title: A model for anisotropic reflection

abstract: A reflection and refraction model for anisotropic surfaces is introduced. The anisotropy is simulated by small cylinders (added or subtracted) distributed on the anisotropic surface. Different levels of anisotropy are achieved by varying the distance between each cylinder and/or rising the cylinders more or less from the surface. Multidirectional anisotropy is modelled by orienting groups of cylinders in different direction. The intensity of the reflected light is computed by determining the visible and illuminated portion of the cylinders, taking self-blocking into account. We present two techniques to compute this in practice. In one the intensity is computed by sampling the surface of the cylinders. The other is an analytic solution. In the case of the diffuse component, the solution is exact. In the case of the specular component, an approximation is developed using a Chebyshev polynomial approximation of the specular term, and integrating the polynomial.This model can be implemented easily within most rendering system, given a suitable mechanism to define and alter surface tangents. The effectiveness of the model and the visual importance of anisotropy are illustrated with some pictures.


Reverse Engineering Flash EEPROM Memories Using Scanning Electron Microscopy


title: Semantic annotation of unstructured and ungrammatical text

abstract: There are vast amounts of free text on the internet that are neither grammatical nor formally structured, such as item descriptions on Ebay or internet classifieds like Craig's list. These sources of data, called "posts," are full of useful information for agents scouring the Semantic Web, but they lack the semantic annotation to make them searchable. Annotating these posts is difficult since the text generally exhibits little formal grammar and the structure of the posts varies. However, by leveraging collections of known entities and their common attributes, called "reference sets," we can annotate these posts despite their lack of grammar and structure. To use this reference data, we align a post to a member of the reference set, and then exploit this matched member during information extraction. We compare this extraction approach to more traditional information extraction methods that rely on structural and grammatical characteristics, and we show that our approach outperforms traditional methods on this type of data.


title: A semantic federated search engine for domain-specific document retrieval

abstract: Retrieval of domain-specific documents became attractive for the Semantic Web community due to the possibility of integrating classic Information Retrieval (IR) techniques with semantic knowledge. Unfortunately, the gap between the construction of a full semantic search engine and the possibility of exploiting a repository of ontologies covering all possible domains is far from being filled. Recent solutions focused on the aggregation of different domain-specific repositories managed by third-parties. In this paper, we present a semantic federated search engine developed in the context of the EEXCESS EU project. Through the developed platform, users are able to perform federated queries over repositories in a transparent way, i.e. without knowing how their original queries are transformed before being actually submitted. The platform implements a facility for plugging new repositories and for creating, with the support of general purpose knowledge bases, knowledge graphs describing the content of each connected repository. Such knowledge graphs are then exploited for enriching queries performed by users.


title: Clustering With Side Information: From a Probabilistic Model to a Deterministic Algorithm

abstract: In this paper, we propose a model-based clustering method (TVClust) that robustly incorporates noisy side information as soft-constraints and aims to seek a consensus between side information and the observed data. Our method is based on a nonparametric Bayesian hierarchical model that combines the probabilistic model for the data instance and the one for the side-information. An efficient Gibbs sampling algorithm is proposed for posterior inference. Using the small-variance asymptotics of our probabilistic model, we then derive a new deterministic clustering algorithm (RDP-means). It can be viewed as an extension of K-means that allows for the inclusion of side information and has the additional property that the number of clusters does not need to be specified a priori. Empirical studies have been carried out to compare our work with many constrained clustering algorithms from the literature on both a variety of data sets and under a variety of conditions such as using noisy side information and erroneous k values. The results of our experiments show strong results for our probabilistic and deterministic approaches under these conditions when compared to other algorithms in the literature.


title: Guided Manipulation Planning at the DARPA Robotics Challenge Trials

abstract: We document the empirical results from Carnegie Mellon University’s entry into the DARPA Robotics Challenge Trials. Our system seamlessly and intelligently integrates recent advances in autonomous manipulation with the perspective and intuition of an expert human operator. Virtual fixtures are used as the common language between the operator and the motion planner. The planning system then solves a guided manipulation problem to perform disaster-response tasks.


title: Experiments in adjustable autonomy

abstract: Human-robot interaction is becoming an increasingly important research area. In this paper, we present our work on designing a human-robot system with adjustable autonomy and describe not only the prototype interface but also the corresponding, robot behaviors. In our approach, we grant the human meta-level control over the level of robot autonomy, but we allow the robot a varying amount of self-direction with each level. Within this framework of adjustable autonomy, we explore how existing, robot control approaches can be adapted and extended to be compatible with adjustable autonomy.


title: A quality by design approach to investigate the effect of mannitol and dicalcium phosphate qualities on roll compaction

abstract: Roll compaction is a continuous process for solid dosage form manufacturing increasingly popular within pharmaceutical industry. Although roll compaction has become an established technique for dry ...


title: Integrated overcurrent protection system for class-D audio power amplifiers

abstract: A fully integrated overcurrent protection system is presented suitable for application in integrated class-D audio power amplifiers. Accurate overcurrent detection is used based on parallel measurement of the voltage drop across the DMOS power transistors. A logic circuit enables continuous current limiting during overload situations. Actual short circuits can be distinguished from load impedance minima using a simple short-circuit discrimination method.


title: Scalable Pattern Sharing on Event Streams*

abstract: Complex Event Processing (CEP) has emerged as a technology of choice for high performance event analytics in time-critical decision-making applications. Yet it is becoming increasingly difficult to support high-performance event processing due to the rising number and complexity of event pattern queries and the increasingly high velocity of event streams. In this work we design the SPASS framework that successfully tackles these demanding CEP workloads. Our SPASS optimizer identifies opportunities for effective shared processing among CEP queries by leveraging time-based event correlations among queries. The problem of pattern sharing is shown to be NP-hard by reducing the Minimum Substring Cover problem to our CEP pattern sharing problem. The SPASS optimizer is designed that finds a shared pattern plan in polynomial-time covering all sequence patterns while still guaranteeing an optimality bound. To execute this shared pattern plan, the SPASS runtime employs stream transactions that assure concurrent shared maintenance and re-use of sub-patterns across queries. Our experimental study confirms that the SPASS framework achieves over 16 fold performance improvement for a wide range of experiments compared to the state-of-the-art solution.


title: Generating Visual Representations for Zero-Shot Classification

abstract: This paper addresses the task of learning an image classifier when some categories are defined by semantic descriptions only (e.g. visual attributes) while the others are defined by exemplar images as well. This task is often referred to as the Zero-Shot classification task (ZSC). Most of the previous methods rely on learning a common embedding space allowing to compare visual features of unknown categories with semantic descriptions. This paper argues that these approaches are limited as i) efficient discriminative classifiers can't be used ii) classification tasks with seen and unseen categories (Generalized Zero-Shot Classification or GZSC) can't be addressed efficiently. In contrast, this paper suggests to address ZSC and GZSC by i) learning a conditional generator using seen classes ii) generate artificial training examples for the categories without exemplars. ZSC is then turned into a standard supervised learning problem. Experiments with 4 generative models and 5 datasets experimentally validate the approach, giving state-of-the-art results on both ZSC and GZSC.


title: Shake-your-head: revisiting walking-in-place for desktop virtual reality

abstract: The Walking-In-Place interaction technique was introduced to navigate infinitely in 3D virtual worlds by walking in place in the real world. The technique has been initially developed for users standing in immersive setups and was built upon sophisticated visual displays and tracking equipments. In this paper, we propose to revisit the whole pipeline of the Walking-In-Place technique to match a larger set of configurations and apply it notably to the context of desktop Virtual Reality. With our novel "Shake-Your-Head" technique, the user is left with the possibility to sit down, and to use small screens and standard input devices such as a basic webcam for tracking. The locomotion simulation can compute various motions such as turning, jumping and crawling, using as sole input the head movements of the user. We also introduce the use of additional visual feedback based on camera motions to enhance the walking sensations. An experiment was conducted to compare our technique with classical input devices used for navigating in desktop VR. Interestingly, the results showed that our technique could even allow faster navigations when sitting, after a short learning. Our technique was also perceived as more fun and increasing presence, and was generally more appreciated for VR navigation.


title: On the Evolution of Mobile Platform Ecosystem Structure and Strategy

abstract: Platforms have become a core fundament of many technology industries. Platforms not only enable new products and services but have also been shown to influence strategies, shape business models, and even transform entire industries. Platforms play a particularly important role in the mobile ecosystem. The success of smartphones has led to an intense battle of mobile platforms, each looking for ways to become the system of choice for mobile device manufacturers, mobile network operators, and mobile application developers. Drawing on theories of platform markets, strategic networks, and business ecosystems, this paper uses a visualization approach to study the evolving global interfirm structure and examines strategies used in the mobile platform ecosystem over the past five years. We identify important differences between mobile platform strategies and discuss their implications for both mobile ecosystem participants and the future of the app economy.


title: An Investigation into Usability Aspects of E-Commerce Websites Using Users' Preferences

abstract: We investigate the usability problems of e-commerce online shopping websites from user’s preferences and determine the relative importance of factors such as navigability, content, design, ease of use, and structure through user survey. The main intent of this ranking of web site characteristics is that a designer can relatively give higher efforts on designing features that may lead to higher merit and better usability. As such, our research work help us capture the data by involving user testing (usability testing) and open source automated tools such as Camtasia. Hence, the outcomes of the above approach show the navigation, content, design were the first, second, and third priority for evaluating the usability of e-commerce websites whereas ease of use and structure were the fourth and fifth features from the overall usability value calculation. There is a significant statistical difference between novice and expert users only for navigation feature. The maximum number of users feels satisfied with navigation, content, and design features whereas they are dissatisfied with ease of use and structural features of the websites.


title: Agent-based and regression models of social influence

abstract: This paper studies social influence (i.e., adoption of belief) using agent-based simulation and regression models. Each agent is modeled by a linear regression model. Agents interact with neighbors by exchanging social beliefs. It is observed that if individual belief is linear in neighbors' beliefs, system-level belief and aggregated neighbors' beliefs can also be described by a linear regression model. Analysis is conducted on a simplified 2-node network to provide insight into the interactions and results of general models. Least squares estimates are developed. Explicit expressions are obtained to explain relationship between initial belief and current belief.


title: Right ventricle segmentation from cardiac MRI: A collation study

abstract: Magnetic Resonance Imaging (MRI), a reference examination for cardiac morphology and function in humans, allows to image the cardiac right ventricle (RV) with high spatial resolution. The segmentation of the RV is a difficult task due to the variable shape of the RV and its ill-defined borders in these images. The aim of this paper is to evaluate several RV segmentation algorithms on common data. More precisely, we report here the results of the Right Ventricle Segmentation Challenge (RVSC), concretized during the MICCAI'12 Conference with an on-site competition. Seven automated and semi-automated methods have been considered, along them three atlas-based methods, two prior based methods, and two prior-free, image-driven methods that make use of cardiac motion. The obtained contours were compared against a manual tracing by an expert cardiac radiologist, taken as a reference, using Dice metric and Hausdorff distance. We herein describe the cardiac data composed of 48 patients, the evaluation protocol and the results. Best results show that an average 80% Dice accuracy and a 1cm Hausdorff distance can be expected from semi-automated algorithms for this challenging task on the datasets, and that an automated algorithm can reach similar performance, at the expense of a high computational burden. Data are now publicly available and the website remains open for new submissions (http://www.litislab.eu/rvsc/).


title: Integrated decision support systems: A data warehousing perspective

abstract: Successfully supporting managerial decision-making is critically dependent upon the availability of integrated, high quality information organized and presented in a timely and easily understood manner. Data warehouses have emerged to meet this need. They serve as an integrated repository for internal and external data-intelligence critical to understanding and evaluating the business within its environmental context. With the addition of models, analytic tools, and user interfaces, they have the potential to provide actionable information resources-business intelligence that supports effective problem and opportunity identification, critical decision-making, and strategy formulation, implementation, and evaluation. Four themes frame our analysis: integration, implementation, intelligence, and innovation.


title: Deep multimodal fusion for persuasiveness prediction

abstract: Persuasiveness is a high-level personality trait that quantifies the influence a speaker has on the beliefs, attitudes, intentions, motivations, and behavior of the audience. With social multimedia becoming an important channel in propagating ideas and opinions, analyzing persuasiveness is very important. In this work, we use the publicly available Persuasive Opinion Multimedia (POM) dataset to study persuasion. One of the challenges associated with this problem is the limited amount of annotated data. To tackle this challenge, we present a deep multimodal fusion architecture which is able to leverage complementary information from individual modalities for predicting persuasiveness. Our methods show significant improvement in performance over previous approaches.


title: DDD17: End-To-End DAVIS Driving Dataset

abstract: Event cameras, such as dynamic vision sensors (DVS), and dynamic and active-pixel vision sensors (DAVIS) can supplement other autonomous driving sensors by providing a concurrent stream of standard active pixel sensor (APS) images and DVS temporal contrast events. The APS stream is a sequence of standard grayscale global-shutter image sensor frames. The DVS events represent brightness changes occurring at a particular moment, with a jitter of about a millisecond under most lighting conditions. They have a dynamic range of >120 dB and effective frame rates >1 kHz at data rates comparable to 30 fps (frames/second) image sensors. To overcome some of the limitations of current image acquisition technology, we investigate in this work the use of the combined DVS and APS streams in end-to-end driving applications. The dataset DDD17 accompanying this paper is the first open dataset of annotated DAVIS driving recordings. DDD17 has over 12 h of a 346x260 pixel DAVIS sensor recording highway and city driving in daytime, evening, night, dry and wet weather conditions, along with vehicle speed, GPS position, driver steering, throttle, and brake captured from the car's on-board diagnostics interface. As an example application, we performed a preliminary end-to-end learning study of using a convolutional neural network that is trained to predict the instantaneous steering angle from DVS and APS visual data.


BotDCAT-AP: An Extension of the DCAT Application Profile for Describing Datasets for Chatbot Systems


title: IoT based smart surveillance security system using raspberry Pi

abstract: Communication mainly is the transfer of whatever thing or exchanging of data, so that the Internet of things is naught but the transferring or exchanging of anything with several other things. The using of internet authorized system or devices roughly calculated as that by 2020 there will be nearly about billions. The purpose of the paper is to define a safekeeping alert device spending little handling power by Internet of things which help out to observer plus alerts when gestures or else motion are there then send images to a cloud server. Besides, internet of things centered use can be used tenuously to observe the action as well as acquire warning when gestures or else indication are there. The images are showed straight to a cloud attendant, when the cloud attendant is not accessible at that time the records are put in storage close by on a Raspberry Pi. A credit card size Raspberry Pi with a advantage of Open Source Computer Vision (Open-CV) software knobs the image processing, control algorithms used for the attentiveness then shows taken images to concern persons email by the use of Wi-Fi module. The system uses ordinary webcam.


title: Using natural zeolite for contamination reduction of agricultural soil irrigated with treated urban wastewater

abstract: AbstractNowadays, due to the global water crisis, reusing treated wastewater is being considered as water resources for different purposes, particularly for agricultural applications. Irrigating soil using urban wastewaters which are not treated properly, however, has negative effect on the soil and its subsequent drained water. Because of that, selective adsorption properties of the natural zeolite were used to improve filtering properties of the soil irrigated with wastewater. The effects of application method, dosage, and particle size of natural zeolite were studied on the EC, pH, BOD5, Na+, Ca2++Mg2+, and nitrate concentration of an urban wastewater by passing it through zeolite-added soil columns. The result shows that by adding zeolite to the soil column, the value of pH, EC, and Na+ of the column outlet were increased, while its Ca2++Mg2+, nitrate concentration, as well as the BOD5 were decreased. BOD5 level of the column effluent in the control, mixed, and layered treated soil with zeolite were l...


An approach for smart parking system based on cloud using IoT


title: Application of the Artificial Neural Network for blood pressure evaluation with smartphones

abstract: The smartphone is proposed to evaluate the Blood Pressure (BP) anywhere and anytime. The tasks performed by smartphone are (i) extraction of the PhotoPlethysmoGram (PPG) signal from a frame sequence acquired by the integrated camera, and (ii) processing it by Artificial Neural Network for the evaluation of the BP. The PPG signal is evaluated by analyzing the volumetric blood variation of the fingertip on the frame sequence. Successively, parameters characterizing the pulses of the PPG signal are sent to the Fit Forward Neural Network for the simultaneously evaluation of the systolic and the diastolic BP. The validation of the results is performed by comparing them with the ones obtained by the Ambulatory Blood Pressure monitor ABP Spacelabs 90207. Preliminary experimental results show useful information to address the future research devoted to reduce the maximum error.


title: Big data integration

abstract: The Big Data era is upon us: data is being generated, collected and analyzed at an unprecedented scale, and data-driven decision making is sweeping through all aspects of society. Since the value of data explodes when it can be linked and fused with other data, addressing the big data integration (BDI) challenge is critical to realizing the promise of Big Data. BDI differs from traditional data integration in many dimensions: (i) the number of data sources, even for a single domain, has grown to be in the tens of thousands, (ii) many of the data sources are very dynamic, as a huge amount of newly collected data are continuously made available, (iii) the data sources are extremely heterogeneous in their structure, with considerable variety even for substantially similar entities, and (iv) the data sources are of widely differing qualities, with significant differences in the coverage, accuracy and timeliness of data provided. This talk explores the progress that has been made by the data integration community in addressing these novel challenges faced by big data integration, and identifies a range of open problems for the community.


title: Augmenting End-to-End Dialog Systems with Commonsense Knowledge

abstract: Building dialog agents that can converse naturally with humans is a challenging yet intriguing problem of artificial intelligence. In open-domain human-computer conversation, where the conversational agent is expected to respond to human responses in an interesting and engaging way, commonsense knowledge has to be integrated into the model effectively. In this paper, we investigate the impact of providing commonsense knowledge about the concepts covered in the dialog. Our model represents the first attempt to integrating a large commonsense knowledge base into end-to-end conversational models. In the retrieval-based scenario, we propose the Tri-LSTM model to jointly take into account message and commonsense for selecting an appropriate response. Our experiments suggest that the knowledge-augmented models are superior to their knowledge-free counterparts in automatic evaluation.


title: Automated Heart Wall Motion Abnormality Detection from Ultrasound Images Using Bayesian Networks

abstract: Coronary Heart Disease can be diagnosed by measuring and scoring regional motion of the heart wall in ultrasound images of the left ventricle (LV) of the heart. We describe a completely automated and robust technique that detects diseased hearts based on detection and automatic tracking of the endocardium and epicardium of the LV. The local wall regions and the entire heart are then classified as normal or abnormal based on the regional and global LV wall motion. In order to leverage structural information about the heart we applied Bayesian Networks to this problem, and learned the relations among the wall regions off of the data using a structure learning algorithm. We checked the validity of the obtained structure using anatomical knowledge of the heart and medical rules as described by doctors. The resultant Bayesian Network classifier depends only on a small subset of numerical features extracted from dual-contours tracked through time and selected using a filter-based approach. Our numerical results confirm that our system is robust and accurate on echocardiograms collected in routine clinical practice at one hospital; our system is built to be used in real-time.


title: Fronts propagating with curvature dependent speed: algorithms based on Hamilton–Jacobi formulations

abstract: We devise new numerical algorithms, called PSC algorithms, for following fronts propagating with curvature-dependent speed. The speed may be an arbitrary function of curvature, and the front also can be passively advected by an underlying flow. These algorithms approximate the equations of motion, which resemble Hamilton-Jacobi equations with parabolic right-hand sides, by using techniques from hyperbolic conservation laws. Non-oscillatory schemes of various orders of accuracy are used to solve the equations, providing methods that accurately capture the formation of sharp gradients and cusps in the moving fronts. The algorithms handle topological merging and breaking naturally, work in any number of space dimensions, and do not require that the moving surface be written as a function. The methods can be also used for more general Hamilton-Jacobi-type problems. We demonstrate our algorithms by computing the solution to a variety of surface motion problems.


title: Molecular Simulations with Solvent Competition Quantify Water Displaceability and Provide Accurate Interaction Maps of Protein Binding Sites

abstract: Binding sites present well-defined interaction patterns that putative ligands must meet. Knowing them is essential to guide structure-based drug discovery projects. However, complex aspects of molecular recognition—such as protein flexibility or the effect of aqueous solvation—hinder accurate predictions. This is particularly true for polar contacts, which are heavily influenced by the local environment and the behavior of discrete water molecules. Here we present and validate MDmix (Molecular Dynamics simulations with mixed solvents) as a method that provides much more accurate interaction maps than ordinary potentials (e.g., GRID). Additionally, MDmix also affords water displaceability predictions, with advantages over methods that use pure water as solvent (e.g., inhomogeneous fluid solvation theory). With current MD software and hardware solutions, predictions can be obtained in a matter of hours and visualized in a very intuitive manner. Thus, MDmix is an ideal complement in everyday structure-based ...


title: Ambient influence: can twinkly lights lure and abstract representations trigger behavioral change?

abstract: Can ubiquitous technologies be designed to nudge people to change their behavior? If so, how? We describe an ambient installation that was intended to help people decide - and to encourage them to reflect - when confronted with a choice. In this particular case, it was whether to take the stairs or the elevator in their place of work. The rationale was to push people towards a desired behavior at the point of decision-making and to reflect upon theirs and others' aggregate behavior. We describe the ambient displays that were developed and the prototyping studies in which they were evaluated. The findings from an in-the-wild study are then presented. They reveal that even though people said they were not aware of changing their behavior, logged data of their actual behavior showed a significant change. We discuss these mixed findings in relation to whether ambient displays can influence at an unconscious or conscious level.


title: Habits of programming in scratch

abstract: Visual programming environments are widely used to introduce young people to computer science and programming; in particular, they encourage learning by exploration. During our research on teaching and learning computer science concepts with Scratch, we discovered that Scratch engenders certain habits of programming: (a) a totally bottom-up development process that starts with the individual Scratch blocks, and (b) a tendency to extremely fine-grained programming. Both these behaviors are at odds with accepted practice in computer science that encourages one: (a) to start by designing an algorithm to solve a problem, and (b) to use programming constructs to cleanly structure programs. Our results raise the question of whether exploratory learning with a visual programming environment might actually be detrimental to more advanced study.


title: On Multipath Link Characterization and Adaptation for Device-Free Human Detection

abstract: Wireless-based device-free human sensing has raised increasing research interest and stimulated a range of novel location-based services and human-computer interaction applications for recreation, asset security and elderly care. A primary functionality of these applications is to first detect the presence of humans before extracting higher-level contexts such as physical coordinates, body gestures, or even daily activities. In the presence of dense multipath propagation, however, it is non-trivial to even reliably identify the presence of humans. The multipath effect can invalidate simplified propagation models and distort received signal signatures, thus deteriorating detection rates and shrinking detection range. In this paper, we characterize the impact of human presence on wireless signals via ray-bouncing models, and propose a measurable metric on commodity WiFi infrastructure as a proxy for detection sensitivity. To achieve higher detection rate and wider sensing coverage in multipath-dense indoor scenarios, we design a lightweight sub carrier and path configuration scheme harnessing frequency diversity and spatial diversity. We prototype our scheme with standard WiFi devices. Evaluations conducted in two typical office environments demonstrate a detection rate of 92.0% with a false positive of 4.5%, and almost 1x gain in detection range given a minimal detection rate of 90%.


title: Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival Prediction

abstract: This paper introduces a novel methodology to integrate human brain connectomics and parcellation for brain tumor segmentation and survival prediction. For segmentation, we utilize an existing brain parcellation atlas in the MNI152 1 mm space and map this parcellation to each individual subject data. We use deep neural network architectures together with hard negative mining to achieve the final voxel level classification. For survival prediction, we present a new method for combining features from connectomics data, brain parcellation information, and the brain tumor mask. We leverage the average connectome information from the Human Connectome Project and map each subject brain volume onto this common connectome space. From this, we compute tractographic features that describe potential neural disruptions due to the brain tumor. These features are then used to predict the overall survival of the subjects. The main novelty in the proposed methods is the use of normalized brain parcellation data and tractography data from the human connectome project for analyzing MR images for segmentation and survival prediction. Experimental results are reported on the BraTS2018 dataset.


title: A Comparison of Preschool and Elementary School Children Learning Computer Science Concepts through a Multilanguage Robot Programming Platform

abstract: This paper describes a school intervention to teach fundamental Computer Science (CS) concepts to 3-11 year old students with a multilanguage robot programming platform (using drag and drop, Python and C++ languages) in Argentina. We analyze students' performance and learning process based on multiple choice test and classroom observations. Data show that all students can intuitively learn sequence, conditional, loops and parameters and that girls performed slightly better than boys. Older students can easily combine these concepts to write a program. The multilanguage platform promotes student spontaneous exploration of more sophisticated CS concepts and languages. These findings imply that introducing CS in mandatory schooling from an inquiry based approach is both achievable and beneficial.


title: Smart traffic light control system

abstract: Traffic light control systems are widely used to monitor and control the flow of automobiles through the junction of many roads. They aim to realize smooth motion of cars in the transportation routes. However, the synchronization of multiple traffic light systems at adjacent intersections is a complicated problem given the various parameters involved. Conventional systems do not handle variable flows approaching the junctions. In addition, the mutual interference between adjacent traffic light systems, the disparity of cars flow with time, the accidents, the passage of emergency vehicles, and the pedestrian crossing are not implemented in the existing traffic system. This leads to traffic jam and congestion. We propose a system based on PIC microcontroller that evaluates the traffic density using IR sensors and accomplishes dynamic timing slots with different levels. Moreover, a portable controller device is designed to solve the problem of emergency vehicles stuck in the overcrowded roads.


title: Decision making and knowledge management in inquiring organizations : toward a new decision-making paradigm for DSS

abstract: Abstract Organizational decisions of the future may include social, environmental, and economic concerns, and be much more “wicked” [Policy Sciences, 4 (1973) 155], complex and interconnected than those of the past. Organizations and their decision support systems must embrace procedures that can deal with this complexity and go beyond the technical orientation of previous DSS. Singerian inquiring organizations [Australian Journal of Information Systems, 6 (1) (1998) 3; http://www.cba.uh.edu/~parks/fis/fis.htm (1998); Proceedings of 3rd Americas Conference on Information Systems, Indianapolis, August 1997, p. 293; Proceedings of the 1999 Meeting of the America's Conference on Information Systems, Milwaukee, August 1999; Special Issue of Information Systems Frontiers on Philosophical Reasoning in Information Systems Research (in press)], based on Churchman's [The Design of Inquiring Systems: Basic Concepts of Systems and Organization, Basic Books, New York, NY, 1971] inquiring systems and Mitroff and Linstone's [The Unbounded Mind: Breaking the Chains of Traditional Business Thinking, Oxford Univ. Press, New York, 1993] unbounded systems thinking (UST), are designed to deal with wicked decision situations. This paper discusses DSS and knowledge management in Singerian organizations and calls for a new decision-making paradigm for DSS.


title: The p-median problem: A survey of metaheuristic approaches

abstract: The p-median problem is one of the basic models in discrete location theory. As with most location problems, it is classified as NP-hard, and so, heuristic methods are usually used to solve it. Metaheuristics are frameworks for building heuristics. In this survey, we examine the p-median, with the aim of providing an overview on advances in solving it using recent procedures based on metaheuristic rules.


title: Search-Based Drama Management in the Interactive Fiction Anchorhead

abstract: Drama managers guide a user through a story experience by modifying the experience in reaction to the user's actions. Search-based drama management (SBDM) casts the drama-management problem as a search problem: Given a set of plot points, a set of actions the drama manager can take, and an evaluation of story quality, search can be used to optimize the user's experience. SBDM was first investigated by Peter Weyhrauch in 1997, but little explored since. We return to SBDM to investigate algorithmic and authorship issues, including the extension of SBDM to different kinds of stories, especially stories with subplots and multiple endings, and issues of scalability. In this paper we report on experiments applying SBDM to an abstract story search space based on the text-based interactive fiction Anchorhead. We describe the features employed by the story evaluation function, investigate design issues in the selection of a set of drama management actions, and report results for drama managed versus unmanaged stories for a simulated random user.


title: Detection of Syn Flooding Attacks using Linear Prediction Analysis

abstract: This paper presents a simple but fast and effective method to detect TCP SYN flooding attacks. Linear prediction analysis is proposed as a new paradigm for DoS attack detection. The proposed SYN flooding detection mechanism makes use of the exponential backoff property of TCP used during timeouts. By modeling the difference of SYN and SYN+ ACK packets, we are successfully able to detect an attack within short delays. We use this method at leaf routers and firewalls to detect the attack without the need of maintaining any state.


title: Mechanisms of antibiotic resistance in Pseudomonas aeruginosa.

abstract: Pseudomonas aeruginosa is a notoriously difficult organism to control with antibiotics or disinfectants1. Recent reports on the antibiotic sensitivity patterns of P. aeruginosa in the UK have highlighted the problem of antibiotic resistance in cystic fibrosis (CF) strains in comparison with other hospital isolates2,3. Table 1 summarizes the current position in which the resistance rates of P. aeruginosa strains from CF patients are all significantly higher than those from non-CF patients. Extensive use of these antibiotics to treat P. aeruginosa in CF has generated the selective pressure to encourage resistance development. Why is P. aeruginosa resistant to antibiotics and how can it become more resistant following exposure to antibiotics? Its general resistance is due to a combination of factors:


title: Recommending Movies Based on Mise-en-Scene Design

abstract: In this paper, we present an ongoing work that will ultimately result in a movie recommender system based on the Mise-en-Scene characteristics of the movies. We believe that the preferences of users on movies can be well described in terms of the mise-en-scene, i.e., the design aspects of movie making influencing aesthetic and style. Examples of mise-en-scene characteristics are lighting, colors, background, and movements. Our recommender system opens new opportunities in the design of new user interfaces able to offer a personalized way to search for interesting movies through the analysis of film styles rather than using the traditional classifications of movies based on explicit attributes such as genre and cast.


title: A Novel Single-Stage High-Power-Factor AC-to-DC LED Driving Circuit With Leakage Inductance Energy Recycling

abstract: This paper proposes a novel single-stage ac-to-dc light-emitting-diode (LED) driver circuit. A buck-boost power factor (PF) corrector is integrated with a flyback converter. A recycling path is built to recover the inductive leakage energy. In this way, the presented circuit can provide not only high PF and low total harmonic distortion but also high conversion efficiency and low switching voltage spikes. Above 0.95 PF and 90% efficiency are obtained from an 8-W LED lamp driver prototype.


title: Variational Inference with Normalizing Flows

abstract: The choice of approximate posterior distribution is one of the core problems in variational inference. Most applications of variational inference employ simple families of posterior approximations in order to allow for efficient inference, focusing on mean-field or other simple structured approximations. This restriction has a significant impact on the quality of inferences made using variational methods. We introduce a new approach for specifying flexible, arbitrarily complex and scalable approximate posterior distributions. Our approximations are distributions constructed through a normalizing flow, whereby a simple initial density is transformed into a more complex one by applying a sequence of invertible transformations until a desired level of complexity is attained. We use this view of normalizing flows to develop categories of finite and infinitesimal flows and provide a unified view of approaches for constructing rich posterior approximations. We demonstrate that the theoretical advantages of having posteriors that better match the true posterior, combined with the scalability of amortized variational approaches, provides a clear improvement in performance and applicability of variational inference.


title: Self-regulation of time management: Mental contrasting with implementation intentions

abstract: Mental contrasting with implementation intentions (MCII) has been found to improve self-regulation across many life domains. The present research investigates whether MCII can benefit time management. In Study 1, we asked students to apply MCII to a pressing academic problem and assessed how they scheduled their time for the upcoming week. MCII participants scheduled more time than control participants who in their thoughts either reflected on similar contents using different cognitive procedures (content-control group) or applied the same cognitive procedures on different contents (format-control group). In Study 2, students were taught MCII as a metacognitive strategy to be used on any upcoming concerns of the subsequent week. As compared with the week prior to the training, students in the MCII (vs. format control) condition improved in self-reported time management. In Study 3, MCII (vs. format control) helped working mothers who enrolled in a vocational business program to attend classes more regularly. The findings suggest that performing MCII on one’s everyday concerns improves time management. Copyright © 2015 John Wiley & Sons, Ltd.


title: Transformation of Wolffia arrhiza (L.) Horkel ex Wimm

abstract: To date, Lemna and Spirodela species of the family Lemnaceae are the only species that have been used to produce recombinant proteins for pharmaceutical and veterinary purposes. Wolffia arrhiza is the most evolutionarily advanced species of the Lemnaceae. A rootless duckweed, it is the most promising target for biopharmingas a candidate for submerged cultivation in a fermenter. As a first step toward future biotechnological use of Wolffia, we established a stable transformation system for it based on Agrobacterium-mediated transformation. Following inoculation with the bacteria, Wolffia cluster explants were cultured for 2 weeks on media containing 2.0 mg l−1 2,4-dichlorophenoxyacetic acid and 2.0 mg l−1 N6-benzyladenine. Explants were then transferred to growth regulator-free media in the presence of 5.0 mg l−1 hygromycin B to select antibiotic-resistant plants. Other selective agents—kanamycin and phosphinothricin—were not suitable for Wolffia, nor was application of particle bombardment for the delivery of foreign DNA to Wolffia explants. The developed agro transformation conditions yielded stably transformed lines of Wolffia, confirmed by Southern blotting, with an efficiency of 0.2–0.4 % transgenes per 100 explants.


Quantum Reinforcement Learning


title: Modelling users, context and devices for adaptive user interface systems

abstract: Purpose – The purpose of this paper is to review the state-of-the-art in adaptive user interface systems by studying their historical development over the past 20 years. Moreover, this paper contributes with a specific model combining three main entities (users, context and devices) that have been demonstrated to be always represented in these environments. Novel concepts that should be taken into account in these systems are also presented. Design/methodology/approach – The authors first provide a review and a comparison of current user interface adaptive systems. Next, the authors detail the most significant models and the set of techniques used to, finally, propose a novel model based on the studied literature. Findings – Literature solutions for adaptive user interface systems tend to be very domain dependant. This situation restricts the possibility of sharing and exporting the information between such systems. Furthermore, the studied approaches barely highlight the dynamism of these models. Origina...


title: A Kalman-Filter Approach to Estimating the Natural Rate of Unemployment

abstract: The terms "NAIRU" and "natural rate of unemployment" are not interchangeable. Further, while there is a consensus that the NAIRU represents an empirical macroeconomic relationship, little agreement exists regarding what is meant by the "natural rate of unemployment." This paper estimates the natural rate of unemployment, defined here as the unemployment rate corresponding to equilibrium in the labor market. Rather than employ a Phillips curve, the model utilizes a macroeconomic-principles-level decomposition of the unemployment rate into its cyclical and noncyclical components. We then evaluate the estimated natural rate series by examining its relationship to structural variables from the labor market.


title: The differences between latent topics in abstracts and citation contexts of citing papers

abstract: Although it is commonly expected that the citation context of a reference is likely to provide more detailed and direct information about the nature of a citation, few studies in the literature have specifically addressed the extent to which the information in different parts of a scientific publication differs. Do abstracts tend to use conceptually broader terms than sentences in a citation context in the body of a publication? In this article, we propose a method to analyze and compare latent topics in scientific publications, in particular, from abstracts of papers that cited a target reference and from sentences that cited the target reference. We conducted an experiment and applied topical modeling techniques to full‐text papers in eight biomedicine journals. Topics derived from the two sources are compared in terms of their similarities and broad‐narrow relationships defined based on information entropy. The results show that abstracts and citation contexts are characterized by distinct sets of topics with moderate overlaps. Furthermore, the results confirm that topics from abstracts of citing papers have broader terms than topics from citation contexts formed by citing sentences. The method and the findings could be used to enhance and extend the current methodologies for research evaluation and citation evaluation.


title: Design and Implementation of a Distributed File System

abstract: SUMMARY We introduce a new model for replication in distributed systems. The primary motivation for replication lies in fault tolerance. Although there are different kinds of replication approaches, our model combines the advantages of modular redundancy and primary-stand-by approaches to give more flexibility with respect to system configuration. To implement such a model, we select the IBM PC-net with MS-DOS environment as our base. Transparency as well as fault-tolerance file access are the highlights of our system design. To fulfil these requirements, we incorporate the idea of directory-oriented replication and extended prefix tab/es in the system design. The implementation consists of a command shell, a DOS manager, and a recovery manager. Through this design, we can simulate a UNIX-like distributed file system whose function is compatible with MS-DOS.


title: Multimodal Graph-Based Reranking for Web Image Search

abstract: This paper introduces a web image search reranking approach that explores multiple modalities in a graph-based learning scheme. Different from the conventional methods that usually adopt a single modality or integrate multiple modalities into a long feature vector, our approach can effectively integrate the learning of relevance scores, weights of modalities, and the distance metric and its scaling for each modality into a unified scheme. In this way, the effects of different modalities can be adaptively modulated and better reranking performance can be achieved. We conduct experiments on a large dataset that contains more than 1000 queries and 1 million images to evaluate our approach. Experimental results demonstrate that the proposed reranking approach is more robust than using each individual modality, and it also performs better than many existing methods.


title: Combined effects of valence and attributes of e-WOM on consumer judgment for message and product The moderating effect of brand community type

abstract: Purpose – The purpose of this paper is to propose a model to test whether the combined effects of valence and objectivity/subjectivity of online review have an effect on consumer judgment and whether e-WOM platforms have a moderating effect. Design/methodology/approach – In total, 480 respondents participated in online experiments with a four (positive+objective, positive+subjective, negative+objective, and negative+subjective online review) by two (marketer-generated vs consumer-generated brand community web sites) between subject design. Findings – The experiment showed that: an objective negative online review was rated higher in terms of message usefulness compared to the other types of online reviews; positive reviews, whether they are objective or subjective, were rated higher in terms of attitudes toward and intention to purchase the reviewed product, and the effects of online reviews moderated by e-WOM platforms on consumer judgment were supported. Research limitations/implications – The present s...


title: A 3.4 W Digital-In Class-D Audio Amplifier in 0.14 $\mu $m CMOS

abstract: In this paper a class-D audio amplifier for mobile applications is presented realized in a 0.14 μm CMOS technology tailored for mobile applications. The amplifier has a simple PDM-based digital interface for audio and control that requires only two pins and enables assembly in 9-bump WL-CSP. The complete audio path is discussed that consists of a Parser, Digital PWM controller, 1-bit DA-converters, analog feedback loop and the Class-D power stage. A reconfigurable gate driver is used that reduces quiescent current consumption and radiated emission.


title: 3D Model Classification and Retrieval Based on Semantic and Ontology

abstract: Classifying 3D models into classes is an important step in 3D model retrieval process. However, most classification system does not include semantic information. In this paper, a new method has been proposed to classify and to retrieve 3D model using semantic concepts and ontology. First, we use the machine learning methods to label 3D models by k-means algorithm in shape indexes space. Second, classification of 3D models into classes based on semantic annotation is applied. Finally, ontology is constructed with instances for each class, which contain spatial relationships, shape indexes and measures. Therefore, the 3D model retrieval system is comprised of lowlevel and high-level visual features. We interpret our results using the Princeton Shape Benchmark Database and our prototypical 3D Model Search System (3DMSS). The results show the performance of the proposed new approach to classify and to retrieve 3D models.


title: Camera-GPS-IMU sensor fusion for autonomous flying

abstract: GPS-IMU based sensor fusion is widely used for autonomous flying, which yet suffers from the inaccuracy and drift of the GPS signal and also the failure with the loss of GPS (e.g., indoor flying). To circumvent this issue, in this paper, we propose a new framework for camera-GPS-IMU sensor fusion, which, by fusing monocular camera information with that from GPS and IMU, can improve the accuracy and robustness of the autonomous flying. For camera and GPS-IMU calibration, a new Kalman filter is also proposed, which runs in parallel with the state estimation EKF and also utilize multiple key frames generated from the camera information. An autonomous flying experiment is performed to validate the theory.


Forecasting short-term taxi demand using boosting-GCRF


title: 5.85-GHz radio path loss and penetration loss measurements in and around homes and trees

abstract: This letter contains measured data and empirical models for 5.85-GHz radio propagation path loss in and around residential areas for the newly allocated National Information Infrastructure (NII) band in the U.S. Three homes and two stands of trees were studied for outdoor path loss, tree loss, and house penetration loss in a narrow-band measurement campaign that included 270 local area path loss measurements and over 276000 instantaneous power measurements. The data will aid the development of futuristic outdoor-to-indoor wireless unlicensed NII systems (in the U.S.) and HIPERLAN systems (in Europe) for home Internet access, telecommunications, and wireless local loops.


title: Visual-inertial SLAM for a small helicopter in large outdoor environments

abstract: In this video, we present our latest results towards fully autonomous flights with a small helicopter. Using a monocular camera as the only exteroceptive sensor, we fuse inertial measurements to achieve a self-calibrating power-on-and-go system, able to perform autonomous flights in previously unknown, large, outdoor spaces. Our framework achieves Simultaneous Localization And Mapping (SLAM) with previously unseen robustness in onboard aerial navigation for small platforms with natural restrictions on weight and computational power. We demonstrate successful operation in flights with altitude between 0.2–70 m, trajectories with 350 m length, as well as dynamic maneuvers with track speed of 2 m/s. All flights shown are performed autonomously using vision in the loop, with only high-level waypoints given as directions.


title: Convergent Expansions for Solutions of Linear Ordinary Differential Equations Having a Simple Turning Point, with an Application to Bessel Functions

abstract: Second-order linear ordinary differential equations with a large parameter u are examined. Classic asymptotic expansions involving Airy functions are applicable for the case where the argument z lies in complex domain containing a simple turning point. In this article, such asymptotic expansions are converted into convergent series, where u appears in an inverse factorial, rather than an inverse power. The domain of convergence of the new expansions is rigorously established and is found to be an unbounded domain containing the turning point. The theory is then applied to obtain convergent expansions for Bessel functions of complex argument and large positive order.


title: Automatic analysis of in-the-wild mobile eye-tracking experiments using object, face and person detection

abstract: In this paper we present a novel method for the automatic analysis of mobile eye-tracking data in natural environments. Mobile eye-trackers generate large amounts of data, making manual analysis very time-consuming. Available solutions, such as marker-based analysis minimize the manual labour but require experimental control, making real-life experiments practically unfeasible. We present a novel method for processing this mobile eye-tracking data by applying object, face and person detection algorithms. Furthermore we present a temporal smoothing technique to improve the detection rate and we trained a new detection model for occluded person and face detections. This enables the analysis to be performed on the object level rather than the traditionally used coordinate level. We present speed and accuracy results of our novel detection scheme on challenging, large-scale real-life experiments.


title: Assessing social anxiety using gps trajectories and point-of-interest data

abstract: Mental health problems are highly prevalent and appear to be increasing in frequency and severity among the college student population. The upsurge in mobile and wearable wireless technologies capable of intense, longitudinal tracking of individuals, provide valuable opportunities to examine temporal patterns and dynamic interactions of key variables in mental health research. In this paper, we present a feasibility study leveraging non-invasive mobile sensing technology to passively assess college students' social anxiety, one of the most common disorders in the college student population. We have first developed a smartphone application to continuously track GPS locations of college students, then we built an analytic infrastructure to collect the GPS trajectories and finally we analyzed student behaviors (e.g. studying or staying at home) using Point-Of-Interest (POI). The whole framework supports intense, longitudinal, dynamic tracking of college students to evaluate how their anxiety and behaviors change in the college campus environment. The collected data provides critical information about how students' social anxiety levels and their mobility patterns are correlated. Our primary analysis based on 18 college students demonstrated that social anxiety level is significantly correlated with places students' visited and location transitions.


title: The CoNLL 2008 Shared Task on Joint Parsing of Syntactic and Semantic Dependencies

abstract: The Conference on Computational Natural Language Learning is accompanied every year by a shared task whose purpose is to promote natural language processing applications and evaluate them in a standard setting. In 2008 the shared task was dedicated to the joint parsing of syntactic and semantic dependencies. This shared task not only unifies the shared tasks of the previous four years under a unique dependency-based formalism, but also extends them significantly: this year's syntactic dependencies include more information such as named-entity boundaries; the semantic dependencies model roles of both verbal and nominal predicates. In this paper, we define the shared task and describe how the data sets were created. Furthermore, we report and analyze the results and describe the approaches of the participating systems.


title: Automatic heart sounds detection and systolic murmur characterization using wavelet transform and AR modeling

abstract: This paper describes a signal processing procedure that identifies the first and the second heart sounds (S1 and S2), extracts the systole from the diastole, detects and characterizes the systolic murmur found within. The identification of heart sounds was facilitated by discrete wavelet transform (DWT) approximation using the Coiflet wavelet and followed by using indicators that quantify signal activity and strength. The systole was isolated and divided into smaller short segments where the signal activity measure and absolute amplitude were computed. S1 and S2, and the onset and duration of a systolic murmur were marked. Using the indices derived from AR modeling, a systolic murmur can be characterized by its timing, duration, pitch, and shape either as crescendo, decrescendo, crescendo-decrescendo, or plateau. The performance of the proposed procedure was evaluated and proved with clinically recorded systolic murmur episodes.


title: Intention-Based EMG Control for Powered Exoskeletons

abstract: Electromyographical (EMG) signals have been frequently used to estimate human muscular torques. In the field of human-assistive robotics, these methods provide valuable information to provide effectively support to the user. However, their usability is strongly limited by the necessity of complex user-dependent and session-dependent calibration procedures, which confine their use to the laboratory environment. Nonetheless, an accurate estimate of muscle torque could be unnecessary to provide effective movement assistance to users. The natural ability of human central nervous system of adapting to external disturbances could compensate for a lower accuracy of the torque provided by the robot and maintain the movement accuracy unaltered, while the effort is reduced. In order to explore this possibility, in this paper we study the reaction of ten healthy subjects to the assistance provided through a proportional EMG control applied by an elbow powered exoskeleton. This system gives only a rough estimate of the user muscular torque but does not require any specific calibration. Experimental results clearly show that subjects adapt almost instantaneously to the assistance provided by the robot and can reduce their effort while keeping full control of the movement under different dynamic conditions (i.e., no alterations of movement accuracy are observed).


title: Matrix Completion on Graphs

abstract: The problem of finding the missing values of a matrix given a few of its entries, called matrix completion, has gathered a lot of attention in the recent years. Although the problem under the standard low rank assumption is NP-hard, Cand\`es and Recht showed that it can be exactly relaxed if the number of observed entries is sufficiently large. In this work, we introduce a novel matrix completion model that makes use of proximity information about rows and columns by assuming they form communities. This assumption makes sense in several real-world problems like in recommender systems, where there are communities of people sharing preferences, while products form clusters that receive similar ratings. Our main goal is thus to find a low-rank solution that is structured by the proximities of rows and columns encoded by graphs. We borrow ideas from manifold learning to constrain our solution to be smooth on these graphs, in order to implicitly force row and column proximities. Our matrix recovery model is formulated as a convex non-smooth optimization problem, for which a well-posed iterative scheme is provided. We study and evaluate the proposed matrix completion on synthetic and real data, showing that the proposed structured low-rank recovery model outperforms the standard matrix completion model in many situations.


title: Aggregate channel features for multi-view face detection

abstract: Face detection has drawn much attention in recent decades since the seminal work by Viola and Jones. While many subsequences have improved the work with more powerful learning algorithms, the feature representation used for face detection still can’t meet the demand for effectively and efficiently handling faces with large appearance variance in the wild. To solve this bottleneck, we borrow the concept of channel features to the face detection domain, which extends the image channel to diverse types like gradient magnitude and oriented gradient histograms and therefore encodes rich information in a simple form. We adopt a novel variant called aggregate channel features, make a full exploration of feature design, and discover a multiscale version of features with better performance. To deal with poses of faces in the wild, we propose a multi-view detection approach featuring score re-ranking and detection adjustment. Following the learning pipelines in ViolaJones framework, the multi-view face detector using aggregate channel features surpasses current state-of-the-art detectors on AFW and FDDB testsets, while runs at 42 FPS


title: From testing intelligence to assessing competences: A pluralistic view of intellect

abstract: The term “intelligence” is frequently used to refer to a general intellectual competence and is usually measured by tests of language and logic skills. In response, Howard Gardner and David Feldman have stressed the variety of human abilities and the numerous ways in which those abilities can be expressed. They call for a consideration of a wide range of competences in both definitions as well as tests of intelligence. Further, they point out that many competences cannot be adequately surveyed on standard paper and pencil tests. Therefore, they have advocated developing new means of assessment to examine natural performances in specific domains. Project Spectrum is designed to address these issues.


title: P274 Clinical utility of measuring serum TNF alpha level, anti TNF alpha levels and antibody titers in critical situations in inflammatory bowel disease and in psoriasis

abstract: Background: Bowel ultrasonography (US) is a non-invasive tool in the evaluation of inflammatory bowel diseases, especially Crohn’s disease. There are only few data on its role in Ulcerative Colitis (UC), particularly in children. We aimed to evaluate the usefulness of bowel US to assess pediatric UC pts and compare US findings with clinical and endoscopic data. Methods: 30 pediatric pts (median age 15 years; range 2 21; 14 males) were prospectively enrolled. Eight pts had a clinical suspicion of UC, 22 had an already established diagnosis and showed a flare-up of disease. All pts underwent clinical evaluation, bowel US with Color-Doppler examination (Toshiba equipment, 3.5 MHz convex and 7.5 MHz linear transducers) and ileo-colonoscopy. US and endoscopy were carried out by different operators, blind to the results of the other technique. For each patient Pediatric Ulcerative Colitis Activity Index (PUCAI) and Mayo endoscopic subscore were calculated. The US parameters assessed were Bowel Wall Thickness (BWT> 3mm), BW stratification, vascularity and presence of austrae: each parameter was assigned a value of 0 or 1 depending on the presence or absence of alteration. Results: 27/30 pts were finally diagnosed as UC. Extension of disease according to Paris Classification was: E2 (left-side colitis) in 12/27 (45%), E4 (pancolitis) in 15/27 (55%) pts. This extension was independently confirmed in 25/27 pts by US, that yielded a 85% concordance with endoscopy. Disease activity was mild (PUCAI 10 34) in 7 pts (25%), moderate (PUCAI 35 64) in 12 (45%) and severe (PUCAI >65) in 8 (30%). The mean values of PUCAI, Mayo score and US score respectively were: 40.5±24.4, 2±1 and 2.8±1.4. The mean BWT in affected colonic segments was 5±2mm. The US score strongly correlated with PUCAI (r = 0.85, p < 0.0001) and Mayo index (r = 0.90, p < 0.0001). A positive correlation was also found between PUCAI and Mayo score (r = 0.87, p < 0.05). Multiple regression analysis showed that variables making a significant contribution to the final value of Mayo score were BWT (p < 0.007) and vascularity (p < 0.038). Conclusions: Our preliminary data show a strong relationship between US and clinical and endoscopic findings, thus suggesting that colonic US might represent a useful first line tool in the evaluation of pediatric UC pts. It allows to assess in a relatively rapid manner the extension and activity of disease and to estimate the severity of a flare-up, prior to further invasive tests.


title: Discovering interpretable geo-social communities for user behavior prediction

abstract: Social community detection is a growing field of interest in the area of social network applications, and many approaches have been developed, including graph partitioning, latent space model, block model and spectral clustering. Most existing work purely focuses on network structure information which is, however, often sparse, noisy and lack of interpretability. To improve the accuracy and interpretability of community discovery, we propose to infer users' social communities by incorporating their spatiotemporal data and semantic information. Technically, we propose a unified probabilistic generative model, User-Community-Geo-Topic (UCGT), to simulate the generative process of communities as a result of network proximities, spatiotemporal co-occurrences and semantic similarity. With a well-designed multi-component model structure and a parallel inference implementation to leverage the power of multicores and clusters, our UCGT model is expressive while remaining efficient and scalable to growing large-scale geo-social networking data. We deploy UCGT to two application scenarios of user behavior predictions: check-in prediction and social interaction prediction. Extensive experiments on two large-scale geo-social networking datasets show that UCGT achieves better performance than existing state-of-the-art comparison methods.


title: Improvement in cancer-related knowledge following use of a psychoeducational video game for adolescents and young adults with cancer.

abstract: PURPOSE ::: Adolescents with chronic illnesses have been shown to have management and treatment issues resulting in poor outcomes. These issues may arise from non-interest in self care and illness knowledge. A video game, "Re-Mission," was developed to actively involve young people with cancer in their own treatment. Re-Mission provides opportunities to learn about cancer and its treatment. ::: ::: ::: METHOD ::: The efficacy of Re-Mission was investigated in a multi-site, randomized, controlled study with 375 adolescent and young adult cancer patients. Participants received either a regular commercial game (control) or both the regular game plus Re-Mission (Re-Mission group). Participants were given a mini-PC with the games installed and requested to play for an hour each week for 3 months. A test on cancer-related knowledge was given prior to game play (baseline) and again after 1 and 3 months. At 3 months the Re-Mission group also rated the acceptability and credibility of Re-Mission. ::: ::: ::: RESULTS ::: Analyses of the knowledge test scores showed that whereas scores of both groups improved significantly over the follow-up periods, the scores of the Re-Mission group improved significantly more. The size of this effect was related to usage of Re-Mission. Credibility scores were negatively correlated with level of knowledge but not with change in knowledge level at 1 month or 3 months. ::: ::: ::: CONCLUSIONS ::: The results indicate a specific effect of Re-Mission play on cancer knowledge that is not attributable to patients' expectations. It is concluded that video games can be an effective vehicle for health education in adolescents and young adults with chronic illnesses.


title: HSim: A MapReduce simulator in enabling Cloud Computing

abstract: Abstract MapReduce is an enabling technology in support of Cloud Computing. Hadoop which is a MapReduce implementation has been widely used in developing MapReduce applications. This paper presents HSim, a MapReduce simulator which builds on top of Hadoop. HSim models a large number of parameters that can affect the behaviors of MapReduce nodes, and thus it can be used to tune the performance of a MapReduce cluster. HSim is validated with both benchmark results and user customized MapReduce applications.


title: Reconstruction of Partially Occluded Face by Fast Recursive PCA

abstract: This paper proposes a fast recursive PCA (principal component analysis) algorithm to remove face occlusions. In training phase, all faces are normalized by two eye centers and two mouth corners, and eigenvectors (eigenfaces) were obtained by PCA analysis. In test phase, face occlusion is removed by iteratively perform two steps of analysis and synthesis. New damaged face is first normalized by clicking four feature points, and PCA coefficients are obtained in analysis step. In synthesis step, reconstructed face is obtained by linear combining eigenfaces, and coefficients error between two successive analyses is used for fast PCA compensation. Experimental results on training and test faces show that the proposed algorithm convergences faster than classical PCA compensation and reconstructed faces are natural.


title: HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks

abstract: Topology control in a sensor network balances load on sensor nodes and increases network scalability and lifetime. Clustering sensor nodes is an effective topology control approach. In this paper, we propose a novel distributed clustering approach for long-lived ad hoc sensor networks. Our proposed approach does not make any assumptions about the presence of infrastructure or about node capabilities, other than the availability of multiple power levels in sensor nodes. We present a protocol, HEED (Hybrid Energy-Efficient Distributed clustering), that periodically selects cluster heads according to a hybrid of the node residual energy and a secondary parameter, such as node proximity to its neighbors or node degree. HEED terminates in O(1) iterations, incurs low message overhead, and achieves fairly uniform cluster head distribution across the network. We prove that, with appropriate bounds on node density and intracluster and intercluster transmission ranges, HEED can asymptotically almost surely guarantee connectivity of clustered networks. Simulation results demonstrate that our proposed approach is effective in prolonging the network lifetime and supporting scalable data aggregation.


title: Graph Attention Networks

abstract: We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. By stacking layers in which nodes are able to attend over their neighborhoods' features, we enable (implicitly) specifying different weights to different nodes in a neighborhood, without requiring any kind of costly matrix operation (such as inversion) or depending on knowing the graph structure upfront. In this way, we address several key challenges of spectral-based graph neural networks simultaneously, and make our model readily applicable to inductive as well as transductive problems. Our GAT models have achieved or matched state-of-the-art results across four established transductive and inductive graph benchmarks: the Cora, Citeseer and Pubmed citation network datasets, as well as a protein-protein interaction dataset (wherein test graphs remain unseen during training).


title: Sampling efficiency in digital camera performance standards

abstract: One of the first ISO digital camera standards to address image microstructure was ISO 12233, which introduced the SFR, spatial frequency response, based on the analysis of edge features in digital images. The SFR, whether derived from edges or periodic signals, describes the capture of image detail as a function of spatial frequency. Often during camera testing, however, there is an interest in distilling SFR results down to a single value that can be compared with acceptable tolerances. As a measure of limiting resolution, it has been suggested that the frequency at which the SFR falls to, e.g., 10%, can be used. We use this limiting resolution to introduce a sampling efficiency measure, being considered under the current ISO 12233 standard revision effort. The measure is the ratio of limiting resolution frequency to that implied by the image (sensor) sampling alone. The rationale and details of this measure are described, as are example measurements. One-dimensional sampling efficiency calculations for multiple directions are included in a two-dimensional analysis.


title: Learning Common and Feature-Specific Patterns: A Novel Multiple-Sparse-Representation-Based Tracker

abstract: The use of multiple features has been shown to be an effective strategy for visual tracking because of their complementary contributions to appearance modeling. The key problem is how to learn a fused representation from multiple features for appearance modeling. Different features extracted from the same object should share some commonalities in their representations while each feature should also have some feature-specific representation patterns which reflect its complementarity in appearance modeling. Different from existing multi-feature sparse trackers which only consider the commonalities among the sparsity patterns of multiple features, this paper proposes a novel multiple sparse representation framework for visual tracking which jointly exploits the shared and feature-specific properties of different features by decomposing multiple sparsity patterns. Moreover, we introduce a novel online multiple metric learning to efficiently and adaptively incorporate the appearance proximity constraint, which ensures that the learned commonalities of multiple features are more representative. Experimental results on tracking benchmark videos and other challenging videos demonstrate the effectiveness of the proposed tracker.


title: Object Classification and Grasp Planning Using Visual and Tactile Sensing

abstract: The perception of the tactile and vision modalities plays a crucial role in object classification and robot precise operations. On one hand, the visual perception helps acquire the objects apparent characteristics (e.g., shape and color) that are paramount for grasp planning. On the other hand, when grasping the object, the tactile sensors can detect the softness or stiffness and the surface texture of the object, thus can be used to classify the objects. In this paper, two different tactile models are first developed to model the tactile sequences, namely bag-of-system (BoS) and deep dynamical system (DDS). To be specific, BoS applies the extreme learning machine method as the classifier, and DDS employs deep neural networks as the feature mapping functions in order to deal with the highly nonlinear data. As for visual sensing, we formulate a signature of histograms of orientations descriptor to model the shape of objects. Moreover, by using human experience, we identify the graspable components of objects on the category level instead of the object level. Finally, a fast planning method considering both contact points exaction and hand kinematics is proposed to accomplish the grasping manipulation. Comparative experiments demonstrate the effectiveness of our proposed methods on the tasks including the robot grasping and the object classification.


title: New Modeling and Analysis of Three-Stage Electro-Hydraulic Servo Valve

abstract: To develop higher performance three-stage electro-hydraulic servo valve, a new modeling software, AMEsim, is used to develop its non-linear model according to the construction and principles presented in this paper. The results of simulation and experimental test about an existing valve are consistent, which proves the model is right. Based on the analysis of the model, the performances of three-stage electro-hydraulic servo valve are mainly affected by pilot valve performance, spool and sleeve performance, position transducer performance and servo controller performance. The methods of modeling and analysis possess universal significance to other similarity system.


title: How to adapt applications for the Cloud environment

abstract: The migration of existing applications to the Cloud requires adapting them to a new computing paradigm. Existing works have focused on migrating the whole application stack by means of virtualization and deployment on the Cloud, delegating the required adaptation effort to the level of resource management. With the proliferation of Cloud services allowing for more flexibility and better control over the application migration, the migration of individual application layers, or even individual architectural components to the Cloud, becomes possible. Towards this goal, in this work we focus on the challenges and solutions for each layer when migrating different parts of the application to the Cloud. We categorize different migration types and identify the potential impact and adaptation needs for each of these types on the application layers based on an exhaustive survey of the State of the Art. We also investigate various cross-cutting concerns that need to be considered for the migration of the application, and position them with respect to the identified migration types. Finally, we present some of the open research issues in the field and position our future work targeting these research questions.


title: EULAide: Interpretation of End-User License Agreements using Ontology-Based Information Extraction

abstract: Ignoring End-User License Agreements (EULAs) for online services due to their length and complexity is a risk undertaken by the majority of online and mobile service users. This paper presents an Ontology-Based Information Extraction (OBIE) method for EULA term and phrase extraction to facilitate a better understanding by humans. An ontology capturing important terms and relationships has been developed and used to guide the OBIE process. Through a feedback cycle we have improved its domain-specific coverage by identifying additional concepts. In the detection and extraction, we focus on three key rights and conditions: permission, prohibition and duty. We present the EULAide system, which comprises a custom information extraction pipeline and a number of custom extraction rules tailored for EULA processing. To evaluate our approach, we created and manually annotated a corpus of 20 well-known licenses. For the gold standard we achieved an Inter-Annotator Agreement (IAA) of 90%, resulting in 193 permissions, 185 prohibitions and 168 duties. An evaluation of the OBIE pipeline against this gold standard resulted in an F-measure of 70-74% which, in the context of the IAA, proves the feasibility of the approach.


title: Chat mining : Predicting user and message attributes in computer-mediated communication

abstract: The focus of this paper is to investigate the possibility of predicting several user and message attributes in text-based, real-time, online messaging services. For this purpose, a large collection of chat messages is examined. The applicability of various supervised classification techniques for extracting information from the chat messages is evaluated. Two competing models are used for defining the chat mining problem. A term-based approach is used to investigate the user and message attributes in the context of vocabulary use while a style-based approach is used to examine the chat messages according to the variations in the authors' writing styles. Among 100 authors, the identity of an author is correctly predicted with 99.7% accuracy. Moreover, the reverse problem is exploited, and the effect of author attributes on computer-mediated communications is discussed.


title: Learning Generalized Linear Models Over Normalized Data

abstract: Enterprise data analytics is a booming area in the data management industry. Many companies are racing to develop toolkits that closely integrate statistical and machine learning techniques with data management systems. Almost all such toolkits assume that the input to a learning algorithm is a single table. However, most relational datasets are not stored as single tables due to normalization. Thus, analysts often perform key-foreign key joins before learning on the join output. This strategy of learning after joins introduces redundancy avoided by normalization, which could lead to poorer end-to-end performance and maintenance overheads due to data duplication. In this work, we take a step towards enabling and optimizing learning over joins for a common class of machine learning techniques called generalized linear models that are solved using gradient descent algorithms in an RDBMS setting. We present alternative approaches to learn over a join that are easy to implement over existing RDBMSs. We introduce a new approach named factorized learning that pushes ML computations through joins and avoids redundancy in both I/O and computations. We study the tradeoff space for all our approaches both analytically and empirically. Our results show that factorized learning is often substantially faster than the alternatives, but is not always the fastest, necessitating a cost-based approach. We also discuss extensions of all our approaches to multi-table joins as well as to Hive.


title: Modified Playfair Cipher using Rectangular Matrix

abstract: One of the well known polyalphabetic ciphers is the Playfair cipher. In this cipher digrams or groups of 2 letters in the plain text is converted to cipher text digrams during encryption using a key. Similarly during decryption cipher text digrams are converted to plain text digrams using the same key. However the original 5 x 5 Playfair cipher can support only 25 uppercase alphabets. To overcome this drawback we propose a rectangular matrix having 10 columns and 9 rows which can support almost all the printable characters including white space. This paper analyses the original Playfair cipher, the different variations that have been proposed and the modified Playfair cipher that we propose. Cryptanalysis is done to show that the proposed cipher is a strong one.


title: Rethinking Machine Learning Development and Deployment for Edge Devices

abstract: Machine learning (ML), especially deep learning is made possible by the availability of big data, enormous compute power and, often overlooked, development tools or frameworks. As the algorithms become mature and efficient, more and more ML inference is moving out of datacenters/cloud and deployed on edge devices. This model deployment process can be challenging as the deployment environment and requirements can be substantially different from those during model development. In this paper, we propose a new ML development and deployment approach that is specially designed and optimized for inference-only deployment on edge devices. We build a prototype and demonstrate that this approach can address all the deployment challenges and result in more efficient and high-quality solutions.


title: Fast CapsNet for Lung Cancer Screening

abstract: Lung cancer is the leading cause of cancer-related deaths in the past several years. A major challenge in lung cancer screening is the detection of lung nodules from computed tomography (CT) scans. State-of-the-art approaches in automated lung nodule classification use deep convolutional neural networks (CNNs). However, these networks require a large number of training samples to generalize well. This paper investigates the use of capsule networks (CapsNets) as an alternative to CNNs. We show that CapsNets significantly outperforms CNNs when the number of training samples is small. To increase the computational efficiency, our paper proposes a consistent dynamic routing mechanism that results in \(3\times \) speedup of CapsNet. Finally, we show that the original image reconstruction method of CapNets performs poorly on lung nodule data. We propose an efficient alternative, called convolutional decoder, that yields lower reconstruction error and higher classification accuracy.


title: Focus Groups for Artifact Refinement and Evaluation in Design Research

abstract: Focus groups to investigate new ideas are widely used in many research fields. The use of focus groups in design research poses interesting opportunities and challenges. Traditional focus group methods must be adapted to meet two specific goals of design research. For the refinement of an artifact design, exploratory focus groups (EFGs) study the artifact to propose improvements in the design. The cycle of build and evaluate using EFGs continues until the artifact is released for field test in the application environment. Then, the field test of the design artifact may employ confirmatory focus groups (CFGs) to establish the utility of the artifact in field use. Rigorous investigation of the artifact requires multiple CFGs to be run with opportunities for quantitative and qualitative data collection and analyses across the multiple CFGs. In this paper, we discuss the adaptation of focus groups to design research projects. We demonstrate the use of both EFGs and CFGs in a design research project in the health care field.


title: Rapid Analysis of Active Cell Balancing Circuits

abstract: Active cell balancing improves the performance of a battery pack by transferring charge from one cell to another. Associated design questions require multiple simulations with 100 cells over several hours. Since the most efficient transfer methods switch between phases in the kilohertz range, these simulations require high computational effort or reduced accuracy. To enable detailed analysis on a large scale, this paper includes state-of-the-art electrical battery models in active balancing simulation while keeping the computation effort for one transfer in the low millisecond range. This is achieved in three steps. First, we model the dynamics of each transfer phase using standard equivalent circuit abstraction. Next, we find closed form equations for the so-defined phase dynamics, yielding an iterative approach that saves computation time by replacing the numerical solver. Finally, we employ error control techniques to aggregate phases in that iteration, systematically reducing the millions of phase evaluations that would be necessary otherwise. Our experiments show that the speedup from equivalent circuit dynamics to error-controlled aggregation almost reaches five orders of magnitude while introducing virtually no additional error. This enables simulations of realistic balancing scenarios in less than a second and is hence suitable for design space exploration.


title: On Controller Performance in Software-Defined Networks

abstract: Network architectures in which the control plane is decoupled from the data plane have been growing in popularity. Among the main arguments for this approach is that it provides a more structured software environment for developing network-wide abstractions while potentially simplifying the data plane. As has been adopted elsewhere [11], we refer to this split architecture as Software-Defined Networking (SDN). While it has been argued that SDN is suitable for some deployment environments (such as homes [17, 13], data centers [1], and the enterprise [5]), delegating control to a remote system has raised a number of questions on control-plane scaling implications of such an approach. Two of the most often voiced concerns are: (a) how fast can the controller respond to data path requests?; and (b) how many data path requests can it handle per second? There are some references to the performance of SDN systems in the literature [16, 5, 3]. For example, an oft-cited study shows that a popular network controller (NOX) handles around 30k flow initiation events1 per second while maintaining a sub-10ms flow install time [14]. Unfortunately, recent measurements of some deployment environments suggests that these numbers are far from sufficient. For example, Kandula et al. [9] found that a 1500-server cluster has a median flow arrival rate of 100k flows per second. Also, Benson et al. [2] show that a network with 100 switches can have spikes of 10M flows arrivals per second in the worst case. In addition, the 10ms flow setup delay of an SDN controller would add a 10% delay to the majority of flows (short-lived) in such a network. This disconnect between relatively poor controller performance and high network demands has motivated a


title: A method for text localization and recognition in real-world images

abstract: A general method for text localization and recognition in real-world images is presented. The proposed method is novel, as it (i) departs from a strict feed-forward pipeline and replaces it by a hypothesesverification framework simultaneously processing multiple text line hypotheses, (ii) uses synthetic fonts to train the algorithm eliminating the need for time-consuming acquisition and labeling of real-world training data and (iii) exploits Maximally Stable Extremal Regions (MSERs) which provides robustness to geometric and illumination conditions. ::: ::: The performance of the method is evaluated on two standard datasets. On the Char74k dataset, a recognition rate of 72% is achieved, 18% higher than the state-of-the-art. The paper is first to report both text detection and recognition results on the standard and rather challenging ICDAR 2003 dataset. The text localization works for number of alphabets and the method is easily adapted to recognition of other scripts, e.g. cyrillics.


title: Innovation trajectories for information visualizations: Comparing treemaps, cone trees, and hyperbolic trees

abstract: This paper reviews the trajectory of three information visualization innovations: treemaps, cone trees, and hyperbolic trees. These three ideas were first published around the same time in the early 1990s, so we are able to track academic publications, patents, and trade press articles over almost two decades. We describe the early history of each approach, problems with data collection from differing sources, appropriate metrics, and strategies for visualizing these longitudinal data sets. This paper makes two contributions: (1) it offers the information visualization community a history of how certain ideas evolved, influenced others, and were adopted for widespread use and (2) it provides an example of how such scientometric trajectories of innovations can be gathered and visualized. Guidance for designers is offered, but these conjectures may also be useful to researchers, research managers, science policy analysts, and venture capitalists.


title: Semisupervised learning using denoising autoencoders for brain lesion detection and segmentation

abstract: The work explores the use of denoising autoencoders (DAEs) for brain lesion detection, segmentation, and false-positive reduction. Stacked denoising autoencoders (SDAEs) were pretrained using a large number of unlabeled patient volumes and fine-tuned with patches drawn from a limited number of patients (n=20, 40, 65). The results show negligible loss in performance even when SDAE was fine-tuned using 20 labeled patients. Low grade glioma (LGG) segmentation was achieved using a transfer learning approach in which a network pretrained with high grade glioma data was fine-tuned using LGG image patches. The networks were also shown to generalize well and provide good segmentation on unseen BraTS 2013 and BraTS 2015 test data. The manuscript also includes the use of a single layer DAE, referred to as novelty detector (ND). ND was trained to accurately reconstruct nonlesion patches. The reconstruction error maps of test data were used to localize lesions. The error maps were shown to assign unique error distributions to various constituents of the glioma, enabling localization. The ND learns the nonlesion brain accurately as it was also shown to provide good segmentation performance on ischemic brain lesions in images from a different database.


title: Max-Margin Early Event Detectors

abstract: The need for early detection of temporal events from sequential data arises in a wide spectrum of applications ranging from human-robot interaction to video security. While temporal event detection has been extensively studied, early detection is a relatively unexplored problem. This paper proposes a maximum-margin framework for training temporal event detectors to recognize partial events, enabling early detection. Our method is based on Structured Output SVM, but extends it to accommodate sequential data. Experiments on datasets of varying complexity, for detecting facial expressions, hand gestures, and human activities, demonstrate the benefits of our approach.


title: Robot-Aided Upper-Limb Rehabilitation Based on Motor Imagery EEG

abstract: Stroke is a leading cause of disability worldwide. In this paper, a novel robot-assisted rehabilitation system based on motor imagery electroencephalography (EEG) is developed for regular training of neurological rehabilitation for upper limb stroke patients. Firstly, three-dimensional animation was used to guide the patient image the upper limb movement and EEG signals were acquired by EEG amplifier. Secondly, eigenvectors were extracted by harmonic wavelet transform (HWT) and linear discriminant analysis (LDA) classifier was utilized to classify the pattern of the left and right upper limb motor imagery EEG signals. Finally, PC triggered the upper limb rehabilitation robot to perform motor therapy and gave the virtual feedback. Using this robot-assisted upper limb rehabilitation system, the patientʹs EEG of upper limb movement imagination is translated to control rehabilitation robot directly. Consequently, the proposed rehabilitation system can fully explore the patientʹs motivation and attention and directly facilitate upper limb post-stroke rehabilitation therapy. Experimental results on unimpaired participants were presented to demonstrate the feasibility of the rehabilitation system. Combining robot-assisted training with motor imagery- based BCI will make future rehabilitation therapy more effective. Clinical testing is still required for further proving this assumption.


title: Challenges of introducing and implementing mobile payments : A Qualitative study of the Swedish mobile payment application WyWallet

abstract: Citizens of Sweden are now facing a large shift in their habits of managing money transferring using mobile phones. They are now forced to use an application called WyWallet when making purchases w ...


title: Term Set Expansion based on Multi-Context Term Embeddings: an End-to-end Workflow

abstract: We present SetExpander, a corpus-based system for expanding a seed set of terms into a more complete set of terms that belong to the same semantic class. SetExpander implements an iterative end-to end workflow for term set expansion. It enables users to easily select a seed set of terms, expand it, view the expanded set, validate it, re-expand the validated set and store it, thus simplifying the extraction of domain-specific fine-grained semantic classes. SetExpander has been used for solving real-life use cases including integration in an automated recruitment system and an issues and defects resolution system. A video demo of SetExpander is available at this https URL (some images were blurred for privacy reasons).