title: Learning normalized inputs for iterative estimation in medical image segmentation

abstract: Abstract In this paper, we introduce a simple, yet powerful pipeline for medical image segmentation that combines Fully Convolutional Networks (FCNs) with Fully Convolutional Residual Networks (FC-ResNets). We propose and examine a design that takes particular advantage of recent advances in the understanding of both Convolutional Neural Networks as well as ResNets. Our approach focuses upon the importance of a trainable pre-processing when using FC-ResNets and we show that a low-capacity FCN model can serve as a pre-processor to normalize medical input data. In our image segmentation pipeline, we use FCNs to obtain normalized images, which are then iteratively refined by means of a FC-ResNet to generate a segmentation prediction. As in other fully convolutional approaches, our pipeline can be used off-the-shelf on different image modalities. We show that using this pipeline, we exhibit state-of-the-art performance on the challenging Electron Microscopy benchmark, when compared to other 2D methods. We improve segmentation results on CT images of liver lesions, when contrasting with standard FCN methods. Moreover, when applying our 2D pipeline on a challenging 3D MRI prostate segmentation challenge we reach results that are competitive even when compared to 3D methods. The obtained results illustrate the strong potential and versatility of the pipeline by achieving accurate segmentations on a variety of image modalities and different anatomical regions.


title: Comparing Predictive Accuracy

abstract: We propose and evaluate explicit tests of the null hypothesis of no difference in the accuracy of two competing forecasts. In contrast to previously developed tests, a wide variety of accuracy measures can be used (in particular, the loss function need not be quadratic and need not even be symmetric), and forecast errors can be non-Gaussian, nonzero mean, serially correlated, and contemporaneously correlated. Asymptotic and exact finite-sample tests are proposed, evaluated, and illustrated.


title: Power Control in Ad-Hoc Networks: Theory, Architecture, Algorithm and Implementation of the COMPOW Protocol

abstract: We present a new protocol for power control in ad hoc networks. We describe the issues in conceptualizing the power control problem, and provide an architecturally simple as well as theoretically well founded solution. The solution is shown to simultaneously satisfy the three objectives of maximizing the traffic carrying capacity of the entire network, extending battery life through providing low power routes, and reducing the contention at the MAC layer. Further, the protocol has the plug and play feature that it can be employed in conjunction with any routing protocol that pro-actively maintains a routing table. The protocol, called COMPOW, has been implemented in the Linux kernel and we describe the software architecture and implementation details.


title: Linus Pauling: A Life in Science and Politics

abstract: Ted Goertzel, Ben Goertzel Basic Books (Harper Collins) £18.99, pp 300 ISBN 0 465 00672 8 ::: ::: Among 20th century scientists Linus Pauling (1901-94) occupies a conspicuous position. For one thing, he was a recipient of two Nobel prizes, for chemistry (1954) and peace (1962), and the authors of this highly readable, unauthorised, biography tell us that “he felt he deserved a third.” ::: ::: It is also a useful book for understanding the politics—in the broadest sense of the word—of the Nobel prize awards. Pauling was given the Nobel prize in chemistry for “research into the nature of the chemical bond and its application to …


HCTI at SemEval-2017 Task 1: Use convolutional neural network to evaluate Semantic Textual Similarity


title: Robotic systems in orthopaedic surgery.

abstract: Robots have been used in surgery since the late 1980s. Orthopaedic surgery began to incorporate robotic technology in 1992, with the introduction of ROBODOC, for the planning and performance of total hip replacement. The use of robotic systems has subsequently increased, with promising short-term radiological outcomes when compared with traditional orthopaedic procedures. Robotic systems can be classified into two categories: autonomous and haptic (or surgeon-guided). Passive surgery systems, which represent a third type of technology, have also been adopted recently by orthopaedic surgeons. While autonomous systems have fallen out of favour, tactile systems with technological improvements have become widely used. Specifically, the use of tactile and passive robotic systems in unicompartmental knee replacement (UKR) has addressed some of the historical mechanisms of failure of non-robotic UKR. These systems assist with increasing the accuracy of the alignment of the components and produce more consistent ligament balance. Short-term improvements in clinical and radiological outcomes have increased the popularity of robot-assisted UKR. Robot-assisted orthopaedic surgery has the potential for improving surgical outcomes. We discuss the different types of robotic systems available for use in orthopaedics and consider the indication, contraindications and limitations of these technologies.


title: Online Active Learning with Imbalanced Classes

abstract: This paper proposes an online algorithm for active learning that switches between different candidate instance selection strategies (ISS) for classification in imbalanced data sets. This is important for two reasons: 1) many real-world problems have imbalanced class distributions and 2) there is no ISS that always outperforms all the other techniques. We first empirically compare the performance of existing techniques on imbalanced data sets and show that different strategies work better on different data sets and some techniques even hurt compared to random selection. We then propose an unsupervised score to track and predict the performance of individual instance selection techniques, allowing us to select an effective technique without using a holdout set and wasting valuable labeled data. This score is used in a simple online learning approach that switches between different ISS at each iteration. The proposed approach performs better than the best individual strategy available to the online algorithm over data sets in this paper and provides a way to build practical and effective active learning system for imbalanced data sets.


title: Fast Hydraulic Erosion Simulation and Visualization on GPU

abstract: Natural mountains and valleys are gradually eroded by rainfall and river flows. Physically-based modeling of this complex phenomenon is a major concern in producing realistic synthesized terrains. However, despite some recent improvements, existing algorithms are still computationally expensive, leading to a time-consuming process fairly impractical for terrain designers and 3D artists. In this paper, we present a new method to model the hydraulic erosion phenomenon which runs at interactive rates on today's computers. The method is based on the velocity field of the running water, which is created with an efficient shallow-water fluid model. The velocity field is used to calculate the erosion and deposition process, and the sediment transportation process. The method has been carefully designed to be implemented totally on GPU, and thus takes full advantage of the parallelism of current graphics hardware. Results from experiments demonstrate that the proposed method is effective and efficient. It can create realistic erosion effects by rainfall and river flows, and produce fast simulation results for terrains with large sizes.


title: MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization

abstract: Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of magnetic resonance (MR) images. Unfortunately, MR images always contain a significant amount of noise caused by operator performance, equipment, and the environment, which can lead to serious inaccuracies with segmentation. A robust segmentation technique based on an extension to the traditional fuzzy c-means (FCM) clustering algorithm is proposed in this paper. A neighborhood attraction, which is dependent on the relative location and features of neighboring pixels, is shown to improve the segmentation performance dramatically. The degree of attraction is optimized by a neural-network model. Simulated and real brain MR images with different noise levels are segmented to demonstrate the superiority of the proposed technique compared to other FCM-based methods. This segmentation method is a key component of an MR image-based classification system for brain tumors, currently being developed.


title: Current sharing in three-phase LLC interleaved resonant converter

abstract: In this paper, a novel approach for multi-phase interleaved LLC resonant converter is presented. The proposed solution, based on the use of three LLC modules with star connection of transformer primary windings, allows a drastic reduction of the output current ripple and consequently of the output filter capacitor size. Differently from other multi-phase solutions, that are greatly susceptible to resonant components' tolerance causing current imbalance, the proposed topology exhibits an inherent current sharing capability. Moreover, a closed-loop phase-shift control is introduced to additionally compensate for current mismatch and completely balance the current supplied by each module. The benefit of such solution on the reduction of output current ripple and the phase-shift control interaction and effect on load-step variations are also investigated. Measurements on a prototype are added to simulations as validation of the assertions and proposals.


title: The Impact of Instagram "Call-to-Action" Buttons on Customers' Impulse Buying

abstract: High penetration of social media in Indonesia gives more opportunities for sellers to promote their products, as well as for consumers to buy. Instagram is a popular social media for e-commerce in Indonesia. In 2016 Instagram introduced the Call-to-Action (CTA) buttons to trigger more e-commerce transactions on Instagram. On the other hand, impulse buying of social commerce consumers can be explained by the Stimulus-Organism-Response (S-O-R) model. Therefore, the objective of this study is to investigate the impact of the CTA buttons on consumers' impulse buying using the S-O-R model. A questionnaire was administered to collect data from 163 consumers. The data were analyzed and tested using Partial Least Squares (PLS). The results were validated by interviews with 3 sellers and 3 consumers. It shows that the CTA buttons have a positive impact on consumers' impulsive purchase, but less effective to improve consumers' perceived enjoyment. The main contribution of this study is the understanding of consumers' impulse buying response on promotional ads feature, which is related to the CTA buttons.


title: Learning to Navigate: Exploiting Deep Networks to Inform Sample-Based Planning During Vision-Based Navigation

abstract: Recent applications of deep learning to navigation have generated end-to-end navigation solutions whereby visual sensor input is mapped to control signals or to motion primitives. The resulting visual navigation strategies work very well at collision avoidance and have performance that matches traditional reactive navigation algorithms while operating in real-time. It is accepted that these solutions cannot provide the same level of performance as a global planner. However, it is less clear how such end-to-end systems should be integrated into a full navigation pipeline. We evaluate the typical end-to-end solution within a full navigation pipeline in order to expose its weaknesses. Doing so illuminates how to better integrate deep learning methods into the navigation pipeline. In particular, we show that they are an efficient means to provide informed samples for sample-based planners. Controlled simulations with comparison against traditional planners show that the number of samples can be reduced by an order of magnitude while preserving navigation performance. Implementation on a mobile robot matches the simulated performance outcomes.


title: Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals

abstract: Data analysis applications typically aggregate data across many dimensions looking for unusual patterns. The SQL aggregate functions and the GROUP BY operator produce zero-dimensional or one-dimensional answers. Applications need the N-dimensional generalization of these operators. The paper defines that operator, called the data cube or simply cube. The cube operator generalizes the histogram, cross-tabulation, roll-up, drill-down, and sub-total constructs found in most report writers. The cube treats each of the N aggregation attributes as a dimension of N-space. The aggregate of a particular set of attribute values is a point in this space. The set of points forms an N-dimensionaI cube. Super-aggregates are computed by aggregating the N-cube to lower dimensional spaces. Aggregation points are represented by an "infinite value": ALL, so the point (ALL,ALL,...,ALL, sum(*)) represents the global sum of all items. Each ALL value actually represents the set of values contributing to that aggregation.


title: Enforceable security policies

abstract: A precise characterization is given for the class of security policies enforceable with mechanisms that work by monitoring system execution, and automata are introduced for specifying exactly that class of security policies. Techniques to enforce security policies specified by such automata are also discussed.


title: An RCP Packaged Transceiver Chipset for Automotive LRR and SRR Systems in SiGe BiCMOS Technology

abstract: We present a transceiver chipset consisting of a four channel receiver (Rx) and a single-channel transmitter (Tx) designed in a 200-GHz SiGe BiCMOS technology. Each Rx channel has a conversion gain of 19 dB with a typical single sideband noise figure of 10 dB at 1-MHz offset. The Tx includes two exclusively-enabled voltage-controlled oscillators on the same die to switch between two bands at 76-77 and 77-81 GHz. The phase noise is -97 dBc/Hz at 1-MHz offset. On-wafer, the output power is 2 × 13 dBm. At 3.3-V supply, the Rx chip draws 240 mA, while the Tx draws 530 mA. The power dissipation for the complete chipset is 2.5 W. The two chips are used as vehicles for a 77-GHz package test. The chips are packaged using the redistribution chip package technology. We compare on-wafer measurements with on-board results. The loss at the RF port due to the transition in the package results to be less than 1 dB at 77 GHz. The results demonstrate an excellent potential of the presented millimeter-wave package concept for millimeter-wave applications.


title: Face Recognition for Smart Environments

abstract: Smart environments, wearable computers, and ubiquitous computing in general are the coming "fourth generation" of computing and information technology. But that technology will be a stillbirth without new interfaces for interaction, minus a keyboard or mouse. To win wide consumer acceptance, these interactions must be friendly and personalized; the next generation interfaces must recognize people in their immediate environment and, at a minimum, know who they are. In this article, the authors discuss face recognition technology, how it works, problems to be overcome, current technologies, and future developments and possible applications. Twenty years ago, the problem of face recognition was considered among the most difficult in artificial intelligence and computer vision. Today, however, there are several companies that sell commercial face recognition software that is capable of high-accuracy recognition with databases of more than 1,000 people. The authors describe the face recognition technology used, explaining the algorithms for face recognition as well as novel applications, such as behavior monitoring that assesses emotions based on facial expressions.


Whitepaper GPU-Based Deep Learning Inference : A Performance and Power Analysis


title: Randomness Testing of the Advanced Encryption Standard Finalist Candidates

abstract: Abstract : Mars, RC6, Rijndael, Serpent and Twofish were selected as finalists for the Advanced Encryption Standard (AES). To evaluate the finalists' suitability as random number generators, empirical statistical testing is commonly employed. Although it is widely believed that these five algorithms are indeed random, randomness testing was conducted to show that there is empirical evidence supporting this belief. In this paper, NIST reports on the studies that were conducted on the finalists for the 192-bit key size and 256-bit key size. The results to date suggest that all five of the finalists appear to be random.


title: Learning Photography Aesthetics with Deep CNNs

abstract: Automatic photo aesthetic assessment is a challenging artificial intelligence task. Existing computational approaches have focused on modeling a single aesthetic score or a class (good or bad), however these do not provide any details on why the photograph is good or bad, or which attributes contribute to the quality of the photograph. To obtain both accuracy and human interpretation of the score, we advocate learning the aesthetic attributes along with the prediction of the overall score. For this purpose, we propose a novel multitask deep convolution neural network, which jointly learns eight aesthetic attributes along with the overall aesthetic score. We report near human performance in the prediction of the overall aesthetic score. To understand the internal representation of these attributes in the learned model, we also develop the visualization technique using back propagation of gradients. These visualizations highlight the important image regions for the corresponding attributes, thus providing insights about model's representation of these attributes. We showcase the diversity and complexity associated with different attributes through a qualitative analysis of the activation maps.


title: A class AB CMOS operational amplifier for application as rail–to–rail high current drive output buffer

abstract: A class AB CMOS operational amplifier specially designed to be used as unity gain buffer is presented. The novelty of the presented topology consists in using a classical rail-to-rail OTA with duplicated output port to drive the gates of the op-amp complementary output transistors and relying on a separate feedback loop to limit the quiescent current. The simplicity of classical two stage op-amps is maintained with advantages in terms of die size, transient response and process independence. The circuit, fabricated using the BCD3S process of STMicroelectronics, occupies a die size of 0.06 mm2and operates from a single 2.2V power supply with resistive loads as low as 300 Ω.


Pre-Training CNNs Using Convolutional Autoencoders


title: Visualizing and Quantifying Discriminative Features for Face Recognition

abstract: Deep convolutional networks have generated significant performance improvements in the domain of face recognition. However, these improvements do not provide insight into which facial features lead to classification decisions. In this paper, we explore the problem of visualizing discriminative information in faces, to show which properties of images and subjects influence classification. We compare six different techniques for computing a network saliency map, which identifies influential local features in an image, using a metric called the "hiding game" to directly evaluate these techniques on classification performance. Results show that contrastive excitation backprop (cEBP) [26] best localizes features that lead to face identification. However, these maps are nearly identical across subjects, which can result in an unstable network saliency map. We introduce a robust improvement called truncated cEBP and demonstrate the capability to predict the performance of a given map. Our evaluation provides the first application of network saliency to face recognition, and we provide a robust new tool for face recognition analysts to explore which facial regions lead to changes in match scores.


title: A Domain Ontology Approach in the ETL Process of Data Warehousing

abstract: Extract-Transform-Loading (ETL) tools integrate data from source side to target in building data warehouse. However data structure and semantic heterogeneity exits widely in the enterprise information systems. On the purpose of eliminate data heterogeneity so as to construct data warehouse, this paper introduces domain ontology into ETL process of finding the data sources, defining the rules of data transformation, and eliminating the heterogeneity. In this method, the domain ontology is embedded in the metadata of the data warehouse. Hence, the data record could be mapped from data bases to ontology classes of Web Ontology Language (OWL). As result, the accessing of information resources could be done more efficiently. The method is testing in a hospital data warehouse project, and the result shows that ontology method plays an important role in the process of data integration by providing common descriptions of the concepts and relationships of data items, and medical domain ontology in the ETL process is of practical feasibility.


title: Comparing Electric Motors: An Analysis Using Four Standard Driving Schedules

abstract: Permanent magnets with rare-earth materials are widely used in interior permanent magnet synchronous motors (IPMSMs) in hybrid electric vehicles (HEVs). The recent price increase in rare-earth materials has become a serious concern. A switched reluctance motor (SRM) is one of the candidates for HE V rare-earth-free motors. An SRM has been developed with dimensions, maximum torque, operating area, and maximum efficiency that all compete with the IPMSM. The efficiency map of the designed SRM is different from that of the IPMSM; thus, direct comparisons have been rather difficult. In this article, a comparison of the energy consumption between the SRM and the IPMSM using four standard driving schedules is carried out. In the Highway Fuel Economy Driving Schedule (HWFET) and the New European Driving Cycle (NEDC), the SRM is found to have better efficiency because its efficiency is high at the high rotational speed region.


title: Improving the representation of legal case texts with information extraction methods

abstract: The prohibitive cost of assigning indices to textual cases is a major obstacle for the practical use of AI and Law systems supporting reasoning and arguing with cases. While progress has been made toward extracting certain facts from well-structured case texts or classifying case abstracts under Key Number concepts, these methods still do not suffice for the complexity of indexing concepts in CBR systems. In this paper, we lay out how a better example representation may facilitate classification-based indexing. Our hypotheses are that (1) abstracting from the individual actors and events in cases, (2) capturing actions in multi-word features, and (3) recognizing negation, can lead to a better representation of legal case texts for automatic indexing. We discuss how to implement these techniques with state-of-the-art NLP tools. Preliminary experimental results suggest that a combination of domain-specific knowledge and information extraction techniques can be used to generalize from the examples and derive more powerful features.


title: Basal ganglia output to the thalamus: still a paradox

abstract: The basal ganglia (BG)-recipient thalamus controls motor output but it remains unclear how its activity is regulated. Several studies report that thalamic activation occurs via disinhibition during pauses in the firing of inhibitory pallidal inputs from the BG. Other studies indicate that thalamic spiking is triggered by pallidal inputs via post-inhibitory ‘rebound' calcium spikes. Finally excitatory cortical inputs can drive thalamic activity, which becomes entrained, or time-locked, to pallidal spikes. We present a unifying framework where these seemingly distinct results arise from a continuum of thalamic firing ‘modes' controlled by excitatory inputs. We provide a mechanistic explanation for paradoxical pallidothalamic coactivations observed during behavior that raises new questions about what information is integrated in the thalamus to control behavior.


title: A Unified Building Model for 3D Urban GIS

abstract: Several tasks in urban and architectural design are today undertaken in a geospatial context. Building Information Models (BIM) and geospatial technologies offer 3D data models that provide information about buildings and the surrounding environment. The Industry Foundation Classes (IFC) and CityGML are today the two most prominent semantic models for representation of BIM and geospatial models respectively. CityGML has emerged as a standard for modeling city models while IFC has been developed as a reference model for building objects and sites. Current CAD and geospatial software provide tools that allow the conversion of information from one format to the other. These tools are however fairly limited in their capabilities, often resulting in data and information losses in the transformations. This paper describes a new approach for data integration based on a unified building model (UBM) which encapsulates both the CityGML and IFC models, thus avoiding translations between the models and loss of information. To build the UBM, all classes and related concepts were initially collected from both models, overlapping concepts were merged, new objects were created to ensure the capturing of both indoor and outdoor objects, and finally, spatial relationships between the objects were redefined. Unified Modeling Language (UML) notations were used for representing its objects and relationships between them. There are two use-case scenarios, both set in a hospital: “evacuation” and “allocating spaces for patient wards” were developed to validate and test the proposed UBM data model. Based on these two scenarios, four validation queries were defined in order to validate the appropriateness of the proposed unified building model. It has been validated, through the case scenarios and four queries, that the UBM being developed is able to integrate CityGML data as well as IFC data in an apparently seamless way. Constraints and enrichment functions are used for populating empty database tables and fields. The motivation scenarios also show the needs and benefits of having an integrated approach to the modeling of indoor and outdoor spatial features.


title: A Smart Home Appliances Power Management System for Handicapped, Elder and Blind People

abstract: Smart home has become a visible concept that attracts the collaboration of various areas of science and engineering. Increasing the power efficiency and power management has become an important research topic since a decade. In this research article, the focus is to ease the life of elder and handicapped people. In point of fact as compare to the healthy people, elderly and disabled people experiences difficulties to perform their everyday activities. Elderly and disabled people can be supported by using smart homes by providing them secure, safe, and controlled environments. The developed system allows the users to be able to control the appliances with least physical efforts. More particularly, the designed system allow users to switch the home appliances ON and OFF just by sending message command by using Android application or SMS with the help of a cell phone. Not only the far remote monitoring, but the local management of appliances is made possible by using the Bluetooth technology. The experimental results demonstrate that the user can control the home appliances with the designed Android application by using the voice, Bluetooth, and SMS inputs.


title: Automatic reconstruction of 3D objects using a mobile monoscopic camera

abstract: A method for the automatic reconstruction of 3D objects from multiple camera views for 3D multimedia applications is presented. Conventional 3D reconstruction techniques use equipment that restrict the flexibility of the user. In order to increase this flexibility, the presented method is characterized by a simple measurement environment, that consists of a new calibration pattern placed below the object allowing object and pattern acquisition simultaneously. This ensures, that each view can be calibrated individually. From these obtained calibrated camera views, a textured 3D wireframe model is estimated using a shape-from-silhouette approach and texture mapping of the original camera views. Experiments with this system have confirmed a significant gain of flexibility for the user and a drastic reduction of costs for technical equipment while ensuring comparable model quality as conventional reconstruction techniques at the same time.


title: Live Programming of Mobile Apps in App Inventor

abstract: MIT App Inventor is a programming environment that lowers the barriers to creating mobile apps for Android devices, especially for people with little or no programming experience. App Inventor apps for a mobile device are constructed by arranging components with a WYSIWYG editor in a computer web browser, where the development computer is connected to the device by WiFi or USB. The behavior of the components is specified using a blocks-based graphical programming language. A key feature in making App Inventor accessible to beginning programmers is live programming: developers interact directly with the state of the evolving program as it is being constructed, and changes made in the web browser are realized instantaneously in the running app on the device. This paper describes the live programming features of App Inventor and explains how they are implemented.


title: Detect Rumors Using Time Series of Social Context Information on Microblogging Websites

abstract: Automatically identifying rumors from online social media especially microblogging websites is an important research issue. Most of existing work for rumor detection focuses on modeling features related to microblog contents, users and propagation patterns, but ignore the importance of the variation of these social context features during the message propagation over time. In this study, we propose a novel approach to capture the temporal characteristics of these features based on the time series of rumor's lifecycle, for which time series modeling technique is applied to incorporate various social context information. Our experiments using the events in two microblog datasets confirm that the method outperforms state-of-the-art rumor detection approaches by large margins. Moreover, our model demonstrates strong performance on detecting rumors at early stage after their initial broadcast.


title: Fingerprint Local Invariant Feature Extraction on GPU with CUDA

abstract: Driven from its uniqueness, immutability, acceptability, and low cost, fingerprint is in a forefront between biometric traits. Recently, the GPU has been considered as a promising parallel processi ...


title: MoodLight: Exploring Personal and Social Implications of Ambient Display of Biosensor Data

abstract: MoodLight is an interactive ambient lighting system that responds to biosensor input related to an individual's current level of arousal. Changes in levels of arousal correspond to fluctuations in the color of light provided by the system, altering the immediate environment in ways intimately related to the user's private internal state. We use this intervention to explore personal and social implications of the ambient display of biosensor data. A design probe study conducted with university students provided the opportunity to observe MoodLight being used by individuals and dyads. Discussion of findings highlights key tensions associated with the dialectics of technology-mediated self-awareness and automated disclosure of personal information, addressing issues of agency, skepticism and uncertainty. This study provides greater understanding of the ways in which the representations of personal informatics, with a focus on ambient feedback, influence our perceptions of ourselves and those around us.


title: Comdb2: Bloomberg's Highly Available Relational Database System

abstract: Comdb2 is a distributed database system designed for geographical replication and high availability. In contrast with the latest trends in this field, Comdb2 offers full transactional support, a standard relational model, and the expressivity of SQL. Moreover, the system allows for rich stored procedures using a dialect of Lua. Comdb2 implements a serializable system in which reads from any node always return current values. Comdb2 provides transparent High Availability through built-in service discovery and sophisticated retry logic embedded in the standard API. ::: ::: In addition to the relational data model, Comdb2 implements queues for publisher-to-subscriber message delivery. Queues can be combined with table triggers for time-consistent log distribution, providing functionality commonly needed in modern OLTP. ::: ::: In this paper we give an overview of our last twelve years of work. We focus on the design choices that have made Comdb2 the primary database solution within our company, Bloomberg LP (BLP).


title: Mapping the mental space of game genres

abstract: The gaming community currently uses an informal classification of games into genres such as first-person shooters, real-time strategy games, etc. While this classification is generally accepted, producing a more formal taxonomy of game types directly from data has several scholarly and commercial advantages. These include providing a basis for analysis of age- and gender-related data, statistically meaningful grouping in critical literature, improved game recommendations on retail websites, and better evaluation of a game's market potential before production. Mapping the mental space of game genres is challenging, both because it involves subjective evaluations and because there are many axes on which games can vary. We collected pairwise similarity metrics of games from game players through an online survey to build a large similarity matrix that is the projection of a highdimensional space representing the unknown and hypothetical true mental space of game genres. We then applied previous techniques in manifold learning and psychology to the problem of reconstructing the most significant dimensions into maps that can be meaningfully interpreted. We believe this is the first application of these techniques to games and one of the first to work with conceptual (instead of physical) data. The resulting maps arrange related games into spontaneously arising clusters that sometimes contradict current marketing genres. We analyze several of these clusters and propose both interpretations for these "true genres" as well as axes that game players appear to use in discriminating between them. Our initial results indicate that game players tend to primarily distinguish games not by traditional genres but instead by aesthetic and mechanics, which is closely related to how developers construct games.


AUTOMATIC DETECTION OF PLANT LEAF DISEASE USING K-MEANS CLUSTERING AND SEGMENTATION


title: Generic Demand Model Considering the Impact of Prosumers for Future Grid Scenario Analysis

abstract: The increasing uptake of residential PV-battery systems is bound to significantly change demand patterns of future power systems and, consequently, their dynamic performance. In this paper, we propose a generic demand model that captures the aggregated effect of a large population of price-responsive users equipped with small-scale PV-battery systems, called prosumers , for market simulation in future grid scenario analysis. The model is formulated as a bi-level program in which the upper-level unit commitment problem minimizes the total generation cost, and the lower-level problem maximizes prosumers’ aggregate self-consumption. Unlike in the existing bi-level optimization frameworks that focus on the interaction between the wholesale market and an aggregator, the coupling is through the prosumers’ demand, not through the electricity price. That renders the proposed model market structure agnostic, making it suitable for future grid studies where the market structure is potentially unknown. As a case study, we perform steady-state voltage stability analysis of a simplified model of the Australian National Electricity Market with a significant penetration of renewable generation. The simulation results show that a high prosumer penetration changes the demand profile in ways that significantly improve the system loadability, which confirms the suitability of the proposed model for future grid studies.


Graph Community Discovery Algorithms in Neo4j with a Regularization-based Evaluation Metric.


title: Upvoting hurricane Sandy: event-based news production processes on a social news site

abstract: This paper uses the case of Hurricane Sandy and reddit's topical community (subreddit) /r/sandy to examine the production and curation of news content around events on a social news site. Through qualitative analysis, we provide a coded topology of produced content and describe how types of networked gatekeeping impact the framing of a crisis situation. This study also examines, through quantitative modeling, what kind of information becomes negotiated and voted as relevant. We suggest that highly scored content shared in a social news setting focused more on human-interest media and perspective-based citizen journalism than professional news reports. We conclude by discussing how the mechanisms of social news sites conflict with the social norms and culture of reddit to produce differing expectations around news.


title: Offline and Online Search: UAV Multiobjective Path Planning Under Dynamic Urban Environment

abstract: This paper is concerned with path planning for unmanned aerial vehicles (UAVs) flying through low altitude urban environment. Although many different path planning algorithms have been proposed to find optimal or near-optimal collision-free paths for UAVs, most of them either do not consider dynamic obstacle avoidance or do not incorporate multiple objectives. In this paper, we propose a multiobjective path planning (MOPP) framework to explore a suitable path for a UAV operating in a dynamic urban environment, where safety level is considered in the proposed framework to guarantee the safety of UAV in addition to travel time. To this aim, two types of safety index maps (SIMs) are developed first to capture static obstacles in the geography map and unexpected obstacles that are unavailable in the geography map. Then an MOPP method is proposed by jointly using offline and online search, where the offline search is based on the static SIM and helps shorten the travel time and avoid static obstacles, while the online search is based on the dynamic SIM of unexpected obstacles and helps bypass unexpected obstacles quickly. Extensive experimental results verify the effectiveness of the proposed framework under the dynamic urban environment.


title: The Effect of On-Line Consumer Reviews on Consumer Purchasing Intention: The Moderating Role of Involvement

abstract: On-line consumer reviews, functioning both as informants and as recommenders, are important in making purchase decisions and for product sales. Their persuasive impact depends on both their quality and their quantity. This paper uses the elaboration likelihood model to explain how level of involvement with a product moderates these relationships. The study produces three major findings: (1) the quality of on-line reviews has a positive effect on consumers' purchasing intention, (2) purchasing intention increases as the number of reviews increases, and (3) low-involvement consumers are affected by the quantity rather than the quality of reviews, but high-involvement consumers are affected by review quantity mainly when the review quality is high. These findings have implications for on-line sellers in terms of how to manage on-line consumer reviews.


Binary Cuckoo Optimization Algorithm for Feature Selection in High-Dimensional Datasetse


Packing and deploying Soft Origami to and from cylindrical volumes with application to automotive airbags


title: Question Generation With Doubly Adversarial Nets

abstract: We study the problem of question generation on a specific domain, where there are no labeled data. To address this problem, we propose a novel neural question generation approach called DoubAN, or doubly adversarial nets, which fully utilizes labeled data from other domains (source domains) and unlabeled data from the target domain. Learning a DoubAN involves two adversarial procedures between a question generator and two adversaries. One adversary is a domain-classification discriminator (DC-Dis), which is designed to help the generator learn domain-general representations of the input text. The other is a question-answering discriminator (QA-Dis), which provides more training data with estimated reward scores for generated text-question pairs. We conduct experiments on the SQuAD dataset as target-domain unlabeled data and the NewsQA dataset as source-domain labeled data. Experiment results show that our DoubAN achieves better results than baselines. Compared to model variants, which adopt only DC-Dis or QA-Dis, we find that the DC-Dis and QA-Dis indirectly interact with each other and jointly improve the quality of generated questions on the target domain. Moreover, extensive analysis and discussion prove the reasonableness and effectiveness of our proposed approach.


title: Indoor Visual Positioning Aided by CNN-Based Image Retrieval: Training-Free, 3D Modeling-Free

abstract: Indoor localization is one of the fundamentals of location-based services (LBS) such as seamless indoor and outdoor navigation, location-based precision marketing, spatial cognition of robotics, etc. Visual features take up a dominant part of the information that helps human and robotics understand the environment, and many visual localization systems have been proposed. However, the problem of indoor visual localization has not been well settled due to the tough trade-off of accuracy and cost. To better address this problem, a localization method based on image retrieval is proposed in this paper, which mainly consists of two parts. The first one is CNN-based image retrieval phase, CNN features extracted by pre-trained deep convolutional neural networks (DCNNs) from images are utilized to compare the similarity, and the output of this part are the matched images of the target image. The second one is pose estimation phase that computes accurate localization result. Owing to the robust CNN feature extractor, our scheme is applicable to complex indoor environments and easily transplanted to outdoor environments. The pose estimation scheme was inspired by monocular visual odometer, therefore, only RGB images and poses of reference images are needed for accurate image geo-localization. Furthermore, our method attempts to use lightweight datum to present the scene. To evaluate the performance, experiments are conducted, and the result demonstrates that our scheme can efficiently result in high location accuracy as well as orientation estimation. Currently the positioning accuracy and usability enhanced compared with similar solutions. Furthermore, our idea has a good application foreground, because the algorithms of data acquisition and pose estimation are compatible with the current state of data expansion.


title: Mining manufacturing data for discovery of high productivity process characteristics.

abstract: Modern manufacturing facilities for bioproducts are highly automated with advanced process monitoring and data archiving systems. The time dynamics of hundreds of process parameters and outcome variables over a large number of production runs are archived in the data warehouse. This vast amount of data is a vital resource to comprehend the complex characteristics of bioprocesses and enhance production robustness. Cell culture process data from 108 'trains' comprising production as well as inoculum bioreactors from Genentech's manufacturing facility were investigated. Each run constitutes over one-hundred on-line and off-line temporal parameters. A kernel-based approach combined with a maximum margin-based support vector regression algorithm was used to integrate all the process parameters and develop predictive models for a key cell culture performance parameter. The model was also used to identify and rank process parameters according to their relevance in predicting process outcome. Evaluation of cell culture stage-specific models indicates that production performance can be reliably predicted days prior to harvest. Strong associations between several temporal parameters at various manufacturing stages and final process outcome were uncovered. This model-based data mining represents an important step forward in establishing a process data-driven knowledge discovery in bioprocesses. Implementation of this methodology on the manufacturing floor can facilitate a real-time decision making process and thereby improve the robustness of large scale bioprocesses.


title: Captioning Images with Diverse Objects

abstract: Recent captioning models are limited in their ability to scale and describe concepts unseen in paired image-text corpora. We propose the Novel Object Captioner (NOC), a deep visual semantic captioning model that can describe a large number of object categories not present in existing image-caption datasets. Our model takes advantage of external sources -- labeled images from object recognition datasets, and semantic knowledge extracted from unannotated text. We propose minimizing a joint objective which can learn from these diverse data sources and leverage distributional semantic embeddings, enabling the model to generalize and describe novel objects outside of image-caption datasets. We demonstrate that our model exploits semantic information to generate captions for hundreds of object categories in the ImageNet object recognition dataset that are not observed in MSCOCO image-caption training data, as well as many categories that are observed very rarely. Both automatic evaluations and human judgements show that our model considerably outperforms prior work in being able to describe many more categories of objects.


title: Recent Advances of Generative Adversarial Networks in Computer Vision

abstract: The appearance of generative adversarial networks (GAN) provides a new approach and framework for computer vision. Compared with traditional machine learning algorithms, GAN works via adversarial training concept and is more powerful in both feature learning and representation. GAN also exhibits some problems, such as non-convergence, model collapse, and uncontrollability due to high degree of freedom. How to improve the theory of GAN and apply it to computer-vision-related tasks have now attracted much research efforts. In this paper, recently proposed GAN models and their applications in computer vision are systematically reviewed. In particular, we firstly survey the history and development of generative algorithms, the mechanism of GAN, its fundamental network structures, and theoretical analysis of the original GAN. Classical GAN algorithms are then compared comprehensively in terms of the mechanism, visual results of generated samples, and Frechet Inception Distance. These networks are further evaluated from network construction, performance, and applicability aspects by extensive experiments conducted over public datasets. After that, several typical applications of GAN in computer vision, including high-quality samples generation, style transfer, and image translation, are examined. Finally, some existing problems of GAN are summarized and discussed and potential future research topics are forecasted.


title: Fuzzy Cognitive Map software

abstract: Fuzzy Cognitive Map (FCM) is a soft computing modelling methodology for complex systems. Beyond the mathematical formulation of the FCM theory, there was a need of developing a software tool to facilitate the implementation of FCMs. This paper describes the use of a software tool that was developed to construct FCM models. Some theoretical elements of Fuzzy Cognitive Maps are presented. Then, it is discussed how the software tool is used to develop a model and simulate the use of Fuzzy Cognitive Maps. The user interface of the software tool is described and how the FCM-Analyst is used to facilitate the implementation and simulation of Fuzzy Cognitive Maps.


title: Towards Benchmarking Scene Background Initialization

abstract: Given a set of images of a scene taken at different times, the availability of an initial background model that describes the scene without foreground objects is the prerequisite for a wide range of applications, ranging from video surveillance to computational photography. Even though several methods have been proposed for scene background initialization, the lack of a common groundtruthed dataset and of a common set of metrics makes it difficult to compare their performance. To move first steps towards an easy and fair comparison of these methods, we assembled a dataset of sequences frequently adopted for background initialization, selected or created ground truths for quantitative evaluation through a selected suite of metrics, and compared results obtained by some existing methods, making all the material publicly available.


title: There Is No AI Without IA

abstract: Artificial intelligence (AI) is increasingly hyped by everyone from well-funded startups to well-known software brands. Although it's receiving a lot of visibility, the fact that AI technologies all require some element of knowledge engineering, information architecture, and high-quality data sources is not well known. Here, the author describes the need for high-quality, structured data before AI technologies can be of use to organizations and their customers.


title: Human Abilities: Emotional Intelligence

abstract: Emotional intelligence (EI) involves the ability to carry out accurate reasoning about emotions and the ability to use emotions and emotional knowledge to enhance thought. We discuss the origins of the EI concept, define EI, and describe the scope of the field today. We review three approaches taken to date from both a theoretical and methodological perspective. We find that Specific-Ability and Integrative-Model approaches adequately conceptualize and measure EI. Pivotal in this review are those studies that address the relation between EI measures and meaningful criteria including social outcomes, performance, and psychological and physical well-being. The Discussion section is followed by a list of summary points and recommended issues for future research.


title: F1: A Distributed SQL Database That Scales

abstract: F1 is a distributed relational database system built at Google to support the AdWords business. F1 is a hybrid database that combines high availability, the scalability of NoSQL systems like Bigtable, and the consistency and usability of traditional SQL databases. F1 is built on Spanner, which provides synchronous cross-datacenter replication and strong consistency. Synchronous replication implies higher commit latency, but we mitigate that latency by using a hierarchical schema model with structured data types and through smart application design. F1 also includes a fully functional distributed SQL query engine and automatic change tracking and publishing.


title: A reusable BIST with software assisted repair technology for improved memory and IO debug, validation and test time

abstract: As silicon integration complexity increases with 3D stacking and Through-Silicon-Via (TSV), so does the occurrence of memory and IO defects and associated test and validation time. This ultimately leads to an overall cost increase. On a 14nm Intel SOC, a reusable BIST engine called Converged-Pattern-Generator-Checker (CPGC) are architected to detect memory and IO defects, and combined with the software assisted repair technology to automatically repair memory cell defects on 3D stacked Wide-IO DRAM. Additionally, we also present the CPGC gate count, power, simulation, and silicon results. The reusable CPGC IP is designed to connect to a standard IP interface, which enables a quick turn-key SOC development cycle. Silicon results show CPGC can speed up validation by 5x, improve test time from minutes down to seconds, and decrease debug time by 5x including root-cause of boot failures of the memory interface. CPGC is also used in memory training and initialization, which makes it a critical part of Intel SOC.


title: Chaos-based cryptography: a brief overview

abstract: Over the past decade, there has been tremendous interest in studying the behavior of chaotic systems. They are characterized by sensitive dependence on initial conditions, similarity to random behavior, and continuous broad-band power spectrum. Chaos has potential applications in several functional blocks of a digital communication system: compression, encryption and modulation. The possibility for self-synchronization of chaotic oscillations has sparked an avalanche of works on application of chaos in cryptography. In this paper, chaos-based cryptography is discussed from a point of view which the author believes is closer to the spirit of both cryptography and chaos theory than the way the subject has been treated recently by many researchers.


title: Visual Big Data Analytics for Traffic Monitoring in Smart City

abstract: The application such as video surveillance for traffic control in smart cities needs to analyze the large amount (hours/days) of video footage in order to locate the people who are violating the traffic rules. The traditional computer vision techniques are unable to analyze such a huge amount of visual data generated in real-time. So, there is a need for visual big data analytics which involves processing and analyzing large scale visual data such as images or videos to find semantic patterns that are useful for interpretation. In this paper, we propose a framework for visual big data analytics for automatic detection of bike-riders without helmet in city traffic. We also discuss challenges involved in visual big data analytics for traffic control in a city scale surveillance data and explore opportunities for future research.


title: Neural Turing Machine for sequential learning of human mobility patterns

abstract: The capacity of recurrent neural networks to learn complex sequential patterns is improving. Recent developments such as Clockwork RNN, Stack RNN, Memory networks and Neural Turing Machine all aim to increase long-term memory capacity of recurrent neural networks. In this study, we investigate properties of Neural Turing Machine, compare it with ensembles of Stack RNN on artificial benchmarks and applied it to learn human mobility patterns. We show, that Neural Turing Machine based predictor outperformed not only n-gram based prediction, but also neighborhood based predictor, that was designed to solve this particular problem. Our models will be deployed in anti-drug police department to predict mobility of suspects.


title: A new routing protocol for the reconfigurable wireless networks

abstract: We propose a new routing protocol, the Zone Routing Protocol (ZRP), for the reconfigurable wireless networks, a large scale, highly mobile ad-hoc networking environment. The novelty of the ZRP protocol is that it is applicable to large flat-routed networks. Furthermore, through the use of the zone radius parameter, the scheme exhibits the adjustable hybrid behavior of proactive and reactive routing schemes. We evaluate the performance of the protocol, showing the reduction in the number of control messages, as compared with other reactive schemes, such as flooding.


title: Quantified UX: Towards a Common Organizational Understanding of User Experience

abstract: User Experience (UX) is increasingly being recognized as an important factor for the commercial success of digital products. In fact, it has become a buzzword, which is interpreted differently by different parties. This lack of common understanding inevitably leads to misunderstandings and inefficiency in industrial practice. We therefore propose a quantifiable way of describing User Experience (QUX). Based on the analysis of 84 UX evaluation methods, a sample of UX characteristics from literature, and 24 interviews with experts from academia and practice, we propose a formalism and a corresponding tool to measure, visualize, and communicate a product's UX within organizations. We showcase the benefits of our approach by integrating it into the product development processes of companies from three different industries.


title: Autonomous vehicles control in the VisLab Intercontinental Autonomous Challenge

abstract: Autonomous driving is one of the most interesting fields of research, with a number of important applications, like agricultural, military and, most significantly, safety. This paper addresses the problem of designing a general purpose path planner and its associated low level control for autonomous vehicles operating in unknown environments. Different kinds of inputs, like the results of obstacle detection, ditch localization, lane detection, and global path planning information are merged together using potential fields to build a representation of the environment in real-time; kinematically feasible trajectories, based on vehicle dynamics, are generated on a cost map. This approach demonstrated both flexibility and reliability for vehicle driving in very different environments, including extreme road conditions. This controller was extensively tested during VIAC, the VisLab Intercontinental Autonomous Challenge, a 13,000 km long test for intelligent vehicle applications. The results, collected during the development stage and the experiment itself, are presented in the final part of this article.


title: Nanoscale Memristor Device as Synapse in Neuromorphic Systems

abstract: A memristor is a two-terminal electronic device whose conductance can be precisely modulated by charge or flux through it. Here we experimentally demonstrate a nanoscale silicon-based memristor device and show that a hybrid system composed of complementary metal−oxide semiconductor neurons and memristor synapses can support important synaptic functions such as spike timing dependent plasticity. Using memristors as synapses in neuromorphic circuits can potentially offer both high connectivity and high density required for efficient computing.


title: Review of leukocyte classification techniques for microscopic blood images

abstract: An automated system for leukocyte classification can make manual analysis of peripheral blood smear faster and less tedious task for a pathologist. Hematological disorders of leukocytes (white blood corpuscles) are very frequent in medical practices. Microscopic analysis of peripheral blood smear is a critical step in diagnosing the hematological disorders. However, the nonspecific nature of white cell with symptoms of Acute Lymphoblastic Leukemia (ALL) often leads to erroneous identification. Hence, the hematologists face difficulty to classify white blood cells. Light microscopic assessment is time consuming and inherently subjective, hence to avoid such problems an efficient computer aided classification technique for quantitative analysis of peripheral blood samples is required. This paper provides a review of computer-based classification techniques for leukocytes which have been effectively implemented by different researchers.


title: Estimation of receivable distance for radiated disturbance containing information signal from information technology equipment

abstract: The present study was conducted to evaluate a leakage threat of information that can be reconstructed using radiated electromagnetic disturbance of information technology equipment. In this paper, the information leakage threat was estimated from a viewpoint of the receivable distance from the information technology equipment. The radiated electromagnetic disturbance of the information technology equipment is then based on the limits of the radiated disturbance for the information technology equipment of class B provided in CISPR 22. The estimation result reveals that the receivable distance exceeds hundreds of meters from the information technology equipment, taking account of the receivable capacity of a receiver with an antenna assumed in this paper. In addition, when the receiving frequency is higher, the receivable distance is shorter, in the defined frequency range with the constant limit value defined in CISPR 22. However, the receivable distance is stepped up at the receiving frequency of 230 [MHz], because at which the limit values of the radiated disturbance for the ITE of class B is stepped up.


title: Automatic Emotion Recognition Based on EEG and ECG Signals While Listening to Quranic Recitation Compared with Listening to Music

abstract: Relaxation and calmness are two emotions that people continually seek. One popular method people frequently use to reduce their tension and pressure levels is listening to various types of relaxing music. However, the Quran is composed of Allah's words, which were ultimately given for the benefit of humanity. Muslims strongly believe that listening to or reading the Quran brings them comfort, pleasure and confidence that would otherwise elude them, however, scientific evidence is still required to prove that this belief has a scientific basis. Recently, researchers have used electrophysiology to explore the relationships between electrical phenomena and body processes. This research aims to study and analyse the electrical activity of people's brains and hearts when listening to Quranic recitation compared with listening to relaxing music. Two types of electrophysiology readings are used in this research: electroencephalograms (EEGs) and electrocardiograms (ECGs). An EEG measures brain electrical activity, and an ECG measures heart electrical activity. EEG and ECG data were collected from twenty-five subjects. Then, machine learning algorithms were applied to the EEG and ECG signals. The result showed that Quranic recitation demonstrated a positive transformation of the subjects' emotions: from negative precursor emotions to calmness and happiness conditions denoted by a positive valence for the EEG and ECG signals. In contrast, relaxing music showed a positive transformation with regard to the valence in the EEG analysis, however, with respect to the ECG music data analysis, the results revealed a negative transformation for most of the music tracks.


title: FSIM: A Feature Similarity Index for Image Quality Assessment

abstract: Image quality assessment (IQA) aims to use computational models to measure the image quality consistently with subjective evaluations. The well-known structural similarity index brings IQA from pixel- to structure-based stage. In this paper, a novel feature similarity (FSIM) index for full reference IQA is proposed based on the fact that human visual system (HVS) understands an image mainly according to its low-level features. Specifically, the phase congruency (PC), which is a dimensionless measure of the significance of a local structure, is used as the primary feature in FSIM. Considering that PC is contrast invariant while the contrast information does affect HVS' perception of image quality, the image gradient magnitude (GM) is employed as the secondary feature in FSIM. PC and GM play complementary roles in characterizing the image local quality. After obtaining the local quality map, we use PC again as a weighting function to derive a single quality score. Extensive experiments performed on six benchmark IQA databases demonstrate that FSIM can achieve much higher consistency with the subjective evaluations than state-of-the-art IQA metrics.


title: Impact of Users’ Security Awareness on Desktop Security Behavior: A Protection Motivation Theory Perspective

abstract: This article uses the protection motivation theory to study the impact of information security awareness on desktop security behavior. It contributes to the literature by examining the roles played by awareness, an important antecedent to the cognitive processes in the protection motivation theory. The findings indicate that security awareness significantly affects perceived severity, response efficacy, self-efficacy, and response cost. Constructs in the coping appraisal process except response cost, in turn, significantly impact recommended security behavior.


abstract: The status of many carnivore populations is of growing concern to scientists and conservationists, making the need for data pertaining to carnivore distribution, abundance, and habitat use ever more pressing. Recent developments in "noninvasive" research techniques - those that minimize disturbance to the animal being studied - have resulted in a greatly expanded toolbox for the wildlife practitioner.Presented in a straightforward and readable style, "Noninvasive Survey Methods for Carnivores" is a comprehensive guide for wildlife researchers who seek to conduct carnivore surveys using the most up-to-date scientific approaches. Twenty-five experts from throughout North America discuss strategies for implementing surveys across a broad range of habitats, providing input on survey design, sample collection, DNA and endocrine analyses, and data analysis. Photographs from the field, line drawings, and detailed case studies further illustrate on-the-ground application of the survey methods discussed.Coupled with cutting-edge laboratory and statistical techniques, which are also described in the book, noninvasive survey methods are efficient and effective tools for sampling carnivore populations. "Noninvasive Survey Methods for Carnivores" allows practitioners to carefully evaluate a diversity of detection methods and to develop protocols specific to their survey objectives, study area, and species of interest. It is an essential resource for anyone interested in the study of carnivores, from scientists engaged in primary research to agencies or organizations requiring carnivore detection data to develop management or conservation plans.


title: A Dynamic ID-based Remote User Authentication Scheme

abstract: Password-based authentication schemes are the most widely used techniques for remote user authentication. Many static ID-based remote user authentication schemes both with and without smart cards have been proposed. Most of the schemes do not allow the users to choose and change their passwords, and maintain a verifier table to verify the validity of the user login. In this paper we present a dynamic ID-based remote user authentication scheme using smart cards. Our scheme allows the users to choose and change their passwords freely, and do not maintain any verifier table. The scheme is secure against ID-theft, and can resist the reply attacks, forgery attacks, guessing attacks, insider attacks and stolen verifier attacks.


title: A Machine Learning Approach to Twitter User Classification

abstract: This paper addresses the task of user classification in social media, with an application to Twitter. We automatically infer the values of user attributes such as political orientation or ethnicity by leveraging observable information such as the user behavior, network structure and the linguistic content of the user’s Twitter feed. We employ a machine learning approach which relies on a comprehensive set of features derived from such user information. We report encouraging experimental results on 3 tasks with different characteristics: political affiliation detection, ethnicity identification and detecting affinity for a particular business. Finally, our analysis shows that rich linguistic features prove consistently valuable across the 3 tasks and show great promise for additional user classification needs.


title: Innovation, Openness, and Platform Control

abstract: Suppose that a firm in charge of a business ecosystem is a firm in charge of a microeconomy. To achieve the highest growth rate, how open should that economy be? To encourage third-party developers, how long should their intellectual property interests last? We develop a sequential innovation model that addresses the trade-offs inherent in these two decisions: i Closing the platform increases the sponsor's ability to charge for access, while opening the platform increases developer ability to build upon it. ii The longer third-party developers retain rights to their innovations, the higher the royalties they and the sponsor earn, but the sooner those developers' rights expire, the sooner their innovations become a public good upon which other developers can build. Our model allows us to characterize the optimal levels of openness and of intellectual property IP duration in a platform ecosystem. We use standard Cobb-Douglas production technologies to derive our results. These findings can inform innovation strategy, choice of organizational form, IP noncompete decisions, and regulation policy. ::: ::: This paper was accepted by Chris Forman, information systems.


title: Fruits Sorting and Grading using Fuzzy Logic

abstract: Now a days, the classification and grading is performed based on observations and through experience. The system utilizes image-processing techniques to classify and grade fruits. The developed system starts the process by capturing the fruit's image using a regular digital camera. Then, the image is transmitted to the processing level where feature extraction, classification and grading is done using MATLAB. The fruits are classified based on color and graded based on size. Both classification and grading are realized by Fuzzy Logic approach. The results obtained are very promising.


StackReader: An RNN-Free Reading Comprehension Model


title: Maestro: A System for Scalable OpenFlow Control

abstract: The fundamental feature of an OpenFlow network is that the controller is responsible for the initial establishment of every flow by contacting related switches. Thus the performance of the controller could be a bottleneck. This paper shows how this fundamental problem is addressed by parallelism. The state of the art OpenFlow controller, called NOX, achieves a simple programming model for control function development by having a single-threaded event-loop. Yet NOX has not considered exploiting parallelism. We propose Maestro which keeps the simple programming model for programmers, and exploits parallelism in every corner together with additional throughput optimization techniques. We experimentally show that the throughput of Maestro can achieve near linear scalability on an eight core server machine. Keywords-OpenFlow, network management, multithreading, performance optimization


title: Performance of Point and Range Queries for In-memory Databases Using Radix Trees on GPUs

abstract: In in-memory database systems augmented by hardware accelerators, accelerating the index searching operations can greatly increase the runtime performance of database queries. Recently, adaptive radix trees (ART) have been shown to provide very fast index search implementation on the CPU. Here, we focus on an accelerator-based implementation of ART. We present a detailed performance study of our GPU-based adaptive radix tree (GRT) implementation over a variety of key distributions, synthetic benchmarks, and actual keys from music and book data sets. The performance is also compared with other index-searching schemes on the GPU. GRT on modern GPUs achieves some of the highest rates of index searches reported in the literature. For point queries, a throughput of up to 106 million and 130 million lookups per second is achieved for sparse and dense keys, respectively. For range queries, GRT yields 600 million and 1000 million lookups per second for sparse and dense keys, respectively, on a large dataset of 64 million 32-bit keys.


title: Pathway graphical lasso

abstract: Graphical models provide a rich framework for summarizing the dependencies among variables. The graphical lasso approach attempts to learn the structure of a Gaussian graphical model (GGM) by maximizing the log likelihood of the data, subject to an l1 penalty on the elements of the inverse co-variance matrix. Most algorithms for solving the graphical lasso problem do not scale to a very large number of variables. Furthermore, the learned network structure is hard to interpret. To overcome these challenges, we propose a novel GGM structure learning method that exploits the fact that for many real-world problems we have prior knowledge that certain edges are unlikely to be present. For example, in gene regulatory networks, a pair of genes that does not participate together in any of the cellular processes, typically referred to as pathways, is less likely to be connected. In computer vision applications in which each variable corresponds to a pixel, each variable is likely to be connected to the nearby variables. In this paper, we propose the pathway graphical lasso, which learns the structure of a GGM subject to pathway-based constraints. In order to solve this problem, we decompose the network into smaller parts, and use a message-passing algorithm in order to communicate among the subnetworks. Our algorithm has orders of magnitude improvement in run time compared to the state-of-the-art optimization methods for the graphical lasso problem that were modified to handle pathway-based constraints.


title: Cata-Fisheye Camera for Panoramic Imaging

abstract: We present a novel panoramic imaging system which uses a curved mirror as a simple optical attachment to a fish- eye lens. When compared to existing panoramic cameras, our "'cata-fisheye" camera has a simple, compact and inexpensive design, and yet yields high optical performance. It captures the desired panoramic field of view in two parts. The upper part is obtained directly by the fisheye lens and the lower part after reflection by the curved mirror. These two parts of the field of view have a small overlap that is used to stitch them into a single seamless panorama. The cata-fisheye concept allows us to design cameras with a wide range of fields of view by simply varying the parameters and position of the curved mirror. We provide an automatic method for the one-time calibration needed to stitch the two parts of the panoramic field of view. We have done a complete performance evaluation of our concept with respect to (i) the optical quality of the captured images, (ii) the working range of the camera over which the parallax is negligible, and (iii) the spatial resolution of the computed panorama. Finally, we have built a prototype cata-fisheye video camera with a spherical mirror that can capture high resolution panoramic images (3600times550pixels) with a 360deg (horizontal) x 55deg (vertical) field of view.


title: A learner-centred approach to developing team skills through web-based learning and assessment

abstract: In traditional face-to-face classrooms, collaborative learning has been endorsed as an effective pedagogy that fosters skills of analysis, communication and higher order thinking. Increasingly, as online learning environments for tertiary learners make use of course support systems, there is greater recognition of the potential of communications technologies to foster dialogue, networking and team skills among learners. Such skills are now among the recognised core attributes that graduates are expected to develop. In face-toface classroom students learn the skills of collaboration and teamwork by engaging in tasks defined and supervised by a teacher. In online environments teacher presence is often limited to task definition, management and feedback functions. Team skills and collaboration therefore need to be supported though different pedagogies and processes such as establishing a climate of trust and openness, communication protocols, resolution of conflict and group processes that provide sanctions and support. In this paper we describe an innovative approach to building the skills of decision-making and conflict resolution, leadership and clarity in goal setting and communication. A case study of tertiary learners illustrates how the essential aspects of task definition, resources to support group learning and integrated online assessment foster team skills.


A framework for assessing the global diffusion of the internet


title: An induction motor design for urban use electric vehicle

abstract: Because of fossil fuel shortage and its damages on environment, electric vehicles (EV) are going to be more popular in science and car industry as well. So, EV's take an opportunity to spread over the traffic finally. But battery, which is the main energy supplier of the motor, has disadvantages as big volume and weight. Scientists try to enhance energy densities, ranges and quicken charging. All of research results force us to drive EV in a loop, close to charging stations for near future. So EV's should be designed by considering urban road and speed conditions as well. In this survey, we designed a new two pole 7,5 kW induction motor for a certain electric vehicle, which was designed to use urban drive. We considered urban traffic rules and conditions, while designing the motor. We simulated and verified analytical and finite element analysis of the designed induction motor. We also modelled and simulated the EV with the induction motor on MATLAB to observe control performance of field oriented control (FOC) on several urban use scenarios. According to our results, EV with designed motor can be realized and controlled.


title: Defect Management Life Cycle Process for Software Quality Improvement

abstract: Software quality issues require special attention especially in view of the demands of quality software product to meet customer satisfaction. Software development projects in most organisations need proper defect management process in order to produce high quality software product and reduce the number of defects. The research question of this study is how to produce high quality software and reducing the number of defects. Therefore, the objective of this paper is to provide a framework for managing software defects by following defined life cycle processes. The methodology starts by reviewing defects, defect models, best practices and standards. A framework for defect management life cycle is proposed. The major contribution of this study is to define a defect management roadmap in software development. The adoption of an effective defect management process helps to achieve the ultimate goal of producing high quality software products and contributes towards continuous software process improvement.


title: Frequency reconfigurable mobile antenna with multi-band characteristics based on metamaterials

abstract: In this paper, a compact multi-band frequency reconfigurable metamaterial (MTM) antenna has been proposed for mobiles. PIN diodes have been used to realize the switching application by controlling the bias voltage. With suitable selection of switch configuration, the proposed antenna offers frequency re-configurability by exhibiting wideband/multi-band behavior and covers various wireless standards such as GPS, GSM 1900, WCDMA, Bluetooth, LTE, Wi-Fi, WiMAX and WLAN. The radiation characteristics such as gain, radiation efficiency and patterns are consistent throughout the working bands. The proposed MTM antenna offers a compact size with dimensions of 35 × 35 × 1.6 mm3 which makes it suitable for use in mobile units.


title: CALPAN: Countermeasure against Leakage Power Analysis attack by normalized DDPL

abstract: This paper proposes novel techniques against Leakage Power Analysis (LPA) attack on Dual-Rail Precharge Logic (DDPL) which is a Differential Power Analysis (DPA) resistant logic style. DDPL is first applied with sleep transistors technique and second with Normalization technique by which the Cryptosystem is made LPA resistant. Security metrics used for assessing the proposed logic styles are the leakage power and Coefficient of Deviation (CoD). By analyzing the leakage power traces secret key can be extracted, as the leakage power is data dependent. The leakage power dissipated by the Cryptosystem cannot be avoided completely but it can be reduced by which LPA attack can be avoided. To avoid LPA attack, leakage power should be made independent of data that is getting processed in the Cryptosystem. This paper concentrates on leakage power analysis of DDPL NAND applied with novel techniques and defines the leakage power and CoD. The logic styles are implemented and analyzed using Cadence© GPDK 45nm technology node. In the proposed method sleep transistor technique is applied to DDPL by which the power consumption is double the normal DDPL but the data dependency CoD has been reduced by 20% and with the novel Normalized DDPL technique, power consumption is reduced by 30% and the CoD has been reduced by 89.4% which implies that the power traces are made equal for all the input combinations by which LPA attack can be avoided. The power traces are measured at various temperatures by which dependency of Logic style on temperature i.e., thermal Stability is also studied.


INTELLIGENT COGNITIVE ASSISTANTS


title: Vectorial representation of spatial goals in the hippocampus of bats

abstract: To navigate, animals need to represent not only their own position and orientation, but also the location of their goal. Neural representations of an animal’s own position and orientation have been extensively studied. However, it is unknown how navigational goals are encoded in the brain. We recorded from hippocampal CA1 neurons of bats flying in complex trajectories toward a spatial goal. We discovered a subpopulation of neurons with angular tuning to the goal direction. Many of these neurons were tuned to an occluded goal, suggesting that goal-direction representation is memory-based. We also found cells that encoded the distance to the goal, often in conjunction with goal direction. The goal-direction and goal-distance signals make up a vectorial representation of spatial goals, suggesting a previously unrecognized neuronal mechanism for goal-directed navigation.


title: Evaluation of SiC MOSFET in Buck converter

abstract: SiC device technology has gained much progress. However the advantages of the device level have not been clearly shown in a power conversion circuit. The purpose of this work is to apply the SiC device in a Buck converter and to evaluate the MOSFETs' driving performance and switching losses for high switching frequency operation. The analysis is verified by the design and test results for a high frequency hard switching silicon carbide based Buck converter.


title: Person Spotting: Video Shot Retrieval for Face Sets

abstract: Matching people based on their imaged face is hard because of the well known problems of illumination, pose, size and expression variation. Indeed these variations can exceed those due to identity. Fortunately, videos of people have the happy benefit of containing multiple exemplars of each person in a form that can easily be associated automatically using straightforward visual tracking. We describe progress in harnessing these multiple exemplars in order to retrieve humans automatically in videos, given a query face in a shot. There are three areas of interest: (i) the matching of sets of exemplars provided by “tubes” of the spatial-temporal volume; (ii) the description of the face using a spatial orientation field; and, (iii) the structuring of the problem so that retrieval is immediate at run time. ::: ::: The result is a person retrieval system, able to retrieve a ranked list of shots containing a particular person in the manner of Google. The method has been implemented and tested on two feature length movies.


Position Control Method For Pick And Place Robot Arm For Object Sorting System


title: Multi sensor wireless system optimized for elderly health monitoring

abstract: In the field of wireless health monitoring for specially old age people cognitive wireless sensors which can monitor the daily activities have been reported to be of great importance. Usually a large number of sensors like motion sensor, current sensor, force sensor and flow sensors are widely employed. However there are redundancies in the system and it is required to reduce the number of sensor nodes to lower the electrical load and cost. In this paper we have tried to optimize the selection of the sensor nodes through a fuzzy decision logic based on performance parameters like ease of installation, power consumption, cost and occurrence of false signals. It has been observed that the performance of current sensor and motion sensor are better than the other sensors. A multi sensor wireless system using current and motion sensor nodes have been fabricated and installed in the house of an elderly woman. It has been observed that the optimized system has been able to record faithfully the daily activities.


title: Urban event detection with big data of taxi OD trips: A time series decomposition approach

abstract: Big urban mobility data, such as taxi trips, cell phone records, and geo-social media check-ins, offer great opportunities for analyzing the dynamics, events, and spatiotemporal trends of the urban social landscape. In this article, we present a new approach to the detection of urban events based on location-specific time series decomposition and outlier detection. The approach first extracts long-term temporal trends and seasonal periodicity patterns. Events are defined as anomalies that deviate significantly from the prediction with the discovered temporal patterns, i.e., trend and periodicity. Specifically, we adopt the STL approach, i.e., seasonal and trend decomposition using LOESS (locally weighted scatterplot smoothing), to decompose the time series for each location into three components: long-term trend, seasonal periodicity, and the remainder. Events are extracted from the remainder component for each location with an outlier detection method. We analyze over a billion taxi trips for over seven years in Manhattan (New York City) to detect and map urban events at different temporal resolutions. Results show that the approach is effective and robust in detecting events and revealing urban dynamics with both holistic understandings and location-specific interpretations.


title: Spatial analysis and characteristics of pig farming in Thailand

abstract: Background ::: In Thailand, pig production intensified significantly during the last decade, with many economic, epidemiological and environmental implications. Strategies toward more sustainable future developments are currently investigated, and these could be informed by a detailed assessment of the main trends in the pig sector, and on how different production systems are geographically distributed. This study had two main objectives. First, we aimed to describe the main trends and geographic patterns of pig production systems in Thailand in terms of pig type (native, breeding, and fattening pigs), farm scales (smallholder and large-scale farming systems) and type of farming systems (farrow-to-finish, nursery, and finishing systems) based on a very detailed 2010 census. Second, we aimed to study the statistical spatial association between these different types of pig farming distribution and a set of spatial variables describing access to feed and markets.


title: Implementation Challenges of Self Service Business Intelligence: A Literature Review

abstract: In a traditional Business Intelligence (BI) system, power users serve less experienced casual users. Power users analyze and gather data requested by casual users, and produce the reports and visua ...


title: Online Actions with Offline Impact: How Online Social Networks Influence Online and Offline User Behavior

abstract: Many of today's most widely used computing applications utilize social networking features and allow users to connect, follow each other, share content, and comment on others' posts. However, despite the widespread adoption of these features, there is little understanding of the consequences that social networking has on user retention, engagement, and online as well as offline behavior. Here, we study how social networks influence user behavior in a physical activity tracking application. We analyze 791 million online and offline actions of 6 million users over the course of 5 years, and show that social networking leads to a significant increase in users' online as well as offline activities. Specifically, we establish a causal effect of how social networks influence user behavior. We show that the creation of new social connections increases user online in-application activity by 30%, user retention by 17%, and user offline real-world physical activity by 7% (about 400 steps per day). By exploiting a natural experiment we distinguish the effect of social influence of new social connections from the simultaneous increase in user's motivation to use the app and take more steps. We show that social influence accounts for 55% of the observed changes in user behavior, while the remaining 45% can be explained by the user's increased motivation to use the app. Further, we show that subsequent, individual edge formations in the social network lead to significant increases in daily steps. These effects diminish with each additional edge and vary based on edge attributes and user demographics. Finally, we utilize these insights to develop a model that accurately predicts which users will be most influenced by the creation of new social network connections.


title: Bayesian approaches to failure prediction for disk drives

abstract: Hard disk drive failures are rare but are often costly. The ability to predict failures is important to consumers, drive manufacturers, and computer system manufacturers alike. In this paper we investigate the abilities of two Bayesian methods to predict disk drive failures based on measurements of drive internal conditions. We first view the problem from an anomaly detection stance. We introduce a mixture model of naive Bayes submodels (i.e. clusters) that is trained using expectation-maximization. The second method is a naive Bayes classifier, a supervised learning approach. Both methods are tested on realworld data concerning 1936 drives. The predictive accuracy of both algorithms is far higher than the accuracy of thresholding methods used in the disk drive industry today.


title: Convolutional Neural Network for Intermediate View Enhancement in Multiview Streaming

abstract: Multiview video streaming continues to gain popularity due to the great viewing experience it offers, as well as its availability that has been enabled by increased network throughput and other recent technical developments. User demand for interactive multiview video streaming that provides seamless view switching upon request is also increasing. However, it is a highly challenging task to stream stable and high quality videos that allow real-time scene navigation within the bandwidth constraint. In this paper, a convolutional neural network (ConvNet)-assisted seamless multiview video streaming system is proposed to tackle the challenge. The proposed method solves the problem from two perspectives. First, a ConvNet-assisted multiview representation method is proposed, which provides flexible interactivity without compromising on multiview video compression efficiency. Second, a bit allocation mechanism guided by a navigation model is developed to provide seamless navigation and adapt to network bandwidth fluctuations at the same time. These two blocks work closely to provide an optimized viewing experience to users. They can be integrated into any existing multiview video streaming framework to enhance overall performance. Experimental results demonstrate the effectiveness of the proposed method for seamless multiview streaming.


title: Server Siblings: Identifying Shared IPv4/IPv6 Infrastructure via Active Fingerprinting

abstract: We present, validate, and apply an active measurement technique that ascertains whether candidate IPv4 and IPv6 server addresses are “siblings,” i.e., assigned to the same physical machine. In contrast to prior efforts limited to passive monitoring, opportunistic measurements, or end-client populations, we propose an active methodology that generalizes to all TCP-reachable devices, including servers. Our method extends prior device fingerprinting techniques to improve their feasibility in modern environments, and uses them to support measurement-based detection of sibling interfaces. We validate our technique against a diverse set of 61 web servers with known sibling addresses and find it to be over 97 % accurate with 99 % precision. Finally, we apply the technique to characterize the top \(\sim \)6,400 Alexa IPv6-capable web domains, and discover that a DNS name in common does not imply that the corresponding IPv4 and IPv6 addresses are on the same machine, network, or even autonomous system. Understanding sibling and non-sibling relationships gives insight not only into IPv6 deployment and evolution, but also helps characterize the potential for correlated failures and susceptibility to certain attacks.


title: Hierarchical Recurrent Attention Network for Response Generation

abstract: We study multi-turn response generation in chatbots where a response is generated according to a conversation context. Existing work has modeled the hierarchy of the context, but does not pay enough attention to the fact that words and utterances in the context are differentially important. As a result, they may lose important information in context and generate irrelevant responses. We propose a hierarchical recurrent attention network (HRAN) to model both aspects in a unified framework. In HRAN, a hierarchical attention mechanism attends to important parts within and among utterances with word level attention and utterance level attention respectively. With the word level attention, hidden vectors of a word level encoder are synthesized as utterance vectors and fed to an utterance level encoder to construct hidden representations of the context. The hidden vectors of the context are then processed by the utterance level attention and formed as context vectors for decoding the response. Empirical studies on both automatic evaluation and human judgment show that HRAN can significantly outperform state-of-the-art models for multi-turn response generation.


title: An Android Malware Detection Approach Using Community Structures of Weighted Function Call Graphs

abstract: With the development of code obfuscation and application repackaging technologies, an increasing number of structural information-based methods have been proposed for malware detection. Although, many offer improved detection accuracy via a similarity comparison of specific graphs, they still face limitations in terms of computation time and the need for manual operation. In this paper, we present a new malware detection method that automatically divides a function call graph into community structures. The features of these community structures can then be used to detect malware. Our method reduces the computation time by improving the Girvan–Newman algorithm and using machine learning classification instead of a similarity comparison of subgraphs. To evaluate our method, 5040 malware samples and 8750 benign samples were collected as an experimental data set. The evaluation results show that the detection accuracy of our method is higher than that of three well-known anti-virus software and two previous control flow graph-based methods for many malware families. The runtime performance of our method exhibits a clear improvement over the GN algorithm for community structure generation.


title: Evaluation of Predictive Data Mining Algorithms in Erythemato-Squamous Disease Diagnosis

abstract: A lot of time is spent searching for the most performing data mining algorithms applied in clinical diagnosis. The study set out to identify the most performing predictive data mining algorithms applied in the diagnosis of Erythemato-squamous diseases. The study used Naive Bayes, Multilayer Perceptron and J48 decision tree induction to build predictive data mining models on 366 instances of Erythemato-squamous diseases datasets. Also, 10-fold cross-validation and sets of performance metrics were used to evaluate the baseline predictive performance of the classifiers. The comparative analysis shows that the Naive Bayes performed best with accuracy of 97.4%, Multilayer Perceptron came out second with accuracy of 96.6%, and J48 came out the worst with accuracy of 93.5%. The evaluation of these classifiers on clinical datasets, gave an insight into the predictive ability of different data mining algorithms applicable in clinical diagnosis especially in the diagnosis of Erythemato-squamous diseases.


LEAR-INRIA submission for the THUMOS workshop


title: Digital Predistortion of 75-110GHz W-Band Frequency Multiplier for Fiber Wireless Short Range Access Systems

abstract: We present a digital predistortion technique to effectively compensate high nonlinearity of a sextuple multiplier operating at 99.6GHz. An 18.9dB adjacent-channel power ratio (ACPR) improvement is guaranteed and a W-band fiber-wireless system is experimentally investigated.


title: An On-line Strategy based on Rolling State Prediction for Hybrid Energy Storage System of Tram

abstract: This paper aims at a hybrid tram with the onboard battery-supercapacitor storage system. This paper proposes an on-line strategy based on rolling state prediction (ROS). A wavelet neural network (WNN) is used to predict the running state of the tram in a certain period of time in the future according to the historic data, and the dynamic programming (DP) algorithm is used to optimize the energy management strategy (EMS) during the period. The strategy aims to optimize the system efficiency and battery life. Compared with classical rule-based (RB) strategy, ROS greatly increases the system efficiency and lengthens lifespan of battery and achieves real-time optimization.


Cross Selling of Financial Products – A Study Based on Customers in Kerala