title: Active pebbles: parallel programming for data-driven applications

abstract: The scope of scientific computing continues to grow and now includes diverse application areas such as network analysis, combinatorialcomputing, and knowledge discovery, to name just a few. Large problems in these application areas require HPC resources, but they exhibit computation and communication patterns that are irregular, fine-grained, and non-local, making it difficult to apply traditional HPC approaches to achieve scalable solutions. In this paper we present Active Pebbles, a programming and execution model developed explicitly to enable the development of scalable software for these emerging application areas. Our approach relies on five main techniques--scalable addressing, active routing, message coalescing, message reduction, and termination detection--to separate algorithm expression from communication optimization. Using this approach, algorithms can be expressed in their natural forms, with their natural levels of granularity, while optimizations necessary for scalability can be applied automatically to match the characteristics of particular machines. We implement several example kernels using both Active Pebbles and existing programming models, evaluating both programmability and performance. Our experimental results demonstrate that the Active Pebbles model can succinctly and directly express irregular application kernels, while still achieving performance comparable to MPI-based implementations that are significantly more complex.


title: Emphasis on testing assimilation using cloud computing for improvised agile SCRUM framework

abstract: In today's era service development demand increasing day by day and client's ever changing requirements which made agile methodology to come in existence due to faster production, flexible, improved quality and moreover it can accommodate changes, faster feedbacks and transparent communication. But now Due to geographical distribution of team members and client, leading IT thinkers thought to strengthen the agile methodology by giving user's access an environment that has wide-spread access i.e CLOUD. This combination of agile and cloud provide a positive impact to IT companies. But for achieving Quality, we need to test all functionality so in our work we emphasis on testing assimilation using cloud services in agile environment. Cloud team members can perform testing easily using Test-as-a-Service without increasing the cost of the project and get the results faster. We can also use agile management tools to reduce the problem of communication and for all the updates in the product backlog as well as Sprint backlog list.


title: Internet Web Servers: Workload Characterization and Performance Implications

abstract: This paper presents a workload characterization study for Internet Web servers. Six different data sets are used in the study: three from academic environments, two from scientific research organizations, and one from a commercial Internet provider. These data sets represent three different orders of magnitude in server activity, and two different orders of magnitude in time duration, ranging from one week of activity to one year. The workload characterization focuses on the document type distribution, the document size distribution, the document referencing behavior, and the geographic distribution of server requests. Throughout the study, emphasis is placed on finding workload characteristics that are common to all the data sets studied. Ten such characteristics are identified. The paper concludes with a discussion of caching and performance issues, using the observed workload characteristics to suggest performance enhancements that seem promising for Internet Web servers.


title: Model-Based Dispatch Strategies for Lithium-Ion Battery Energy Storage Applied to Pay-as-Bid Markets for Secondary Reserve

abstract: Due to their decreasing cost, lithium-ion batteries (LiB) are becoming increasingly attractive for grid-scale applications. In this paper, we investigate the use of LiB for providing secondary reserve and show how the achieved cost savings could be increased by using model-based optimization techniques. In particular, we compare a maximum use dispatch strategy with two different cost-minimizing strategies. For the estimation of state-dependent battery usage cost, we combine an existing electro-thermal LiB model of a mature lithium-iron-phosphate battery cell with corresponding semiempirical calendar and cycle aging models. We estimate the benefit of storage operation from the system operator's point of view by gauging the avoided cost of activated reserve. Our evaluation is based on two years worth of data from the German reserve market. The proposed cost minimizing dispatch strategies yield significantly better results than a dispatch strategy that maximizes battery utilization.


title: Detecting Loop Closure with Scene Sequences

abstract: This paper is concerned with "loop closing" for mobile robots. Loop closing is the problem of correctly asserting that a robot has returned to a previously visited area. It is a particularly hard but important component of the Simultaneous Localization and Mapping (SLAM) problem. Here a mobile robot explores an a-priori unknown environment performing on-the-fly mapping while the map is used to localize the vehicle. Many SLAM implementations look to internal map and vehicle estimates (p.d.fs) to make decisions about whether a vehicle is revisiting a previously mapped area or is exploring a new region of workspace. We suggest that one of the reasons loop closing is hard in SLAM is precisely because these internal estimates can, despite best efforts, be in gross error. The "loop closer" we propose, analyze and demonstrate makes no recourse to the metric estimates of the SLAM system it supports and aids---it is entirely independent. At regular intervals the vehicle captures the appearance of the local scene (with camera and laser). We encode the similarity between all possible pairings of scenes in a "similarity matrix". We then pose the loop closing problem as the task of extracting statistically significant sequences of similar scenes from this matrix. We show how suitable analysis (introspection) and decomposition (remediation) of the similarity matrix allows for the reliable detection of loops despite the presence of repetitive and visually ambiguous scenes. We demonstrate the technique supporting a SLAM system driven by scan-matching laser data in a variety of settings. Some of the outdoor settings are beyond the capability of the SLAM system itself in which case GPS was used to provide a ground truth. We further show how the techniques can equally be applied to detect loop closure using spatial images taken with a scanning laser. We conclude with an extension of the loop closing technique to a multi-robot mapping problem in which the outputs of several, uncoordinated and SLAM-enabled robots are fused without requiring inter-vehicle observations or a-priori frame alignment.


title: Joint Super Resolution and Denoising From a Single Depth Image

abstract: This paper describes a new algorithm for depth image super resolution and denoising using a single depth image as input. A robust coupled dictionary learning method with locality coordinate constraints is introduced to reconstruct the corresponding high resolution depth map. The local constraints effectively reduce the prediction uncertainty and prevent the dictionary from over-fitting. We also incorporate an adaptively regularized shock filter to simultaneously reduce the jagged noise and sharpen the edges. Furthermore, a joint reconstruction and smoothing framework is proposed with an L0 gradient smooth constraint, making the reconstruction more robust to noise. Experimental results demonstrate the effectiveness of our proposed algorithm compared to previously reported methods.


title: Fragmented social media: a look into selective exposure to political news

abstract: The hypothesis of selective exposure assumes that people crave like-minded information and eschew information that conflicts with their beliefs, and that has negative consequences on political life. Yet, despite decades of research, this hypothesis remains theoretically promising but empirically difficult to test. We look into news articles shared on Facebook and examine whether selective exposure exists or not in social media. We find a concrete evidence for a tendency that users predominantly share like-minded news articles and avoid conflicting ones, and partisans are more likely to do that. Building tools to counter partisanship on social media would require the ability to identify partisan users first. We will show that those users cannot be distinguished from the average user as the two subgroups do not show any demographic difference.


Byzantine Agreement , Made Trivial


PROMOTING ORGANIZATIONAL LEARNING CULTURE THROUGH WORK-BASED-LEARNING


title: Supernetwork Model of Intra-organizational Knowledge Transfer

abstract: Knowledge transfer has become a hot issue for knowledge management. Nevertheless the application of heterogeneous network model to the analysis of the internal processes of intra-organization knowledge transfer is still rare. In this paper, we propose a super network model of intra-organizational knowledge transfer. The model is composite of a social network and a knowledge network. It consists of individual nodes, knowledge nodes, and the directed or undirected edges among them, which represent the organization members, the organization knowledge and their complex relationship respectively. The results of model simulation on the Net logo platform show that the higher the average degree of nodes is in the knowledge transfer network, the more innovation is created by the organization. Moreover, the results demonstrate that the knowledge of network achieve a specific equilibrium more quickly if the number of initial transfer nodes is higher. The proposed super network model provides a novel thinking and method for further in-depth study of intra-organizational knowledge transfer.


title: Moral Foundations Theory: The Pragmatic Validity of Moral Pluralism

abstract: Where does morality come from? Why are moral judgments often so similar across cultures, yet sometimes so variable? Is morality one thing, or many? Moral Foundations Theory (MFT) was created to answer these questions. In this chapter we describe the origins, assumptions, and current conceptualization of the theory, and detail the empirical findings that MFT has made possible, both within social psychology and beyond. Looking toward the future, we embrace several critiques of the theory, and specify five criteria for determining what should be considered a foundation of human morality. Finally, we suggest a variety of future directions for MFT and for moral psychology.


title: Nipah virus infection in bats (order Chiroptera) in peninsular Malaysia.

abstract: Nipah virus, family Paramyxoviridae, caused disease in pigs and humans in peninsular Malaysia in 1998-99. Because Nipah virus appears closely related to Hendra virus, wildlife surveillance focused primarily on pteropid bats (suborder Megachiroptera), a natural host of Hendra virus in Australia. We collected 324 bats from 14 species on peninsular Malaysia. Neutralizing antibodies to Nipah virus were demonstrated in five species, suggesting widespread infection in bat populations in peninsular Malaysia.


title: An Area and Power-Efficient Analog Li-Ion Battery Charger Circuit

abstract: The demand for greater battery life in low-power consumer electronics and implantable medical devices presents a need for improved energy efficiency in the management of small rechargeable cells. This paper describes an ultra-compact analog lithium-ion (Li-ion) battery charger with high energy efficiency. The charger presented here utilizes the tanh basis function of a subthreshold operational transconductance amplifier to smoothly transition between constant-current and constant-voltage charging regimes without the need for additional area- and power-consuming control circuitry. Current-domain circuitry for end-of-charge detection negates the need for precision-sense resistors in either the charging path or control loop. We show theoretically and experimentally that the low-frequency pole-zero nature of most battery impedances leads to inherent stability of the analog control loop. The circuit was fabricated in an AMI 0.5-μm complementary metal-oxide semiconductor process, and achieves 89.7% average power efficiency and an end voltage accuracy of 99.9% relative to the desired target 4.2 V, while consuming 0.16 mm2 of chip area. To date and to the best of our knowledge, this design represents the most area-efficient and most energy-efficient battery charger circuit reported in the literature.


title: Terahertz surface plasmons in grating-coupled graphene

abstract: Pronounced plasmonic absorption features at terahertz frequencies are measured in large-area graphene sheets coupled to periodic arrays of metallic nanoparticles, and limitations on their tunability caused by carrier density inhomogeneities are investigated.


title: First flight tests for a quadrotor UAV with tilting propellers

abstract: In this work we present a novel concept of a quadrotor UAV with tilting propellers. Standard quadrotors are limited in their mobility because of their intrinsic underactuation (only 4 independent control inputs vs. their 6-dof pose in space). The quadrotor prototype discussed in this paper, on the other hand, has the ability to also control the orientation of its 4 propellers, thus making it possible to overcome the aforementioned underactuation and behave as a fully-actuated flying vehicle. We first illustrate the hardware/software specifications of our recently developed prototype, and then report the experimental results of some preliminary, but promising, flight tests which show the capabilities of this new UAV concept.


title: Single source photoplethysmograph transducer for local pulse wave velocity measurement

abstract: Cuffless evaluation of arterial blood pressure (BP) using pulse wave velocity (PWV) has received attraction over the years. Local PWV based techniques for cuffless BP measurement has more potential in accurate estimation of BP parameters. In this work, we present the design and experimental validation of a novel single-source Photoplethysmograph (PPG) transducer for arterial blood pulse detection and cycle-to-cycle local PWV measurement. The ability of the transducer to continuously measure local PWV was verified using arterial flow phantom as well as by conducting an in-vivo study on 17 volunteers. The single-source PPG transducer could reliably acquire dual blood pulse waveforms, along small artery sections of length less than 28 mm. The transducer was able to perform repeatable measurements of carotid local PWV on multiple subjects with maximum beat-to-beat variation less than 12%. The correlation between measured carotid local PWV and brachial BP parameters were also investigated during the in-vivo study. Study results prove the potential use of newly proposed single-source PPG transducers in continuous cuffless BP measurement systems.


Anodic Bonding for Microsystem Applications


title: Learning to segment moving objects in videos

abstract: We segment moving objects in videos by ranking spatio-temporal segment proposals according to “moving objectness”; how likely they are to contain a moving object. In each video frame, we compute segment proposals using multiple figure-ground segmentations on per frame motion boundaries. We rank them with a Moving Objectness Detector trained on image and motion fields to detect moving objects and discard over/under segmentations or background parts of the scene. We extend the top ranked segments into spatio-temporal tubes using random walkers on motion affinities of dense point trajectories. Our final tube ranking consistently outperforms previous segmentation methods in the two largest video segmentation benchmarks currently available, for any number of proposals. Further, our per frame moving object proposals increase the detection rate up to 7% over previous state-of-the-art static proposal methods.


title: Positioning and presenting design science research for maximum impact

abstract: Design science research (DSR) has staked its rightful ground as an important and legitimate Information Systems (IS) research paradigm. We contend that DSR has yet to attain its full potential impact on the development and use of information systems due to gaps in the understanding and application of DSR concepts and methods. This essay aims to help researchers (1) appreciate the levels of artifact abstractions that may be DSR contributions, (2) identify appropriate ways of consuming and producing knowledge when they are preparing journal articles or other scholarly works, (3) understand and position the knowledge contributions of their research projects, and (4) structure a DSR article so that it emphasizes significant contributions to the knowledge base. Our focal contribution is the DSR knowledge contribution framework with two dimensions based on the existing state of knowledge in both the problem and solution domains for the research opportunity under study. In addition, we propose a DSR communication schema with similarities to more conventional publication patterns, but which substitutes the description of the DSR artifact in place of a traditional results section. We evaluate the DSR contribution framework and the DSR communication schema via examinations of DSR exemplar publications.


title: Developing an early warning system to predict currency crises

abstract: The purpose of this paper is to develop an early warning system to predict currency crises. In this study, a data set covering the period of January 1992–December 2011 of Turkish economy is used, and an early warning system is developed with artificial neural networks (ANN), decision trees, and logistic regression models. Financial Pressure Index (FPI) is an aggregated value, composed of the percentage changes in dollar exchange rate, gross foreign exchange reserves of the Central Bank, and overnight interest rate. In this study, FPI is the dependent variable, and thirty-two macroeconomic indicators are the independent variables. Three models, which are tested in Turkish crisis cases, have given clear signals that predicted the 1994 and 2001 crises 12months earlier. Considering all three prediction model results, Turkey’s economy is not expected to have a currency crisis (ceteris paribus) until the end of 2012. This study presents uniqueness in that decision support model developed in this study uses basic macroeconomic indicators to predict crises up to a year before they actually happened with an accuracy rate of approximately 95%. It also ranks the leading factors of currency crisis with regard to their importance in predicting the crisis.


title: Multi-view kernel-based data analysis

abstract: The input data features set for many data driven tasks is high-dimensional while the intrinsic dimension of the data is low. Data analysis methods aim to uncover the underlying low dimensional structure imposed by the low dimensional hidden parameters by utilizing distance metrics that considers the set of attributes as a single monolithic set. However, the transformation of the low dimensional phenomena into the measured high dimensional observations might distort the distance metric. This distortion can affect the desired estimated low dimensional geometric structure. In this paper, we suggest to utilize the redundancy in the feature domain by partitioning the features into multiple subsets that are called views. The proposed method utilize the agreement also called consensus between different views to extract valuable geometric information that unifies multiple views about the intrinsic relationships among several different observations. This unification enhances the information that a single view or a simple concatenations of views provides.


title: Methodologies for Prediction of Stock Market: An Artificial Neural Network

abstract: Peoples tend to invest in stocks because of its high returns over time. Stock markets are affected by many highly interrelated economic, social, political and even psychological factors, and these factors interact with each other in a very complicated manner. Therefore it is generally challenging task to predict the movement of stock market. It is observed that conventional statistical techniques for prediction have reached their limitation in applications with nonlinearities in the data set. Artificial Neural Network, a computing system containing many simple nonlinear computing units as neurons interconnected by links, is a well-tested method for financial analysis on the stock market. This paper explains in detail various prediction methodologies for stock market and found that Artificial Neural network could be useful for stock market prediction.


title: Enhanced thermoelectric performance of rough silicon nanowires

abstract: Approximately 90 per cent of the world's power is generated by heat engines that use fossil fuel combustion as a heat source and typically operate at 30-40 per cent efficiency, such that roughly 15 terawatts of heat is lost to the environment. Thermoelectric modules could potentially convert part of this low-grade waste heat to electricity. Their efficiency depends on the thermoelectric figure of merit ZT of their material components, which is a function of the Seebeck coefficient, electrical resistivity, thermal conductivity and absolute temperature. Over the past five decades it has been challenging to increase ZT > 1, since the parameters of ZT are generally interdependent. While nanostructured thermoelectric materials can increase ZT > 1 (refs 2-4), the materials (Bi, Te, Pb, Sb, and Ag) and processes used are not often easy to scale to practically useful dimensions. Here we report the electrochemical synthesis of large-area, wafer-scale arrays of rough Si nanowires that are 20-300 nm in diameter. These nanowires have Seebeck coefficient and electrical resistivity values that are the same as doped bulk Si, but those with diameters of about 50 nm exhibit 100-fold reduction in thermal conductivity, yielding ZT = 0.6 at room temperature. For such nanowires, the lattice contribution to thermal conductivity approaches the amorphous limit for Si, which cannot be explained by current theories. Although bulk Si is a poor thermoelectric material, by greatly reducing thermal conductivity without much affecting the Seebeck coefficient and electrical resistivity, Si nanowire arrays show promise as high-performance, scalable thermoelectric materials.


Editorial: special issue on learning from imbalanced data sets


title: Extreme Value Analysis for Mobile Active User Authentication

abstract: In this paper, we propose to improve the performance of mobile Active Authentication (AA) systems in the low false alarm region using the statistical Extreme Value Theory (EVT). The problem is studied under a Bayesian framework where extremal observations that contribute to mis-verification are given more prominence. We prop~e modeling !he.tail. of the match distribution using a Generalized Pareto Distrfbution (GPD) in order to make better inferences about the extremal observations. A method based on the mean excess function is introduced for parameter estimation of the GPD. Effectiveness of the proposed framework is demonstrated using publicly available unconstrained mobile active authentication datasets. It is shown that the proposed EVT-based method can significantly enhance the performance of traditional AA systems in the low false alarm rate region.


title: Kinematics and the Implementation of an Elephant's Trunk Manipulator and Other Continuum Style Robots

abstract: Traditionally, robot manipulators have been a simple arrangement of a small number of serially connected links and actuated joints. Though these manipulators prove to be very effective for many tasks, they are not without their limitations, due mainly to their lack of maneuverability or total degrees of freedom. Continuum style (i.e., continuous “back-bone”) robots, on the other hand, exhibit a wide range of maneuverability, and can have a large number of degrees of freedom. The motion of continuum style robots is generated through the bending of the robot over a given section; unlike traditional robots where the motion occurs in discrete locations, i.e., joints. The motion of continuum manipulators is often compared to that of biological manipulators such as trunks and tentacles. These continuum style robots can achieve motions that could only be obtainable by a conventionally designed robot with many more degrees of freedom. In this paper we present a detailed formulation and explanation of a novel kinematic model for continuum style robots. The design, construction, and implementation of our continuum style robot called the elephant trunk manipulator is presented. Experimental results are then provided to verify the legitimacy of our model when applied to our physical manipulator. We also provide a set of obstacle avoidance experiments that help to exhibit the practical implementation of both our manipulator and our kinematic model. © 2003 Wiley Periodicals, Inc.


Hand and Finger Motion-Controlled Audio Mixing Interface


title: Big data analytics of IoT based Health care monitoring system

abstract: New prospects for health care monitoring is engendered by the augmentation of Internet of Things (IoT) and Big data, as well as the ubiquitous nature of tiny wearable bio sensors. Many confront have yet to be addressed to create a dependable and flexible system for health care monitoring. In this paper Big data analytics on IoT based Health care system is proposed. IoT based health care monitoring system comprises of “Internet of health sensor things”. These things produce huge volumes of data that could not be handled by the physician. The Physicians important concern is that they need to make critical decisions about their patient's health from these huge volumes of health information. He/she has to segregate the information about one particular patient from the flood of health care information arriving from the massive number of patients. Intel Galileo Gen 2 is acting as IoT agent in this paper and is used to deploy the health information of patients into the Cloud. The Cloud could manage the increasing volume of health data, resourcefully share the information across healthcare systems and provide viability for Big data analytics. Real time alerting of patient health information is an important exercise in Big data which is crucial in the proposed work. The Big health sensor data are analyzed using the Hadoop framework. Since the response time of the proposed system is less, it is suitable for real time alerting.


title: Feedforward contraction of transversus abdominis is not influenced by the direction of arm movement

abstract: Because the structure of the spine is inherently unstable, muscle activation is essential for the maintenance of trunk posture and intervertebral control when the limbs are moved. To investigate how the central nervous system deals with this situation the temporal components of the response of the muscles of the trunk were evaluated during rapid limb movement performed in response to a visual stimulus. Fine-wire electromyography (EMG) electrodes were inserted into transversus abdominis (TrA), obliquus internus abdominis (OI) and obliquus externus abdominis (OE) of 15 subjects under the guidance of real-time ultrasound imaging. Surface electrodes were placed over rectus abdominis (RA), lumbar multifidus (MF) and the three parts of deltoid. In a standing position, ten repetitions of shoulder flexion, abduction and extension were performed by the subjects as fast as possible in response to a visual stimulus. The onset of TrA EMG occurred in advance of deltoid irrespective of the movement direction. The time to onset of EMG activity of OI, OE, RA and MF varied with the movement direction, being activated earliest when the prime action of the muscle opposed the reactive forces associated with the specific limb movement. It is postulated that the non-direction-specific contraction of TrA may be related to the control of trunk stability independent of the requirement for direction-specific control of the centre of gravity in relation to the base of support.


title: Towards 5G Enabled Tactile Robotic Telesurgery

abstract: Robotic telesurgery has a potential to provide extreme and urgent health care services and bring unprecedented opportunities to deliver highly specialized skills globally. It has a significant societal impact and is regarded as one of the appealing use cases of Tactile Internet and 5G applications. However, the performance of robotic telesurgery largely depends on the network performance in terms of latency, jitter and packet loss, especially when telesurgical system is equipped with haptic feedback. This imposes significant challenges to design a reliable and secure but cost-effective communication solution. This article aims to give a better understanding of the characteristics of robotic telesurgical system, and the limiting factors, the possible telesurgery services and the communication quality of service (QoS) requirements of the multi-modal sensory data. Based on this, a viable network architecture enabled by the converged edge and core cloud is presented and the relevant research challenges, open issues and enabling technologies in the 5G communication system are discussed.


title: Commanding a Brain-Controlled Wheelchair Using Steady-State Somatosensory Evoked Potentials

abstract: In this work, we propose a novel brain-controlled wheelchair, one of the major applications of brain–machine interfaces (BMIs), that allows an individual with mobility impairments to perform daily living activities independently. Specifically, we propose to use a steady-state somatosensory evoked potential (SSSEP) paradigm, which elicits brain responses to tactile stimulation of specific frequencies, for a user’s intention to control a wheelchair. In our system, a user had three possible commands by concentrating on one of three vibration stimuli, which were attached to the left-hand, right-hand, and right-foot, to selectively control the wheelchair. The three stimuli were associated with three wheelchair commands: turn-left, turn-right, and move-forward. From a machine learning perspective, we also devise a novel feature representation by combining spatial and spectral characteristics of brain signals. In order to validate the effectiveness of the proposed SSSEP-based system, we considered two different tasks: 1) a simple obstacle-avoidance task within a limited time and; 2) a driving task along the predefined trajectory of about 40 m length, where there were a narrow pathway, a door, and obstacles. In both experiments, we recruited 12 subjects and compared the average time of motor imagery (MI) and SSSEP-based controls to complete the task. With the SSSEP-based control, all subjects successfully completed the task without making any collision while four subjects failed it with MI-based control. It is also noteworthy that in terms of the average time to complete the task, the SSSEP-based control outperformed the MI-based control. In the other more challenging task, all subjects successfully reached the target location.


title: Size Estimation of Cloud Migration Projects with Cloud Migration Point (CMP)

abstract: One major obstacle to enterprise adoption of cloud technologies has been the lack of visibility into migration effort and cost. In this paper, we present a methodology, called Cloud Migration Point (CMP), for estimating the size of cloud migration projects, by recasting a well-known software size estimation model called Function Point (FP) into the context of cloud migration. We empirically evaluate our CMP model by performing a cross-validation on six different small-scale cloud migration projects and show that our size estimation model can be used as a reliable predictor for effort estimation. Furthermore, we prove that our CMP model satisfies the fundamental properties of a software size measure.


title: Automatic modeling of virtual humans and body clothing

abstract: Highly realistic virtual human models are rapidly becoming commonplace in computer graphics. These models, often represented by complex shape and requiring labor-intensive process, challenge the problem of automatic modeling. The problem and solutions to automatic modeling of animatable virtual humans are studied. Methods for capturing the shape of real people, parameterization techniques for modeling static shape (the variety of human body shapes) and dynamic shape (how the body shape changes as it moves) of virtual humans are classified, summarized and compared. Finally, methods for clothed virtual humans are reviewed.


Flex And Pinch: A Case Study Of Whole Hand Input Design For Virtual Environment Interaction


title: Easy freshness with Pequod cache joins

abstract: Pequod is a distributed application-level key-value cache that supports declaratively defined, incrementally maintained, dynamic, partially-materialized views. These views, which we call cache joins, can simplify application development by shifting the burden of view maintenance onto the cache. Cache joins define relationships among key ranges; using cache joins, Pequod calculates views on demand, incrementally updates them as required, and in many cases improves performance by reducing client communication. To build Pequod, we had to design a view abstraction for volatile, relationless key-value caches and make it work across servers in a distributed system. Pequod performs as well as other in-memory key-value caches and, like those caches, outperforms databases with view support.


Chapter 18 Scripting Group Processes in VMT


title: A Survey of Interaction Techniques for Interactive 3D Environments

abstract: Various interaction techniques have been developed for interactive 3D environments. This paper presents an up-to-date and comprehensive review of the state of the art of non-immersive interaction techniques for Navigation, Selection & Manipulation, and System Control, including a basic introduction to the topic, the challenges, and an examination of a number of popular approaches. We hope that this survey can aid both researchers and developers of interactive 3D applications in having a clearer overview of the topic and in particular can be useful for practitioners and researchers that are new to the field of interactive 3D graphics.


title: Bootstrapping Semantic Parsers from Conversations

abstract: Conversations provide rich opportunities for interactive, continuous learning. When something goes wrong, a system can ask for clarification, rewording, or otherwise redirect the interaction to achieve its goals. In this paper, we present an approach for using conversational interactions of this type to induce semantic parsers. We demonstrate learning without any explicit annotation of the meanings of user utterances. Instead, we model meaning with latent variables, and introduce a loss function to measure how well potential meanings match the conversation. This loss drives the overall learning approach, which induces a weighted CCG grammar that could be used to automatically bootstrap the semantic analysis component in a complete dialog system. Experiments on DARPA Communicator conversational logs demonstrate effective learning, despite requiring no explicit meaning annotations.


title: Pyramidal neurons: dendritic structure and synaptic integration

abstract: Pyramidal neurons are characterized by their distinct apical and basal dendritic trees and the pyramidal shape of their soma. They are found in several regions of the CNS and, although the reasons for their abundance remain unclear, functional studies--especially of CA1 hippocampal and layer V neocortical pyramidal neurons--have offered insights into the functions of their unique cellular architecture. Pyramidal neurons are not all identical, but some shared functional principles can be identified. In particular, the existence of dendritic domains with distinct synaptic inputs, excitability, modulation and plasticity appears to be a common feature that allows synapses throughout the dendritic tree to contribute to action-potential generation. These properties support a variety of coincidence-detection mechanisms, which are likely to be crucial for synaptic integration and plasticity.


title: Sulphonamides: Deserving class as MMP inhibitors?

abstract: The importance of sulphonamide moiety in medicinal chemistry cannot be ignored as it constitutes an important class of extensively used drugs. Recently, sulphonamides have also been reported for their matrix metalloproteinase (MMP) inhibitory activity. MMPs are calcium- and zinc-dependent endopeptidases, involved in both inter- and intra-cellular activity. This review documents the emergence of sulphonamides as matrix metalloproteinase inhibitors (MMPIs) from the first generation to the recent third generation MMPIs, their mode of action - how sulphonamides act on MMPs? as well as the structure activity relationship along with their therapeutic uses in chronic obstructive pulmonary disease (COPD), ulcer, asthma, arthritis and cancer. From this review, readers can get answer for the question- is sulphonamides a potential class of MMPIs?


Design Considerations and Performance Evaluation of Single-Stage TAIPEI Rectifier for HVDC Distribution Applications


title: An Evaluation of Reading Comprehension of Expository Text in Adults With Traumatic Brain Injury

abstract: Purpose This project was conducted to obtain information about reading problems of adults with traumatic brain injury (TBI) with mild-to-moderate cognitive impairments and to investigate how these ...


title: Planning and control algorithms for enhanced rough-terrain rover mobility

abstract: Future planetary exploration missions will require rovers to perform difficult tasks in rough terrain, with limited human supervision. Many current motion planning and control algorithms do not consider the physical characteristics of the rover and its environment, which limits their effectiveness in rough terrain. This paper presents an overview of physics-based rover and terrain modeling techniques that allow improved prediction of rough-terrain rover mobility. Kinematic and force models of a rover in rough terrain are presented. A method for on-line estimation of critical terrain physical parameters is presented. A method for on-line estimation of wheel-terrain contact geometry is also presented. It is shown that these modeling techniques can be used in advanced control and motion planning algorithms to improve a rover's rough-terrain mobility.


title: Rebound Modeling of Spinning Ping-Pong Ball Based on Multiple Visual Measurements

abstract: The research on the collision between a spinning ball and a table, as a general issue for the trajectory analysis of the spinning-flying objects, is important but complicated. Traditionally, it is studied either by simplifying the collision process as a black box in which the ball’s motion state changes abruptly before and after collision, or by assuming that the coefficient of restitution and the coefficient of friction are simply piecewise linear or even constant during the whole collision process. In this paper, we first analyze the mechanism of the collision dynamics and conclude from mathematical derivations that the collision between a ping-pong ball and a table is a continuous transition process and its collision duration is a constant irrelevant to the ball’s motion state. These two conclusions are also verified by the visual measurement using an ultrahigh-speed camera. Then, we propose a novel rebound model using the mean value theorem, momentum theorem, and angular momentum theorem. The novelty of the proposed model is that the forces and the parameters in it are formulated as the continuous functions related to the ball’s motion state, which is more accurate, rather than as constants. The integrations of these functions over time can be formulated by using the mean value theorem, and their expressions are learned using multilayer perception with a large data set collected by the position vision system and the pan-tilt vision system. The experimental results verify the effectiveness and accuracy of the proposed model.


title: Dynamic modeling, sensitivity analysis and parameter estimation of industrial yeast fermenters

abstract: Abstract Reliable simulation of industrial scale fermenters is a challenging problem that needs to be solved for effective operation and control of such nonlinear and complex systems. Contrary to the available studies, which mostly rely on laboratory data, this work addresses the development and test of a rigorous dynamic model incorporating two phases and transport terms for industrial fermenters used for baker's yeast production. The influence of various parameters on model behaviour is determined by sensitivity analysis; and inspired by these results, those parameters that strongly effect the biomass production are identified by a parameter estimation technique. Comparison of the model predictions to two sets of actual process data for fed-batch and batch operating conditions verify the validity of model. The results provide valuable insights into the reliable simulation of large scale fermenters, and also bring an effective tool for further studies on optimization and control of such reactors.


title: On-line personalization of a touch screen based keyboard

abstract: The user expectations for usability and personalization along with decreasing size of handheld devices challenge traditional keypad layout design. We have developed a method for on-line adaptation of a touch pad keyboard layout. The method starts from an original layout and monitors the usage of the keyboard by recording and analyzing the keystrokes. An on-line learning algorithm subtly moves the keys according to the spatial distribution of keystrokes. In consequence, the keyboard matches better to the users physical extensions and grasp of the device, and makes the physical trajectories during typing more comfortable. We present two implementations that apply different vector quantization algorithms to produce an adaptive keyboard with visual on-line feedback. Both qualitative and quantitative results show that the changes in the keyboard are consistent, and related to the user's handedness and hand extensions. The testees found the on-line personalization positive. The method can either be applied for on-line personalization of keyboards or for ergonomics research


title: Cover trees for nearest neighbor

abstract: We present a tree data structure for fast nearest neighbor operations in general n-point metric spaces (where the data set consists of n points). The data structure requires O(n) space regardless of the metric's structure yet maintains all performance properties of a navigating net (Krauthgamer & Lee, 2004b). If the point set has a bounded expansion constant c, which is a measure of the intrinsic dimensionality, as defined in (Karger & Ruhl, 2002), the cover tree data structure can be constructed in O (c6n log n) time. Furthermore, nearest neighbor queries require time only logarithmic in n, in particular O (c12 log n) time. Our experimental results show speedups over the brute force search varying between one and several orders of magnitude on natural machine learning datasets.


Fraudes en Internet


title: Nanoparticle assembly and sintering towards all-copper flip chip interconnects

abstract: The current feed capability of typical flip chip electrical interconnects is constrained by the solder alloy, as it is more susceptible to electromigration than the copper used for the pads and wires. Hence, interconnects formed by copper only mitigate the electromigration risk and/or allow to increase the current limit of the all-copper interconnect. In this work, two methods to form all-copper flip chip interconnects at an annealing temperature of 250 °C are presented. The interconnects in the contact region between Cu pillars and Cu pads with a pitch down to 150 µm are formed by Cu nanoparticle self-assembly and sintering. In the first method, the entire gap between a Cu pillar chip and a substrate was filled with a Cu nano-suspension. The formation of capillary bridges during the evaporation of the dispersant directed the self-assembly of the nanoparticles towards the contact region between Cu pillars and Cu pad. In the second method, the Cu pillar chip was dipped into a film of the Cu nano-suspension, followed by a transfer, placement and release with a die bonder onto pads on a substrate. The annealing of the Cu nanoparticles is performed in both cases in a reducing formic acid atmosphere. The first method was more susceptible to the formation of shorts between pillars, whereas the second method resulted in electrical functional chip to substrate assemblies. Interconnects with a mean electrical resistance of 26 ± 3 mΩ and a shear strength ranging from 4.6 to 12.3 MPa were achieved. The sintered Cu nanoparticles bridged gaps up to 10 µm between copper pillars and pads, demonstrating the potential to apply the joint also on non-planar substrates. Nevertheless, imperfections such as voids and cracks are still present in the joints and need further process development, to improve the quality and process robustness further.


title: Screen Space Rendering Solution for Multiphase SPH Simulation

abstract: Fluid simulation using meshless methods has increasingly become a robust way to solve mechanics problems that require dealing with large deformations, and has become very popular in many applications such as naval engineering, mechanical engineering, movies and games. One of the main methods is the Smoothed Particle Hydrodynamics (SPH). This work has two main goals: to propose a multiphase SPH formulation by extending the work of Silva et al. and to propose a shader based render solution for this kind of simulation. The proposed SPH method was able to simulate multiphase fluids with up to one million particles and the renderer was able to generate visually plausible results up to 60fps.


title: Online 6 DOF augmented reality registration from natural features

abstract: We present a complete scalable system for 6 DOF camera tracking based on natural features. Crucially, the calculation is based only on pre-captured reference images and previous estimates of the camera pose and is hence suitable for online applications. We match natural features in the current frame to two spatially separated reference images. We overcome the wide baseline matching problem by matching to the previous frame and transferring point positions to the reference images. We then minimize deviations from the two-view and three-view constraints between the reference images and the current frame as a function of camera position parameters. We stabilize this calculation using a recursive form of temporal regularization that is similar in spirit to the Kalman filter. We can track camera pose over hundreds of frames and realistically integrate virtual objects with only slight jitter.


title: Cell-Phone–Induced Driver Distraction

abstract: Our research examined the effects of hands-free cell-phone conversations on simulated driving. We found that even when participants looked directly at objects in the driving environment, they were less likely to create a durable memory of those objects if they were conversing on a cell phone. This pattern was obtained for objects of both high and low relevance, suggesting that very little semantic analysis of the objects occurs outside the restricted focus of attention. Moreover, in-vehicle conversations do not interfere with driving as much as cell-phone conversations do, because drivers are better able to synchronize the processing demands of driving with in-vehicle conversations than with cell-phone conversations. Together, these data support an inattention-blindness interpretation wherein the disruptive effects of cell-phone conversations on driving are due in large part to the diversion of attention from driving to the phone conversation.


title: Empowering one-vs-one decomposition with ensemble learning for multi-class imbalanced data

abstract: Extending binary ensemble techniques to multi-class imbalanced data.OVO scheme enhancement for multi-class imbalanced data by ensemble learning.A complete experimental study of comparison of the ensemble learning techniques with OVO.Study of the impact of base classifiers used in the proposed scenario. Multi-class imbalance classification problems occur in many real-world applications, which suffer from the quite different distribution of classes. Decomposition strategies are well-known techniques to address the classification problems involving multiple classes. Among them binary approaches using one-vs-one and one-vs-all has gained a significant attention from the research community. They allow to divide multi-class problems into several easier-to-solve two-class sub-problems. In this study we develop an exhaustive empirical analysis to explore the possibility of empowering the one-vs-one scheme for multi-class imbalance classification problems with applying binary ensemble learning approaches. We examine several state-of-the-art ensemble learning methods proposed for addressing the imbalance problems to solve the pairwise tasks derived from the multi-class data set. Then the aggregation strategy is employed to combine the binary ensemble outputs to reconstruct the original multi-class task. We present a detailed experimental study of the proposed approach, supported by the statistical analysis. The results indicate the high effectiveness of ensemble learning with one-vs-one scheme in dealing with the multi-class imbalance classification problems.


title: Implementation of Web Crawler

abstract: The World Wide Web is an interlinked collection of billions of documents formatted using HTML. Ironically the very size of this collection has become an obstacle for information retrieval. The user has to shift through scores of pages to come upon the information he/she desires. Web crawlers are the heart of search engines. Web crawlers continuously keep on crawling the web and find any new web pages that have been added to the web, pages that have been removed from the web. Due to growing and dynamic nature of the web; it has become a challenge to traverse all URLs in the web documents and to handle these URLs. A focused crawler is an agent that targets a particular topic and visits and gathers only relevant web pages. In this dissertation I had worked on design and working of web crawler that can be used for copyright infringement. We will take one seed URL as input and search with a keyword, the searching result is based on keyword and it will fetch the web pages where it will find that keyword. This focused based crawler approach retrieve documents that contain particular keyword from the user's query; we are implementing this using breadth-first search. Now, when we retrieved the web pages we will apply pattern recognition over text. We will give one file as input and apply the pattern recognition algorithms. Here, pattern symbolizes text only and check how much text is available on the web page. The algorithms that I had used for pattern search are Knutt-Morri-Pratt, Boyer-Moore, Finite Automata algorithm.


title: Current limitations and opportunities in mobile augmented reality applications

abstract: Mobile AR has evolved from the bulkiness of head-mounted device and backpack device to smart device (smartphone, tablet etc.). To date, the current implementation has made what AR is today. However, the advancement of AR technology has met with limitation and challenges on its own, which resulted in not able to reach mass-market. This paper in turn presents current limitations and challenges that need to overcome. We have done a review based on past research papers on limitation in technical (hardware, algorithms and interaction technique) and non-technical (social acceptance, privacy and usefulness) aspects of developing and implementing mobile augmented reality applications. We also presented some future opportunities in mobile AR applications.


title: FPGA2: An open source framework for FPGA-GPU PCIe communication

abstract: In recent years two main platforms emerged as powerful key players in the domain of parallel computing: GPUs and FPGAs. Many researches investigate interaction and benefits of coupling them with a general purpose processor (CPU), but very few, and only very recently, integrate the two in the same computational system. Even less research are focusing on direct interaction of the two platforms [1]. This paper presents an open source framework enabling easy integration of GPU and FPGA resources; Our work provides direct data transfer between the two platforms with minimal CPU coordination at high data rate and low latency. Finally, at the best of our knowledge, this is the first proposition of an open source implementation of a system including an FPGA and a GPU that provides code for both sides. Notwithstanding the generality of the presented framework, we present in this paper an actual implementation consisting of a single GPU board and a FPGA board connected through a PCIe link. Measures on this implementation demonstrate achieved data rate that are close to the theoretical maximum.


title: Simulation of Multipath Impulse Response for Indoor Wireless Optical Channels

abstract: A recursive method for evaluating the impulse response of an indoor free-space optical channel with Lambertian reflectors is presented. The method, which accounts for multiple reflections of any order, enables accurate analysis of the effects of multipath dispersion on high-speed indoor optical communication systems. A simple algorithm for computer implementation of the technique and computer simulation results for both line-of-sight and diffuse transmitter configurations are also presented. In both cases, it is shown that reflections of multiple order are a significant source of intersymbol interference. Experimental measurements of optical multipath, which help verify the accuracy of the simulations, are discussed. >


title: An Internet of Things (IoT) architecture for embedded appliances

abstract: Although much of the work has been done until today to realize the Internet of Things (IoT) into practice, most of the work focuses on resource-constrained nodes, rather than linking the existing embedded systems to the IoT network. In this paper, we propose the uID-CoAP architecture, a new architecture designed to host IoT services on common embedded systems, like usual consumer appliances. As they often need to provide a number of sophisticated functions compared to simple sensor nodes, we combine the constrained application protocol (CoAP) with the ubiquitous ID (uID) architecture. The latter plays a crucial role for keeping the knowledge and data required for practical complex IoT services. In addition, we provide a software framework for embedded appliance nodes, designed to reduce the burden of embedded appliance manufacturers by providing an intuitive, consistent, and easy-to-use APL Based on this idea, our framework provides functions to build RESTful services in addition to the low-level communication API. We have evaluated our system through a case-study, and showed that our framework can be used effectively to implement practical IoT applications over existing embedded systems with a small programming effort.


title: Visual Analytics of Heterogeneous Data Using Hypergraph Learning

abstract: For real-world learning tasks (e.g., classification), graph-based models are commonly used to fuse the information distributed in diverse data sources, which can be heterogeneous, redundant, and incomplete. These models represent the relations in different datasets as pairwise links. However, these links cannot deal with high-order relations which connect multiple objects (e.g., in public health datasets, more than two patient groups admitted by the same hospital in 2014). In this article, we propose a visual analytics approach for the classification on heterogeneous datasets using the hypergraph model. The hypergraph is an extension to traditional graphs in which a hyperedge connects multiple vertices instead of just two. We model various high-order relations in heterogeneous datasets as hyperedges and fuse different datasets with a unified hypergraph structure. We use the hypergraph learning algorithm for predicting missing labels in the datasets. To allow users to inject their domain knowledge into the model-learning process, we augment the traditional learning algorithm in a number of ways. In addition, we also propose a set of visualizations which enable the user to construct the hypergraph structure and the parameters of the learning model interactively during the analysis. We demonstrate the capability of our approach via two real-world cases.


title: A Chat-Based Group Recommender System for Tourism.

abstract: Group recommender systems are information filtering and decision support applications that are aimed at aiding a group of users in making decisions when they are considering a set of alternatives. State of the art solutions aggregate users’ preferences acquired before the actual decision making process and suggest items that fit the aggregated model. However, it has been shown that the recommendation needs of groups go beyond the identification of such items, and it is essential to take into account, in the recommendation process, the dynamic of users’ interactions in their real group context. In this paper, we therefore illustrate a novel approach to group recommendation, which is implemented in a mobile system, that monitors and exploits users’ interactions during a group discussion, and offers appropriate recommendations as well as other types of suggestions, to guide and help the group members in settling on an agreement. We have carried out a preliminary user study of the proposed approach to assess and analyze the usability of the system along with the perceived recommendation quality and choice satisfaction. The results of this study are encouraging as they show that the proposed approach attains a high usability score, and has good user-perceived recommendation quality as well as choice satisfaction.


title: Visual Teach and Repeat using appearance-based lidar

abstract: Visual Teach and Repeat (VT&R) has proven to be an effective method to allow a vehicle to autonomously repeat any previously driven route without the need for a global positioning system. One of the major challenges for a method that relies on visual input to recognize previously visited places is lighting change, as this can make the appearance of a scene look drastically different. For this reason, passive sensors, such as cameras, are not ideal for outdoor environments with inconsistent/inadequate light. However, camera-based systems have been very successful for localization and mapping in outdoor, unstructured terrain, which can be largely attributed to the use of sparse, appearance-based computer vision techniques. Thus, in an effort to achieve lighting invariance and to continue to exploit the heritage of the appearance-based vision techniques traditionally used with cameras, this paper presents the first VT&R system that uses appearance-based techniques with laser scanners for motion estimation. The system has been field tested in a planetary analogue environment for an entire diurnal cycle, covering more than 11km with an autonomy rate of 99.7% of the distance traveled.


title: Scalable Anomaly Detection for Smart City Infrastructure Networks

abstract: Dynamically detecting anomalies can be difficult in very large-scale infrastructure networks. The authors' approach addresses spatiotemporal anomaly detection in a smarter city context with large numbers of sensors deployed. They propose a scalable, hybrid Internet infrastructure for dynamically detecting potential anomalies in real time using stream processing. The infrastructure enables analytically inspecting and comparing anomalies globally using large-scale array processing. Deployed on a real pipe network topology of 1,891 nodes, this approach can effectively detect and characterize anomalies while minimizing the amount of data shared across the network.


title: The Algorithmic Beauty of Plants

abstract: 1 Graphical modeling using L-systems.- 1.1 Rewriting systems.- 1.2 DOL-systems.- 1.3 Turtle interpretation of strings.- 1.4 Synthesis of DOL-systems.- 1.4.1 Edge rewriting.- 1.4.2 Node rewriting.- 1.4.3 Relationship between edge and node rewriting.- 1.5 Modeling in three dimensions.- 1.6 Branching structures.- 1.6.1 Axial trees.- 1.6.2 Tree OL-systems.- 1.6.3 Bracketed OL-systems.- 1.7 Stochastic L-systems.- 1.8 Context-sensitive L-systems.- 1.9 Growth functions.- 1.10 Parametric L-systems.- 1.10.1 Parametric OL-systems.- 1.10.2 Parametric 2L-systems.- 1.10.3 Turtle interpretation of parametric words.- 2 Modeling of trees.- 3 Developmental models of herbaceous plants.- 3.1 Levels of model specification.- 3.1.1 Partial L-systems.- 3.1.2 Control mechanisms in plants.- 3.1.3 Complete models.- 3.2 Branching patterns.- 3.3 Models of inflorescences.- 3.3.1 Monopodial inflorescences.- 3.3.2 Sympodial inflorescences.- 3.3.3 Polypodial inflorescences.- 3.3.4 Modified racemes.- 4 Phyllotaxis.- 4.1 The planar model.- 4.2 The cylindrical model.- 5 Models of plant organs.- 5.1 Predefined surfaces.- 5.2 Developmental surface models.- 5.3 Models of compound leaves.- 6 Animation of plant development.- 6.1 Timed DOL-systems.- 6.2 Selection of growth functions.- 6.2.1 Development of nonbranching filaments.- 6.2.2 Development of branching structures.- 7 Modeling of cellular layers.- 7.1 Map L-systems.- 7.2 Graphical interpretation of maps.- 7.3 Microsorium linguaeforme.- 7.4 Dryopteris thelypteris.- 7.5 Modeling spherical cell layers.- 7.6 Modeling 3D cellular structures.- 8 Fractal properties of plants.- 8.1 Symmetry and self-similarity.- 8.2 Plant models and iterated function systems.- Epilogue.- Appendix A Software environment for plant modeling.- A.1 A virtual laboratory in botany.- A.2 List of laboratory programs.- Appendix B About the figures.- Turtle interpretation of symbols.


title: Can transcranial electrical stimulation improve learning difficulties in atypical brain development? A future possibility for cognitive training

abstract: Learning difficulties in atypical brain development represent serious obstacles to an individual's future achievements and can have broad societal consequences. Cognitive training can improve learning impairments only to a certain degree. Recent evidence from normal and clinical adult populations suggests that transcranial electrical stimulation (TES), a portable, painless, inexpensive, and relatively safe neuroenhancement tool, applied in conjunction with cognitive training can enhance cognitive intervention outcomes. This includes, for instance, numerical processing, language skills and response inhibition deficits commonly associated with profound learning difficulties and attention-deficit hyperactivity disorder (ADHD). The current review introduces the functional principles, current applications and promising results, and potential pitfalls of TES. Unfortunately, research in child populations is limited at present. We suggest that TES has considerable promise as a tool for increasing neuroplasticity in atypically developing children and may be an effective adjunct to cognitive training in clinical settings if it proves safe. The efficacy and both short- and long-term effects of TES on the developing brain need to be critically assessed before it can be recommended for clinical settings.


SecureNN: Efficient and Private Neural Network Training


title: Sentiments Analysis in GitHub Repositories: An Empirical Study

abstract: There have been more than 12 million open source software in GitHub so far, but the existing studies about sentiments analysis in open source software are not sufcient for the sentiments analysis in GitHub. This paper proposes an approach to analyze the correlation of comment sentiments and bug fixing speed. It's proved the existence of certain factors among some relevance after experiments. The experimental results also give some suggestions on GitHub open source software development process.


title: Memory management for billions of small objects in a distributed in-memory storage

abstract: Large-scale interactive applications and online analytic processing on graphs require fast data access to huge sets of small data objects. DXRAM addresses these challenges by keeping all data always in memory of potentially many nodes aggregated in a data center. In this paper we focus on the efficient memory management and mapping of global IDs to local memory addresses, which is not trivial as each node may store up to one billion of small data objects (16–64 byte) in its local memory. We present an efficient paging-like translation scheme for global IDs and a memory management optimized for many small data objects. The latter includes an efficient incremental defragmentation supporting changing allocation granularities for dynamic data. Our evaluations show that the proposed memory management approach has only a 4–5% overhead compared to state of the art memory allocators with around 20% and the paging-like mapping of globals IDs is faster and more efficient than hash-table based approaches. Furthermore, we compare memory overhead and read performance of DXRAM with RAMCloud.


title: Sentiment-Based Features for Predicting Election Polls: A Case Study on the Brazilian Scenario

abstract: The success of opinion mining for automatically processing vast amounts of opinionated content available on the Internet has been demonstrated as a less expensive and lower latency solution for gathering public opinion. In this paper, we investigate whether it is possible to predict variations in vote intention based on sentiment time series extracted from news comments, using three Brazilian elections as case study. The contributions of this case study are: a) the comparison of two approaches for opinion mining in user-generated content in Brazilian Portuguese, b) the proposition of two types of features to represent sentiment behavior towards political candidates that can be used for prediction, c) an approach to predict polls vote intention variations that is adequate for scenarios of sparse data. We developed experiments to assess the influence on the forecasting accuracy of the proposed features, and their respective preparation. Our results display an accuracy of 70% in predicting positive and negative variations. These are important contributions towards a more general framework that is able to blend opinions from several different sources to find representativeness of the target population, and make more reliable predictions.


title: Asymptotic Behavior Of A Markovian Stochastic Algorithm With Constant Step

abstract: We first derive from abstract results on Feller transition kernels that, under some mild assumptions, a Markov stochastic algorithm with constant step size $\varepsilon$ usually has a tight family of invariant distributions $\nu^{\varepsilon}$, $\varepsilon \in(0,\varepsilon_0]$, whose weak limiting distributions as $\varepsilon\downarrow 0$ are all flow-invariant for its ODE. Then the main part of the paper deals with a kind of converse: what are the possible limiting distributions among all flow-invariant distributions of the ODE? We first show that no repulsive invariant (thin) set can belong to their supports. When the ODE is a stochastic pseudogradient descent, these supports cannot contain saddle or spurious equilibrium points either, so that they are eventually supported by the set of local minima of their potential. Such results require only the random perturbation to lie in L2. Various examples are treated, showing that these results yield some weak convergence results for the $\nu^{\varepsilon}$'s, sometimes toward a saddle point when the algorithm is not a pseudogradient.


title: Control of a mobile inverted pendulum robot system

abstract: In this paper, the mobile inverted pendulum robot system is developed and controlled. Kinematics and dynamics are derived. Frequency responses of a gyro sensor and a tilt sensor are analyzed. A sensor fusion technique is used to compensate for the gyro sensor drift. Instead of using expensive sensors, a cost effective gyro sensor and a tilt sensor are fused together by filtering to estimate angle of the pendulum. For experimental studies, circular trajectory tracking control has been performed experimentally.


title: First analysis and experiments in aerial manipulation using fully actuated redundant robot arm

abstract: In this paper we describe a system for aerial manipulation composed of a helicopter platform and a fully actuated seven Degree of Freedom (DoF) redundant industrial robotic arm. We present the first analysis of such kind of systems and show that the dynamic coupling between helicopter and arm can generate diverging oscillations with very slow frequency which we called phase circles. Based on the presented analysis, we propose a control approach for the whole system. The partial decoupling between helicopter and arm - which eliminates the phase circles - is achieved by means of special movement of robotic arm utilizing its redundant DoF. For the underlying arm control a specially designed impedance controller was proposed. In different flight experiments we showcase that the proposed kind of system type might be used in the future for practically relevant tasks. In an integrated experiment we demonstrate a basic manipulation task - impedance based grasping of an object from the environment underlaying a visual object tracking control loop.


title: Bayesian integration in sensorimotor learning

abstract: When we learn a new motor skill, such as playing an approaching tennis ball, both our sensors and the task possess variability. Our sensors provide imperfect information about the ball's velocity, so we can only estimate it. Combining information from multiple modalities can reduce the error in this estimate1,2,3,4. On a longer time scale, not all velocities are a priori equally probable, and over the course of a match there will be a probability distribution of velocities. According to bayesian theory5,6, an optimal estimate results from combining information about the distribution of velocities—the prior—with evidence from sensory feedback. As uncertainty increases, when playing in fog or at dusk, the system should increasingly rely on prior knowledge. To use a bayesian strategy, the brain would need to represent the prior distribution and the level of uncertainty in the sensory feedback. Here we control the statistical variations of a new sensorimotor task and manipulate the uncertainty of the sensory feedback. We show that subjects internally represent both the statistical distribution of the task and their sensory uncertainty, combining them in a manner consistent with a performance-optimizing bayesian process4,5. The central nervous system therefore employs probabilistic models during sensorimotor learning.


title: A primer on the GCF Repo® Service

abstract: This primer provides a detailed description of the GCF Repo® Service, a financial service provided by the Fixed Income Clearing Corporation. The primer is composed of an introductory note and two separate papers. {{p}} The first paper focuses on the clearance and settlement of GCF Repo. These financial plumbing details are especially important because the settlement of GCF Repo has been and will continue to be impacted by the current reforms to the tri-party repo settlement platform. In particular, the authors lay out the various ways that intraday credit was used pre-reform to facilitate the settlement of GCF Repo and why this use of credit is problematic. They also describe the reforms that are planned or in effect already, and consider how these reforms affect the use of intraday credit. {{p}} The second paper examines how dealers use the GCF Repo Service. The authors first describe the various strategies that dealers employ when trading GCF Repo and then use empirical analysis to quantify the predominance of these strategies. {{p}} Although they focus on different aspects of the GCF Repo Service, the two papers are complementary. This is because the strategies that dealers follow in trading GCF Repos are influenced by the clearance and settlement procedures in place. Furthermore, when gauging the risks of potential changes to the clearance and settlement of GCF Repo, it is important to take into account how GCF Repos are traded. Consequently, the papers are presented jointly so as to encourage readers to become familiar with both aspects of the GCF Repo Service.


title: How can we improve school discipline

abstract: School discipline addresses schoolwide, classroom, and individual student needs through broad prevention, targeted intervention, and development of self-discipline. Schools often respond to disruptive students with exclusionary and punitive approaches that have limited value. This article surveys three approaches to improving school discipline practices and student behavior: ecological approaches to classroom management; schoolwide positive behavioral supports; and social and emotional learning. The article examines their epistemological and empirical roots and supporting research, suggesting ways to combine approaches.


title: Biochemical method for inserting new genetic information into DNA of Simian Virus 40: circular SV40 DNA molecules containing lambda phage genes and the galactose operon of Escherichia coli.

abstract: We have developed methods for covalently joining duplex DNA molecules to one another and have used these techniques to construct circular dimers of SV40 DNA and to insert a DNA segment containing lambda phage genes and the galactose operon of E. coli into SV40 DNA. The method involves: (a) converting circular SV40 DNA to a linear form, (b) adding single-stranded homodeoxypolymeric extensions of defined composition and length to the 3' ends of one of the DNA strands with the enzyme terminal deoxynucleotidyl transferase (c) adding complementary homodeoxypolymeric extensions to the other DNA strand, (d) annealing the two DNA molecules to form a circular duplex structure, and (e) filling the gaps and sealing nicks in this structure with E. coli DNA polymerase and DNA ligase to form a covalently closed-circular DNA molecule.


title: Low-Cost X/Ku/Ka-Band Dual-Polarized Array With Shared Aperture

abstract: This paper presents a novel tri-band (X/Ku/Ka-band) planar antenna array with dual polarizations and shared aperture. Compared with traditional dual-polarized arrays, the proposed array has advantages of low cost, low profile, and high integration. Three types of antennas resonating at different frequencies, including the perforated patch, stacked patch, and slim crosspatch, are innovatively interleaved in the same aperture. The crosspatch fed by proximity coupling is presented as Ku-band element for its advantages of compact size, high isolation, and pure polarization. The techniques such as series feed and reverse feed are utilized to implement the six feed networks in a compact size with reduced cross polarizations. Measured results agree well with the simulations, showing three operation bands at X-, Ku-, and Ka-bands with the bandwidths of 3.6%, 6.7%, and 5.3%, respectively. The antenna also exhibits excellent radiation performance with the cross-polarization discrimination over 25 dB at the three bands. To the best of the author’s knowledge, this is the first shared-aperture X/Ku/Ka-band dual-polarized antenna array reported, which is useful for potential synthetic aperture radar applications.


title: The Effect of Cognitive-Behavioral Consultation on Sexual Function among Women: a Randomized Clinical Trial

abstract: Introduction: Sexual dysfunction is one of the most common causes of family breakdowns. In recent years, various approaches have been proposed to resolve this issue. The present study was carried out in order to examine the effect of cognitive-behavioral consultation on sexual function among women who had referred to health centers in Hamadan. Method: A randomized clinical trial was conducted in two groups. It focused on 198 qualified women who had referred to selected health centers of Hamadan in 2016. The participants were selected by simple random sampling (99 people in each group).The intervention group received four 2-hour sessions of cognitive-behavioral group consultation. The required data were collected using a questionnaire of demographic characteristics and Female Sexual Function Index (FSFI). For ethical considerations, by the end of the study, sexual counseling for the control group was provided. To analyze the collected data, SPSS 13 was employed; t-test was used for independent samples. ANCOVA was also utilized. The significance level was set at 0.05. Results: The results showed that the two groups were not significantly different in terms of mean pretest scores in all domains, except for sexual satisfaction and total sexual function. However, after cognitive-behavioral consultation sessions, the intervention group experienced a significant increase in all behavioral domains and total sexual function as compared to the control group. Conclusion: Cognitive-behavioral consultation among women can play a positive and effective role in improving sexual relationships. Therefore, the effectiveness of this type of consultation can be confirmed with more certainty.


title: Stochastic Contextual Bandits with Known Reward Functions

abstract: Many sequential decision-making problems in communication networks can be modeled as contextual bandit problems, which are natural extensions of the well-known multi-armed bandit problem. In contextual bandit problems, at each time, an agent observes some side information or context, pulls one arm and receives the reward for that arm. We consider a stochastic formulation where the context-reward tuples are independently drawn from an unknown distribution in each trial. Motivated by networking applications, we analyze a setting where the reward is a known non-linear function of the context and the chosen arm's current state. We first consider the case of discrete and finite context-spaces and propose DCB($\epsilon$), an algorithm that we prove, through a careful analysis, yields regret (cumulative reward gap compared to a distribution-aware genie) scaling logarithmically in time and linearly in the number of arms that are not optimal for any context, improving over existing algorithms where the regret scales linearly in the total number of arms. We then study continuous context-spaces with Lipschitz reward functions and propose CCB($\epsilon, \delta$), an algorithm that uses DCB($\epsilon$) as a subroutine. CCB($\epsilon, \delta$) reveals a novel regret-storage trade-off that is parametrized by $\delta$. Tuning $\delta$ to the time horizon allows us to obtain sub-linear regret bounds, while requiring sub-linear storage. By exploiting joint learning for all contexts we get regret bounds for CCB($\epsilon, \delta$) that are unachievable by any existing contextual bandit algorithm for continuous context-spaces. We also show similar performance bounds for the unknown horizon case.


title: Deep Watershed Transform for Instance Segmentation

abstract: Most contemporary approaches to instance segmentation use complex pipelines involving conditional random fields, recurrent neural networks, object proposals, or template matching schemes. In this paper, we present a simple yet powerful end-to-end convolutional neural network to tackle this task. Our approach combines intuitions from the classical watershed transform and modern deep learning to produce an energy map of the image where object instances are unambiguously represented as energy basins. We then perform a cut at a single energy level to directly yield connected components corresponding to object instances. Our model achieves more than double the performance over the state-of-the-art on the challenging Cityscapes Instance Level Segmentation task.


title: A fast and efficient semantic short text similarity metric

abstract: The semantic comparison of short sections of text is an emerging aspect of Natural Language Processing (NLP). In this paper we present a novel Short Text Semantic Similarity (STSS) method, Lightweight Semantic Similarity (LSS), to address the issues that arise with sparse text representation. The proposed approach captures the semantic information contained when comparing text to process the similarity. The methodology combines semantic term similarities with a vector similarity method used within statistical analysis. A modification of the term vectors using synset similarity values addresses issues that are encountered with sparse text. LSS is shown to be comparable to current semantic similarity approaches, LSA and STASIS, whilst having a lower computational footprint.


title: PERSONALITY AND PERFORMANCE 9 Personality and Performance at the Beginning of the New Millennium: What Do We Know and Where Do We Go Next?

abstract: As we begin the new millennium, it is an appropriate time to examine what we have learned about personality-performance relationships over the past century and to embark on new directions for research. In this study we quantitatively summarize the results of 15 prior meta-analytic studies that have investigated the relationship between the Five Factor Model (FFM) personality traits and job performance. Results support the previous findings that conscientiousness is a valid predictor across performance measures in all occupations studied. Emotional stability was also found to be a generalizable predictor when overall work performance was the criterion, but its relationship to specific performance criteria and occupations was less consistent than was conscientiousness. Though the other three Big Five traits (extraversion, openness and agreeableness) did not predict overall work performance, they did predict success in specific occupations or relate to specific criteria. The studies upon which these results are based comprise most of the research that has been conducted on this topic in the past century. Consequently, we call for a moratorium on meta-analytic studies of the type reviewed in our study and recommend that researchers embark on a new research agenda designed to further our understanding of personalityperformance linkages.


PersoNER: Persian Named-Entity Recognition


title: Alternatives to Peer Review: Novel Approaches for Research Evaluation

abstract: In this paper we review several novel approaches for research evaluation. We start with a brief overview of the peer review, its controversies, and metrics for assessing efficiency and overall quality of the peer review. We then discuss five approaches, including reputation-based ones, that come out of the research carried out by the LiquidPub project and research groups collaborated with LiquidPub. Those approaches are alternative or complementary to traditional peer review. We discuss pros and cons of the proposed approaches and conclude with a vision for the future of the research evaluation, arguing that no single system can suit all stakeholders in various communities.


title: Characterization of UWB transmit-receive antenna system

abstract: An ultra-wideband (UWB), stripline-fed Vivaldi antenna is characterized both numerically and experimentally. Three-dimensional far-field measurements are conducted and accurate antenna gain and efficiency as well as gain variation versus frequency in the boresight direction are measured. Using two Vivaldi antennas, a free-space communication link is set up. The impulse response of the cascaded antenna system is obtained using full-wave numerical electromagnetic time-domain simulations. These results are compared with frequency-domain measurements using a network analyzer. Full-wave numerical simulation of the free-space channel is performed using a two step process to circumvent the computationally intense simulation problem. Vector transfer function concept is used to obtain the overall system transfer function and the impulse response.


title: Estimating average growth trajectories in shape-space using kernel smoothing

abstract: In this paper, we show how a dense surface point distribution model of the human face can be computed and demonstrate the usefulness of the high-dimensional shape-space for expressing the shape changes associated with growth and aging. We show how average growth trajectories for the human face can be computed in the absence of longitudinal data by using kernel smoothing across a population. A training set of three-dimensional surface scans of 199 male and 201 female subjects of between 0 and 50 years of age is used to build the model.


title: Space-time block coding for wireless communications: performance results

abstract: We document the performance of space-time block codes, which provide a new paradigm for transmission over Rayleigh fading channels using multiple transmit antennas. Data is encoded using a space-time block code, and the encoded data is split into n streams which are simultaneously transmitted using n transmit antennas. The received signal at each receive antenna is a linear superposition of the n transmitted signals perturbed by noise. Maximum likelihood decoding is achieved in a simple way through decoupling of the signals transmitted from different antennas rather than joint detection. This uses the orthogonal structure of the space-time block code and gives a maximum likelihood decoding algorithm which is based only on linear processing at the receiver. We review the encoding and decoding algorithms for various codes and provide simulation results demonstrating their performance. It is shown that using multiple transmit antennas and space-time block coding provides remarkable performance at the expense of almost no extra processing.


title: Aggregate Query Answering on Anonymized Tables

abstract: Privacy is a serious concern when microdata need to be released for ad hoc analyses. The privacy goals of existing privacy protection approaches (e.g., k-anonymity and l-diversity) are suitable only for categorical sensitive attributes. Since applying them directly to numerical sensitive attributes (e.g., salary) may result in undesirable information leakage, we propose privacy goals to better capture the need of privacy protection for numerical sensitive attributes. Complementing the desire for privacy is the need to support ad hoc aggregate analyses over microdata. Existing generalization-based anonymization approaches cannot answer aggregate queries with reasonable accuracy. We present a general framework of permutation-based anonymization to support accurate answering of aggregate queries and show that, for the same grouping, permutation-based techniques can always answer aggregate queries more accurately than generalization-based approaches. We further propose several criteria to optimize permutations for accurate answering of aggregate queries, and develop efficient algorithms for each criterion.


title: A study on exception detection and handling using aspect-oriented programming

abstract: Aspect oriented programming (AOP) is intended to ease situations that involve many kinds of code tangling. The paper reports on a study to investigate AOP's ability to ease tangling related to exception detection and handling. We took an existing framework written in Java/sup TM/, the JWAM framework, and partially reengineered its exception detection and handling aspects using AspectJ/sup TM/, an aspect oriented programming extension to Java. We found that AspectJ supported implementations that drastically reduced the portion of the code related to exception detection and handling. In one scenario, we were able to reduce that code by a factor of 4. We also found that, with respect to the original implementation in plain Java, AspectJ provided better support for different configurations of exceptional behaviors, more tolerance for changes in the specifications of exceptional behaviors, better support for incremental development, better reuse, automatic enforcement of contracts in applications that use the framework, and cleaner program texts. We also found some weaknesses of AspectJ that should be addressed in the future.


title: TripleGeo-CSW: A Middleware for Exposing Geospatial Catalogue Services on the Semantic Web

abstract: A wealth of data and services are available on the Web, and often have geographical context as well. But the vast quantity of offered geospatial information is rather difficult to explore, and its quality hard to assess, due to lack of sufficient metadata. Hence, the Open Geospatial Consortium has specified application profiles for publishing, accessing, and searching over collections of spatial metadata with standardized Catalogue Services for the Web (CSW). Unfortunately, existing spatial metadata remain largely unexploited by Semantic Web technologies. In this paper, we introduce TripleGeo-CSW, a middleware that can be used to discover metadata from existing CSWs through a virtual SPARQL endpoint. Acting as broker between a request (in SPARQL) and catalogue services (in XML/GML), this platform can provide on-the-fly information (as RDF triples) on available geodata according to multiple, user-specified criteria (e.g., area of interest, date of last update, keywords). As a proof of concept, we have set up an instance of this middleware against CSWs from public authorities across Europe, which involve datasets complying with the EU INSPIRE Directive. Our experience testifies that TripleGeo-CSW can assist stakeholders to repurpose existing CSWs with minimal overhead and readily expose spatial metadata on the Semantic Web.


title: Retouched bloom filters: allowing networked applications to trade off selected false positives against false negatives

abstract: Where distributed agents must share voluminous set membership information, Bloom filters provide a compact, though lossy, way for them to do so. Numerous recent networking papers have examined the trade-offs between the bandwidth consumed by the transmission of Bloom filters, and the error rate, which takes the form of false positives, and which rises the more the filters are compressed. In this paper, we introduce the retouched Bloom filter (RBF), an extension that makes the Bloom filter more flexible by permitting the removal of selected false positives at the expense of generating random false negatives. We analytically show that RBFs created through a random process maintain an overall error rate, expressed as a combination of the false positive rate and the false negative rate, that is equal to the false positive rate of the corresponding Bloom filters. We further provide some simple heuristics that decrease the false positive rate more than than the corresponding increase in the false negative rate, when creating RBFs. Finally, we demonstrate the advantages of an RBF over a Bloom filter in a distributed network topology measurement application, where information about large stop sets must be shared among route tracing monitors.


title: Model Composition from Interchangeable Components

abstract: Following the increasing demand to make the creation and manipulation of 3D geometry simpler and more accessible, we introduce a modeling approach that allows even novice users to create sophisticated models in minutes. Our approach is based on the observation that in many modeling settings users create models which belong to a small set of model classes, such as humans or quadrupeds. The models within each class typically share a common component structure. Following this observation, we introduce a modeling system which utilizes this common component structure allowing users to create new models by shuffling interchangeable components between existing models. To enable shuffling, we develop a method for computing a compatible segmentation of input models into meaningful, interchangeable components. Using this segmentation our system lets users create new models with a few mouse clicks, in a fraction of the time required by previous composition techniques. We demonstrate that the shuffling paradigm allows for easy and fast creation of a rich geometric content.


title: Analysis and Design for Intrusion Detection System Based on Data Mining

abstract: Network and host Intrusion Detection Systems (IDS) have become a standard component in security infrastructures. As the action of intrusion represents variable, complicated, and uncertainty characteristic, they face so many problems to resolve for intrusion detection. Each approach has its strengths and weaknesses. A truly effective intrusion detection system will employ both technologies. We discusses the differences in host- and network-based intrusion detection techniques to demonstrate how the two can work together to provide additionally effective intrusion detection and protection. We propose a hybrid IDS, which combines network and host IDS, with anomaly and misuse detection mode, utilizes auditing programs to extract an extensive set of features that describe each network connection or host session, and applies data mining programs to learn rules that accurately capture the behavior of intrusions and normal activities.


title: XES Tensorflow - Process Prediction using the Tensorflow Deep-Learning Framework

abstract: Predicting the next activity of a running process is an important aspect of process management. Recently, artificial neural networks, so called deep-learning approaches, have been proposed to address this challenge. This demo paper describes a software application that applies the Tensorflow deep-learning framework to process prediction. The software application reads industry-standard XES files for training and presents the user with an easy-to-use graphical user interface for both training and prediction. The system provides several improvements over earlier work. This demo paper focuses on the software implementation and describes the architecture and user interface.


INTEGRATING BLOCKCHAIN WITH ERP FOR A TRANSPARENT SUPPLY CHAIN


title: Intelligent speed bump system with dynamic license plate recognition

abstract: We present an intelligent bump system incorporated with the speed detection and dynamic license plate recognition techniques. The idea is to determine the enforcement of the speed bump based on the speed and identification of the vehicle. As a part of intelligent transportation systems for providing the traffic flow control, the proposed technique makes the driving safer and more comfortable. In the dynamic license plate recognition stage, the adaptive tracking is carried out for license plate allocation, followed by character segmentation and identification. "Tesseract OCR" is then used to construct sample images and perform the character recognition task. The experiments are carried out on a prototype system with embedded computation to demonstrate the feasibility of this work.


title: Summary Analysis of the 2017 GitHub Open Source Survey

abstract: This report is a high-level summary analysis of the 2017 GitHub Open Source Survey dataset, presenting frequency counts, proportions, and frequency or proportion bar plots for every question asked in the survey.


title: Consumer Behavior in Online Auctions: an Examination of Partitioned Prices on EBay

abstract: eBay is one of the most successful consumer-related online auction sites. This paper describes a field study to determine the effects of partitioned prices—that is, separate item costs and shipping charges—on the winning bids submitted by consumers on this site. Consumers appear to be subject to an anchoring and adjustment heuristic that leads many to ignore the less-salient shipping costs when bidding. Even experienced bidders exhibit these effects. This behavior is particularly striking in an environment where sellers set variable shipping costs. The results have implications for consumers and sellers on eBay and similar online auction sites that allow sellers to set their own prices for shipping and other ancillary costs. Retailers conducting auctions on other Web sites should find the results useful as well.


title: Cellular Mechanisms in Acid Secretion

abstract: The secretion of acid by the upper gastrointestinal tract first appeared among the lowest vertebrates on the phylogenetic tree and has since been retained by most species (1). The obvious physiological role for this acid is in the digestion of food by pepsin; however the importance of acid-peptic digestion in man appears quite limited in light of the normal nutritional status of persons with achlorhydria. The stomach may have retained the ability to secrete acid not primarily for digestion, but rather to sterilize the upper gastrointestinal tract. Although secretion of acid appears to be a dispensable function in man, acid does play a major role in the pathophysiology of peptic ulcer disease and of most of the bleeding disorders of the upper gastrointestinal tract. This review concerns our current, admittedly limited, knowledge of the cellular mechanisms of gastric acid secretion. It attempts to lay a groundwork for understanding certain aspects of acid-peptic disorders, particularly the pathophysiology and the mechanisms underlying the effectiveness of some of the therapeutic modalities used.


title: Automated SLA Monitoring for Web Services

abstract: SLA monitoring is difficult to automate as it would need precise and unambiguous specification and a customizable engine that collects the right measurement, models the data and evaluates the SLA at certain times or when certain events happen. Also most of the SLA neglect client side measurement or restrict SLAs to measurements based only on server side. In a cross-enterprise scenario like web services it will be important to obtain measurements at multiple sites and to guarantee SLAs on them. In this article we propose an automated and distributed SLA monitoring engine.