional connectivity. Results showed that the athlete group had greater GMC and ALFF in the right and medial cerebellar regions, respectively. The athlete group also demonstrated smaller ALFF in the left superior parietal lobule and altered functional connectivity between the left superior parietal and frontal regions. These findings indicate that badminton expertise is associated with not only plastic structural changes in terms of enlarged gray matter density in the cerebellum, but also functional alterations in fronto-parietal connectivity. Such structural and functional alterations may reflect specific experiences of badminton training and practice, including high-capacity visuo-spatial processing and hand-eye coordination in addition to refined motor skills.


title: Concentri Cloud: Word Cloud Visualization for Multiple Text Documents

abstract: Word clouds provide a simple and effective means to visually communicate the most frequent words of text documents. However, only few word cloud visualizations support the contrastive analysis of multiple texts. This paper introduces Concentri Cloud, a layered word cloud layout that merges the words from several text documents into a single visualization. The weighted words are arranged in a concentric layout, with those representing the individual documents on the outer circle and the merged ones on inner circles. Interaction techniques allow to analyze the word cloud composition and to provide details on demand. The approach has been implemented and tested on several examples. A qualitative evaluation indicates the general value of Concentri Cloud and reveals benefits and limitations.


title: Music generation with variational recurrent autoencoder supported by history

abstract: A new architecture of an artificial neural network that helps to generate longer melodic patterns is introduced alongside with methods for post-generation filtering. The proposed approach called variational autoencoder supported by history is based on a recurrent highway gated network combined with a variational autoencoder. Combination of this architecture with filtering heuristics allows generating pseudo-live acoustically pleasing and melodically diverse music.


title: On the Prospects for a Science of Visualization

abstract: This paper explores the extent to which a scientific framework for visualization might be possible. It presents several potential parts of a framework, illustrated by application to the visualization of correlation in scatterplots. The first is an extended-vision thesis, which posits that a viewer and visualization system can be usefully considered as a single system that perceives structure in a dataset, much like “basic” vision perceives structure in the world. This characterization is then used to suggest approaches to evaluation that take advantage of techniques used in vision science. Next, an optimal-reduction thesis is presented, which posits that an optimal visualization enables the given task to be reduced to the most suitable operations in the extended system. A systematic comparison of alternative designs is then proposed, guided by what is known about perceptual mechanisms. It is shown that these elements can be extended in various ways—some even overlapping with parts of vision science. As such, a science of some kind appears possible for at least some parts of visualization. It would remain distinct from design practice, but could nevertheless assist with the design of visualizations that better engage human perception and cognition.


title: Silica-based rib waveguides for integrated optics using multimode interference

abstract: Integrated optical waveguides, as well as splitters and switches utilizing the principle of multimode interference have been realized in PECVD SiON/SiO 2 technology. The waveguide design has been optimized to meet a number of requirements, such as small device size, polarization- independent operation, and low-power active operation. The design, fabrication, and initial experimental results for these devices are discussed.


title: Structure-Aware and Temporally Coherent 3D Human Pose Estimation

abstract: Deep learning methods for 3D human pose estimation from RGB images require a huge amount of domain-specific labeled data for good in-the-wild performance. However, obtaining annotated 3D pose data requires a complex motion capture setup which is generally limited to controlled settings. We propose a semi-supervised learning method using a structure-aware loss function which is able to utilize abundant 2D data to learn 3D information. Furthermore, we present a simple temporal network which uses additional context present in pose sequences to improve and temporally harmonize the pose estimates. Our complete pipeline improves upon the state-of-the-art by 11.8% and works at 30 FPS on a commodity graphics card.


Towards Diverse Text Generation with Inverse Reinforcement Learning


Discovering repeated patterns in music : state of knowledge , challenges , perspectives


title: A Robust ISFET pH-Measuring Front-End for Chemical Reaction Monitoring

abstract: This paper presents a robust, low-power and compact ion-sensitive field-effect transistor (ISFET) sensing front-end for pH reaction monitoring using unmodified CMOS. Robustness is achieved by overcoming problems of DC offset due to trapped charge and transcoductance reduction due to capacitive division, which commonly exist with implementation of ISFETs in CMOS. Through direct feedback to the floating gate and a low-leakage switching scheme, all the unwanted factors are eliminated while the output is capable of tracking a pH reaction which occurs at the sensing surface. This is confirmed through measured results of multiple devices of different sensing areas, achieving a mean amplification of 1.28 over all fabricated devices and pH sensitivity of 42.1 mV/pH. The front-end is also capable of compensating for accumulated drift using the designed switching scheme by resetting the floating gate voltage. The circuit has been implemented in a commercially-available 0.35 $\mu$ m CMOS technology achieving a combined chemical and electrical output RMS noise of 3.1 mV at a power consumption of 848.1 nW which is capable of detecting pH changes as small as 0.06 pH.


title: Split-level I/O scheduling

abstract: We introduce split-level I/O scheduling, a new framework that splits I/O scheduling logic across handlers at three layers of the storage stack: block, system call, and page cache. We demonstrate that traditional block-level I/O schedulers are unable to meet throughput, latency, and isolation goals. By utilizing the split-level framework, we build a variety of novel schedulers to readily achieve these goals: our Actually Fair Queuing scheduler reduces priority-misallocation by 28x; our Split-Deadline scheduler reduces tail latencies by 4x; our Split-Token scheduler reduces sensitivity to interference by 6x. We show that the framework is general and operates correctly with disparate file systems (ext4 and XFS). Finally, we demonstrate that split-level scheduling serves as a useful foundation for databases (SQLite and PostgreSQL), hypervisors (QEMU), and distributed file systems (HDFS), delivering improved isolation and performance in these important application scenarios.


title: Increasing Prevalence of the Metabolic Syndrome Among U.S. Adults

abstract: OBJECTIVE —The prevalence of the metabolic syndrome is high among U.S. adults. Our purpose was to determine whether the prevalence of this syndrome has changed since 1988–1994. RESEARCH DESIGN AND METHODS —A total of 6,436 men and women aged ≥20 years from the National Health and Nutrition Examination Survey (NHANES) III (1988–1994) and 1,677 participants from NHANES 1999–2000 were included in the analyses. We used the definition of the metabolic syndrome developed by the Third Report of the National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults. RESULTS —The unadjusted prevalence of the metabolic syndrome was 23.1% in NHANES III and 26.7% in NHANES 1999–2000 ( P = 0.043), and the age-adjusted prevalences were 24.1 and 27.0% ( P = 0.088), respectively. The age-adjusted prevalence increased by 23.5% among women ( P = 0.021) and 2.2% among men ( P = 0.831). Increases in high blood pressure, waist circumference, and hypertriglyceridemia accounted for much of the increase in the prevalence of the metabolic syndrome, particularly among women. CONCLUSIONS —The increased prevalence of the metabolic syndrome is likely to lead to future increases in diabetes and cardiovascular disease.


title: On the Generalization Ability of Online Strongly Convex Programming Algorithms

abstract: This paper examines the generalization properties of online convex programming algorithms when the loss function is Lipschitz and strongly convex. Our main result is a sharp bound, that holds with high probability, on the excess risk of the output of an online algorithm in terms of the average regret. This allows one to use recent algorithms with logarithmic cumulative regret guarantees to achieve fast convergence rates for the excess risk with high probability. As a corollary, we characterize the convergence rate of PEGASOS (with high probability), a recently proposed method for solving the SVM optimization problem.


title: Exploring Effects of Multi-Touch Tabletop on Collaborative Fraction Learning and the Relationship of Learning Behavior and Interaction with Learning Achievement

abstract: Introduction Mathematics refers to acquiring the basic concepts of figures, shapes and quantity (Ministry of Education, 2008). It is suggested that mathematics is an important subject as it is extensively applied in many fields. Being able to calculate, organize and apply mathematics knowledge and skills in daily life becomes prerequisites in everyday life and workspace. Therefore, mathematics learning needs to be emphasized, promoted and reflected in the aims of national education's curriculum. General guidelines of grade 1-9 curriculum of elementary and junior high school education (Ministry of Education, 2008) set the following goals and claimed that educators must guide learners to achieve them: (1) to develop take-away mathematics skill so that learners can easily apply learned knowledge in real life situations; (2) to cultivate a positive attitude towards mathematics; (3) to teach mathematics by taking into consideration needs and development levels of learners; and (4) to ensure that learners are able to appropriately utilize technology in mathematics learning and problem-solving processes. However, realization of such ideas may encounter some obstacles which can easily be found in mathematics classrooms. For instance, according to House (2006) and Yu and Chang (2009), in most Taiwanese classrooms, mathematics classes emphasize that learners memorize formulas and calculate well in the condition of not knowing why and how. Under such circumstances, learners seldom explore meanings behind mathematical concepts and they hardly develop multiple mathematical competencies. As a result, learners fail to understand mathematics concept completely and to solve related problems. Furthermore, this issue leads to learners' negative perceptions toward necessity of mathematics skills and importance of such skills in the real life. This issue is especially relevant when learners learn some difficult mathematics concepts, e.g., fraction (Booker, 1998). House (2006) and Yu and Chang (2009) suggested that to merely memorize information is not enough. Learners have to be taught in such way so that they understand mathematics concepts well enough, acquire facts and skills in a meaningful manner and have appropriate learning experience. In this case, when learners experience real-life mathematics problems outside of school, they will be able to solve them. Research on mathematics teaching and learning suggests that representations (e.g., graphical and symbolical) are important mediums not only for communication and expression but also for learning mathematics concepts. According to related studies, some concepts can be easily remembered and comprehended if they are presented in different representations (Nakahara, 2008). If learners learn difficult mathematics concepts (e.g., fraction) and use appropriately variety of their representations, learners will understanding them much easier and deeper compared to a situation when the same concepts presented in a sole representation (Dreyfus & Eisenberg, 1996). Another way to facilitate learning of difficult concepts is to learn them collaboratively (Hwang et al., 2014; Shadiev, Hwang, Chen, & Huang, 2014b; Shadiev, Hwang, & Huang, 2015). According to related studies, learning collaboratively enables learners to achieve more than they could do alone (Argyle, 1991; Wang, 2009). One reason to explain this is that learners take advantage of one another during collaboration in terms of their resources and skills; so learners may ask for help from peers or provide necessary assistance to others (Lehtinen et al., 2001). It is suggested that collaborative learning of fraction can be better enhanced with educational computing technologies, such as tablet PC (Chen, 2011; Kong, 2011; Looi & Chen, 2010) or touch-operated tabletop (Jackson, Brummel, Pollet, & Greer, 2013; Rick, Rogers, Haig, & Yuill, 2009; Schafer et al., 2013). However, several issues still exist with respect to these studies. …


title: Multi-Agent Consensus With Relative-State-Dependent Measurement Noises

abstract: In this note, the distributed consensus corrupted by relative-state-dependent measurement noises is considered. Each agent can measure or receive its neighbors' state information with random noises, whose intensity is a vector function of agents' relative states. By investigating the structure of this interaction and the tools of stochastic differential equations, we develop several small consensus gain theorems to give sufficient conditions in terms of the control gain, the number of agents and the noise intensity function to ensure mean square (m. s.) and almost sure (a. s.) consensus and quantify the convergence rate and the steady-state error. Especially, for the case with homogeneous communication and control channels, a necessary and sufficient condition to ensure m. s. consensus on the control gain is given and it is shown that the control gain is independent of the specific network topology, but only depends on the number of nodes and the noise coefficient constant. For symmetric measurement models, the almost sure convergence rate is estimated by the Iterated Logarithm Law of Brownian motions.


title: Recognition of Action Units in the Wild with Deep Nets and a New Global-Local Loss

abstract: Most previous algorithms for the recognition of Action Units (AUs) were trained on a small number of sample images. This was due to the limited amount of labeled data available at the time. This meant that data-hungry deep neural networks, which have shown their potential in other computer vision problems, could not be successfully trained to detect AUs. A recent publicly available database with close to a million labeled images has made this training possible. Image and individual variability (e.g., pose, scale, illumination, ethnicity) in this set is very large. Unfortunately, the labels in this dataset are not perfect (i.e., they are noisy), making convergence of deep nets difficult. To harness the richness of this dataset while being robust to the inaccuracies of the labels, we derive a novel global-local loss. This new loss function is shown to yield fast globally meaningful convergences and locally accurate results. Comparative results with those of the EmotioNet challenge demonstrate that our newly derived loss yields superior recognition of AUs than state-of-the-art algorithms.


title: Correction by Projection: Denoising Images with Generative Adversarial Networks

abstract: Generative adversarial networks (GANs) transform low-dimensional latent vectors into visually plausible images. If the real dataset contains only clean images, then ostensibly, the manifold learned by the GAN should contain only clean images. In this paper, we propose to denoise corrupted images by finding the nearest point on the GAN manifold, recovering latent vectors by minimizing distances in image space. We first demonstrate that given a corrupted version of an image that truly lies on the GAN manifold, we can approximately recover the latent vector and denoise the image, obtaining significantly higher quality, comparing with BM3D. Next, we demonstrate that latent vectors recovered from noisy images exhibit a consistent bias. By subtracting this bias before projecting back to image space, we improve denoising results even further. Finally, even for unseen images, our method performs better at denoising better than BM3D. Notably, the basic version of our method (without bias correction) requires no prior knowledge on the noise variance. To achieve the highest possible denoising quality, the best performing signal processing based methods, such as BM3D, require an estimate of the blur kernel.


title: Risk as Feelings

abstract: Virtually all current theories of choice under risk or uncertainty are cognitive and consequentialist. They assume that people assess the desirability and likelihood of possible outcomes of choice alternatives and integrate this information through some type of expectation-based calculus to arrive at decision. The authors propose an alternative theoretical perspective, the risk-as-feelings hypothesis, that highlights the role of affect experienced at the moment of decision making. Drawing on research from clinical, physiological, and other subfield of psychology, they show that emotional reactions to risky situations often drive behavior. The risk-as-feelings hypothesis is shown to explain a wide range of phenomena that have resisted interpretation in cognitive-consequentialist terms.


title: Mirror therapy improves hand function in subacute stroke: a randomized controlled trial.

abstract: OBJECTIVE ::: To evaluate the effects of mirror therapy on upper-extremity motor recovery, spasticity, and hand-related functioning of inpatients with subacute stroke. ::: ::: ::: DESIGN ::: Randomized, controlled, assessor-blinded, 4-week trial, with follow-up at 6 months. ::: ::: ::: SETTING ::: Rehabilitation education and research hospital. ::: ::: ::: PARTICIPANTS ::: A total of 40 inpatients with stroke (mean age, 63.2y), all within 12 months poststroke. ::: ::: ::: INTERVENTIONS ::: Thirty minutes of mirror therapy program a day consisting of wrist and finger flexion and extension movements or sham therapy in addition to conventional stroke rehabilitation program, 5 days a week, 2 to 5 hours a day, for 4 weeks. ::: ::: ::: MAIN OUTCOME MEASURES ::: The Brunnstrom stages of motor recovery, spasticity assessed by the Modified Ashworth Scale (MAS), and hand-related functioning (self-care items of the FIM instrument). ::: ::: ::: RESULTS ::: The scores of the Brunnstrom stages for the hand and upper extremity and the FIM self-care score improved more in the mirror group than in the control group after 4 weeks of treatment (by 0.83, 0.89, and 4.10, respectively; all P<.01) and at the 6-month follow-up (by 0.16, 0.43, and 2.34, respectively; all P<.05). No significant differences were found between the groups for the MAS. ::: ::: ::: CONCLUSIONS ::: In our group of subacute stroke patients, hand functioning improved more after mirror therapy in addition to a conventional rehabilitation program compared with a control treatment immediately after 4 weeks of treatment and at the 6-month follow-up, whereas mirror therapy did not affect spasticity.


title: A multimode-beamforming 77-GHz FMCW radar system

abstract: In this work a multimode-beamforming 77-GHz frequency-modulated continuous-wave radar system is presented. Four transceiver chips with integrated inphase/quadrature modulators in the transmit path are used in order to simultaneously realize a short-range frequency-division multiple-access (FDMA) multiple-input multiple-output (MIMO) and a long-range transmit phased-array (PA) radar system with the same antennas. It combines the high angular resolution of FDMA MIMO radars and the high-gain and steerable beam of PA transmit antennas. Several measurements were carried out to show the potential benefits of using this concept for a linear antenna array with four antennas and methods of digital beamforming in the receive path.


title: A matheuristic approach for the Pollution-Routing Problem

abstract: This paper deals with the Pollution-Routing Problem (PRP), a Vehicle Routing Problem (VRP) with environmental considerations, recently introduced in the literature by [Bektas and Laporte (2011), Transport. Res. B-Meth. 45 (8), 1232-1250]. The objective is to minimize operational and environmental costs while respecting capacity constraints and service time windows. Costs are based on driver wages and fuel consumption, which depends on many factors, such as travel distance and vehicle load. The vehicle speeds are considered as decision variables. They complement routing decisions, impacting the total cost, the travel time between locations, and thus the set of feasible routes. We propose a method which combines a local search-based metaheuristic with an integer programming approach over a set covering formulation and a recursive speed-optimization algorithm. This hybridization enables to integrate more tightly route and speed decisions. Moreover, two other "green" VRP variants, the Fuel Consumption VRP (FCVRP) and the Energy Minimizing VRP (EMVRP), are addressed. The proposed method compares very favorably with previous algorithms from the literature and many new improved solutions are reported.


title: From FAST to E-FAST: an overview of the evolution of ultrasound-based traumatic injury assessment

abstract: Ultrasound is a ubiquitous and versatile diagnostic tool. In the setting of acute injury, ultrasound enhances the basic trauma evaluation, influences bedside decision-making, and helps determine whether or not an unstable patient requires emergent procedural intervention. Consequently, continued education of surgeons and other acute care practitioners in performing focused emergency ultrasound is of great importance. This article provides a synopsis of focused assessment with sonography for trauma (FAST) and the extended FAST (E-FAST) that incorporates basic thoracic injury assessment. The authors also review key pitfalls, limitations, controversies, and advances related to FAST, E-FAST, and ultrasound education.


title: Using Blockchain Technology to Build Trust in Sharing LoRaWAN IoT

abstract: With1 the rapid growth of the internet of things (IoT) market and requirement, low power wide area (LPWA) technologies have become popular. In various LPWA technologies, Narrow Band IoT (NB-IoT) and long range (LoRa) are two main leading competitive technologies. Comparing to NB-IoT network that mainly built and managed by mobile network operators, LoRa wide-area network (LoRaWAN) is mainly operated by private companies or organizations, which will bring the trust issues between application customers and network operations. In this paper, we proposed a blockchain technology based solution to build an open, trusted, decentralized and tamper-proof system for LoRaWAN. To the best of our knowledge, this is the first work that integrating blockchain technology and LoRaWAN IoT technology.


title: Information Security Policy Compliance Behavior Based on Comprehensive Dimensions of Information Security Culture: A Conceptual Framework

abstract: The adherence of employees towards Information Security Policy (ISP) established in the organization is crucial in reducing information security risks. Some scholars have suggested that employees' compliance to ISP could be influenced by Information Security Culture (ISC) cultivated in the organization. Several studies on the impact of ISC towards ISP compliance have proposed different dimensions and factors associated to ISC with substantial differences in each finding. This paper is discussing an enhanced conceptual framework of ISP compliance behavior by addressing ISC as a multidimensional concept which consist of seven comprehensive dimensions. These new proposed ISC dimensions developed using all the key factors of ISC in literature and were aligned with the widely accepted concept of organizational culture and ISC. The framework also integrated with the most significant behavioral theory in this domain of study, which is Theory of Planned Behavior to provide more deep understanding and richer findings of the compliance behavior. This framework is expected to give more accurate findings on the relationships between ISC and ISP compliance behavior.


title: Unlocking the Value of Privacy: Trading Aggregate Statistics over Private Correlated Data

abstract: With the commoditization of personal privacy, pricing private data has become an intriguing problem. In this paper, we study noisy aggregate statistics trading from the perspective of a data broker in data markets. We thus propose ERATO, which enables aggrEgate statistics pRicing over privATe cOrrelated data. On one hand, ERATO guarantees arbitrage freeness against cunning data consumers. On the other hand, ERATO compensates data owners for their privacy losses using both bottom-up and top-down designs. We further apply ERATO to three practical aggregate statistics, namely weighted sum, probability distribution fitting, and degree distribution, and extensively evaluate their performances on MovieLens dataset, 2009 RECS dataset, and two SNAP large social network datasets, respectively. Our analysis and evaluation results reveal that ERATO well balances utility and privacy, achieves arbitrage freeness, and compensates data owners more fairly than differential privacy based approaches.


title: Performance comparison of LQR and ANFIS controller for stabilizing double inverted pendulum system

abstract: In this paper performance of LQR and ANFIS control for a Double Inverted Pendulum system is compared. The double inverted pendulum system is highly unstable and nonlinear. Mathematical model is presented by linearizing the system about its vertical position. The analysis of the system is performed for its stability, controllability and observability. Furthermore, the LQR controller and ANFIS controller based on the state variable fusion is proposed for the control of the double inverted pendulum system and simulation results show that ANFIS controller has better tracking performance and disturbance rejecting performance as compared to LQR controller.


title: A testing platform for on-drone computation

abstract: This paper describes the development of a test bed for an on-drone computation system, in which the drone plays the game of ping-pong competitively (YCCD: The Yorktown Cognitive Competition Drone). Unlike other drone systems and demonstrators YCCD will be completely autonomous with no external support from cameras, servers, GPS etc. YCCD will have ultra-low power computation capabilities including on-drone real-time processing for vision and localization (non-GPS based). Architectural design and processing algorithms of the system are discussed in detail.


title: An Improved Clustering Algorithm for Text Mining: Multi-cluster Spherical K-means

abstract: Thanks to advances in information and communication technologies, there is a prominent increase in the amount of information produced specifically in the form of text documents. In order to, effectively deal with this "information explosion" problem and utilize the huge amount of text databases, efficient and scalable tools and techniques are indispensable. In this study, text clustering which is one of the most important techniques of text mining that aims at extracting useful information by processing data in textual form is addressed. An improved variant of Spherical K-means algorithm named multi-cluster spherical K-means is developed for clustering high dimensional document collections with high performance and efficiency. Experiments were performed on several document data sets and it is shown that the new algorithm provides significant increase in clustering quality without causing considerable difference in CPU time usage when compared to Spherical K- means algorithm.


title: Intelligent risk management tools for software development

abstract: Software tools have been used in software development for a long time now. They are used for, among other things, performance analysis, testing and verification, debugging and building applications. Software tools can be very simple and lightweight, e.g. linkers, or very large and complex, e.g. computer-assisted software engineering (CASE) tools and integrated development environments (IDEs). Some tools support particular phases of the project cycle while others can be used with a speicfic software development model or technology. Some aspects of software development, like risk management, are done throughout the whole project from inception to commissioning. The aim of this paper is to demonstrate the need for an intelligent risk assessment and management tool for both agile or traditional (or their combination) methods in software development. The authors propose a model, whose development is subject of further research, which can be investigated for use in developing intelligent risk management tools


title: Acceleration of k-Means Algorithm Using Altera SDK for OpenCL

abstract: A K-means clustering algorithm involves partitioning of data iteratively into k clusters. It is one of the most popular data-mining algorithms lWu et al. 2007r, and is widely used in other applications, such as image processing and machine learning. However, k-means is highly time-consuming when data or cluster size is large. Traditionally, FPGAs have shown great promise for accelerating computationally intensive algorithms, but they are harder to use for acceleration if we rely on traditional HD-based design methods. The recent introduction of Altera SDK for the OpenCL high-level synthesis tool allows developers to utilize FPGA's potential without long development periods and extensive hardware knowledge. This article presents an optimized implementation of a k-means clustering algorithm on an FPGA using Altera SDK for OpenCL. Performance and power consumption is measured with various data, cluster, and dimension sizes. When compared to state-of-the-art solutions, this implementation supports larger cluster sizes, offers up to 21x speed over a CPU and is more power efficient than a GPU. Unlike previous implementations, it can deliver consistently high throughput across large or small feature dimensions given reasonable cluster sizes and large enough data size.


Comparative Study of Deep Learning Models for Network Intrusion Detection


title: Single Image Dehazing With Depth-Aware Non-Local Total Variation Regularization

abstract: Single image dehazing can benefit many computer vision applications hence has attracted much more attention in recent years. However, it still remains a challenging task due to its double uncertainty of scene transmission and scene radiance. The existing image dehazing methods usually impair edges in the estimated transmission which leads to halo effects in the dehazing results. Besides, most existing methods suffer from noise and artifacts amplification in dense haze region after dehazing. To address these challenges, we propose a transmission adaptive regularized image recovery method for high quality single image dehazing. An initial transmission map is first obtained by a boundary constraint on the haze model. Then it is refined by applying a non-local total variation (NLTV) regularization to keep depth structures while smoothing excessive details. Noticing that the artifacts amplification effect depends on scene transmission, a transmission adaptive regularized recovery method based on NLTV is proposed to simultaneously suppress visual artifacts and preserve image details in the final dehazing result. An efficient alternating optimization algorithm is also proposed to solve the regularization model. Thorough experimental results demonstrate that the proposed method can effectively suppress visual artifacts for degraded hazy images, and yields high-quality results comparative to the state-of-the-art dehazing methods both quantitatively and qualitatively.


title: MixedFusion: Real-Time Reconstruction of an Indoor Scene with Dynamic Objects

abstract: Real-time indoor scene reconstruction aims to recover the 3D geometry of an indoor scene in real time with a sensor scanning the scene. Previous works of this topic consider pure static scenes, but in this paper, we focus on more challenging cases that the scene contains dynamic objects, for example, moving people and floating curtains, which are quite common in reality and thus are eagerly required to be handled. We develop an end-to-end system using a depth sensor to scan a scene on the fly. By proposing a Sigmoid-based Iterative Closest Point (S-ICP) method, we decouple the camera motion and the scene motion from the input sequence and segment the scene into static and dynamic parts accordingly. The static part is used to estimate the camera rigid motion, while for the dynamic part, graph node-based motion representation and model-to-depth fitting are applied to reconstruct the scene motions. With the camera and scene motions reconstructed, we further propose a novel mixed voxel allocation scheme to handle static and dynamic scene parts with different mechanisms, which helps to gradually fuse a large scene with both static and dynamic objects. Experiments show that our technique successfully fuses the geometry of both the static and dynamic objects in a scene in real time, which extends the usage of the current techniques for indoor scene reconstruction.


title: Design of multi-beam holographic leaky-wave antenna with one/two-dimension frequency sweep

abstract: We propose a method to design the holographic leaky-wave antenna composed of the artificial impedance surface, which can radiate the multi-beam with frequency scanning in one-dimension or in two-dimension space. Two samples corresponding to different characteristics of the frequency sweep are simulated to show the validity of our design.


title: Contingencies of Self-Worth and Social-Networking-Site Behavior

abstract: Abstract Social-networking sites like Facebook enable people to share a range of personal information with expansive groups of “friends.” With the growing popularity of media sharing online, many questions remain regarding antecedent conditions for this behavior. Contingencies of self-worth afford a more nuanced approach to variable traits that affect self-esteem, and may help explain online behavior. A total of 311 participants completed an online survey measuring such contingencies and typical behaviors on Facebook. First, exploratory factor analyses revealed an underlying structure to the seven dimensions of self-worth. Public-based contingencies explained online photo sharing (β = 0.158, p < 0.01), while private-based contingencies demonstrated a negative relationship with time online (β = −0.186, p < 0.001). Finally, the appearance contingency for self-worth had the strongest relationship with the intensity of online photo sharing (β = 0.242), although no relationship was evident for time spent manag...


title: 7.6 1GB/s 2Tb NAND flash multi-chip package with frequency-boosting interface chip

abstract: NAND Flash-based solid-state drives (SSDs) have been adopted in enterprise storage applications that require high capacity and high-throughput performance. In recent years, a SATA interface supporting only up to 600MB/S throughput has hindered the accelerating performance growth of SSDs due to the host bandwidth limit. A PCI Express (PCIe) has emerged to close the limit because it can deliver 1GB/s throughput per lane and can be scaled to multi-lane to improve bandwidth [1]. Accordingly, the SSD performance bottleneck has moved from the host interface to the NAND flash interface. In a memory system, a die-stacking technology in a NAND flash multi-chip package (MCP) effectively increases capacity and throughput performance in terms of PCB complexities and power consumption compared to a method increasing the number of channels. However, the multi-drop bus topology on NAND interfaces severely affects I/O speed degradations due to channel reflections and inter-symbol interference (ISI) resulting from large capacitive loadings [2]. The undeniable paradox between larger storage capacity and higher I/O bandwidth has become a key challenge to reach enterprise-class SSDs. To overcome this issue, this paper presents a frequency-boosting interface chip (F-Chip) to boost I/O speeds while meeting capacity requirements. A 2Tb NAND flash MCP with 1GB/s toggle DDR interface is accomplished by incorporating the F-Chip into the NAND MCP including a 16-die stacked 128Gb NAND flash [3].


title: ?Hybrid Particle Swarm Optimization and Genetic Algorithm for Multi-UAV Formation Reconfiguration

abstract: The initial state of an Unmanned Aerial Vehicle (UAV) system and the relative state of the system, the continuous inputs of each flight unit are piecewise linear by a Control Parameterization and Time Discretization (CPTD) method. The approximation piecewise linearization control inputs are used to substitute for the continuous inputs. In this way, the multi-UAV formation reconfiguration problem can be formulated as an optimal control problem with dynamical and algebraic constraints. With strict constraints and mutual interference, the multi-UAV formation reconfiguration in 3-D space is a complicated problem. The recent boom of bio-inspired algorithms has attracted many researchers to the field of applying such intelligent approaches to complicated optimization problems in multi-UAVs. In this paper, a Hybrid Particle Swarm Optimization and Genetic Algorithm (HPSOGA) is proposed to solve the multi-UAV formation reconfiguration problem, which is modeled as a parameter optimization problem. This new approach combines the advantages of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), which can find the time-optimal solutions simultaneously. The proposed HPSOGA will also be compared with basic PSO algorithm and the series of experimental results will show that our HPSOGA outperforms PSO in solving multi-UAV formation reconfiguration problem under complicated environments.


title: A Dataset and Taxonomy for Urban Sound Research

abstract: Automatic urban sound classification is a growing area of research with applications in multimedia retrieval and urban informatics. In this paper we identify two main barriers to research in this area - the lack of a common taxonomy and the scarceness of large, real-world, annotated data. To address these issues we present a taxonomy of urban sounds and a new dataset, UrbanSound, containing 27 hours of audio with 18.5 hours of annotated sound event occurrences across 10 sound classes. The challenges presented by the new dataset are studied through a series of experiments using a baseline classification system.


Climate change and human health : Impacts , vulnerability and public health *


title: Parenthetical - A Special Type of Prosodic Reduction in Continuous Speech

abstract: The current study investigates parenthetical, a type of prosodic reduction in multi-phrase speech paragraphs. Structurally a modifier of its antecedent to provide supplementary information, such reduction creates a lower level in the prosodic hierarchy nested within a discourse-prosodic unit. Perceptual annotation of parenthetical turned out to be consistent across listeners; their acoustic profiles distinctive. Further calculation of information density in relation to allocation of perceived emphasis also demonstrates that parenthetical triggered prosodic reductions are patterned and accountable. Therefore, in spite of low information standing, their existence in the prosodic hierarchy helps facilitate more precise information expression. In sum, current evidence illustrates how information planning is manifested via both emphases and reductions in global context prosody, why parenthetical caused reductions should be understood from a hierarchical perspective within speech context, and how prosodic reduction also plays a crucial role in contributing to comprehensive understanding toward context prosody.


title: Web Application Security by SQL Injection DetectionTools

abstract: SQL injection is a type of attack which the attacker adds Structured Query Language code to a web form input box to gain access or make changes to data. SQL injection vulnerability allows an attacker to flow commands directly to a web application's underlying database and destroy functionality or confidentiality. Researchers have proposed different tools to detect and prevent this vulnerability. In this paper we present all SQL injection attack types and also current tools which can detect or prevent these attacks. Finally we evaluate these tools.


title: The Effects of Memory Replay in Reinforcement Learning

abstract: Experience replay is a key technique behind many recent advances in deep reinforcement learning. Allowing the agent to learn from earlier memories can speed up learning and break undesirable temporal correlations. Despite its wide-spread application, very little is understood about the properties of experience replay. How does the amount of memory kept affect learning dynamics? Does it help to prioritize certain experiences? In this paper, we address these questions by formulating a dynamical systems ODE model of Q-learning with experience replay. We derive analytic solutions of the ODE for a simple setting. We show that even in this very simple setting, the amount of memory kept can substantially affect the agent's performance. Too much or too little memory both slow down learning. Moreover, we characterize regimes where prioritized replay harms the agent's learning. We show that our analytic solutions have excellent agreement with experiments. Finally, we propose a simple algorithm for adaptively changing the memory buffer size which achieves consistently good empirical performance.


Arnold: An Autonomous Agent to Play FPS Games.


title: Change detection in time series data using wavelet footprints

abstract: In this paper, we propose a novel approach to address the problem of change detection in time series data. Our approach is based on wavelet footprints proposed originally by the signal processing community for signal compression. We, however, exploit the properties of footprints to capture discontinuities in a signal. We show that transforming data using footprints generates nonzero coefficients only at the change points. Exploiting this property, we propose a change detection query processing scheme which employs footprint-transformed data to identify change points, their amplitudes, and degrees of change efficiently and accurately. Our analytical and empirical results show that our approach outperforms the best known change detection approach in terms of both performance and accuracy. Furthermore, unlike the state of the art approaches, our query response time is independent of the number of change points and the user-defined change threshold.


title: Business model metrics: an open repository

abstract: Development of successful business models has become a necessity in turbulent business environments, but compared to research on business modeling tools, attention to the role of metrics in designing business models in literature is limited. Building on existing approaches to business models and performance measurement literature, we develop a generic open repository of metrics related to core business model concepts. We validate and assess the practical value of the repository based on four e-Business model cases. The repository can be utilized in designing business models, specifically for networked enterprises. It is neither complete nor universally applicable, but needs adjustment with each design. Ultimately, with this paper, we hope measurement will become an integral part of the business model innovation discussion.


title: The contribution of outdoor air pollution sources to premature mortality on a global scale

abstract: Assessment of the global burden of disease is based on epidemiological cohort studies that connect premature mortality to a wide range of causes, including the long-term health impacts of ozone and fine particulate matter with a diameter smaller than 2.5 micrometres (PM2.5). It has proved difficult to quantify premature mortality related to air pollution, notably in regions where air quality is not monitored, and also because the toxicity of particles from various sources may vary. Here we use a global atmospheric chemistry model to investigate the link between premature mortality and seven emission source categories in urban and rural environments. In accord with the global burden of disease for 2010 (ref. 5), we calculate that outdoor air pollution, mostly by PM2.5, leads to 3.3 (95 per cent confidence interval 1.61-4.81) million premature deaths per year worldwide, predominantly in Asia. We primarily assume that all particles are equally toxic, but also include a sensitivity study that accounts for differential toxicity. We find that emissions from residential energy use such as heating and cooking, prevalent in India and China, have the largest impact on premature mortality globally, being even more dominant if carbonaceous particles are assumed to be most toxic. Whereas in much of the USA and in a few other countries emissions from traffic and power generation are important, in eastern USA, Europe, Russia and East Asia agricultural emissions make the largest relative contribution to PM2.5, with the estimate of overall health impact depending on assumptions regarding particle toxicity. Model projections based on a business-as-usual emission scenario indicate that the contribution of outdoor air pollution to premature mortality could double by 2050.


title: Friction observer and compensation for control of robots with joint torque measurement

abstract: In this paper we introduce a friction observer for robots with joint torque sensing (in particular for the DLR medical robot) in order to increase the positioning accuracy and the performance of torque control. The observer output corresponds to the low-pass filtered friction torque. It is used for friction compensation in conjunction with a MIMO controller designed for flexible joint arms. A passivity analysis is done for this friction compensation, allowing a Lyapunov based convergence analysis in the context of the nonlinear robot dynamics. For the complete controlled system, global asymptotic stability can be shown. Experimental results validate the practical efficiency of the approach.


title: Enhanced vector space models for content-based recommender systems

abstract: The use of Vector Space Models (VSM) in the area of Information Retrieval is an established practice within the scientific community. The reason is twofold: first, its very clean and solid formalism allows us to represent objects in a vector space and to perform calculations on them. On the other hand, as proved by many contributions, its simplicity does not hurt the effectiveness of the model. Although Information Retrieval and Information Filtering undoubtedly represent two related research areas, the use of VSM in Information Filtering is much less analzyed. The goal of this work is to investigate the impact of vector space models in the Information Filtering area. Specifically, I will introduce two approaches: the first one, based on a technique called Random Indexing, reduces the impact of two classical VSM problems, this is to say its high dimensionality and the inability to manage the semantics of documents. The second extends the previous one by integrating a negation operator implemented in the Semantic Vectors1 open-source package. The results emerged from an experimental evaluation performed on a large dataset and the applicative scenarios opened by these approaches confirmed the effectiveness of the model and induced to investigate more these techniques.


title: Research on School Bullying and Victimization: What Have We Learned and Where Do We Go from Here? [Mini-Series]

abstract: This special i,sue nn bullying and victimization in Schooll',I'rc!l%gr Rel'ieu' highlights current research elTol1:> in American schools on bullying and peer victimization. and how this research can inform prevention and intervention planning. nlis introductory article provides a brief overview of several major insights gained over the last decade from research on bullying in school-aged youth and sets the stage for the special issue. Research on psychosOl:ial currelates in bullying behaviors is reviewed and four insights that provide directions fur future research are derived. The contributing authors in the special i,sue augment these insights by examining the intluence of the peer ecology on bullying (Rodkin & Hodges. 20031. using longitudinal and multivariate melhodologies in bullying research (Long & Pellegrini. 20(3). assessing the climales within the schuol where bullying typically occurs (letT. Power. Cosligan. & Manz. 20m I. exploring implementation issues of school-wide bullying prevention programming (Orpinas. Home. & Staniszewski. 2003 I. reviewing laws and policies lo address bullying (Limher & Small. 20(3), and challenging researchers to reach a consensus on bullying research (Furlong. Morrison. & Greif. 20m). School bullying among children and adolescents has been the focus of many international studies over the last 30 years. In his seminal research, Norwegian scholar Daniel Olweus ( 1972) coined bullying a" "mobbing." and defined it as an individual or a group of individuals harassing, tcasing. or pestering another person. However, it was not until 1982 that school officials in Norway turned their attention to school bullying, and did so only after three 14-year-old boys committed suicide as a result of extreme harassment from classmates (Olweus, 1993). Following these events. the Ministry of Education in Norway launched a national campaign against bullying in which a prevention program was implemented in every primary and secondary school. Indeed. many other countries have recognized bullying as a serious concern. including England. Italy. Canada. Japan. the United States. and Australia. to name a few. Recent events in the United States raise some issues about the transportability of international findings to the culture of American Address correspondcnce rcgarding this article to Dorothy L. Espelagc. Ph.D.. Derallmcnt of Educational Psychology. University of Illinois, Urbana-Champaign. College of Education. 226 Educatiun Building. 1310 South Sixth Street. Champaign. lL 61820-6lJ90; E-mail: espelage@uiuc.edu. l65 Copyright 2003, National Association of School Psychologists. Used by permission.


title: Design of reconfigurable manufacturing systems

abstract: This paper explains the rationale for the development of reconfigurable manufacturing systems, which possess the advantages both of dedicated lines and of flexible systems. The paper defines the core characteristics and design principles of reconfigurable manufacturing systems (RMS) and describes the structure recommended for practical RMS with RMS core characteristics. After that, a rigorous mathematical method is introduced for designing RMS with this recommended structure. An example is provided to demonstrate how this RMS design method is used. The paper concludes with a discussion of reconfigurable assembly systems.


title: Revision of achievement goal theory: Necessary and illuminating

abstract: C. Midgley et al. (2001) raised important questions about the effects of performance-approach goals. The present authors disagree with their characterization of the research findings and implications for theory. They discuss 3 reasons to revise goal theory: (a) the importance of separating approach from avoidance strivings, (b) the positive potential of performance-approach goals, and (c) identification of the ways performance-approach goals can combine with mastery goals to promote optimal motivation. The authors review theory and research to substantiate their claim that goal theory is in need of revision, and they endorse a multiple goal perspective. The revision of goal theory is underway and offers a more complex, but necessary, perspective on important issues of motivation, learning, and achievement. In tins response. the authors dispel interpretation of their critical review of research on performance-approach goals as support for a dichotomous perspective of achievement goal theory. Second, the authors challenge the suggestion that accepting recent research findings and adopting a multiple goals perspective constitutes a theoretical revision of the assumption that mastery goals are always good and performance goals are always bad (J. M. Harackiewicz. K. E. Barron, P. R. Pintrich, P. R. Elliot, & T. M. Thrash. 2002, p. 643). The authors make a distinction between developments that contribute to the explanatory power of the theory and value-laden interpretations of theory and research. The authors argue that phrasing the latter in terms of the former is misleading and that it masks the necessity for a critical discussion over the desired purposes in different types of achievement contexts.


title: Detecting Android malware using sequences of system calls

abstract: The increasing diffusion of smart devices, along with the dynamism of the mobile applications ecosystem, are boosting the production of malware for the Android platform. So far, many different methods have been developed for detecting Android malware, based on either static or dynamic analysis. The main limitations of existing methods include: low accuracy, proneness to evasion techniques, and weak validation, often limited to emulators or modified kernels. We propose an Android malware detection method, based on sequences of system calls, that overcomes these limitations. The assumption is that malicious behaviors (e.g., sending high premium rate SMS, cyphering data for ransom, botnet capabilities, and so on) are implemented by specific system calls sequences: yet, no apriori knowledge is available about which sequences are associated with which malicious behaviors, in particular in the mobile applications ecosystem where new malware and non-malware applications continuously arise. Hence, we use Machine Learning to automatically learn these associations (a sort of "fingerprint" of the malware); then we exploit them to actually detect malware. Experimentation on 20000 execution traces of 2000 applications (1000 of them being malware belonging to different malware families), performed on a real device, shows promising results: we obtain a detection accuracy of 97%. Moreover, we show that the proposed method can cope with the dynamism of the mobile apps ecosystem, since it can detect unknown malware.


title: Global Analytic Solution for Variational Bayesian Matrix Factorization

abstract: Bayesian methods of matrix factorization (MF) have been actively explored recently as promising alternatives to classical singular value decomposition. In this paper, we show that, despite the fact that the optimization problem is non-convex, the global optimal solution of variational Bayesian (VB) MF can be computed analytically by solving a quartic equation. This is highly advantageous over a popular VBMF algorithm based on iterated conditional modes since it can only find a local optimal solution after iterations. We further show that the global optimal solution of empirical VBMF (hyperparameters are also learned from data) can also be analytically computed. We illustrate the usefulness of our results through experiments.


title: History of the FIGO cancer staging system

abstract: The main objectives of any good staging system – essential to an evidence-based approach to cancer – are: to aid the clinician in planning treatment; to provide indication of prognosis; to assist the physician in evaluating the results of treatment; to facilitate the exchange of information between treatment centers, thus disseminating knowledge; and to contribute to continuing investigations into human malignancies. A good staging system must have 3 basic characteristics: it must be valid, reliable, and practical. The first staging system for gynecological cancers appeared around the turn of the 20th century and applied to the carcinoma of the cervix uteri—the most common cancer affecting women in high income countries at that time. The classification and staging of the other gynecological malignancies was not put forward until the 1950s. Over the years, these staging classifications – with the exception of cervical cancer and gestational trophoblastic neoplasia – have shifted from a clinical to a surgical–pathological basis. This paper reviews the history of the International Federation of Gynecology and Obstetrics (FIGO) cancer staging system, how it was developed, and why.


title: High-Temperature Characteristics of 3-kV 4H-SiC Reverse Blocking MOSFET for High-Performance Bidirectional Switch

abstract: Novel 3-kV 4H-SiC reverse blocking (RB) metal–oxide–semiconductor field-effect transistors (MOSFETs) have been demonstrated for high-voltage bidirectional switching applications. To achieve RB capability, a series Schottky barrier diode structure was introduced onto the backside of the 4H-SiC MOSFET. The developed SiC RB MOSFET exhibits bidirectional blocking voltage over 3 kV and a differential specific on-resistance of 20 $\text{m}\Omega ~\cdot $ cm2 at room temperature. In an inductive-load switching measurement, the RB MOSFET showed good turn-ON/-OFF characteristics at 1 kV. The bidirectional switch configured by the developed RB MOSFETs exhibited lower ON-state power loss than the series connection of the standard SiC MOSFETs at wide range of temperature and operation current, demonstrating the advantage of the SiC RB MOSFET as a high-performance bidirectional switch.


title: Magnetostatic Wave Frequency Selective Limiters

abstract: Frequency selective limiters (FSLs), which selectively attenuate above threshold signals while allowing low power signals to pass with little attenuation, have potential application in maintaining the dynamic range of RF receivers in the presence of interference. Stripline FSLs, fabricated using thick yttrium iron garnet (YIG) films, are suitable for broadband, microwave applications but have typical threshold power levels of 0 dBm which is too high for many RF applications. Here magnetostatic wave (MSW) FSLs are described with frequency selectivity and threshold power levels at least 10 times lower than achieved with stripline devices. The performance of an MSW FSL operating in the UHF band will be presented and compared with a model for threshold power. Based on these results the performance limits of FSLs will be discussed with emphasis on reducing threshold power levels significantly below -40 dBm as required in potential GPS applications.


title: Practical design considerations for application of GIS MV switchgear

abstract: In a recent biotechnology facility project, the decision was made to require arc-resistant medium-voltage (MV) switchgear for the safety of operating personnel. The available service voltage was 26.4 kV, limiting the availability of proven designs of arc-resistant switchgear. As a result, the owner considered use of medium-voltage gas-insulated switchgear (GIS) with vacuum circuit breakers. Once this decision was made, consideration was given to using the same class of switchgear for the 4.16-kV voltage equipment in the facilities. This offered advantages of uniformity, commonality of spares, and simplification of operator training. This paper focuses on the rationale for selection of GIS switchgear, and the design considerations that had to be addressed as the project moved from concept to installation to operation. Among the issues discussed are the following: space requirements for conventional and GIS equipment; arc-resistant performance of GIS equipment; design commonality between 4.16- and 26.4-kV equipment; installation (cable issues, foundations, workspace, etc.); environmental considerations; total cost of ownership; reliability/availability; operational experience.


title: Growing & changing trends in consumer behavior

abstract: ‘You cannot step into the same water twice’, in other words ‘change is law of life’ and ‘everything changes and nothing stands still’ said Heraclitus, a prominent Greek Philosopher. In this similar line Changes in consumer behavior is not an exception as consumers are a dynamic and progressive and who change themselves according to the signs of time. This paper delves into the fact of changing consumer behavior in the light of marketing and in ‘Make in India’. It explicates how a consumer and more specifically an Indian consumer could update and adapt himself in the growing digital milieu. The impact of ‘Make in India’ on changing consumer behavior are dealt in this workshop. In nut-shell this thesis sheds the light upon the demand, the power of consumer and the challenges of marketers.


title: Epidemiological features of chronic low-back pain

abstract: Summary Although the literature is filled with information about the prevalence and incidence of back pain in general, there is less information about chronic back pain, partly because of a lack of agreement about definition. Chronic back pain is sometimes defined as back pain that lasts for longer than 7–12 weeks. Others define it as pain that lasts beyond the expected period of healing, and acknowledge that chronic pain may not have well-defined underlying pathological causes. Others classify frequently recurring back pain as chronic pain since it intermittently affects an individual over a long period. Most national insurance and industrial sources of data include only those individuals in whom symptoms result in loss of days at work or other disability. Thus, even less is known about the epidemiology of chronic low-back pain with no associated work disability or compensation. Chronic low-back pain has also become a diagnosis of convenience for many perople who are actually disabled for socioeconomic, work-related, or psychological reasons. In fact, some people argue that chronic disability in back pain is primarily related to a psychosocial dysfunction. Because the validity and reliability of some of the existing data are uncertain, caution is needed in an assessment of the information on this type of pain.


title: ASSESSING THE NEW WAY OF WORKING: BRICKS, BYTES AND BEHAVIOUR

abstract: The world in which we work is changing. Information and communication technologies transform the work environment, providing the flexibility of when and where to work. The New Way of Working (NWOW) is a relatively new phenomenon that provides the context for these developments. It consists of three distinct pillars that are referred to as Bricks, Bytes and Behaviour. These pillars formed the basis for the development of the NWOW Analysis Monitor that enables organisations to determine their current level of NWOW adoption and provides guidance for future initiatives in adopting NWOW practices. The level of adoption is determined from both the manager’s and employees’ perspective as they might have a different perception and/or expectations regarding NWOW. The development of the multi-level NWOW Analysis Monitor is based on the Design Science Research approach. The monitor has been evaluated in two cases, forming two iterations in the design science research cycle. It has proved to be a useful assessment tool for organisations in the process of implementing NWOW. In future research the NWOW Analysis Monitor will be used in quantitative research on the effects of the implementation of NWOW on the organisation and its performance.


title: Recommendation Based on Trust Diffusion Model

abstract: Recommender system is emerging as a powerful and popular tool for online information relevant to a given user. The traditional recommendation system suffers from the cold start problem and the data sparsity problem. Many methods have been proposed to solve these problems, but few can achieve satisfactory efficiency. In this paper, we present a method which combines the trust diffusion (DiffTrust) algorithm and the probabilistic matrix factorization (PMF). DiffTrust is first used to study the possible diffusions of trust between various users. It is able to make use of the implicit relationship of the trust network, thus alleviating the data sparsity problem. The probabilistic matrix factorization (PMF) is then employed to combine the users' tastes with their trusted friends' interests. We evaluate the algorithm on Flixster, Moviedata, and Epinions datasets, respectively. The experimental results show that the recommendation based on our proposed DiffTrust + PMF model achieves high performance in terms of the root mean square error (RMSE), Recall, and F Measure.


title: A Model of Supplier Integration into New Product Development*

abstract: In many industries, firms are looking for ways to cut concept-to-customer development time, to improve quality, and to reduce the cost of new products. One approach shown to be successful in Japanese organizations involves the integration of material suppliers early in the new product development cycle. This involvement may range from simple consultation with suppliers on design ideas to making suppliers fully responsible for the design of components or systems they will supply. While prior research shows the benefit of using this approach, execution remains a problem. The processes for identifying and integrating suppliers into the new product development (NPD) process in North American organizations are not understood well. This problem is compounded by the fact that design team members often are reluctant to listen to the technology and cost ideas made by suppliers in new product development efforts. We suggest a model of the key activities required for successful supplier integration into NPD projects, based on case studies with 17 Japanese and American manufacturing organizations. The model is validated using data from a survey of purchasing executives in global corporations with at least one successful and one unsuccessful supplier integration experience. The results suggest that (1) increased knowledge of a supplier is more likely to result in greater information sharing and involvement of the supplier in the product development process; (2) sharing of technology information results in higher levels of supplier involvement and improved outcomes; (3) supplier involvement on teams generally results in a higher achievement of NPD team goals; (4) in cases when technology uncertainty is present, suppliers and buyers are more likely to share information on NPD teams; and (5) the problems associated with technology uncertainty can be mitigated by greater use of technology sharing and direct supplier participation on new product development teams. A supplier’s participation as a true member of a new product development team seems to result in the highest level of benefits, especially in cases when a technology is in its formative stages.


title: Design on Measurement and Control System of Cleaning Robot Based on Sensor Array detection

abstract: A new type of home intelligent cleaner adopted the ultrasonic and infrared sensor array, which has the function of the real-time environment perception, is introduced, and this cleaner driven by step-motor has the ability of autonomous working by itself and the functions of the automatic obstacle detection and obstacle avoidance. This paper adopts the grid scanning algorithm based on electric map realize floor coverage task, and designs synthesis detection system based on sensor array finding method technology according to algorithm characteristics, experimental results for obstacle detection by static finding indicate that the designed detection system improves cleaning robot's environment perception and path search ability greatly.


title: 11.5-Gbps 2.4-pJ/bit 60-GHz OOK demodulator integrated in a SiGe BiCMOS technology

abstract: This paper presents design and characterization of a 60-GHz On-Off Keying (OOK) demodulator for low-power ultra-high-speed wireless communications. The core of the demodulator consists of a fully-balanced envelope detector to improve the detection sensitivity compared to the conventional single-ended configuration. A feed-forward offset-compensation technique is utilized to eliminate the DC saturation in the limiting amplifier of the demodulator. Integrated in a 0.25-μm SiGe BiCMOS technology, the demodulator can operate with data rates from 4 Gbps to 11.5 Gbps with 10-12 BER over a 60-GHz carrier. The demodulator consumes 27.5 mW of DC power from a 1.6-V supply voltage, accounting for all bias circuits and a test output buffer. An energy efficiency of 2.4 pJ/bit is achieved at a data rate of 11.5 Gbps.


title: A high efficiency of a Class-D resonant inverter high power factor for induction heating multi-coils hardening application

abstract: This paper presented a high efficiency Class-D resonant inverter high power factor for induction heating multi-coils hardening application. The main function of the Class-D resonant inverter is to provide high frequency AC from bridge rectifier and LC filter input voltage. The advantages of this technique, the power switched are operated under the zero voltage switching condition and the leakage inductance of transformer can be used the LLC resonant. A prototype Class-D operating at a 102 kHz fixed frequency, 730 W maximum input power. Experimental results, induction hardening had a near unity power factor, total harmonic distortion of the input current THDi 6.5 %, it can be heat up 2 work pieces a same size diameter 9.5-mm from room temperature to 400°C within about 1 minutes and 92 % efficiency at full power.


Locomotion control with inverted pendulum model and hierarchical low-dimensional data


title: An affordable millimeter-wave beam-steerable antenna using interleaved planar subarrays

abstract: Design and fabrication aspects of an affordable planar beam steerable antenna array with a simple architecture are considered in this paper. Grouping the elements of a phased array into a number of partially overlapped subarrays and using a single phase shifter for each subarray, generally results in a considerable reduction in array size and manufacturing costs. However, overlapped subarrays require complicated corporate feed networks and array architectures that cannot be easily implemented using planar technologies. In this paper a novel feed network and array architecture for implementing a planar phased array of microstrip antennas is presented that enables the fabrication of low-sidelobe, compact, beam-steerable millimeter-wave arrays and facilitates integration of the RF front-end electronics with the antenna structure. This design uses a combination of series and parallel feeding schemes to achieve the desired array coefficients. The proposed approach is used to design a three-state switched-beam phased array with a scanning width of /spl plusmn/10/spl deg/. This phased array which is composed of 80 microstrip elements, achieves a gain of >20 dB, a sidelobe level of 6.3% for all states of the beam. The antenna efficiency is measured at 33-36% in X band. It is shown that the proposed feeding scheme is insensitive to the mutual coupling among the elements.


title: Presentation Attack Detection for Cadaver Iris

abstract: This paper presents a deep-learning-based method for iris presentation attack detection (PAD) when iris images are obtained from deceased people. Our approach is based on the VGG-16 architecture fine-tuned with a database of 574 post-mortem, near-infrared iris images from the Warsaw-BioBase-PostMortem-Iris-v1 database, complemented by a dataset of 256 images of live irises, collected within the scope of this study. Experiments described in this paper show that our approach is able to correctly classify iris images as either representing a live or a dead eye in almost 99% of the trials, averaged over 20 subject-disjoint, train/test splits. We also show that the post-mortem iris detection accuracy increases as time since death elapses, and that we are able to construct a classification system with APCER=0%@BPCER=1% (Attack Presentation and Bona Fide Presentation Classification Error Rates, respectively) when only post-mortem samples collected at least 16 hours post-mortem are considered. Since acquisitions of ante- and post-mortem samples differ significantly, we applied countermeasures to minimize bias in our classification methodology caused by image properties that are not related to the PAD. This included using the same iris sensor in collection of ante- and post-mortem samples, and analysis of class activation maps to ensure that discriminant iris regions utilized by our classifier are related to properties of the eye, and not to those of the acquisition protocol. This paper offers the first known to us PAD method in a post-mortem setting, together with an explanation of the decisions made by the convolutional neural network. Along with the paper we offer source codes, weights of the trained network, and a dataset of live iris images to facilitate reproducibility and further research.


title: Learning to Control Redundant Musculoskeletal Systems with Neural Networks and SQP: Exploiting Muscle Properties

abstract: Modeling biomechanical musculoskeletal systems reveals that the mapping from muscle stimulations to movement dynamics is highly nonlinear and complex, which makes it difficult to control those systems with classical techniques. In this work, we not only investigate whether machine learning approaches are capable of learning a controller for such systems. We are especially interested in the question if the structure of the musculoskeletal apparatus exhibits properties that are favorable for the learning task. In particular, we consider learning a control policy from target positions to muscle stimulations. To account for the high actuator redundancy of biomechanical systems, our approach uses a learned forward model represented by a neural network and sequential quadratic programming to obtain the control policy, which also enables us to alternate the co-contraction level and hence allows to change the stiffness of the system and to include optimality criteria like small muscle stimulations. Experiments on both a simulated musculoskeletal model of a human arm and a real biomimetic muscle-driven robot show that our approach is able to learn an accurate controller despite high redundancy and nonlinearity, while retaining sample efficiency.


title: A blockchain framework for smart mobility

abstract: A blockchain framework is presented for addressing the privacy and security challenges associated with the Big Data in smart mobility. It is composed of individuals, companies, government and universities where all the participants collect, own, and control their data. Each participant shares their encrypted data to the blockchain network and can make information transactions with other participants as long as both party agrees to the transaction rules (smart contract) issued by the owner of the data. Data ownership, transparency, auditability and access control are the core principles of the proposed blockchain for smart mobility Big Data.


16-741: Mechanics of Manipulation – Paper Review Randomized Kinodynamic Planning


title: TurkerGaze: Crowdsourcing Saliency with Webcam based Eye Tracking

abstract: Traditional eye tracking requires specialized hardware, which means collecting gaze data from many observers is expensive, tedious and slow. Therefore, existing saliency prediction datasets are order-of-magnitudes smaller than typical datasets for other vision recognition tasks. The small size of these datasets limits the potential for training data intensive algorithms, and causes overfitting in benchmark evaluation. To address this deficiency, this paper introduces a webcam-based gaze tracking system that supports large-scale, crowdsourced eye tracking deployed on Amazon Mechanical Turk (AMTurk). By a combination of careful algorithm and gaming protocol design, our system obtains eye tracking data for saliency prediction comparable to data gathered in a traditional lab setting, with relatively lower cost and less effort on the part of the researchers. Using this tool, we build a saliency dataset for a large number of natural images. We will open-source our tool and provide a web server where researchers can upload their images to get eye tracking results from AMTurk.


title: Nitrogen and phosphorus benefits from faba bean (Vicia faba L.) residues to subsequent wheat crop in the humid highlands of Ethiopia

abstract: Faba bean–wheat rotation is one of the traditional cropping systems in most parts of the temperate, Mediterranean and tropical highland areas. However, the net contribution of legumes to soil nutrient balance is determined by the extent to which crop residue is removed from the field. Therefore, we assessed two possible faba bean residue management scenarios and their role in the faba bean–wheat rotation system in a two-phase field experiment. We further tested to what extent high N2-fixing and P efficient faba bean varieties could benefit subsequently grown wheat. In the first phase, three improved faba bean varieties (Degaga, Moti, Obse) were grown at four levels of P fertilization (0, 10, 20 and 30 kg P ha−1) along with local faba bean and reference wheat but without any fertilization. N2-fixation, soil N balance and P uptake were determined for the faba beans. The N balance was determined via two possible residue management scenarios: scenario-I assumed that all the aboveground biomass is exported from the fields; scenario-II assumed that all the above ground biomass except grains and empty pods is incorporated to the soil. In the second phase, the N and P benefits of faba beans to rotational wheat were assessed. Scenario-I gave a negative net N balance (kg N ha−1) in the range of −86.5 ± 5.8 (Degaga) to −9.4 ± 8.7 (Moti) with significant differences between varieties. Scenario-II showed that all balances were significantly (P < 0.01) improved and the varieties were positively contributing N to the system in the range of 50.6 ± 13.4 (Degaga) to 168.3 ± 13.7 (Moti) kg N ha−1, which is equivalent to 110–365 kg N ha−1 in the form of urea (46 % N). In the second crop phase, biomass and grain yield of wheat grown after the faba beans improved significantly (P < 0.05) by 112 and 82 %, respectively compared to the yield of wheat after wheat. Phosphorus application to the preceding faba bean varieties significantly improved total biomass and grain yield of the succeeding wheat (R2 = 0.97). The incorporated legume root, nodule and straw clearly played a role in improving wheat yield through N addition via BNF and straw P. The study demonstrates the prospects and importance of improved faba bean germplasm and management as a key component for sustainable wheat based cropping systems in the humid tropical highlands.


title: Complications Associated With Isotretinoin Use After Rhinoplasty

abstract: BACKGROUND ::: This study aimed to evaluate the association between the postoperative use of isotretinoin and the formation of nasal tip deformities after rhinoplasty. ::: ::: ::: METHODS ::: A retrospective study investigated a case series of patients who presented with nasal tip deformities subsequent to the use of isotretinoin after rhinoplasty. Patients who had taken isotretinoin after rhinoplasty were identified from a single surgeon's case log. Clinic charts and operative reports were reviewed. Predisposing factors and time intervals to complications were identified. ::: ::: ::: RESULTS ::: Three patients were found to have undergone isotretinoin therapy after rhinoplasty. Nasal tip deformities included bossa formation, asymmetry, and prominence of a composite graft. All three patients required subsequent procedures to repair soft tissue nasal tip deformities. In all three cases, isotretinoin was started within 2 years of the primary rhinoplasty. The nasal tip deformities were first observed within 6 months after isotretinoin was started. ::: ::: ::: CONCLUSIONS ::: Three cases are described in which postoperative use of isotretinoin was associated with nasal tip deformities after rhinoplasty. Further studies are needed to investigate this possible causative relationship and to describe the risks involved.


title: SystemT: An Algebraic Approach to Declarative Information Extraction

abstract: As information extraction (IE) becomes more central to enterprise applications, rule-based IE engines have become increasingly important. In this paper, we describe SystemT, a rule-based IE system whose basic design removes the expressivity and performance limitations of current systems based on cascading grammars. SystemT uses a declarative rule language, AQL, and an optimizer that generates high-performance algebraic execution plans for AQL rules. We compare SystemT's approach against cascading grammars, both theoretically and with a thorough experimental evaluation. Our results show that SystemT can deliver result quality comparable to the state-of-the-art and an order of magnitude higher annotation throughput.


title: Fast and Simple Relational Processing of Uncertain Data

abstract: This paper introduces U-relations, a succinct and purely relational representation system for uncertain databases. U-relations support attribute-level uncertainty using vertical partitioning. If we consider positive relational algebra extended by an operation for computing possible answers, a query on the logical level can be translated into, and evaluated as, a single relational algebra query on the U-relational representation. The translation scheme essentially preserves the size of the query in terms of number of operations and, in particular, number of joins. Standard techniques employed in off-the-shelf relational database management systems are effective for optimizing and processing queries on U-relations. In our experiments we show that query evaluation on U-relations scales to large amounts of data with high degrees of uncertainty.


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title: A Qualitative Exploration of Individuals’ Motivators for Seeking Substance User Treatment

abstract: Substance abuse continues to greatly impact the US population. The purpose of this study is to improve understanding of the reasons why individuals seek substance user treatment. Qualitative interviews were conducted with 50 men (n = 25) and women (n = 25) across eight substance user treatment programs in Durham, North Carolina, in 2010. We identify six motivating themes regarding reasons for seeking treatment and explore how these themes vary by the participants’ mode of entry into treatment. Findings provide a holistic understanding of how and why individuals come into treatment that can enhance the quality and impact of substance user services.


title: GPDTI : A Genetic Programming Decision Tree Induction method to find epistatic effects in common complex diseases

abstract: Motivation: The identification of risk-associated genetic variants in common diseases remains a challenge to the biomedical research community. It has been suggested that common statistical approaches that exclusively measure main effects are often unable to detect interactions between some of these variants. Detecting and interpreting interactions is a challenging open problem from the statistical and computational perspectives. Methods in computing science may improve our understanding on the mechanisms of genetic disease by detecting interactions even in the presence of very low heritabilities. Results: We have implemented a method using Genetic Programming that is able to induce a Decision Tree to detect interactions in genetic variants. This method has a cross-validation strategy for estimating classification and prediction errors and tests for consistencies in the results. To have better estimates, a new consistency measure that takes into account interactions and can be used in a genetic programming environment is proposed. This method detected five different interaction models with heritabilities as low as 0.008 and with prediction errors similar to the generated errors. Availability: Information on the generated data sets and executable code is available upon request. Contact: jestrada@inmegen.gob.mx


OpenIE for Slot Filling at TAC KBP 2017 - System Description


title: A collection of benchmark datasets for systematic evaluations of machine learning on the Semantic Web

abstract: In the recent years, several approaches for machine learning on the Semantic Web have been proposed. However, no extensive comparisons between those approaches have been undertaken, in particular due to a lack of publicly available, acknowledged benchmark datasets. In this paper, we present a collection of 22 benchmark datasets of different sizes. Such a collection of datasets can be used to conduct quantitative performance testing and systematic comparisons of approaches.


title: Data Mining of the Public Version of the FDA Adverse Event Reporting System

abstract: The US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS, formerly AERS) is a database that contains information on adverse event and medication error reports submitted to the FDA. Besides those from manufacturers, reports can be submitted from health care professionals and the public. The original system was started in 1969, but since the last major revision in 1997, reporting has markedly increased. Data mining algorithms have been developed for the quantitative detection of signals from such a large database, where a signal means a statistical association between a drug and an adverse event or a drug-associated adverse event, including the proportional reporting ratio (PRR), the reporting odds ratio (ROR), the information component (IC), and the empirical Bayes geometric mean (EBGM). A survey of our previous reports suggested that the ROR provided the highest number of signals, and the EBGM the lowest. Additionally, an analysis of warfarin-, aspirin- and clopidogrel-associated adverse events suggested that all EBGM-based signals were included in the PRR-based signals, and also in the IC- or ROR-based ones, and that the PRR- and IC-based signals were in the ROR-based ones. In this article, the latest information on this area is summarized for future pharmacoepidemiological studies and/or pharmacovigilance analyses.


title: Comparing Relational and Ontological Triple Stores in Healthcare Domain

abstract: Today’s technological improvements have made ubiquitous healthcare systems that converge into smart healthcare applications in order to solve patients’ problems, to communicate effectively with patients, and to improve healthcare service quality. The first step of building a smart healthcare information system is representing the healthcare data as connected, reachable, and sharable. In order to achieve this representation, ontologies are used to describe the healthcare data. Combining ontological healthcare data with the used and obtained data can be maintained by storing the entire health domain data inside big data stores that support both relational and graph-based ontological data. There are several big data stores and different types of big data sets in the healthcare domain. The goal of this paper is to determine the most applicable ontology data store for storing the big healthcare data. For this purpose, AllegroGraph and Oracle 12c data stores are compared based on their infrastructural capacity, loading time, and query response times. Hence, healthcare ontologies (GENE Ontology, Gene Expression Ontology (GEXO), Regulation of Transcription Ontology (RETO), Regulation of Gene Expression Ontology (REXO)) are used to measure the ontology loading time. Thereafter, various queries are constructed and executed for GENE ontology in order to measure the capacity and query response times for the performance comparison between AllegroGraph and Oracle 12c triple stores.


title: Generalizing Surrogate-Assisted Evolutionary Computation

abstract: Using surrogate models in evolutionary search provides an efficient means of handling today's complex applications plagued with increasing high-computational needs. Recent surrogate-assisted evolutionary frameworks have relied on the use of a variety of different modeling approaches to approximate the complex problem landscape. From these recent studies, one main research issue is with the choice of modeling scheme used, which has been found to affect the performance of evolutionary search significantly. Given that theoretical knowledge available for making a decision on an approximation model a priori is very much limited, this paper describes a generalization of surrogate-assisted evolutionary frameworks for optimization of problems with objectives and constraints that are computationally expensive to evaluate. The generalized evolutionary framework unifies diverse surrogate models synergistically in the evolutionary search. In particular, it focuses on attaining reliable search performance in the surrogate-assisted evolutionary framework by working on two major issues: 1) to mitigate the 'curse of uncertainty' robustly, and 2) to benefit from the 'bless of uncertainty.' The backbone of the generalized framework is a surrogate-assisted memetic algorithm that conducts simultaneous local searches using ensemble and smoothing surrogate models, with the aims of generating reliable fitness prediction and search improvements simultaneously. Empirical study on commonly used optimization benchmark problems indicates that the generalized framework is capable of attaining reliable, high quality, and efficient performance under a limited computational budget.


title: Multilayer feedforward networks are universal approximators

abstract: Abstract This paper rigorously establishes that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available. In this sense, multilayer feedforward networks are a class of universal approximators.


title: Combination Strategies for Semantic Role Labeling

abstract: This paper introduces and analyzes a battery of inference models for the problem of semantic role labeling: one based on constraint satisfaction, and several strategies that model the inference as a meta-learning problem using discriminative classifiers. These classifiers are developed with a rich set of novel features that encode proposition and sentence-level information. To our knowledge, this is the first work that: (a) performs a thorough analysis of learning-based inference models for semantic role labeling, and (b) compares several inference strategies in this context. We evaluate the proposed inference strategies in the framework of the CoNLL-2005 shared task using only automatically-generated syntactic information. The extensive experimental evaluation and analysis indicates that all the proposed inference strategies are successful - they all outperform the current best results reported in the CoNLL-2005 evaluation exercise - but each of the proposed approaches has its advantages and disadvantages. Several important traits of a state-of-the-art SRL combination strategy emerge from this analysis: (i) individual models should be combined at the granularity of candidate arguments rather than at the granularity of complete solutions; (ii) the best combination strategy uses an inference model based in learning; and (iii) the learning-based inference benefits from max-margin classifiers and global feedback.


title: ElastiCon: an elastic distributed sdn controller

abstract: Software Defined Networking (SDN) has become a popular paradigm for centralized control in many modern networking scenarios such as data centers and cloud. For large data centers hosting many hundreds of thousands of servers, there are few thousands of switches that need to be managed in a centralized fashion, which cannot be done using a single controller node. Previous works have proposed distributed controller architectures to address scalability issues. A key limitation of these works, however, is that the mapping between a switch and a controller is statically configured, which may result in uneven load distribution among the controllers as traffic conditions change dynamically. To address this problem, we propose ElastiCon, an elastic distributed controller architecture in which the controller pool is dynamically grown or shrunk according to traffic conditions. To address the load imbalance caused due to spatial and temporal variations in the traffic conditions, ElastiCon automatically balances the load across controllers thus ensuring good performance at all times irrespective of the traffic dynamics. We propose a novel switch migration protocol for enabling such load shifting, which conforms with the Openflow standard. We further design the algorithms for controller load balancing and elasticity. We also build a prototype of ElastiCon and evaluate it extensively to demonstrate the efficacy of our design.


title: Collision-Free and Curvature-Continuous Path Smoothing in Cluttered Environments

abstract: We present a novel trajectory computation algorithm to smooth jerky collision-free paths computed by samplebased motion planners. Our approach uses cubic B-splines to generate G or curvature continuous trajectories. The algorithm performs local spline refinement to compute smooth, collision-free paths in narrow passages and satisfy velocity and acceleration constraints. We also present a fast and reliable algorithm for collision checking between robot and the environment along the B-spline trajectories. We highlight the performance of our algorithm on complex benchmarks, including path computation for rigid and articulated models in tight spaces and cluttered environments.


title: Automated testing of a self-driving vehicle system

abstract: Self-driving vehicle systems promise significant benefits beyond the limited autonomy capabilities demonstrated by currently commercially available and technology demonstration cars. This paper proposes the use of automated testing to assess the performance of a self-driving vehicle system through simulation.


title: Periorbital necrobiosis lipoidica diabeticorum: case report

abstract: Necrobiosis lipoidica diabeticorum (NLD) is a relatively common manifestation of diabetes mellitus that is thought to be related to immune-complex vasculitis. The legs are the most common site for NLD, but other locations, such as the forearms or hands, have been reported. We describe a case of periorbital NLD that was diagnosed on the basis of histopathology.


title: Osteoarthritis as an inflammatory disease (osteoarthritis is not osteoarthrosis!)

abstract: Osteoarthritis (OA) has long been considered a "wear and tear" disease leading to loss of cartilage. OA used to be considered the sole consequence of any process leading to increased pressure on one particular joint or fragility of cartilage matrix. Progress in molecular biology in the 1990s has profoundly modified this paradigm. The discovery that many soluble mediators such as cytokines or prostaglandins can increase the production of matrix metalloproteinases by chondrocytes led to the first steps of an "inflammatory" theory. However, it took a decade before synovitis was accepted as a critical feature of OA, and some studies are now opening the way to consider the condition a driver of the OA process. Recent experimental data have shown that subchondral bone may have a substantial role in the OA process, as a mechanical damper, as well as a source of inflammatory mediators implicated in the OA pain process and in the degradation of the deep layer of cartilage. Thus, initially considered cartilage driven, OA is a much more complex disease with inflammatory mediators released by cartilage, bone and synovium. Low-grade inflammation induced by the metabolic syndrome, innate immunity and inflammaging are some of the more recent arguments in favor of the inflammatory theory of OA and highlighted in this review.