title: Chaotic Map-Based Anonymous User Authentication Scheme With User Biometrics and Fuzzy Extractor for Crowdsourcing Internet of Things

abstract: The recent proliferation of mobile devices, such as smartphones and wearable devices has given rise to crowdsourcing Internet of Things (IoT) applications. E-healthcare service is one of the important services for the crowdsourcing IoT applications that facilitates remote access or storage of medical server data to the authorized users (for example, doctors, patients, and nurses) via wireless communication. As wireless communication is susceptible to various kinds of threats and attacks, remote user authentication is highly essential for a hazard-free use of these services. In this paper, we aim to propose a new secure three-factor user remote user authentication protocol based on the extended chaotic maps. The three factors involved in the proposed scheme are: 1) smart card; 2) password; and 3) personal biometrics. As the proposed scheme avoids computationally expensive elliptic curve point multiplication or modular exponentiation operation, it is lightweight and efficient. The formal security verification using the widely-accepted verification tool, called the ProVerif 1.93, shows that the presented scheme is secure. In addition, we present the formal security analysis using the both widely accepted real-or-random model and Burrows–Abadi–Needham logic. With the combination of high security and appreciably low communication and computational overheads, our scheme is very much practical for battery limited devices for the healthcare applications as compared to other existing related schemes.


title: Volunteer Cloud Computing: MapReduce over the Internet

abstract: Volunteer Computing harnesses computing resources of machines from around the world to perform distributed independent tasks, following a master/worker model. Despite the recent increase in popularity and power in middleware such as BOINC, there are still several limitations in existing systems. Current research is oriented towards optimizing existing applications, while the number of active users and projects has reached a plateau. A programming paradigm that has been significantly popular and is used by several systems on the cloud is MapReduce. The main advantage of this paradigm is that it can be used to solve a vast amount of different problems, by breaking them into simple steps and taking advantage of distributed resources. Volunteer Computing provides these resources, and although it cannot match the conditions offered by a cluster, it has other advantages that can be leveraged. In this paper, we try to increase the computational power of Volunteer Computing systems by allowing more complex applications and paradigms such as MapReduce to be run, thus opening new avenues and possibilities for the use of computational devices scattered through the Internet. We created a BOINC prototype that can run MapReduce jobs (BOINC-MR), using a pull-model in which communication is always initiated by the client. By running experiments on a small cluster, with multiple variables, we were able to evaluate a few initial scenarios with this paradigm. We used a simple MapReduce application, word count, as proof of concept, just to demonstrate a typical execution.


title: An ultra-light and compact design and implementation of head-mounted projective displays

abstract: Head-mounted projective displays (HMPD) have been recently proposed as an alternative to conventional eyepiece-type head-mounted displays. HMPDs consist of a pair of miniature projection lenses and displays mounted on the helmet and retro-reflective sheeting materials placed strategically in the environment. Its novel concept and properties suggest solutions to part of the problems of state-of-the-art visualization devices and make it extremely suitable for multiple-user collaborative applications and wearable systems. A brief review of conventional visualization techniques is followed by an extensive discussion of HMPD technology, which includes a summary of its features and a comparison with conventional head-mounted displays (HMDs), projection-based displays, and HMPDs. An ultra-light and compact design (i.e. 8g) of a projection lens system using diffractive optical element (DOE) as well as plastic components for a HMPD is presented. Through the usage of fast prototyping technology, a compact stereoscopic head-mounted prototype with weight less than 700 grams was implemented, and optomechanical adjustments and ergonomic considerations are discussed. Finally, the motivated application in multiuser tele-collaboration is described.


title: DeepEyes: Progressive Visual Analytics for Designing Deep Neural Networks

abstract: Deep neural networks are now rivaling human accuracy in several pattern recognition problems. Compared to traditional classifiers, where features are handcrafted, neural networks learn increasingly complex features directly from the data. Instead of handcrafting the features, it is now the network architecture that is manually engineered. The network architecture parameters such as the number of layers or the number of filters per layer and their interconnections are essential for good performance. Even though basic design guidelines exist, designing a neural network is an iterative trial-and-error process that takes days or even weeks to perform due to the large datasets used for training. In this paper, we present DeepEyes, a Progressive Visual Analytics system that supports the design of neural networks during training. We present novel visualizations, supporting the identification of layers that learned a stable set of patterns and, therefore, are of interest for a detailed analysis. The system facilitates the identification of problems, such as superfluous filters or layers, and information that is not being captured by the network. We demonstrate the effectiveness of our system through multiple use cases, showing how a trained network can be compressed, reshaped and adapted to different problems.


A Meta-Study of Algorithm Visualization Effectiveness


title: A Multimodal Emotion Detection System during Human-Robot Interaction

abstract: In this paper, a multimodal user-emotion detection system for social robots is presented. This system is intended to be used during human–robot interaction, and it is integrated as part of the overall interaction system of the robot: the Robotics Dialog System (RDS). Two modes are used to detect emotions: the voice and face expression analysis. In order to analyze the voice of the user, a new component has been developed: Gender and Emotion Voice Analysis (GEVA), which is written using the Chuck language. For emotion detection in facial expressions, the system, Gender and Emotion Facial Analysis (GEFA), has been also developed. This last system integrates two third-party solutions: Sophisticated High-speed Object Recognition Engine (SHORE) and Computer Expression Recognition Toolbox (CERT). Once these new components (GEVA and GEFA) give their results, a decision rule is applied in order to combine the information given by both of them. The result of this rule, the detected emotion, is integrated into the dialog system through communicative acts. Hence, each communicative act gives, among other things, the detected emotion of the user to the RDS so it can adapt its strategy in order to get a greater satisfaction degree during the human–robot dialog. Each of the new components, GEVA and GEFA, can also be used individually. Moreover, they are integrated with the robotic control platform ROS (Robot Operating System). Several experiments with real users were performed to determine the accuracy of each component and to set the final decision rule. The results obtained from applying this decision rule in these experiments show a high success rate in automatic user emotion recognition, improving the results given by the two information channels (audio and visual) separately.


title: Catfish Binary Particle Swarm Optimization for Feature Selection

abstract: The feature selection process constitutes a commonly encountered problem of global combin- atorial optimization. This process reduces the number of features by removing irrelevant, noisy, and redundant data, thus resulting in acceptable classification accuracy. Feature selection is a preprocessing technique with great importance in the fields of data analysis and information retrieval processing, pattern classification, and data mining applications. This paper presents a novel optimization algorithm called catfish binary particle swarm optimization (CatfishBPSO), in which the so-called catfish effect is applied to improve the performance of binary particle swarm optimization (BPSO). This effect is the result of the introduction of new particles into the search space ("catfish particles"), which replace particles with the worst fitness by the initialized at extreme points of the search space when the fitness of the global best particle has not improved for a number of consecutive iterations. In this study, the K-nearest neighbor (K-NN) method with leave-one- out cross-validation (LOOCV) was used to evaluate the quality of the solutions. CatfishBPSO was applied and compared to six classification problems taken from the literature. Experimental results show that CatfishBPSO simplifies the feature selection process effectively, and either obtains higher classification accuracy or uses fewer features than other feature selection methods.


title: Scalable Approximation Algorithm for Network Immunization

abstract: The problem of identifying important players in a given network is of pivotal importance for viral marketing, public health management, network security and various other fields of social network analysis. In this work we find the most important vertices in a graph G = (V,E) to immunize so as the chances of an epidemic outbreak is minimized. This problem is directly relevant to minimizing the impact of a contagion spread (e.g. flu virus, computer virus and rumor) in a graph (e.g. social network, computer network) with a limited budget (e.g. the number of available vaccines, antivirus software, filters). It is well known that this problem is computationally intractable (it is NP-hard). In this work we reformulate the problem as a budgeted combinational optimization problem and use techniques from spectral graph theory to design an efficient greedy algorithm to find a subset of vertices to be immunized. We show that our algorithm takes less time compared to the state of the art algorithm. Thus our algorithm is scalable to networks of much larger sizes than best known solutions proposed earlier. We also give analytical bounds on the quality of our algorithm. Furthermore, we evaluate the efficacy of our algorithm on a number of real world networks and demonstrate that the empirical performance of algorithm supplements the theoretical bounds we present, both in terms of approximation guarantees and computational efficiency.


title: Gaussian Bare-Bones Differential Evolution

abstract: Differential evolution (DE) is a well-known algorithm for global optimization over continuous search spaces. However, choosing the optimal control parameters is a challenging task because they are problem oriented. In order to minimize the effects of the control parameters, a Gaussian bare-bones DE (GBDE) and its modified version (MGBDE) are proposed which are almost parameter free. To verify the performance of our approaches, 30 benchmark functions and two real-world problems are utilized. Conducted experiments indicate that the MGBDE performs significantly better than, or at least comparable to, several state-of-the-art DE variants and some existing bare-bones algorithms.


title: Chest Radiograph Pathology Categorization via Transfer Learning

abstract: Abstract The goal of this chapter is to give an overview of the research we have been conducting in automated X-ray pathology detection for the past 10 years, from bag-of-visual-words (BoVW) models to the Convolutional Neural Network (CNN) Deep Learning schemes. Our study was one of the first to suggest the possibility of using non-medical training, using transfer learning from the general imagery to the medical domain. In this chapter we explore deep features that are extracted from intermediate CNN layers in comparison to a set of classical shallow features, including GLCM, PHOG, GABOR, GIST and the more recent, state-of-the-art BoVW model. We investigate the possible benefits of using feature selection techniques on the Deep CNN feature layers. Average AUC results of close to 90% are shown for categorization of 6 different pathologies in a dataset of more than 600 radiographs. This study shows the strength and robustness of the CNN features. We conclude that deep learning with large scale non-medical image databases may be a good substitute, or addition, to domain specific representations which are yet to be available for general medical image recognition tasks. With the BoW schemes, our method won first place in ImageClef competitions. With the DL architectures, we are now able to use the system in real clinical settings.


title: TDMA-ASAP: Sensor Network TDMA Scheduling with Adaptive Slot-Stealing and Parallelism

abstract: TDMA has been proposed as a MAC protocol for wireless sensor networks (WSNs) due to its efficiency in high WSN load. However, TDMA is plagued with shortcomings; we present modifications to TDMA that will allow for the same efficiency of TDMA, while allowing the network to conserve energy during times of low load (when there is no activity being detected). Recognizing that aggregation plays an essential role in WSNs, TDMA-ASAP adds to TDMA: (a) transmission parallelism based on a level-by-level localized graph-coloring, (b) appropriate sleeping between transmissions ("napping"), (c) judicious and controlled TDMA slot stealing to avoid empty slots to be unused and (d) intelligent scheduling/ordering transmissions. Our results show that TDMA-ASAP's unique combination of TDMA, slot-stealing, napping, and message aggregation significantly outperforms other hybrid WSN MAC algorithms and has a performance that is close to optimal in terms of energy consumption and overall delay.


title: Leadership in multiteam systems.

abstract: This study examined 2 leader functions likely to be instrumental in synchronizing large systems of teams (i.e., multiteam systems (MTSs)). Leader strategizing and coordinating were manipulated through training, and effects on functional leadership, interteam coordination, and MTS performance were examined. Three hundred eighty-four undergraduate students participated in a laboratory simulation modeling a 3-team MTS performing an F-22 battle simulation task (N 64 MTSs). Results indicate that both leader training manipulations improved functional leadership and interteam coordination and that functional leader behavior was positively related to MTS-level performance. Functional leadership mediated the effects of both types of training on interteam coordination, and interteam coordination fully mediated the effect of MTS leadership on MTS performance. Multiteam systems (MTSs) are a new and emerging organiza- tional form that present unique challenges for leadership. MTSs are formally defined as "two or more teams that interface directly and interdependently in response to environmental contingencies to- ward the accomplishment of collective goals" (Mathieu, Marks, & Zaccaro, 2001, p. 290). MTSs are tightly coupled constellations of teams offering specialized skills, capabilities, and functions aimed at attaining goals too large to be performed by a single team. MTSs are found in many settings where complex tasks require multiple teams and often diverse expertise. An emergency response MTS requires the coordinated effort of teams of firefighters, EMTs, and surgical units. Successfully bringing a new product to market necessitates marketing, manufacturing, and design teams working in tandem. And oftentimes governmental decision making is the result of long-term collaboration between architects, city planners, municipalities, and various governmental agencies. However, the complexity of MTSs presents clear challenges for leaders as they attempt to coordinate multiple team efforts. How do leaders effec- tively guide the efforts of multiple teams simultaneously working toward both proximal team goals and distal MTS goals? In this study, we extend propositions regarding the team-leader interface to advance and test a model of multiteam leadership in which leaders facilitate cross-team interaction in the context of a highly interdependent MTS performing an F-22 flight simulation task. This research answers Gist, Locke, and Taylor's (1987) call to "extend beyond the study of groups in isolation to the study of groups as part of a system of organizational activity" (p. 253).


title: Practical Confidence and Prediction Intervals

abstract: We propose a new method to compute prediction intervals. Especially for small data sets the width of a prediction interval does not only depend on the variance of the target distribution, but also on the accuracy of our estimator of the mean of the target, i.e., on the width of the confidence interval. The confidence interval follows from the variation in an ensemble of neural networks, each of them trained and stopped on bootstrap replicates of the original data set. A second improvement is the use of the residuals on validation patterns instead of on training patterns for estimation of the variance of the target distribution. As illustrated on a synthetic example, our method is better than existing methods with regard to extrapolation and interpolation in data regimes with a limited amount of data, and yields prediction intervals which actual confidence levels are closer to the desired confidence levels.


title: Software Grand Exposure: SGX Cache Attacks Are Practical

abstract: Intel SGX isolates the memory of security-critical applications from the untrusted OS. However, it has been speculated that SGX may be vulnerable to side-channel attacks through shared caches. We developed new cache attack techniques customized for SGX. Our attack differs from other SGX cache attacks in that it is easy to deploy and avoids known detection approaches. We demonstrate the effectiveness of our attack on two case studies: RSA decryption and genomic processing. While cache timing attacks against RSA and other cryptographic operations can be prevented by using appropriately hardened crypto libraries, the same cannot be easily done for other computations, such as genomic processing. Our second case study therefore shows that attacks on noncryptographic but privacy sensitive operations are a serious threat. We analyze countermeasures and show that none of the known defenses eliminates the attack.


title: Context, learning, and extinction

abstract: A. Redish et al. (2007) proposed a reinforcement learning model of context-dependent learning and extinction in conditioning experiments, using the idea of “state classification” to categorize new observations into states. In the current article, the authors propose an interpretation of this idea in terms of normative statistical inference. They focus on renewal and latent inhibition, 2 conditioning paradigms in which contextual manipulations have been studied extensively, and show that online Bayesian inference within a model that assumes an unbounded number of latent causes can characterize a diverse set of behavioral results from such manipulations, some of which pose problems for the model of Redish et al. Moreover, in both paradigms, context dependence is absent in younger animals, or if hippocampal lesions are made prior to training. The authors suggest an explanation in terms of a restricted capacity to infer new causes.


title: Microcontroller-based single-phase automatic voltage regulator

abstract: This paper proposes the design and implementation of a microcontroller-based single-phase automatic voltage regulator (AVR). The basic building blocks for this design include a PIC 16f 628 microcontroller, a triac, a step-up transformer, a zero crossing circuitry and a load voltage sensing circuitry. This design is based on the principle of phase control of ac voltage using a triac. The trigger pulse for the triac is delayed by the microcontroller to provide the desired regulator terminal voltage. This voltage is always sensed and fed back to the microcontroller via a measuring unit to get a continuous control system. One of the intensions to develop this AVR is to use it in domestic heating and lighting controls. It can also be used as an adjustable voltage source by adjusting a variable resistor in the voltage sensing circuitry. It is also intended to introduce a compact AVR and to demonstrate the usefulness of the PIC microcontroller in power control field.


title: OCV hysteresis effect-based SOC estimation in extended Kalman filter algorithm for a LiFePO4/C cell

abstract: This work investigates the electric characteristics of a carbon-coated LiFePO 4 cell with emphasis on their specific open-circuit voltage (OCV) characteristics, which include very flat OCV curves over the state-of-charge (SOC) and pronounced hysteresis phenomena. Examining discharging/ charging OCV measurement data of a LiFePO 4 /C cell elucidates these phenomena. A simple equivalent circuit model is newly derived and used in an OCV hysteresis effect-based SOC estimation in the extended Kalman filter (EKF) algorithm. The interval of the relationship between the OCV and SOC is decreased to 5% ΔSOC steps to improve the performance of the algorithm. Additionally, to compensate the model errors caused by the equivalent circuit model and variation in parameters, a measurement noise model and data rejection are implemented because the model accuracy is critical in the EKF algorithm in order to obtain a good estimation. All SOC estimation results of the proposed work satisfy the specification within ±5%.


title: Haptic object recognition using passive joints and haptic key features

abstract: This paper presents a novel approach for haptic object recognition with an anthropomorphic robot hand. Firstly, passive degrees of freedom are introduced to the tactile sensor system of the robot hand. This allows the planar tactile sensor patches to optimally adjust themselves to the object's surface and to acquire additional sensor information for shape reconstruction. Secondly, this paper presents an approach to classify an object directly from the haptic sensor data acquired by a palpation sequence with the robot hand - without building a 3d-model of the object. Therefore, a finite set of essential finger positions and tactile contact patterns are identified which can be used to describe a single palpation step. A palpation sequence can then be merged into a simple statistical description of the object and finally be classified. The proposed approach for haptic object recognition and the new tactile sensor system are evaluated with an anthropomorphic robot hand.


title: Grid Computing Devoted to Fusion Applications

abstract: The status of the use of grid computing techniques for fusion simulation is given. It is also given a description of the main applications that are running in the grid and the difficulties that appeared to port them to the grid. The coming developments of grid computing for fusion are shown and the future of this work line is discussed.


title: Floe: A Continuous Dataflow Framework for Dynamic Cloud Applications

abstract: Applications in cyber-physical systems are increasingly coupled with online instruments to perform long running, continuous data processing. Such "always on" dataflow applications are dynamic, where they need to change the applications logic and performance at runtime, in response to external operational needs. Floe is a continuous dataflow framework that is designed to be adaptive for dynamic applications on Cloud infrastructure. It offers advanced dataflow patterns like BSP and MapReduce for flexible and holistic composition of streams and files, and supports dynamic recomposition at runtime with minimal impact on the execution. Adaptive resource allocation strategies allow our framework to effectively use elastic Cloud resources to meet varying data rates. We illustrate the design patterns of Floe by running an integration pipeline and a tweet clustering application from the Smart Power Grids domain on a private Eucalyptus Cloud. The responsiveness of our resource adaptation is validated through simulations for periodic, bursty and random workloads.


Modelling the Multi-Layer Artificial Neural Network for Internet Traffic Forecasting: The Model Selection Design Issues


title: Last-Touch Correlated Data Streaming

abstract: Recent research advocates address-correlating predictors to identify cache block addresses for prefetch. Unfortunately, address-correlating predictors require correlation data storage proportional in size to a program's active memory footprint. As a result, current proposals for this class of predictor are either limited in coverage due to constrained on-chip storage requirements or limited in prediction lookahead due to long off-chip correlation data lookup. In this paper, we propose last-touch correlated data streaming (LT-cords), a practical address-correlating predictor. The key idea of LT-cords is to record correlation data off chip in the order they will be used and stream them into a practically-sized on-chip table shortly before they are needed, thereby obviating the need for scalable on-chip tables and enabling low-latency lookup. We use cycle-accurate simulation of an 8-way out-of-order superscalar processor to show that: (1) LT-cords with 214KB of on-chip storage can achieve the same coverage as a last-touch predictor with unlimited storage, without sacrificing predictor lookahead, and (2) LT-cords improves performance by 60% on average and 385% at best in the benchmarks studied


title: An Experimental Approach to Spectrum Sensing in Cognitive Radio Networks with Off-the-Shelf IEEE 802.11 Devices

abstract: Spectrum sensing is essential to the realization of spectrum agility in cognitive radio (CR) networks. Although fundamental tradeoffs and theoretical limits associated with spectrum sensing have been studied extensively, there have been very few experimental studies focused on building a spectrum "sensor" with commercial off-the-shelf devices. We have therefore built a prototype of CR-based sensor implementation with off- the-shelf IEEE 802.11 devices. 1 In particular, we have explored issues in implementing a spectrum sensor, mostly at the MAC layer, as well as the difficulty in implanting sensing functions into industrial network interfaces. Our experimental results demonstrate the feasibility of building a spectrum sensor and the construction of an incumbent-detection mechanism with off-the- shelf devices. We have also identified technical difficulties, such as device-dependency in determining the detection threshold, in- band signal jamming between secondary devices, and adjacent channel interference due to out-of-band signal emission. This experimental experience has led us to suggest a sensor design guideline for commercial wireless interfaces which can also facilitate other CR-related research.


title: Participation-Contributed Temporal Dynamic Model for Group Activity Recognition

abstract: Group activity recognition, a challenging task that a number of individuals occur in the scene of activity while only a small subset of them participate in, has received increasing attentions. However, most of the previous methods model all the individuals' actions equivalently while ignoring a fact that not all of them are contributed to the discrimination of group activity. That is to say, only a small number of key actors (participants) play important roles in the whole group activity. Inspired by this, we explore a new "One to Key" idea to progressively aggregate temporal dynamics of key actors with different participation degrees over time from each person. Here, we focus on two types of key actors in the whole activity, who steadily move in the whole process (long moving time) or intensely move (but closely related to the group activity) at a significant moment. Based on this, we propose a novel Participation-Contributed Temporal Dynamic Model (PC-TDM) to recognize group activity, which mainly consists of a "One" network and a "One to Key" network. Specifically, "One" network aims at modeling the individual dynamic of each person. "One to Key" network feeds the outputs from the "One" network into a Bidirectional LSTM (Bi-LSTM) according to the order of individual's moving time. Subsequently, each output state of Bi-LSTM weighted by a trainable time-varying attention factor is aggregated by going through LSTM one-by-one. Experimental results on two benchmarks demonstrate that the proposed method improves group activity recognition performance compared to the state-of-the-arts.


title: Attentive Interactive Convolutional Matching for Community Question Answering in Social Multimedia

abstract: Nowadays, community-based question answering (CQA) services have accumulated millions of users to share valuable knowledge. An essential function in CQA tasks is the accurate matching of answers w.r.t given questions. Existing methods usually ignore the redundant, heterogeneous, and multi-modal properties of CQA systems. In this paper, we propose a multi-modal attentive interactive convolutional matching method (MMAICM) to model the multi-modal content and social context jointly for questions and answers in a unified framework for CQA retrieval, which explores the redundant, heterogeneous, and multi-modal properties of CQA systems jointly. A well-designed attention mechanism is proposed to focus on useful word-pair interactions and neglect meaningless and noisy word-pair interactions. Moreover, a multi-modal interaction matrix method and a novel meta-path based network representation approach are proposed to consider the multi-modal content and social context, respectively. The attentive interactive convolutional matching network is proposed to infer the relevance between questions and answers, which can capture both the lexical and the sequential information of the contents. Experiment results on two real-world datasets demonstrate the superior performance of MMAICM compared with other state-of-the-art algorithms.


title: The “Something Something” Video Database for Learning and Evaluating Visual Common Sense

abstract: Neural networks trained on datasets such as ImageNet have led to major advances in visual object classification. One obstacle that prevents networks from reasoning more deeply about complex scenes and situations, and from integrating visual knowledge with natural language, like humans do, is their lack of common sense knowledge about the physical world. Videos, unlike still images, contain a wealth of detailed information about the physical world. However, most labelled video datasets represent high-level concepts rather than detailed physical aspects about actions and scenes. In this work, we describe our ongoing collection of the “something-something” database of video prediction tasks whose solutions require a common sense understanding of the depicted situation. The database currently contains more than 100,000 videos across 174 classes, which are defined as caption-templates. We also describe the challenges in crowd-sourcing this data at scale.


Aesthetics and Entropy III . Aesthetic measures 2


title: Neuroimaging of Wernicke’s Encephalopathy and Korsakoff’s Syndrome

abstract: There is considerable evidence that neuroimaging findings can improve the early diagnosis of Wernicke’s encephalopathy (WE) in clinical settings. The most distinctive neuroimaging finding of acute WE are cytotoxic edema and vasogenic edema, which are represented by bilateral symmetric hyperintensity alterations on T2-weighted MR images in the periphery of the third ventricle, periaqueductal area, mammillary bodies and midbrain tectal plate. An initial bout of WE can result in Korsakoff’s syndrome (KS), but repeated bouts in conjunction with its typical comorbidity, chronic alcoholism, can result in signs of tissue degeneration in vulnerable brain regions. Chronic abnormalities identified with neuroimaging enable examination of brain damage in living patients with KS and have expanded the understanding of the neuropsychological deficits resulting from thiamine deficiency, alcohol neurotoxicity, and their comorbidity. Brain structure and functional studies indicate that the interactions involving the thalamus, mammillary bodies, hippocampus, frontal lobes, and cerebellum are crucial for memory formation and executive functions, and the interruption of these circuits by WE and chronic alcoholism can contribute substantially to the neuropsychological deficits in KS.


title: Can Quirks of Grammar Affect the Way You Think? Grammatical Gender and Object Concepts

abstract: Can the language you speak affect the way you think? Unlike English, many languages have a grammatical gender system whereby all nouns are assigned a gender. Does talking about inanimate objects as if they were masculine or feminine actually lead people to think of inanimate objects as having a gender? A series of studies found effects of grammatical gender on people’s perceptions of similarity between objects and people. This was true even though the tasks were performed in English (a language devoid of grammatical gender), even when the tasks were non-linguistic (e.g., rating similarities between unlabeled pictures), and even while subjects were engaged in a verbal interference task. Finally, results showed that cross-linguistic differences in thought can be produced just by grammatical differences and in the absence of other cultural factors.


title: Least-Squares Temporal Difference Learning

abstract: TD( ) is a popular family of algorithms for approximate policy evaluation in large MDPs. TD( ) works by incrementally updating the value function after each observed transition. It has two major drawbacks: it makes ine cient use of data, and it requires the user to manually tune a stepsize schedule for good performance. For the case of linear value function approximations and = 0, the Least-Squares TD (LSTD) algorithm of Bradtke and Barto (Bradtke and Barto, 1996) eliminates all stepsize parameters and improves data e ciency. This paper extends Bradtke and Barto's work in three signi cant ways. First, it presents a simpler derivation of the LSTD algorithm. Second, it generalizes from = 0 to arbitrary values of ; at the extreme of = 1, the resulting algorithm is shown to be a practical formulation of supervised linear regression. Third, it presents a novel, intuitive interpretation of LSTD as a model-based reinforcement learning technique.


title: Effectiveness of Green Infrastructure for Improvement of Air Quality in Urban Street Canyons

abstract: Street-level concentrations of nitrogen dioxide (NO2) and particulate matter (PM) exceed public health standards in many cities, causing increased mortality and morbidity. Concentrations can be reduced by controlling emissions, increasing dispersion, or increasing deposition rates, but little attention has been paid to the latter as a pollution control method. Both NO2 and PM are deposited onto surfaces at rates that vary according to the nature of the surface; deposition rates to vegetation are much higher than those to hard, built surfaces. Previously, city-scale studies have suggested that deposition to vegetation can make a very modest improvement (<5%) to urban air quality. However, few studies take full account of the interplay between urban form and vegetation, specifically the enhanced residence time of air in street canyons. This study shows that increasing deposition by the planting of vegetation in street canyons can reduce street-level concentrations in those canyons by as much as 40% for NO2 and 60% for PM. Substantial street-level air quality improvements can be gained through action at the scale of a single street canyon or across city-sized areas of canyons. Moreover, vegetation will continue to offer benefits in the reduction of pollution even if the traffic source is removed from city centers. Thus, judicious use of vegetation can create an efficient urban pollutant filter, yielding rapid and sustained improvements in street-level air quality in dense urban areas.


title: A unified approach to salient object detection via low rank matrix recovery

abstract: Salient object detection is not a pure low-level, bottom-up process. Higher-level knowledge is important even for task-independent image saliency. We propose a unified model to incorporate traditional low-level features with higher-level guidance to detect salient objects. In our model, an image is represented as a low-rank matrix plus sparse noises in a certain feature space, where the non-salient regions (or background) can be explained by the low-rank matrix, and the salient regions are indicated by the sparse noises. To ensure the validity of this model, a linear transform for the feature space is introduced and needs to be learned. Given an image, its low-level saliency is then extracted by identifying those sparse noises when recovering the low-rank matrix. Furthermore, higher-level knowledge is fused to compose a prior map, and is treated as a prior term in the objective function to improve the performance. Extensive experiments show that our model can comfortably achieves comparable performance to the existing methods even without the help from high-level knowledge. The integration of top-down priors further improves the performance and achieves the state-of-the-art. Moreover, the proposed model can be considered as a prototype framework not only for general salient object detection, but also for potential task-dependent saliency applications.


title: Using FPGA for real time power monitoring in a NIALM system

abstract: This paper will discuss about the design of a Non Intrusive Application Load Monitoring (NIALM) system using hardware parallel computing in Field Programmable Gate Array (FPGA). This approach relies on the use of Matlab Simulink, Xilinx System Generator, ModelSim and Xilinx EDK to archive the design of FPGA as part of the proposed NIALM system.


title: A Worst-Case Worm

abstract: Worms represent a substantial economic threat to the U.S. computing infrastructure. An important question is how much damage might be caused, as this figure can serve as a guide to evaluating how much to spend on defenses. We construct a parameterized worst-case analysis based on a simple damage model, combined with our understanding of what an attack could accomplish. Although our estimates are at best approximations, we speculate that a plausible worst-case worm could cause $50 billion or more in direct economic damage by attacking widelyused services in Microsoft Windows and carrying a highly destructive payload.


title: The quest for user similarity in mobile societies

abstract: In this paper we explore the notion of mobile users' similarity as a key enabler of innovative applications hinging on opportunistic mobile encounters. In particular, we analyze the performance of known similarity metrics, applicable to our problem domain, as well as propose a novel temporal-based metric, in an attempt to quantify the inherently qualitative notion of similarity. Towards this objective, we first introduce generalized profile structures, beyond mere location, that aim to capture users interests and prior experiences, in the form of a probability distribution. Afterwards, we analyze known and proposed similarity metrics for the proposed profile structures using publicly available data. Apart from the classic Cosine similarity, we identify a distance metric from probability theory, namely Hellinger distance, as a strong candidate for quantifying similarity due to the probability distribution structure of the proposed profiles. In addition, we introduce a novel temporal similarity metric, based on matrix vectorization, to capitalize on the richness in the temporal dimension and maintain low complexity. Finally, the numerical results unveil a number of key insights. First, the temporal metrics yield, on the average, lower similarity indices, compared to the non-temporal ones, due to incorporating the dynamics in the temporal dimension. Second, the Hellinger distance holds great promise for quantifying similarity between probability distribution profiles. Third, vectorized metrics constitute a low-complexity approach towards temporal similarity on resource-limited mobile devices.


title: Clinical use of miniscrew implants as orthodontic anchorage: success rates and postoperative discomfort.

abstract: INTRODUCTION ::: In this study, we evaluated the clinical usefulness of miniscrews as orthodontic anchorage. We examined their success rates, analyzed factors associated with their stability, and evaluated patients' postoperative pain and discomfort with a retrospective questionnaire. ::: ::: ::: METHODS ::: Seventy-five patients, 116 titanium screws of 2 types, and 38 miniplates were retrospectively examined. Each patient was given a questionnaire that included a visual analog scale to indicate discomfort after implantation. ::: ::: ::: RESULTS ::: The success rate for each type of implant was greater than 80%. The analysis of 79 miniscrews with a 1.3-mm diameter showed no significant correlations between success rate and these variables: age, sex, mandibular plane angle, anteroposterior jaw-base relationship, control of periodontitis, temporomandibular disorder symptoms, loading, and screw length. Most patients receiving titanium screws or miniplates with mucoperiosteal-flap surgery reported pain, but half of the patients receiving miniscrews without flap surgery did not report feeling pain at any time after placement. In addition, patients with miniscrews reported minimal discomfort due to swelling, speech difficulty, and difficulty in chewing. ::: ::: ::: CONCLUSIONS ::: Miniscrews placed without flap surgery have high success rates with less pain and discomfort after surgery than miniscrews placed with flap surgery or miniplates placed with either procedure.


Representation and generalization in autonomous reinforcement learning


title: Linguistically-Informed Self-Attention for Semantic Role Labeling

abstract: Current state-of-the-art semantic role labeling (SRL) uses a deep neural network with no explicit linguistic features. However, prior work has shown that gold syntax trees can dramatically improve SRL decoding, suggesting the possibility of increased accuracy from explicit modeling of syntax. In this work, we present linguistically-informed self-attention (LISA): a neural network model that combines multi-head self-attention with multi-task learning across dependency parsing, part-of-speech tagging, predicate detection and SRL. Unlike previous models which require significant pre-processing to prepare linguistic features, LISA can incorporate syntax using merely raw tokens as input, encoding the sequence only once to simultaneously perform parsing, predicate detection and role labeling for all predicates. Syntax is incorporated by training one attention head to attend to syntactic parents for each token. Moreover, if a high-quality syntactic parse is already available, it can be beneficially injected at test time without re-training our SRL model. In experiments on CoNLL-2005 SRL, LISA achieves new state-of-the-art performance for a model using predicted predicates and standard word embeddings, attaining 2.5 F1 absolute higher than the previous state-of-the-art on newswire and more than 3.5 F1 on out-of-domain data, nearly 10% reduction in error. On ConLL-2012 English SRL we also show an improvement of more than 2.5 F1. LISA also out-performs the state-of-the-art with contextually-encoded (ELMo) word representations, by nearly 1.0 F1 on news and more than 2.0 F1 on out-of-domain text.


title: Selection of entropy-measure parameters for knowledge discovery in heart rate variability data

abstract: BackgroundHeart rate variability is the variation of the time interval between consecutive heartbeats. Entropy is a commonly used tool to describe the regularity of data sets. Entropy functions are defined using multiple parameters, the selection of which is controversial and depends on the intended purpose. This study describes the results of tests conducted to support parameter selection, towards the goal of enabling further biomarker discovery.MethodsThis study deals with approximate, sample, fuzzy, and fuzzy measure entropies. All data were obtained from PhysioNet, a free-access, on-line archive of physiological signals, and represent various medical conditions. Five tests were defined and conducted to examine the influence of: varying the threshold value r (as multiples of the sample standard deviation σ, or the entropy-maximizing rChon), the data length N, the weighting factors n for fuzzy and fuzzy measure entropies, and the thresholds rF and rL for fuzzy measure entropy. The results were tested for normality using Lilliefors' composite goodness-of-fit test. Consequently, the p-value was calculated with either a two sample t-test or a Wilcoxon rank sum test.ResultsThe first test shows a cross-over of entropy values with regard to a change of r. Thus, a clear statement that a higher entropy corresponds to a high irregularity is not possible, but is rather an indicator of differences in regularity. N should be at least 200 data points for r = 0.2 σ and should even exceed a length of 1000 for r = rChon. The results for the weighting parameters n for the fuzzy membership function show different behavior when coupled with different r values, therefore the weighting parameters have been chosen independently for the different threshold values. The tests concerning rF and rL showed that there is no optimal choice, but r = rF = rL is reasonable with r = rChon or r = 0.2σ.ConclusionsSome of the tests showed a dependency of the test significance on the data at hand. Nevertheless, as the medical conditions are unknown beforehand, compromises had to be made. Optimal parameter combinations are suggested for the methods considered. Yet, due to the high number of potential parameter combinations, further investigations of entropy for heart rate variability data will be necessary.


A Theory of Term Weighting Based on Exploratory Data Analysis


title: Viscoelastic Properties of the Superficial Musculoaponeurotic System (SMAS): A Microscopic and Mechanical Study

abstract: A study was undertaken to determine the physical properties and microscopic structure of the superficial musculoaponeurotic system (SMAS) tissue. Forty virginal specimens and eight reoperated specimens were examined. The following findings were discovered. 1) Microscopic appearance shows the SMAS to consist of collagen fibers, a relatively high concentration of elastic fibers interspersed with fat cells. 2) On scanning electron microscopy, the virginal SMAS shows the collagen fibers to have a similar convoluted appearance as in the dermis. There is some evidence of parallelization of the collagen fibers in the reexcised SMAS specimens. 3) Mechanical testing (Instron) demonstrates that both the SMAS and preauricular skin were subjected to a series of loading/unloading tests at various rates, amplitudes, and stress relaxation tests. Both sets of specimens indicated definite viscoelastic properties. Although the mechanical behavior of both tissues was somewhat similar, the viscoelastic effect of the SMAS was less pronounced. A slackening effect of the SMAS indicated a gradual expansion of the SMAS postoperatively. These results could provide some indication of the long-term effects of SMAS surgery.


title: Exploiting Wikipedia as external knowledge for document clustering

abstract: In traditional text clustering methods, documents are represented as "bags of words" without considering the semantic information of each document. For instance, if two documents use different collections of core words to represent the same topic, they may be falsely assigned to different clusters due to the lack of shared core words, although the core words they use are probably synonyms or semantically associated in other forms. The most common way to solve this problem is to enrich document representation with the background knowledge in an ontology. There are two major issues for this approach: (1) the coverage of the ontology is limited, even for WordNet or Mesh, (2) using ontology terms as replacement or additional features may cause information loss, or introduce noise. In this paper, we present a novel text clustering method to address these two issues by enriching document representation with Wikipedia concept and category information. We develop two approaches, exact match and relatedness-match, to map text documents to Wikipedia concepts, and further to Wikipedia categories. Then the text documents are clustered based on a similarity metric which combines document content information, concept information as well as category information. The experimental results using the proposed clustering framework on three datasets (20-newsgroup, TDT2, and LA Times) show that clustering performance improves significantly by enriching document representation with Wikipedia concepts and categories.


title: Enhancing MongoDB with Purpose-Based Access Control

abstract: Privacy has become a key requirement for data management systems. Nevertheless, NoSQL datastores, namely highly scalable non relational database management systems, which often support data management of Internet scale applications,still do not provide support for privacy policies enforcement. With this work, we begin to address this issue, by proposing an approach for the integration of purpose based policy enforcement capabilities into MongoDB, a popular NoSQL datastore. Our contribution consists of the enhancement of the MongoDB role based access control model with privacy concepts and related enforcement monitor. The proposed monitor is easily integrable into any MongoDB deployment through simple configurations. Experimental results show that our monitor enforces purpose-based access control with low overhead.


title: Balloon tibioplasty for reduction of depressed tibial plateau fractures: Preliminary radiographic and clinical results

abstract: Purpose ::: In vitro studies have shown promising results for balloon-guided inflation tibioplasty. It was our hypothesis that this technique may be safe and effective for use in depressed lateral tibial plateau fracture.


title: Elliott Waves Recognition Via Neural Networks.

abstract: In this paper we introduce our method that is able to analyze and recognize Elliott waves in time series. Our method uses an artificial neural network that is adapted by backpropagation. Neural network uses Elliot wave’s patterns in order to extract them and recognize. Artificial neural networks are suitable for pattern recognition in time series mainly because of learning only from examples. There is no need to add additional information that could bring more confusion than recognition effect. Neural networks are able to generalize and are resistant to noise. On the other hand, it is generally not possible to determine exactly what a neural network learned and it is also hard to estimate possible recognition error. They are ideal especially when we do not have any other description of the observed series. This paper also includes experimental results of Elliott waves recognition carried out with our method.


title: Blockchain as a Platform for Secure Inter-Organizational Business Processes

abstract: Today, most of the services one may think of are based on a collaborative paradigm (e.g., social media services, IoT-based services, etc.). One of the most relevant representative of such class of services are inter-organizational processes, where an organized group of joined activities is carried out by two or more organizations to achieve a common business goal. Inter-organizational processes are therefore vital to achieve business partnerships among different organizations. However, they may also pose serious security and privacy threats to the data each organization exposes. This is mainly due to the weak trust relationships that may hold among the collaborating parties, which result in a potential lack of trust on how data/operations are managed. In this paper, we discuss, how blockchain, one of today hottest technology, can be used in support of secure inter-organizational processes. We further point out which additional security issues the use of blockchain can bring, illustrate the ongoing research projects in the area and discuss future research directions.


title: Remote Health Monitoring System Using ZigBee Network and GPRS Transmission Technology

abstract: Remote health monitoring system is used to collect and transfer biosignal data from the patient to healthcare center. In this work, a method using the combination of ZigBee and GPRS is presented. These two wireless technologies are integrated to support continuous biosignal monitoring in presence of patient mobility. The principle of remote data transmission through GPRS and short range data transmission through ZigBee is introduced. All data output from medical devices are transferred within a group of wireless personal area network (WPAN) to GPRS gateway. Then, the gateway transmits biosignal data to healthcare center for further analysis. In order to communicate with different medical device, the wireless data transmission devices adopt both RS232 and USB as interface.


title: Transmit/receive (T/R) modules architectures for dual-polarized weather phased array radars

abstract: A discussion of dual-polarized T/R module architectures and technologies for active phased array radar (PAR) for remote sensing application is presented in this paper. Three types of T/R modules developed for weather phased array radar show the evolution and trend of T/R module technology for civil applications. Factors influencing system performance are discussed in terms of the allowable technology and tradeoffs that includes cost. Performance is considered for deployable dual-polarized PAR radar systems. T/R module and antenna aperture Line Replacement Units (LRU) have been evaluated. Performance and cost are derived from affordability constraints imposed by the radar application.


title: FPGA implementation of a FEC decoding subsystem for a DVB-S2 receiver

abstract: This paper presents the implementation of a FEC decoding subsystem for a DVB-S2 compliant receiver. The FEC decoder is composed by three blocks: De-interleaver, LDPC and BCH decoders, and its main goal is correcting the bits that were corrupted by the channel during transmission. The DVB-S2 standard defines several coding schemes and interleaving methods for protecting the data, and all the configurations were considered in this implementation. This work presents the structure and functionality of the FEC subsystem, the platform that was assembled for measuring its performance, and the FPGA synthesis and BER performance results.


title: Reduction of dihydroxyphenylacetic acid by a novel enzyme in the rat brain.

abstract: A novel enzyme that converts dihydroxyphenylacetic acid (DOPAC) to dihydroxyphenylethanol (DOPET) was found to be present in the microdialysate of the rat brain. The enzyme, named DOPAC reductase, was inhibited by EDTA and stimulated by divalent cations like Zn2+, Mn2+, Co2+ and Cu2+. Its Km, pH optimum and temperature optimum were found to be 32 +/- 2 microM, 7.5 and 40 degrees, respectively. The equivalent acid metabolite of noradrenaline, 3,4-dihydroxymandelic acid, and the methoxylated acids of both noradrenaline and dopamine, 3-methoxy-4-hydroxymandelic acid and 3-methoxy-4-hydroxyphenylacetic acid, were found not to be substrates of DOPAC reductase. Thus, DOPAC reductase may be an enzyme that is specifically involved in the one-step conversion of DOPAC to DOPET in the central metabolism of dopamine.


abstract: We propose Graphical Generative Adversarial Networks (Graphical-GAN) to model structured data. Graphical-GAN conjoins the power of Bayesian networks on compactly representing the dependency structures among random variables and that of generative adversarial networks on learning expressive dependency functions. We introduce a structured recognition model to infer the posterior distribution of latent variables given observations. We propose two alternative divergence minimization approaches to learn the generative model and recognition model jointly. The first one treats all variables as a whole, while the second one utilizes the structural information by checking the individual local factors defined by the generative model and works better in practice. Finally, we present two important instances of Graphical-GAN, i.e. Gaussian Mixture GAN (GMGAN) and State Space GAN (SSGAN), which can successfully learn the discrete and temporal structures on visual datasets, respectively.


title: Efficient Multisite Computation Offloading for Mobile Cloud Computing

abstract: Mobile cloud computing is a promising approach to overcome limitations of mobile devices in terms of battery consumption and capability to execute computation-intensive applications. It consists in outsourcing some parts of computation-intensive applications to cloud servers, so that the overall energy consumption and execution time of application's running are optimized. In addition to classical public/private clouds, computations can also be offloaded to nearby clouds, called cloudlets, which may have different access delays, proximity to end users, computation capability and/or monetary cost. In such a heterogeneous context, the offloading decision becomes very complicated and time-consuming, especially when the number of surrogate sites and application units increase. In this paper, we propose a multisite offloading solution which uses the genetic algorithm to timely achieve the best-possible offloading outcome in a heterogeneous multisite context. Simulation results demonstrated that our proposal can outperform other existing multisite alternatives for different number of surrogate sites and various application sizes.


title: Pattern discovery and users classification through web usage mining

abstract: Web usage mining is a special area of web mining which is based upon the discovery and analysis of web usage patterns from web logs so as to effectively and efficiently serve the needs of the users visiting the websites. The main focus of this paper is on doing users classification on the basis of discovered patterns from web logs. Our proposed framework is based on three steps. In the first step, preprocessing is done to remove useless data from web log file so as to reduce its size. In the second step, this cleaned log file is used for discovering usage patterns. Finally, the discovered patterns lead to the classification of users: on the basis of countries; on the basis of direct entry to the site or referred by the other site; on the basis of ti me of access, i.e., either different seasons or different months or different days. This information can then be used by the website administrators for efficient administration and personalization of their websites and thus the specific needs of specific communities of users can be fulfilled and so the profit can be increased.


title: Coupled Inductor Characterization for a High Performance Interleaved Boost Converter

abstract: Interleaved power converter topologies have received increasing attention in recent years for high power and high performance applications. The advantages of interleaved boost converters include increased efficiency, reduced size, reduced electromagnetic emission, faster transient response, and improved reliability. The front end inductors in an interleaved boost converter are magnetically coupled to improve electrical performance and reduce size and weight. Compared to a direct coupled configuration, inverse coupling provides the advantages of lower inductor ripple current and negligible dc flux levels in the core. In this paper, we explore the possible advantages of core geometry on core losses and converter efficiency. Analysis of FEA simulation and empirical characterization data indicates a potential superiority of a square core, with symmetric 45deg energy storage corner gaps, for providing both ac flux balance and maximum dc flux cancellation when wound in an inverse coupled configuration.


title: Isolation of heterorhabditid nematodes from palm tree planted areas and their implications in the Red Palm Weevil control

abstract: Soil samples from 102 sites planted with intercropped palm trees in 8 Egyptian governorates were baited with greater wax moth larvae for the recovery of entomopathogenic nematodes. Only heterorhabditied nematodes were extracted from 5 sites in Alexandria, Behaira, Ismaelia, and Giza governorates. Evaluation of the novel isolates in the laboratory to identify the most efficient ones for biological control of the red palm weevil Rhynchophorus ferrugineus (Oliv.) showed that they are generally more virulent than other entomopathogenic nematode species. However, only two of the 5 strains, isolated from Behaira and Giza, survived a 24 h exposure period in palm weevil-infested tree tissue with a low viability of 19 and 14 %, respectively.


title: Combined committee machine for classifying dengue fever

abstract: The advancement of information technology has made public health management system more efficient on keeping an eye on major outbreaks like dengue fever than before. Vigorous prediction of dengue fever not only helps the management to act faster but also helps medical professionals to treat and suggest a good solution to the patients. In this context, this paper aims to develop an intelligent computing models using multilayer perceptron and support vector machine for classifying positive and negative cases of dengue. Decision taking is vital in medical field, final opinion is sometimes taken by a committee of medical experts. Keeping this fact in mind, Dempster-Shafer based classifier fusion strategy is applied here to form a committee of prediction machine for better outcome. Results show that DS theory gives as best as 96.02% with 4 MLPs and 3 SVMs while that of selected features using Fishers Score gives 96.56% accuracy.


title: A persistence landscapes toolbox for topological statistics

abstract: Topological data analysis provides a multiscale description of the geometry and topology of quantitative data. The persistence landscape is a topological summary that can be easily combined with tools from statistics and machine learning. We give efficient algorithms for calculating persistence landscapes, their averages, and distances between such averages. We discuss an implementation of these algorithms and some related procedures. These are intended to facilitate the combination of statistics and machine learning with topological data analysis. We present an experiment showing that the low-dimensional persistence landscapes of points sampled from spheres (and boxes) of varying dimensions differ.


title: Two integrator loop quadrature oscillators: A review

abstract: A review of the two integrator loop oscillator circuits providing two quadrature sinusoidal output voltages is given. All the circuits considered employ the minimum number of capacitors namely two except one circuit which uses three capacitors. The circuits considered are classified to four different classes. The first class includes floating capacitors and floating resistors and the active building blocks realizing these circuits are the Op Amp or the OTRA. The second class employs grounded capacitors and includes floating resistors and the active building blocks realizing these circuits are the DCVC or the unity gain cells or the CFOA. The third class employs grounded capacitors and grounded resistors and the active building blocks realizing these circuits are the CCII. The fourth class employs grounded capacitors and no resistors and the active building blocks realizing these circuits are the TA. Transformation methods showing the generation of different classes from each other is given in details and this is one of the main objectives of this paper.


title: Towards automated real-time detection of misinformation on Twitter

abstract: Online Social Media (OSM) in general and more specifically micro-blogging site Twitter has outpaced the conventional news dissemination systems. It is often observed that news stories are first broken in Twitter space and then the electronic and print media take them up. However, the distributed structure and lack of moderation in Twitter compounded with the temptation of posting a news worthy story early on Twitter, makes the veracity of information (tweet) a major issue. Our work is an attempt to solve this problem by providing a approach to detect misinformation/rumors on Twitter in real-time automatically. We define a rumor as any information which is circulating in Twitter space and is not in agreement with the information from a credible source. For establishing credibility, our approach is based on the premise that verified News Channel accounts on Twitter would furnish more credible information as compared to the naive unverified account of user (public at large). Our approach has four key steps. Firstly, we extract live streaming tweets corresponding to Twitter trends, identify topics being talked about in each trend based on clustering using hashtags and then collect tweets for each topic. Secondly, we segregate the tweets for each topic based on whether its tweeter is a verified news channel or a general user. Thirdly, we calculate and compare the contextual and sentiment mismatch between tweets comprising of the same topic from verified Twitter accounts of News Channels and other unverified (general) users using semantic and sentiment analysis of the tweets. Lastly, we label the topic as a rumor based on the value of mismatch ratio, which reflects the degree of discrepancy between the news and public on that topic. Results show that a large amount of topics can be flagged as suspicious using this approach without involvement of any manual inspection. In order to validate our proposed algorithm, we implement a prototype called The Twitter Grapevine which targets rumor detection in the Indian domain. The prototype shows how a user can leverage this implementation to monitor the detected rumors using activity timeline, maps and tweet feed. User can also report the rumor as incorrect which can then be updated after manual inspection.


title: Process Mining with the HeuristicsMiner Algorithm

abstract: The basic idea of process mining is to extract knowledge from event logs recorded by an information system. Until recently, the information in these event logs was rarely used to analyze the underlying processes. Process mining aims at improving this by providing techniques and tools for discovering process, organizational, social, and performance information from event logs. Fuelled by the omnipresence of event logs in transactional information systems (cf. WFM, ERP, CRM, SCM, and B2B systems), process mining has become a vivid research area [1, 2]. In this paper we introduce the challenging process mining domain and discuss a heuristics driven process mining algorithm; the so-called “HeuristicsMiner” in detail. HeuristicsMiner is a practical applicable mining algorithm that can deal with noise, and can be used to express the main behavior (i.e. not all details and exceptions) registered in an event log. In the experimental section of this paper we introduce benchmark material (12.000 different event logs) and measurements by which the performance of process mining algorithms can be measured.


title: Controlling the mobility of multiple data transport ferries in a delay-tolerant network

abstract: As technology rapidly progresses, more devices will combine both communication and mobility capabilities. With mobility in devices, we envision a new class of proactive networks that are able to adapt themselves, via physical movement, to meet the needs of applications. To fully realize these opportunities, effective control of device mobility and the interaction between devices is needed. In this paper, we consider the message ferrying (MF) scheme which exploits controlled mobility to transport data in delay-tolerant networks, where end-to-end paths may not exist between nodes. In the MF scheme, a set of special mobile nodes called message ferries are responsible for carrying data for nodes in the network. We study the use of multiple ferries in such networks, which may be necessary to address performance and robustness concerns. We focus on the design of ferry routes. With the possibilities of interaction between ferries, the route design problem is challenging. We present algorithms to calculate routes such that the traffic demand is met and the data delivery delay is minimized. We evaluate these algorithms under a variety of network conditions via simulations. Our goal is to guide the design of MF systems and understand the tradeoff between the incurred cost of multiple ferries and the improved performance. We show that the performance scales well with the number of ferries in terms of throughput, delay and resource requirements in both ferries and nodes.


title: Frodo: Take off the Ring! Practical, Quantum-Secure Key Exchange from LWE

abstract: Lattice-based cryptography offers some of the most attractive primitives believed to be resistant to quantum computers. Following increasing interest from both companies and government agencies in building quantum computers, a number of works have proposed instantiations of practical post-quantum key exchange protocols based on hard problems in ideal lattices, mainly based on the Ring Learning With Errors (R-LWE) problem. While ideal lattices facilitate major efficiency and storage benefits over their non-ideal counterparts, the additional ring structure that enables these advantages also raises concerns about the assumed difficulty of the underlying problems. Thus, a question of significant interest to cryptographers, and especially to those currently placing bets on primitives that will withstand quantum adversaries, is how much of an advantage the additional ring structure actually gives in practice. Despite conventional wisdom that generic lattices might be too slow and unwieldy, we demonstrate that LWE-based key exchange is quite practical: our constant time implementation requires around 1.3ms computation time for each party; compared to the recent NewHope R-LWE scheme, communication sizes increase by a factor of 4.7x, but remain under 12 KiB in each direction. Our protocol is competitive when used for serving web pages over TLS; when partnered with ECDSA signatures, latencies increase by less than a factor of 1.6x, and (even under heavy load) server throughput only decreases by factors of 1.5x and 1.2x when serving typical 1 KiB and 100 KiB pages, respectively. To achieve these practical results, our protocol takes advantage of several innovations. These include techniques to optimize communication bandwidth, dynamic generation of public parameters (which also offers additional security against backdoors), carefully chosen error distributions, and tight security parameters.


title: Cognitive neuroscience: Cognitive neuroscience of human social behaviour

abstract: We are an intensely social species — it has been argued that our social nature defines what makes us human, what makes us conscious or what gave us our large brains. As a new field, the social brain sciences are probing the neural underpinnings of social behaviour and have produced a banquet of data that are both tantalizing and deeply puzzling. We are finding new links between emotion and reason, between action and perception, and between representations of other people and ourselves. No less important are the links that are also being established across disciplines to understand social behaviour, as neuroscientists, social psychologists, anthropologists, ethologists and philosophers forge new collaborations.


Time-of-Flight Sensors in Computer Graphics


title: Predicting the Quality of Short Narratives from Social Media

abstract: An important and difficult challenge in building computational models for narratives is the automatic evaluation of narrative quality. Quality evaluation connects narrative understanding and generation as generation systems need to evaluate their own products. To circumvent difficulties in acquiring annotations, we employ upvotes in social media as an approximate measure for story quality. We collected 54,484 answers from a crowd-powered question-and-answer website, Quora, and then used active learning to build a classifier that labeled 28,320 answers as stories. To predict the number of upvotes without the use of social network features, we create neural networks that model textual regions and the interdependence among regions, which serve as strong benchmarks for future research. To our best knowledge, this is the first large-scale study for automatic evaluation of narrative quality.


title: HPSRouter: A high performance software router based on DPDK

abstract: All aspects of human life have been changed by Internet at an unprecedented rate. And the network appliances has exploded. They bring a series of management problems. In 2012, many large carriers initiated an effort, called Network Function Virtualization (NFV), to replace hardware network appliances with software implementations running in VMs. This approach enabled Network Functions (NFs) to be run on commodity servers. The road to convert hardware middleboxes to software network functions, has never been smooth. The performance gap between software and hardware seems insurmountable when packets are processed through Linux stack. The emergence of data plane development kit (DPDK) brings the chance of bridging the gap by bypassing the Linux kernel stack. We establish a high performance software router based on DPDK framework to offer a solution to the problem and tune the performance to approach the line rate of our 40 Gb network interface card (NIC) using batching, prefetching and affinity technique. We build our system in our four sockets commodity server equipped with 40 Gbps Ethernet interfaces and show that HPSRouter can reach the line rate.


title: Gland segmentation in colon histology images using hand-crafted features and convolutional neural networks

abstract: We investigate glandular structure segmentation in colon histology images as a window-based classification problem. We compare and combine methods based on fine-tuned convolutional neural networks (CNN) and hand-crafted features with support vector machines (HC-SVM). On 85 images of H&E-stained tissue, we find that fine-tuned CNN outperforms HC-SVM in gland segmentation measured by pixel-wise Jaccard and Dice indices. For HC-SVM we further observe that training a second-level window classifier on the posterior probabilities — as an output refinement — can substantially improve the segmentation performance. The final performance of HC-SVM with refinement is comparable to that of CNN. Furthermore, we show that by combining and refining the posterior probability outputs of CNN and HC-SVM together, a further performance boost is obtained.


title: Learning spatio-temporal patterns in the presence of input noise using phase-change memristors

abstract: Neuromorphic systems increasingly attract research interest owing to their ability to provide biologically inspired methods of computing, alternative to the classic von Neumann architecture. In these systems, computing relies on spike-based communication between neurons, and memory is represented by evolving states of the synaptic interconnections. In this work, we first demonstrate how spike-timing-dependent plasticity (STDP) based synapses can be realized using the crystal-growth dynamics of phase-change memristors. Then, we present a novel learning architecture comprising an integrate-and-fire neuron and an array of phase-change synapses that is capable of detecting temporal correlations in parallel input streams. We demonstrate a continuous re-learning operation on a sequence of binary 20×20 pixel images in the presence of significant background noise. Experimental results using an array of phase-change cells as synaptic elements confirm the functionality and performance of the propos ed learning architecture.


title: Automated duplicate detection for bug tracking systems

abstract: Bug tracking systems are important tools that guide the maintenance activities of software developers. The utility of these systems is hampered by an excessive number of duplicate bug reports-in some projects as many as a quarter of all reports are duplicates. Developers must manually identify duplicate bug reports, but this identification process is time-consuming and exacerbates the already high cost of software maintenance. We propose a system that automatically classifies duplicate bug reports as they arrive to save developer time. This system uses surface features, textual semantics, and graph clustering to predict duplicate status. Using a dataset of 29,000 bug reports from the Mozilla project, we perform experiments that include a simulation of a real-time bug reporting environment. Our system is able to reduce development cost by filtering out 8% of duplicate bug reports while allowing at least one report for each real defect to reach developers.


title: Multimodal adaptive social interaction in virtual environment (MASI-VR) for children with Autism spectrum disorders (ASD)

abstract: Difficulties in social interaction, verbal and non-verbal communications as well as repetitive and atypical patterns of behavior, characterizes Autism spectrum disorders (ASD). A number of studies indicated that many children with ASD prefer technology and this preference can be explored to develop systems that may alleviate several challenges of traditional treatment and intervention. As a result, recent advances in computer and robotic technology are ushering in innovative assistive technologies for ASD intervention. The current work presents design, development and a usability study of an adaptive multimodal virtual reality-based social interaction platform for children with ASD. It is hypothesized that endowing a technological system that can detect the processing pattern and mental state of the child using implicit cues from eye tracking and electrophysiological, including peripheral physiological and electroencephalography (EEG), signals and adapt its interaction accordingly is of great importance in assisting and individualizing traditional intervention approaches. The presented VR system is based on a virtual reality based social environment, a school cafeteria, where an individual with ASD interacts with virtual characters. An eye tracker, an EEG monitor and biosensors to measure peripheral electrophysiological signals are integrated with the VR task environment to obtain gaze, EEG signals and several peripheral physiological signals in real-time. In the current work, we show how eye gaze and task performance can be used in real-time to adapt intervention in VR. The other signals are collected for offline analysis. The results from a usability study with 12 subjects with ASD are presented to demonstrate the viability of the proposed concepts within the VR system.


CS 224 N Project : Natural Language Inference for Quora Dataset


Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm


title: Eye Movement Monitoring as a Process Tracing Methodology in Decision Making Research

abstract: Over the past half century, research on human decision making has expanded from a purely behaviorist approach that focuses on decision outcomes, to include a more cognitive approach that focuses on the decision processes that occur prior to the response. This newer approach, known as process tracing, has employed various methods, such as verbal protocols, information search displays, and eye movement monitoring, to identify and track psychological events that occur prior to the response (such as cognitive states, stages, or processes). In the present article, we review empirical studies that have employed eye movement monitoring as a process tracing method in decision making research, and we examine the potential of eye movement monitoring as a process tracing methodology. We also present an experiment that further illustrates the experimental manipulations and analysis techniques that are possible with modern eye tracking technology. In this experiment, a gaze-contingent display was used to manipulate stimulus exposure during decision making, which allowed us to test a specific hypothesis about the role of eye movements in preference decisions (the Gaze Cascade model; Shimojo, Simion, Shimojo, & Scheier, 2003). The results of the experiment did not confirm the predictions of the Gaze Cascade model, but instead support the idea that eye movements in these decisions reflect the screening and evaluation of decision alternatives. In summary, we argue that eye movement monitoring is a valuable tool for capturing decision makers’ information search behaviors, and that modern eye tracking technology is highly compatible with other process tracing methods such as retrospective verbal protocols and neuroimaging techniques, and hence it is poised to be an integral part of the next wave of decision research.


title: A novel multi-articular leg mechanism for biped robots inspired by bi-articular muscle

abstract: This paper proposes a novel multi-articular leg system for biped robot. The structure and actuation scheme of the mechanism are inspired by redundantly-actuated manipulators, of which actuators span over multiple joints like bi-articular muscles. The proposed mechanism for robotic leg contributes to improved capacity in end-effector force exertion, which allows less performance requirements in individual actuators. For the purpose of evaluation, we provide the static torque-force transmission analysis, as well as walking simulation results conducted in the dynamic simulator. The results indicate that the proposed mechanism can benefit from the improved output force capacity.


title: Therapeutic satisfaction and subjective effects of different strains of pharmaceutical-grade cannabis.

abstract: In The Netherlands, pharmaceutical-grade cultivated cannabis is distributed for medicinal purposes as commissioned by the Ministry of Health. Few studies have thus far described its therapeutic efficacy or subjective (adverse) effects in patients. The aims of this study are to assess the therapeutic satisfaction within a group of patients using prescribed pharmaceutical-grade cannabis and to compare the subjective effects among the available strains with special focus on their delta-9tetrahydrocannabinol and cannabidiol content. In a cross-sectional and natural design, users of pharmaceutical-grade cannabis were investigated with questionnaires. Medical background of the patients was asked as well as experienced therapeutic effects and characteristics of cannabis use. Subjective effects were measured with psychometric scales and used to compare among the strains of cannabis used across this group of patients. One hundred two patients were included; their average age was 53 years and 76% used it for more than a year preceding this study. Chronic pain (53%; n = 54) was the most common medical indication for using cannabis followed by multiple sclerosis (23%; n = 23), and 86% (n = 88) of patients (almost) always experienced therapeutic satisfaction when using pharmaceutical cannabis. Dejection, anxiety, and appetite stimulation were found to differ among the 3 strains of cannabis. These results show that patients report therapeutic satisfaction with pharmaceutical cannabis, mainly pain alleviation. Some subjective effects were found to differ among the available strains of cannabis, which is discussed in relation to their different tetrahydrocannabinol/cannabidiol content. These results may aid in further research and critical appraisal for medicinally prescribed cannabis products.


Revitalizing the Concept of Sociotechnical Systems in Social Studies of Technology


title: A pythonic approach for rapid hardware prototyping and instrumentation

abstract: We introduce PyRTL, a Python embedded hardware design language that helps concisely and precisely describe digital hardware structures. Rather than attempt to infer a good design via HLS, PyRTL provides a wrapper over a well-defined "core" set of primitives in a way that empowers digital hardware design teaching and research. The proposed system takes advantage of the programming language features of Python to allow interesting design patterns to be expressed succinctly, and encourage the rapid generation of tooling and transforms over a custom intermediate representation. We describe PyRTL as a language, its core semantics, the transform generation interface, and explore its application to several different design patterns and analysis tools. Also, we demonstrate the integration of PyRTL-generated hardware overlays into Xilinx PYNQ platform. The resulting system provides an almost "pure" pythonic experience for the prototyping and evaluation of FPGA-based SoCs.


On Communicating Finite-State Machines


title: Demographic factors predict magnitude of conditioned fear.

abstract: Abstract There is substantial variability across individuals in the magnitudes of their skin conductance (SC) responses during the acquisition and extinction of conditioned fear. To manage this variability, subjects may be matched for demographic variables, such as age, gender and education. However, limited data exist addressing how much variability in conditioned SC responses is actually explained by these variables. The present study assessed the influence of age, gender and education on the SC responses of 222 subjects who underwent the same differential conditioning paradigm. The demographic variables were found to predict a small but significant amount of variability in conditioned responding during fear acquisition, but not fear extinction learning or extinction recall. A larger differential change in SC during acquisition was associated with more education. Older participants and women showed smaller differential SC during acquisition. Our findings support the need to consider age, gender and education when studying fear acquisition but not necessarily when examining fear extinction learning and recall. Variability in demographic factors across studies may partially explain the difficulty in reproducing some SC findings.


The Storyline Annotation and Representation Scheme (StaR): A Proposal


title: Driving Posture Recognition by Joint Application of Motion History Image and Pyramid Histogram of Oriented Gradients

abstract: In the field of intelligent transportation system (ITS), automatic interpretation of a driver’s behavior is an urgent and ::: challenging topic. This paper studies vision-based driving posture recognition in the human action recognition framework. A driving ::: action dataset was prepared by a side-mounted camera looking at a driver’s left profile. The driving actions, including operating the shift ::: lever, talking on a cell phone, eating, and smoking, are first decomposed into a number of predefined action primitives, that is, interaction ::: with shift lever, operating the shift lever, interaction with head, and interaction with dashboard. A global grid-based representation ::: for the action primitives was emphasized, which first generate the silhouette shape from motion history image, followed by application ::: of the pyramid histogram of oriented gradients (PHOG) for more discriminating characterization. The random forest (RF) classifier ::: was then exploited to classify the action primitives together with comparisons to some other commonly applied classifiers such as NN, ::: multiple layer perceptron, and support vector machine. Classification accuracy is over 94% for the RF classifier in holdout and cross-validation experiments on the four manually decomposed driving actions.


title: A case study in hardware Trojan design and implementation

abstract: As integrated circuits (ICs) continue to have an overwhelming presence in our digital information-dominated world, having trust in their manufacture and distribution mechanisms is crucial. However, with ever-shrinking transistor technologies, the cost of new fabrication facilities is becoming prohibitive, pushing industry to make greater use of potentially less reliable foreign sources for their IC supply. The 2008 Computer Security Awareness Week (CSAW) Embedded Systems Challenge at the Polytechnic Institute of NYU highlighted some of the vulnerabilities of the IC supply chain in the form of a hardware hacking challenge. This paper explores the design and implementation of our winning entry.


title: Non-fragile state estimation for discrete Markovian jumping neural networks

abstract: In this paper, the non-fragile state estimation problem is investigated for a class of discrete-time neural networks subject to Markovian jumping parameters and time delays. In terms of a Markov chain, the mode switching phenomenon at different times is considered in both the parameters and the discrete delays of the neural networks. To account for the possible gain variations occurring in the implementation, the gain of the estimator is assumed to be perturbed by multiplicative norm-bounded uncertainties. We aim to design a non-fragile state estimator such that, in the presence of all admissible gain variations, the estimation error converges to zero exponentially. By adopting the Lyapunov-Krasovskii functional and the stochastic analysis theory, sufficient conditions are established to ensure the existence of the desired state estimator that guarantees the stability of the overall estimation error dynamics. The explicit expression of such estimators is parameterized by solving a convex optimization problem via the semi-definite programming method. A numerical simulation example is provided to verify the usefulness of the proposed methods.


title: Tacotron: Towards End-to-End Speech Synthesis

abstract: A text-to-speech synthesis system typically consists of multiple stages, such as a text analysis frontend, an acoustic model and an audio synthesis module. Building these components often requires extensive domain expertise and may contain brittle design choices. In this paper, we present Tacotron, an end-to-end generative text-to-speech model that synthesizes speech directly from characters. Given pairs, the model can be trained completely from scratch with random initialization. We present several key techniques to make the sequence-to-sequence framework perform well for this challenging task. Tacotron achieves a 3.82 subjective 5-scale mean opinion score on US English, outperforming a production parametric system in terms of naturalness. In addition, since Tacotron generates speech at the frame level, it's substantially faster than sample-level autoregressive methods.


title: Automatic Bug Triage using Semi-Supervised Text Classification

abstract: Abstract —In this paper, we propose a semi-supervised text classification approach for bug triage to avoid the deficiency of labeled bug reports in existing supervised approaches. This new approach combines naive Bayes classifier and expectation-maximization to take advantage of both labeled and unlabeled bug reports. This approach trains a classifier with a fraction of labeled bug reports. Then the approach iteratively labels numerous unlabeled bug reports and trains a new classifier with labels of all the bug reports. We also employ a weighted recommendation list to boost the performance by imposing the weights of multiple developers in training the classifier. Experimental results on bug reports of Eclipse show that our new approach outperforms existing supervised approaches in terms of classification accuracy. Keywords- automatic bug triage; expectation-maximization; semi-supervised text classification; weighted recommendation list I. I NTRODUCTION Most of large software projects employ a bug tracking system (bug repository) to manage bugs and developers. In software development and maintenance, a bug repository is a significant software repository for storing the bugs submitted by


title: SOM: Semantic obviousness metric for image quality assessment

abstract: Image quality assessment (IQA) tries to estimate human perception based image visual quality in an objective manner. Existing approaches target this problem with or without reference images. For no-reference image quality assessment, there is no given reference image or any knowledge of the distortion type of the image. Previous approaches measure the image quality from signal level rather than semantic analysis. They typically depend on various features to represent local characteristic of an image. In this paper we propose a new no-reference (NR) image quality assessment (IQA) framework based on semantic obviousness. We discover that semantic-level factors affect human perception of image quality. With such observation, we explore semantic obviousness as a metric to perceive objects of an image. We propose to extract two types of features, one to measure the semantic obviousness of the image and the other to discover local characteristic. Then the two kinds of features are combined for image quality estimation. The principles proposed in our approach can also be incorporated with many existing IQA algorithms to boost their performance. We evaluate our approach on the LIVE dataset. Our approach is demonstrated to be superior to the existing NR-IQA algorithms and comparable to the state-of-the-art full-reference IQA (FR-IQA) methods. Cross-dataset experiments show the generalization ability of our approach.


title: NUTRISCORE: A new nutritional screening tool for oncological outpatients

abstract: Abstract Objectives The aim of this study was to design a new nutritional screening tool (NUTRISCORE) to detect nutritional risk in outpatients with cancer. Methods A multicenter, cross-sectional study was conducted. We randomly selected outpatients receiving onco-specific, palliative, or symptomatic treatment for malignant neoplasms (including solid tumors and hematologic malignancies). These patients were assessed using the NUTRISCORE tool, the Malnutrition Screening Tool (MST), and the Patient-Generated Subjective Global Assessment (PG-SGA) to detect risk for malnutrition. The new tool included questions regarding the cancer site and active treatment. Sensitivity, specificity, and positive and negative predictive values were calculated for NUTRISCORE and MST using the PG-SGA as a reference method. Results We evaluated 394 patients. According to NUTRISCORE, 22.6% were at risk for malnutrition. The MST detected a risk in 28.2%, and the PG-SGA found that 19% were malnourished or at nutritional risk. Using the PG-SGA as a reference method, the MST had a sensitivity of 84% and a specificity of 85.6%, whereas NUTRISCORE exceeded these values, at 97.3% sensitivity and 95.9% specificity. The better performance of NUTRISCORE as compared with MST was confirmed by the receiver operating characteristic curve analysis, with area under the curve values of 0.95 (95% confidence interval, 0.92–0.98) for NUTRISCORE and 0.84 (95% confidence interval, 0.79–0.89) for the MST. Conclusions NUTRISCORE has been found to be a novel, fast, and valid nutritional screening tool for outpatients with cancer. Its simplicity and high level of accuracy in detecting nutritional risk facilitates its applicability.


title: LSM-trie: an LSM-tree-based ultra-large key-value store for small data

abstract: Key-value (KV) stores have become a backbone of large-scale applications in today's data centers. The data set of the store on a single server can grow to billions of KV items or many terabytes, while individual data items are often small (with their values as small as a couple of bytes). It is a daunting task to efficiently organize such an ultra-large KV store to support fast access. Current KV storage systems have one or more of the following inadequacies: (1) very high data write amplifications, (2) large index set, and (3) dramatic degradation of read performance with overspill index out of memory. ::: ::: To address the issue, we propose LSM-trie, a KV storage system that substantially reduces metadata for locating KV items, reduces write amplification by an order of magnitude, and needs only two disk accesses with each KV read even when only less than 10% of metadata (Bloom filters) can be held in memory. To this end, LSM-trie constructs a trie, or a prefix tree, that stores data in a hierarchical structure and keeps reorganizing them using a compaction method much more efficient than that adopted for LSM-tree. Our experiments show that LSM-trie can improve write and read throughput of LevelDB, a state-of-the-art KV system, by up to 20 times and up to 10 times, respectively.


title: DIFFERENTIAL EFFECTS OF SEATING ARRANGEMENTS ON DISRUPTIVE BEHAVIOR OF FIFTH GRADE STUDENTS DURING INDEPENDENT SEATWORK

abstract: We investigated teacher versus student seat selection in the context of group and individual seating arrangements. Disruptive behavior during group seating occurred at twice the rate when students chose their seats than when the teacher chose. During individual seating, disruptive behavior occurred more than three times as often when the students chose their seats. The results are discussed in relation to choice and the matching law.


title: Voronoi diagrams and offset curves of curvilinear polygons

abstract: This paper studies the practical generation of Voronoi diagrams and offset curves of simply-connected planar areas bounded by straight lines and circular arcs. We present and analyze a wavefront-propagation algorithm for the generation of Voronoi diagrams and compare it experimentally to a tuned version of Lee's divide-and-conquer algorithm. Key performance parameters of these two algorithms are compared based on machine-generated test data. We conclude that the wavefront-propagation algorithm can be expected to outperform the divide-and-conquer algorithm for all, but pathological test data. In particular, its practical running time seems to grow only linearly. We also used our implementation in order to gather statistics on the CPU-consumption of offsetting based on Voronoi diagrams. All tests clearly showed the practical suitability of using Voronoi diagrams for the offsetting of curvilinear polygons. The CPU-time consumptions recorded also compare very favorably with other published codes for computing Voronoi diagrams.