title: Joint Neural Architecture Search and Quantization

abstract: Designing neural architectures is a fundamental step in deep learning applications. As a partner technique, model compression on neural networks has been widely investigated to gear the needs that the deep learning algorithms could be run with the limited computation resources on mobile devices. Currently, both the tasks of architecture design and model compression require expertise tricks and tedious trials. In this paper, we integrate these two tasks into one unified framework, which enables the joint architecture search with quantization (compression) policies for neural networks. This method is named JASQ. Here our goal is to automatically find a compact neural network model with high performance that is suitable for mobile devices. Technically, a multi-objective evolutionary search algorithm is introduced to search the models under the balance between model size and performance accuracy. In experiments, we find that our approach outperforms the methods that search only for architectures or only for quantization policies. 1) Specifically, given existing networks, our approach can provide them with learning-based quantization policies, and outperforms their 2 bits, 4 bits, 8 bits, and 16 bits counterparts. It can yield higher accuracies than the float models, for example, over 1.02% higher accuracy on MobileNet-v1. 2) What is more, under the balance between model size and performance accuracy, two models are obtained with joint search of architectures and quantization policies: a high-accuracy model and a small model, JASQNet and JASQNet-Small that achieves 2.97% error rate with 0.9 MB on CIFAR-10.


title: Practical Deep Reinforcement Learning Approach for Stock Trading

abstract: Stock trading strategy plays a crucial role in investment companies. However, it is challenging to obtain optimal strategy in the complex and dynamic stock market. We explore the potential of deep reinforcement learning to optimize stock trading strategy and thus maximize investment return. 30 stocks are selected as our trading stocks and their daily prices are used as the training and trading market environment. We train a deep reinforcement learning agent and obtain an adaptive trading strategy. The agent's performance is evaluated and compared with Dow Jones Industrial Average and the traditional min-variance portfolio allocation strategy. The proposed deep reinforcement learning approach is shown to outperform the two baselines in terms of both the Sharpe ratio and cumulative returns.


title: Unsupervised Learning in Reservoir Computing for EEG-based Emotion Recognition

abstract: In real-world applications such as emotion recognition from recorded brain activity, data are captured from electrodes over time. These signals constitute a multidimensional time series. In this paper, Echo State Network (ESN), a recurrent neural network with a great success in time series prediction and classification, is optimized with different neural plasticity rules for classification of emotions based on electroencephalogram (EEG) time series. Actually, the neural plasticity rules are a kind of unsupervised learning adapted for the reservoir, i.e. the hidden layer of ESN. More specifically, an investigation of Oja's rule, BCM rule and gaussian intrinsic plasticity rule was carried out in the context of EEG-based emotion recognition. The study, also, includes a comparison of the offline and online training of the ESN. When testing on the well-known affective benchmark "DEAP dataset" which contains EEG signals from 32 subjects, we find that pretraining ESN with gaussian intrinsic plasticity enhanced the classification accuracy and outperformed the results achieved with an ESN pretrained with synaptic plasticity. Four classification problems were conducted in which the system complexity is increased and the discrimination is more challenging, i.e. inter-subject emotion discrimination. Our proposed method achieves higher performance over the state of the art methods.


title: What is best practice for training intensity and duration distribution in endurance athletes

abstract: Successful endurance training involves the manipulation of training intensity, duration, and frequency, with the implicit goals of maximizing performance, minimizing risk of negative training outcomes, and timing peak fitness and performances to be achieved when they matter most. Numerous descriptive studies of the training characteristics of nationally or internationally competitive endurance athletes training 10 to 13 times per week seem to converge on a typical intensity distribution in which about 80% of training sessions are performed at low intensity (2 mM blood lactate), with about 20% dominated by periods of high-intensity work, such as interval training at approx. 90% VO2max. Endurance athletes appear to self-organize toward a high-volume training approach with careful application of high-intensity training incorporated throughout the training cycle. Training intensification studies performed on already well-trained athletes do not provide any convincing evidence that a greater emphasis on high-i...


title: Nonreciprocal Phase-Shift Composite Right/Left Handed Microstrip Lines Using Ferrite-Rod-Embedded Substrate

abstract: Nonreciprocal phase-shift composite right/left handed transmission lines with small nonreciprocity in the amplitude of the transmission coefficients are proposed. The present structures are composed of a microstrip line periodically loaded with series capacitance and shunt inductance, and a ferrite rectangular rod is embedded in the dielectric substrate just below the center strip. The balanced composite right/left handed transmission line with nonreciprocal phase characteristics is designed. The numerical simulation results validate that the nonreciprocity in magnitude of the transmission coefficients is negligibly small whereas the nonreciprocity in the phase is still kept relatively large for the present configuration.


title: Fully Distributed Privacy Preserving Mini-batch Gradient Descent Learning

abstract: In fully distributed machine learning, privacy and security are important issues. These issues are often dealt with using secure multiparty computation MPC. However, in our application domain, known MPC algorithms are not scalable or not robust enough. We propose a light-weight protocol to quickly and securely compute the sum of the inputs of a subset of participants assuming a semi-honest adversary. During the computation the participants learn no individual values. We apply this protocol to efficiently calculate the sum of gradients as part of a fully distributed mini-batch stochastic gradient descent algorithm. The protocol achieves scalability and robustness by exploiting the fact that in this application domain a "quick and dirty" sum computation is acceptable. In other words, speed and robustness takes precedence over precision. We analyze the protocol theoretically as well as experimentally based on churn statistics from a real smartphone trace. We derive a sufficient condition for preventing the leakage of an individual value, and we demonstrate the feasibility of the overhead of the protocol.


title: A 500mA analog-assisted digital-LDO-based on-chip distributed power delivery grid with cooperative regulation and IR-drop reduction in 65nm CMOS

abstract: With the die area of modern processors growing larger and larger, the IR drop across the power supply rail due to its parasitic resistance becomes considerable. There is an urgent demand for local power regulation to reduce the IR drop and to enhance transient response. A distributed power-delivery grid (DPDG) is an attractive solution for large area power-supply applications. The dual-loop distributed micro-regulator in [1] achieves a tight regulation and fast response, but suffers from large ripple and high power consumption due to the comparator-based regulator. Digital low-dropout regulators (DLDOs) [2] can be used as local micro-regulators to implement a DPDG, due to their low-voltage operation and process scalability. Adaptive control [3], asynchronous 3D pipeline control [4], analog-assisted tri-loop control [5], and event-driven PI control [6] are proposed to enhance the transient response speed. However, digital LDOs suffer from the intrinsic limitations of large output ripple and narrow current range. This paper presents an on-chip DPDg with cooperative regulation based on an analog-assisted digital LDO (AADLDO), which inherits the merits of low output ripple and sub-LSB current supply ability from the analog control, and the advantage of low supply voltage operation and adaptive fast response from the digital control.


title: Flow Measurements in a Suction Sump by UVP

abstract: In the present study, to reveal the air entrainment mechanism into a suction pipe in a suction sump, the authors conduct flow-velocity measurements by UDM (Ultrasonic Doppler Method). Here, we consider the simplest geometry as a suction sump, that is, a straight channel with rectangle-cross section and a simple suction pipe near the end of the channel. Ultrasonic transducers are fixed outside the side, bottom and back walls with right/near-right angles and, we get three-dimensional time-mean velocity distributions and equi-vorticity contours. At first, measurement accuracy is checked by comparing velocity profiles by UDM with hydrogen bubble method. As a result, the authors show typical flow fields in the sump, and show the relation between flow pattern and air entrainment. Especially, we compare two cases where the air entrainment is often observed.


Digital Enhancement of Palm Leaf Manuscript Images using Normalization Techniques


title: AROMA: A Recursive Deep Learning Model for Opinion Mining in Arabic as a Low Resource Language

abstract: While research on English opinion mining has already achieved significant progress and success, work on Arabic opinion mining is still lagging. This is mainly due to the relative recency of research efforts in developing natural language processing (NLP) methods for Arabic, handling its morphological complexity, and the lack of large-scale opinion resources for Arabic. To close this gap, we examine the class of models used for English and that do not require extensive use of NLP or opinion resources. In particular, we consider the Recursive Auto Encoder (RAE). However, RAE models are not as successful in Arabic as they are in English, due to their limitations in handling the morphological complexity of Arabic, providing a more complete and comprehensive input features for the auto encoder, and performing semantic composition following the natural way constituents are combined to express the overall meaning. In this article, we propose A Recursive Deep Learning Model for Opinion Mining in Arabic (AROMA) that addresses these limitations. AROMA was evaluated on three Arabic corpora representing different genres and writing styles. Results show that AROMA achieved significant performance improvements compared to the baseline RAE. It also outperformed several well-known approaches in the literature.


title: Transient response control via characteristic ratio assignment

abstract: This note develops an approach to directly control the transient response of linear time-invariant control systems. We begin by considering all-pole transfer functions of order n for which we introduce a set of parameters /spl alpha//sub i/, i=1,...n called the characteristic ratios. We also introduce a generalized time constant /spl tau/. We prove that /spl alpha//sub 1/ and /spl tau/ can be used to characterize the system overshoot to a step input and the speed of response, respectively. By independently adjusting /spl alpha//sub 1/ and /spl tau/ in all-pole systems, arbitrarily small or no overshoot as well as arbitrarily fast speed of response can be achieved. These formulas are used to develop a procedure to design feedback controllers with feedforward or two parameter output feedback type for achieving time response specifications. For a minimum phase plant we show that arbitrary transient response specifications, namely one with independently specified overshoot and specified rise time or speed of response can be exactly attained.


title: Congenital hepatic fibrosis with saccular dilatation of intrahepatic bile ducts and infantile polycystic kidneys

abstract: A case report is presented of a child who died in the neonatal period and who had the sonographic findings of multiple, tubular, fluid-filled spaces in the liver characteristic of Caroli's disease. There was an echodense renal cortex and pyramids. Necropsy findings were consistent with Caroli's disease but the liver also showed evidence of congenital hepatic fibrosis and there was infantile type polycystic disease of the kidneys.


title: DoS & DDoS in Named Data Networking

abstract: With the growing realization that current Internet protocols are reaching the limits of their senescence, several ongoing research efforts aim to design potential next-generation Internet architectures. Although they vary in maturity and scope, in order to avoid past pitfalls, these efforts seek to treat security and privacy as fundamental requirements. Resilience to Denialof-Service (DoS) attacks that plague today’s Internet is a major issue for any new architecture and deserves full attention. In this paper, we focus on DoS in Named Data Networking (NDN) – a specific candidate for next-generation Internet architecture designs. By naming data instead of its locations, NDN transforms data into a first-class entity and makes itself an attractive and viable approach to meet the needs for many current and emerging applications. It also incorporates some basic security features that mitigate classes of attacks that are commonly seen today. However, NDN’s resilience to DoS attacks has not been analyzed to-date. This paper represents a first step towards assessment and possible mitigation of DoS in NDN. After identifying and analyzing several new types of attacks, it investigates their variations, effects and counter-measures. This paper also sheds some light on the debate about relative virtues of self-certifying, as opposed to human-readable, names in the context of content-centric networking.


title: Design recovery for maintenance and reuse

abstract: Software maintenance and harvesting reusable components from software both require that an analyst reconstruct the software's design. Design recovery recreates design abstractions from a combination of code, existing design documentation (if available), personal experience and general knowledge about problem and application domains. The author shows how to extend the automated assistance available to the software engineer for this process. He explains the concept of design recovery, proposes an architecture to implement the concept, illustrates how the architecture operates, describes progress toward implementing it, and compares this work with other similar work such as reverse engineering and program understanding. Much of the discussion is based on a model-based design recovery system called Desire. >


title: Transformer-Less Converter Concept for a Grid-Connection of Thin-Film Photovoltaic Modules

abstract: A transformer-less converter concept for grid- connected photovoltaic systems is proposed that combines a DC/DC converter front-end with a DC/AC inverter. The converter system has an earth-connected DC input, as required from many thin-film photovoltaic modules. The DC/DC converter increases the positive photovoltaic DC-bus voltage by its negative DC output voltage to supply a grid-connected 3-phase inverter. This architecture extends today's power electronic converter topologies for thin-film photovoltaic modules considering their special requirements with the ambition to realize higher power conversion efficiencies at lower cost.


title: A Unified Taxonomic Framework for Information Visualization

abstract: We present a taxonomy for Information Visualization (IV) that characterizes it in terms of data, task, skill and context, as well as a number of dimensions that relate to the input and output hardware, the software tools, as well as user interactions and human perceptual abilities. We illustrate the utility of the taxonomy by focusing particularly on the information retrieval task and the importance of taking into account human perceptual capabilities and limitations. Although the relevance of Psychology to IV is often recognised, we have seen relatively little translation of psychological results and theory to practical IV applications.This paper targets the better development of information visualizations through the introduction of a framework delineating the major factors in interface development. We believe that higher quality visualizations will result from structured developments that take into account these considerations and that the framework will also serve to assist the development of effective evaluation and assessment processes.


1 uvCLAP : a fast , non-radioactive method to identify in vivo targets of RNA-binding proteins


title: A Multiband Slot Antenna for GPS/WiMAX/WLAN Systems

abstract: The design of a four-band slot antenna for the global positioning system (GPS), worldwide interoperability for microwave access (WiMAX), and wireless area network (WLAN) is presented. The antenna consists of a rectangular slot with an area of ${\mathbf{0}}.{\mathbf{37}}{\lambda _g} \times {\mathbf{0}}.{\mathbf{14}}{\lambda _g} = {\mathbf{48}} \times {\mathbf{18}}\;{\bf m}{{\bf m}^{\mathbf{2}}}$ (where ${\lambda _g}$ is the guide wavelength), a $T$ -shaped feed patch, an inverted $T$ -shaped stub, and two $E$ -shaped stubs to generate four frequency bands. The radiating portion and total size of the antenna are less than those of the tri-band antennas studied in literature. Parametric study on the parameters for setting the four frequency bands is presented and hence the methodology of using the design for other frequency bands is proposed. The multiband slot antenna is studied and designed using computer simulation. For verification of simulation results, the antenna is fabricated and measured. The simulated and measured return losses, radiation patterns, realized peak gains, and efficiencies of the antenna are presented. Measured results show that the antenna can be designed to cover the frequency bands from 1.575 to 1.665 GHz for the GPS system, 2.4–2.545 GHz for the IEEE 802.11b&g WLAN systems, 3.27–3.97 GHz for the WiMAX system, and 5.17–5.93 GHz for the IEEE 802.11a WLAN system. The effects of the feeding cable used in measurement and of the cover are also investigated.


title: Participation-based student final performance prediction model through interpretable Genetic Programming

abstract: A theory-based method for a computational student performance prediction.A Genetic Programming model for grade prediction is described and tested.Model evaluation suggests high success rates for predicting student grades. Building a student performance prediction model that is both practical and understandable for users is a challenging task fraught with confounding factors to collect and measure. Most current prediction models are difficult for teachers to interpret. This poses significant problems for model use (e.g. personalizing education and intervention) as well as model evaluation. In this paper, we synthesize learning analytics approaches, educational data mining (EDM) and HCI theory to explore the development of more usable prediction models and prediction model representations using data from a collaborative geometry problem solving environment: Virtual Math Teams with Geogebra (VMTwG). First, based on theory proposed by Hrastinski (2009) establishing online learning as online participation, we operationalized activity theory to holistically quantify students' participation in the CSCL (Computer-supported Collaborative Learning) course. As a result, 6 variables, Subject, Rules, Tools, Division of Labor, Community, and Object, are constructed. This analysis of variables prior to the application of a model distinguishes our approach from prior approaches (feature selection, Ad-hoc guesswork etc.). The approach described diminishes data dimensionality and systematically contextualizes data in a semantic background. Secondly, an advanced modeling technique, Genetic Programming (GP), underlies the developed prediction model. We demonstrate how connecting the structure of VMTwG trace data to a theoretical framework and processing that data using the GP algorithmic approach outperforms traditional models in prediction rate and interpretability. Theoretical and practical implications are then discussed.


title: Deinterleaving pulse trains in unconventional circumstances using multiple hypothesis tracking algorithm

abstract: The main function of Electronic Support Measure (ESM) is to receive, measure and deinterleave pulses, and then identify alternative threat emitters. Among these processes, pulse deinterleaving is vitally important because dense electromagnetic environments could cause an ESM system to receive a seemingly random pulse stream consisting of interleaved pulse trains with high noise levels. Only when we segregate different radar pulse trains from the pulse stream can we proceed with further processing. Traditional deinterleaving algorithms have demonstrated instability in unconventional circumstances (such as agility of pulse repetition interval (PRI), large noise and jitter, missing of intercepted pulses). Based on the dynamic process of different emitters, a new Statistical Association Pulse Deinterleaving (SAPD) approach is proposed based on the Multiple Hypothesis Tracking (MHT) algorithm in Multiple Target Tracking system. Simulation results have shown that the proposed algorithm can successfully identify pulse trains with constant, jittered and staggered PRI, and provide much greater accuracy in PRI estimation and pulse classification than traditional algorithms, with the presence of large noise, frequency jitter, and many missing pulses.


title: Leveraging SDN and WebRTC for Rogue Access Point Security

abstract: Rogue access points (RAPs) are unauthorized devices connected to a network, providing unauthorized wireless access to one or more clients. Such devices pose significant risk to organizations, since they provide a convenient means for hackers and insiders to hide malicious or unsanctioned activities on industry, government, and campus networks. Yet, limitations inherent in traditional networks make detecting and removing such devices expensive, time consuming, and difficult to implement. For software-defined networks (SDNs), the risk of a network compromise due to RAPs is equally concerning, and methods for detecting RAPs within SDN architectures are needed. Hence, this paper leverages the capabilities of an SDN along with a trusted agent to detect and deny RAPs access to networks by using both generic and novel methods with minimal impact to performance. Three other contributions are included in this paper. They include: 1) utilizing an emerging Web architecture to detect hidden subnets; 2) developing the first, security-based, use case for Mininet-WiFi, a software-defined wireless network emulator; and 3) enhancing Ryuretic, a modular programming language for SDN application development.


title: Mechanical designs of active upper-limb exoskeleton robots: State-of-the-art and design difficulties

abstract: Active upper-limb exoskeleton robots have been developing from 1960s. In recent years, the mechanical designs and control algorithms of active upper-limb exoskeleton robots were developed significantly. This paper reviews the state-of-the-art of active upper-limb exoskeleton robots that are applied in the areas of rehabilitation and assistive robotics. In addition, the main requirements of the active upper-limb exoskeleton robot are identified and the mechanical designs of existing active upper-limb exoskeleton robot are classified. The design difficulties of an active upper-limb exoskeleton robot are discussed.


title: Wide-band CMOS low noise amplifier for applications in radio astronomy

abstract: A CMOS low noise amplifier (LNA) is proposed for a proof-of-concept design of the square kilometer array (SKA) radio telescope. A novel variation on a well-known source-degenerated LNA topology is introduced that allows tuning of the power match centre frequency independently from the frequency at which the LNAs noise figure approaches its minimum noise. The 0.7-1.4 GHz LNA designed in 0.18/spl mu/m CMOS achieves better than 11 dB return loss with a noise temperature of 40 K, provides a power gain of 17 dB and IP1dB of -10dBm while consuming 50 mW of power from a 1.8 V supply. The design procedure of the LNA is presented and performance of the LNA is compared with previously published results.


title: Describing economic agent-based models – Dahlem ABM documentation guidelines

abstract: At the 100th Dahlem conference “New Approaches in Economics after the Financial Crisis” a working group devised guidelines for the documentation of computational economic agent-based models, based upon – but differing from – the ODD protocol Grimm et al. (2006, 2010). This paper sketches the motivation for coming up with a new set of guidelines tailored to economic multi-agent modelling, and presents these. While analytical economic models can often be precisely and concisely stated by a few equations together with an economic interpretation of their elements, a computational agentbased model, as a conceptual piece of work, may not always be a very tangible entity. For example, it is represented by but usually not identical to the (many) equations constituting the computer code. It is therefore not always easy to describe the model in a way that provides the reader with a thorough understanding of the model. The present guidelines are an attempt at standardizing such descriptions to support understanding and communication, as well as the comparability of economic multi-agent models.


title: Dominant Local Binary Patterns for Texture Classification

abstract: This paper proposes a novel approach to extract image features for texture classification. The proposed features are robust to image rotation, less sensitive to histogram equalization and noise. It comprises of two sets of features: dominant local binary patterns (DLBP) in a texture image and the supplementary features extracted by using the circularly symmetric Gabor filter responses. The dominant local binary pattern method makes use of the most frequently occurred patterns to capture descriptive textural information, while the Gabor-based features aim at supplying additional global textural information to the DLBP features. Through experiments, the proposed approach has been intensively evaluated by applying a large number of classification tests to histogram-equalized, randomly rotated and noise corrupted images in Outex, Brodatz, Meastex, and CUReT texture image databases. Our method has also been compared with six published texture features in the experiments. It is experimentally demonstrated that the proposed method achieves the highest classification accuracy in various texture databases and image conditions.


title: X-Band Choke Ring Horn Telecom Antenna for Interference Mitigation on NASA’s SWOT Mission

abstract: A low-gain antenna (LGA) is designed for high cross-polarization discrimination (XPD) and low backward radiation within the 8.025–8.4-GHz frequency band to mitigate cross-polarization and multipath interference given the spacecraft layout constraints. The X-band choke ring horn was optimized, fabricated, and measured. The antenna gain remains higher than 2.5 dBi for angles between 0° and 60° off-boresight. The XPD is higher than 15 dB from 0° to 40° and higher than 20 dB from 40° to 60° off-boresight. The calculated and measured data are in excellent agreement.


title: Environmentally safe areas and routes in the Baltic proper using Eulerian tracers.

abstract: In recent years, the shipping of environmentally hazardous cargo has increased considerably in the Baltic proper. In this study, a large number of hypothetical oil spills with an idealized, passive tracer are simulated. From the tracer distributions, statistical measures are calculated to optimize the quantity of tracer from a spill that would stay at sea as long as possible. Increased time may permit action to be taken against the spill before the oil reaches environmentally vulnerable coastal zones. The statistical measures are used to calculate maritime routes with maximum probability that an oil spill will stay at sea as long as possible. Under these assumptions, ships should follow routes that are located south of Bornholm instead of the northern routes in use currently. Our results suggest that the location of the optimal maritime routes depends on the season, although interannual variability is too large to identify statistically significant changes.


title: DiABlO: Optimization based design for improving diversity in recommender system

abstract: Abstract Primary task of a recommender system is to improve user's experience by recommending relevant and interesting items to the users. To this effect, diversity in item suggestion is as important as the accuracy of recommendations. Existing literature aimed at improving diversity primarily suggests a 2-stage mechanism – an existing CF scheme for rating prediction, followed by a modified ranking strategy. This approach requires heuristic selection of parameters and ranking strategies. Also most works focus on diversity from either the user or system's perspective. In this work, we propose a single stage optimization based solution to achieve high diversity while maintaining requisite levels of accuracy. We propose to incorporate additional diversity enhancing constraints, in the matrix factorization model for collaborative filtering. However, unlike traditional MF scheme generating dense user and item latent factor matrices, our base MF model recovers a dense user and a sparse item latent factor matrix; based on a recent work. The idea is motivated by the fact that although a user will demonstrate some affinity towards all latent factors, an item will never possess all features; thereby yielding a sparse structure. We also propose an algorithm for our formulation. The superiority of our model over existing state of the art techniques is demonstrated by the results of experiments conducted on real world movie database.


title: Exploring efficiency differences over time in the Spanish banking industry

abstract: Abstract Over the last 15 years the competitive conditions under which Spanish banking firms operate have become much tighter. Deregulation has affected both banks and savings banks, allowing them to expand geographically and to choose less regulation-conditioned output mixes. This paper analyzes how, in these circumstances, banking efficiency has been affected considering two specifications of output and analyzing the dynamics of the entire distribution, not only mean and standard deviation. Results differ depending on the output definition, but they show that, in general, efficiency scores exhibit dynamic patterns that only two moments of the distribution hardly capture. In particular, regardless of the output definition considered, efficiency scores were more dispersed in 1985 and more concentrated in 1995, and there were substantial changes in firms' efficiency relative positions, as measured by transition probability matrices. In addition, the multi-modality of the distributions has almost disappeared at the end of the period.


title: A monolithic 1200 baud FSK CMOS modem

abstract: An integrated circuit that performs all the modulation, demodulation, filtering, and handshaking required to implement a modem that complies with the Bell 202 and CCITT V.23 standards is described. This double-poly CMOS device uses switched-capacitor techniques to operate at rate of up to 1200, 600, 150, 75, and 5 b/s.


title: Securing Smart Cities Using Blockchain Technology

abstract: A smart city uses information technology to integrate and manage physical, social, and business infrastructures in order to provide better services to its dwellers while ensuring efficient and optimal utilization of available resources. With the proliferation of technologies such as Internet of Things (IoT), cloud computing, and interconnected networks, smart cities can deliver innovative solutions and more direct interaction and collaboration between citizens and the local government. Despite a number of potential benefits, digital disruption poses many challenges related to information security and privacy. This paper proposes a security framework that integrates the blockchain technology with smart devices to provide a secure communication platform in a smart city.


title: Lighting-driven voxels for memory-efficient computation of indirect illumination

abstract: Several recently proposed voxel-based global illumination algorithms rely on the use of reflective shadow maps (RSMs) to interactively compute indirect illumination. However, RSMs do not scale well with the number of light sources because of their high memory consumption when rendering. Observing that, in most cases only a fraction of the voxels really contribute to single-bounce indirect illumination, in this paper we propose the use of lighting-driven voxels (LDVs), which are constructed from a subset of voxels, to reduce the memory burden. They are used in conjunction with a voxel-based global illumination algorithm that enables the interactive indirect illumination of dynamic scenes. We evaluate the memory usage, query performance, and construction speed for various voxel resolutions. Empirically, rendering with LDVs consumes an order of magnitude less memory than rendering with RSMs. Further, it achieves a higher performance for radiance queries when multiple light sources are used. Moreover, we integrated our method into voxel ray tracing and voxel cone tracing. For each of algorithm, we achieve an interactive performance that significantly reduces memory with respect to the reference solution.


title: Criteria Chains: A Novel Multi-Criteria Recommendation Approach

abstract: Recommender systems (RSs) have been successfully applied to alleviate the problem of information overload and assist users' decision makings. Multi-criteria recommender systems is one of the RSs which utilizes users' multiple ratings on different aspects of the items (i.e., multi-criteria ratings) to predict user preferences. Traditional approaches usually predict ratings on each criterion individually and aggregate them together to estimate the user preferences. In this paper, we propose an approach named as "Criteria Chains", where each combination of the criteria can be utilized in a way of contextual situations in order to better predict the multi-criteria ratings. Our experimental results based on the TripAdvisor and YahooMovies rating data sets demonstrate that our proposed approach is able to improve the performance of multi-criteria item recommendations.


title: Building Novel VHF-Based Wireless Sensor Networks for the Internet of Marine Things

abstract: Traditional marine monitoring systems such as oceanographic and hydrographic research vessels use either wireless sensor networks with a limited coverage, or expensive satellite communication that is not suitable for small and mid-sized vessels. This paper proposes a novel Internet of Marine Things data acquisition and cartography system in the marine environment using Very High Frequency (VHF) available on the majority of ships. The proposed system is equipped with many sensors such as sea depth, temperature, wind speed and direction, and the collected data is sent to 5G edge cloudlets connected to sink/base station nodes on shore. The sensory data is ultimately aggregated at a central cloud on the internet to produce up to date cartography systems. Several observations and obstacles unique to the marine environment have been discussed and feed into the solutions presented. The impact of marine sparsity on the network is examined and a novel hybrid Mobile Ad-hoc/Delay Tolerant routing protocol is proposed to switch automatically between Mobile Ad-hoc Network and Delay Tolerant Network routing according to the network connectivity. The low rate data transmission offered by VHF radio has been investigated in terms of the network bottlenecks and the data collection rate achievable near the sinks. A data synchronization and transmission approach has also been proposed at the 5G network core using Information Centric Networks.


title: Decision Boundary Analysis of Adversarial Examples

abstract: Deep neural networks (DNNs) are vulnerable to adversarial examples, which are carefully crafted instances aiming to cause prediction errors for DNNs. Recent research on adversarial examples has examined local neighborhoods in the input space of DNN models. However, previous work has limited what regions to consider, focusing either on low-dimensional subspaces or small balls. In this paper, we argue that information from larger neighborhoods, such as from more directions and from greater distances, will better characterize the relationship between adversarial examples and the DNN models. First, we introduce an attack, OPTMARGIN, that generates adversarial examples robust to small perturbations. These examples successfully evade a defense that only considers a small ball around an input instance. Second, we analyze a larger neighborhood around input instances by looking at properties of surrounding decision boundaries, namely the distances to the boundaries and the adjacent classes. We find that the boundaries around these adversarial examples do not resemble the boundaries around benign examples. Finally, we show that, under scrutiny of the surrounding decision boundaries, our OPTMARGIN examples do not convincingly mimic benign examples. Although our experiments are limited to a few specific attacks, we hope these findings will motivate new, more evasive attacks and ultimately, effective defenses.


title: Cloud Computing - Research Issues, Challenges, Architecture, Platforms and Applications: A Survey

abstract: Abstract — Cloud computing is the development of parallel computing, distributed computing, grid computing and virtualization te chnologies which define the shape of a new era. Cloud computing is an emerging model of business computing. In this paper, we explore the concept of cloud architecture and compares cloud computing with grid computing. We also address the characteristics an d applications of several popular cloud computing platforms. In this paper, we aim to pinpoint the challenges and issues of cloud computing. We identif ied several challenges from the cloud computing adoption perspective and we also highlighted the cloud in teroperability issue that deserves substantial further research and development. However, security and privacy issues present a strong barrier for users to adapt into cloud computing systems. In this paper, we investigate several cloud computing system pro viders about their concerns on security and privacy issues.


title: The extended Segmüller flap.

abstract: A modification of the homodigital lateral V-Y advancement flaps described by Segmuller in 1976 for the treatment of fingertip injuries is described. The extended flap used in this series makes this flap more versatile in the treatment of injuries with considerable soft-tissue loss from the volar aspect of the fingertip. The Segmuller flap technique, its applications, and the results of 100 flaps are reported and discussed.


title: Research on Dynamic Response of Power Transformer Windings under Short Circuit Condition

abstract: The dynamic characteristics of transformer windings under short circuit state are of great significance to the safe operation of transformers. The electromagnetic properties of a 220 kV transformer, including leakage magnetic field and electromagnetic force of windings, are calculated using finite element method (FEM). During the short circuit, the power transformer windings‘ dynamic force, displacement and stress are calculated. The characteristics of windings‘ dynamic motion process because of the big electromagnetic force are analyzed. The analysis method and research results can provide reference for taking measures to enhance the ability to withstand short circuit of transformer windings.


title: A New Control Method for a Bi-Directional Phase-Shift-Controlled DC-DC Converter with an Extended Load Range

abstract: Phase-shifted converters are practically important to provide high conversion efficiencies through soft-switching techniques. However, the limitation on a resonant inductor current in the converters often leads to a non-fulfillment of the requirement of minimum load current. This paper presents a new power electronics control technique to enable the dual features of bi-directional power flow and an extended load range for soft-switching in phase-shift-controlled DC-DC converters. The proposed technique utilizes two identical full bridge converters and inverters in conjunction with a new control logic for gate-driving signals to facilitate both Zero Current Switching (ZCS) and Zero Voltage Switching (ZVS) in a single phase-shift-controlled DC-DC converter. The additional ZCS is designed for light load conditions at which the minimum load current cannot be attained. The bi-directional phase-shift-controlled DC-DC converter can implement the function of synchronous rectification. Its fast dynamic response allows for quick energy recovery during the regenerative braking of traction systems in electrified trains.


title: Motion estimation for human activity surveillance

abstract: This paper represents the system is based on the identification of human body, object and suspicious activity detection. The survey of videos is important solution for people having priorities in market, buildings, train stations, courts, religious places and airport etc. Video provided as input then it converted into sequence of frames and these frames are used for the detection of object. The contributions for activity observation are proposed. It is challenging task for object tracking and video surveillance and because of illumination changes, quick variations in object appearance, similar non target objects in background and obstacle. The algorithm includes of different steps having video compression, object detection, and object localization. The main advantages of this algorithm are accurately moving object and doubtful activity recognition.


title: Personalized click model through collaborative filtering

abstract: Click modeling aims to interpret the users' search click data in order to predict their clicking behavior. Existing models can well characterize the position bias of documents and snippets in relation to users' mainstream click behavior. Yet, current advances depict users' search actions only in a general setting by implicitly assuming that all users act in the same way, regardless of the fact that anyone, motivated with some individual interest, is more likely to click on a link than others. It is in light of this that we put forward a novel personalized click model to describe the user-oriented click preferences, which applies and extends matrix / tensor factorization from the view of collaborative filtering to connect users, queries and documents together. Our model serves as a generalized personalization framework that can be incorporated to the previously proposed click models and, in many cases, to their future extensions. Despite the sparsity of search click data, our personalized model demonstrates its advantage over the best click models previously discussed in the Web-search literature, supported by our large-scale experiments on a real dataset. A delightful bonus is the model's ability to gain insights into queries and documents through latent feature vectors, and hence to handle rare and even new query-document pairs much better than previous click models.


title: Auditory processing skills and phonological representation in Dyslexic children

abstract: It is now well-established that there is a causal connection between children's phonological skills and their acquisition of reading and spelling. Here we study low-level auditory processes that may underpin the development of phonological representations in children. Dyslexic and control children were given a battery of phonological tasks, reading and spelling tasks and auditory processing tasks. Potential relations between deficits in dyslexic performance in the auditory processing tasks and phonological awareness were explored. It was found that individual differences in auditory tasks requiring amplitude envelope rise time processing explained significant variance in phonological processing. It is argued that developmentally, amplitude envelope cues may be primary in establishing well-specified phonological representations, as these cues should yield important rhythmic and syllable-level information about speech. Copyright © 2004 John Wiley & Sons, Ltd.


Smart Home-Control and Monitoring System Using Smart Phone


title: Stock-market participation, intertemporal substitution, and risk-aversion

abstract: Many of the empirical rejections of the consumption CAPM can be explained by the fact that the marginal rate of substitution between present and future consumption, which in the standard model functions as the pricing kernel for all assets for which a consumer is not at a corner, seems to vary too little to be consistent with sensible values of the parameters. Moreover, when one considers more than one asset at a time, one typically gets strong rejections of the overidentifying restrictions implied by the model. The failure seems to be both in terms of unconditional and conditional moments. Two recent papers Attanasio et al., 2002 [henceforth ABT]; Vissing-Jorgensen, 2002 [henceforth VJ] have shown that, if one focuses on the consumption of individuals participating in the stock market, one does not reject some implications of the model. In particular, both VJ and ABT find that, using the consumption of stockholders, conditional Euler equations lead to sensible preference parameters and, in the case of ABT, fail to reject the overidentifying restrictions even when considering two assets (stocks and bonds) at the same time. While these results constitute a first empirical success, they do not necessarily constitute a solution to the equity premium puzzle. As argued by Robert E. Hall (1988) and Attanasio and Guglielmo Weber (1989), the estimates of the coefficient on the interest rate in a loglinearized Euler equation should be interpreted as the elasticity of intertemporal substitution (EIS) and can only be informative about the degree of risk-aversion under more restrictive assumptions (CRRA utility). In this paper we argue that considering the consumption growth of stockholders and two asset returns not only yields values of the EIS that are plausible, but also helps explain the equity premium puzzle. The evidence we have on the consumption, income, and portfolios of stockholders is consistent with fairly plausible values of the coefficient of relative risk-aversion when using the preference specification of Larry G. Epstein and Stanley E. Zin (1991). We use three Euler equations, one for each of the two assets considered, and one for the household’s total wealth portfolio, whose return will be denoted as Rm. Rm is, in principle, an unobservable variable in that it depends on the returns on all assets an individual consumer holds, including human capital. In this paper, we specify the conditional expected return to human capital as a linear function of the conditional expected returns to stocks and bonds. We suggest a novel approach to estimating the coefficients in this function based on conditional Euler equations for stockholders with two asset returns on the right-hand side. Then, considering unconditional moments, we estimate the risk-aversion of stockholders using the log-linearized equation for the equity premium from Epstein-Zin utility also explored by John Y. Campbell (1996). Unlike Campbell, our preferred approach does not substitute out consumption. Furthermore, we emphasize the importance of allowing for bonds in households’ portfolios as well as not restricting the conditional expected human-capital return to equal that of stocks.


title: Exploring Burnout Among Malaysian Junior Doctors Using the Abbreviated Maslach Burnout Inventory

abstract: Objective: The study aimed to explore the prevalence of burnout among the junior doctors and to examine the psychometric properties of abbreviated Maslach Burnout Inventory (aMBI). Methods: A cross-sectional study was conducted using aMBI self-reported questionnaire and Hospital Anxiety Depression Scale which was carried out on a sample of 117 junior doctors working in a teaching hospital in Malaysia. Results: Exploratory factor analysis of aMBI revealed a three-factor structure labelled as emotional exhaustion, depersonalization and personal accomplishment with Crohnbach’s alpha of 0.85, 0.59 and 0.64 respectively. The total variance was 67%. Prevalence of burnout was found at 26.5%. Emotional exhaustion and depersonalization were positively correlated with depression and anxiety. Shorter duration of residence (less than six months) and being posted to Trauma & Emergency unit were associated with higher mean score of burnout. Conclusion: This study provides the knowledge of level of burnout among the junior doctors and the abbreviated Maslach Burnout Inventory has a satisfied psychometirc properties for screening burnout among junior doctors in Malaysia. Please click PDF below to download the full paper...


title: Question Answering in Webclopedia

abstract: SHAPE ADJECTIVE COLOR DISEASE TEXT NARRATIVE* GENERAL-INFO DEFINITION USE EXPRESSION-ORIGIN HISTORY WHY-FAMOUS BIO ANTECEDENT INFLUENCE CONSEQUENT CAUSE-EFFECT METHOD-MEANS CIRCUMSTANCE-MEANS REASON EVALUATION PRO-CON CONTRAST RATING COUNSEL-ADVICE To create the QA Typology, we analyzed 17,384 questions and their answers (downloaded from answers.com); see (Gerber, 2001). The Typology contains 94 nodes, of which 47 are leaf nodes; a section of it appears in Figure 2. Each Typology node has been annotated with examples and typical patterns of expression of both Question and Answer, as indicated in Figure 3 for Proper-Person. Question examples Question templates Who was Johnny Mathis' high school track coach? who be 's Who was Lincoln's Secretary of State? Who was President of Turkmenistan in 1994? who be of Who is the composer of Eugene Onegin? Who is the CEO of General Electric? Actual answers Answer templates Lou Vasquez, track coach of...and Johnny Mathis , of Signed Saparmurad Turkmenbachy [Niyazov], of president of Turkmenistan ...Turkmenistan’s President Saparmurad Niyazov... ’s ...in Tchaikovsky's Eugene Onegin... 's Mr. Jack Welch, GE chairman... ... ...Chairman John Welch said ...GE's | of related role-verb Figure 3. Portion of QA Typology node annotations for Proper-Person.


The Adoption of Online Shopping Assistants: Perceived Similarity as an Antecedent to Evaluative Beliefs


title: Predicting Surface Roughness of AISI 4140 Steel in Hard Turning Process through Artificial Neural Network, Fuzzy Logic and Regression Models

abstract: In this study, the average surface roughness values obtained when turning AISI 4140 grade tempered steel with a hardness of 51 HRC, were modeled using fuzzy logic, artificial neural networks (ANN) and multi-regression equations. Input variables consisted of cutting speed (V), feed rate (f) and depth of cut (a) while output variable was surface roughness (Ra). Fuzzy logic and ANN models were developed using Matlab Toolbox. Variance analysis was conducted using MINITAB. The predicted values of mean squared errors (MSE) were employed to compare the three models. Results showed that the optimum predictive model is the fuzzy logic model. With small errors (e.g MSE = 0.0173166), the model was considered sufficiently accurate.


title: Dexhand: A Space qualified multi-fingered robotic hand

abstract: Despite the progress since the first attempts of mankind to explore space, it appears that sending man in space remains challenging. While robotic systems are not yet ready to replace human presence, they provide an excellent support for astronauts during maintenance and hazardous tasks. This paper presents the development of a space qualified multi-fingered robotic hand and highlights the most interesting challenges. The design concept, the mechanical structure, the electronics architecture and the control system are presented throughout this overview paper.


AERIAL TRIANGULATION OF DIGITAL AERIAL IMAGERY USING HYBRID FEATURES


Vico's Uncanny Humanism: Reading the "New Science" between Modern and Postmodern


title: Automatic Word Sense Discrimination

abstract: This paper presents context-group discrimination, a disambiguation algorithm based on clustering. Senses are interpreted as groups (or clusters) of similar contexts of the ambiguous word. Words, contexts, and senses are represented in Word Space, a high-dimensional, real-valued space in which closeness corresponds to semantic similarity. Similarity in Word Space is based on second-order co-occurrence: two tokens (or contexts) of the ambiguous word are assigned to the same sense cluster if the words they co-occur with in turn occur with similar words in a training corpus. The algorithm is automatic and unsupervised in both training and application: senses are induced from a corpus without labeled training instances or other external knowledge sources. The paper demonstrates good performance of context-group discrimination for a sample of natural and artificial ambiguous words.


title: Ruminating Reader: Reasoning with Gated Multi-Hop Attention

abstract: To answer the question in machine comprehension (MC) task, the models need to establish the interaction between the question and the context. To tackle the problem that the single-pass model cannot reflect on and correct its answer, we present Ruminating Reader. Ruminating Reader adds a second pass of attention and a novel information fusion component to the Bi-Directional Attention Flow model (BiDAF). We propose novel layer structures that construct an query-aware context vector representation and fuse encoding representation with intermediate representation on top of BiDAF model. We show that a multi-hop attention mechanism can be applied to a bi-directional attention structure. In experiments on SQuAD, we find that the Reader outperforms the BiDAF baseline by a substantial margin, and matches or surpasses the performance of all other published systems.


title: Projection based olfactory display with nose tracking

abstract: Most attempts to realize an olfactory display have involved capturing and synthesizing the odor, processes that still pose many challenging problems. These difficulties are mainly due to the mechanism of human olfaction, in which a set of so-called "primary odors" has not been found. Instead, we focus on spatio-temporal control of odor rather than synthesizing odor itself. Many existing interactive olfactory displays simply diffuse the scent into the air, which does not provide the ability of spatio-temporal control of olfaction. Recently, however, several researchers have developed olfactory displays that inject scented air under the nose through tubes. On the analogy of visual displays, these systems correspond to head-mounted displays (HMD). These yield a solid way to achieve spatio-temporal control of olfactory space, but they require the user to wear something on his or her face. Here, we propose an unencumbering olfactory display that does not require the user to attach anything on the face. It works by projecting a clump of scented air from a location near the user's nose through free space. We also aim to display a scent to the restricted space around a specific user's nose, rather than scattering scented air by simply diffusing it into the atmosphere. To implement this concept, we used an "air cannon" that generates toroidal vortices of the scented air. We conducted a preliminary experiment to examine this method's ability to display scent to a restricted space. The results show that we could successfully display incense to the target user. Next, we constructed prototype systems. We could successfully bring the scented air to a specific user by tracking the nose position of the user and controlling the orientation of the air cannon to the user's nose.


title: Deformation and Noise Mitigation for the Linear Switched Reluctance Motor With Skewed Teeth Structure

abstract: In this paper, a skewed teeth structure is proposed for the linear switched reluctance motor (LSRM). First, the normal force, as the main source of the machine deformation as well as the acoustic noise, is analyzed theoretically. Second, the skewed LSRM is designed, and the angle of the skewed structure is analyzed and optimized. Then, the normal force output of the LSRM is calculated using the finite element method (FEM), and the deformation results for the proposed machine are obtained. The relationship of the normal force and the skewed angle is derived. Finally, the experimental results demonstrate that the acoustic noise is reduced, which is a testament to the effectiveness of the proposed structure.


title: Clinical decision support for Alzheimer's disease based on deep learning and brain network

abstract: Modern e-health systems have undergone rapid development thanks to the advances in communications, computing and machine learning technology. Especially, deep learning has great superiority in image analysis and disease prediction. In this paper, we use Alzheimer's Disease (AD) as an example to show advantages of deep learning in diagnosing brain diseases and providing clinical decision support. Firstly, we convert raw functional magnetic resonance imaging (fMRI) to a matrix to represent activity of 90 brain regions. Secondly, to represent the functional connectivity between different brain regions, a correlation matrix is obtained by calculating the correlation between each pair of brain regions. In the next, a targeted autoencoder network is built to classify the correlation matrix, which is sensitive to AD. Finally, the experiment results show that our proposed method for AD prediction achieves much better effects than traditional means. It finds the correlations between different brain regions efficiently, provides strong reference for AD prediction. Compared to Support Vector Machine (SVM), about 25% improvement is gained in prediction accuracy. The e-health field becomes more complete and effective owing to that. Our work helps predict AD at an early stage and take measures to slow down or even prevent the onset of it.


title: EEG-Based Affect and Workload Recognition in a Virtual Driving Environment for ASD Intervention

abstract: Objective: To build group-level classification models capable of recognizing affective states and mental workload of individuals with autism spectrum disorder (ASD) during driving skill training. Methods: Twenty adolescents with ASD participated in a six-session virtual reality driving simulator-based experiment, during which their electroencephalogram (EEG) data were recorded alongside driving events and a therapist's rating of their affective states and mental workload. Five feature generation approaches including statistical features, fractal dimension features, higher order crossings (HOC)-based features, power features from frequency bands, and power features from bins ( $\Delta \boldsymbol{f} = 2\;{\rm{Hz}}$ ) were applied to extract relevant features. Individual differences were removed with a two-step feature calibration method. Finally, binary classification results based on the k -nearest neighbors algorithm and univariate feature selection method were evaluated by leave-one-subject-out nested cross-validation to compare feature types and identify discriminative features. Results: The best classification results were achieved using power features from bins for engagement (0.95) and boredom (0.78), and HOC-based features for enjoyment (0.90), frustration (0.88), and workload (0.86). Conclusion: Offline EEG-based group-level classification models are feasible for recognizing binary low and high intensity of affect and workload of individuals with ASD in the context of driving. However, while promising the applicability of the models in an online adaptive driving task requires further development. Significance: The developed models provide a basis for an EEG-based passive brain computer interface system that has the potential to benefit individuals with ASD with an affect- and workload-based individualized driving skill training intervention.


title: Can We Move Beyond Visual Metaphors? Virtual World Provocations and Second Life

abstract: Is it possible for the human beings, who are the driving forces behind virtual worlds and the avatars that inhabit them, to move beyond Real Life metaphors? What does this sort of questioning mean for teaching and learning in virtual worlds?


title: 2D bipedal walking with knees and feet: A hybrid control approach

abstract: In this paper, we consider an anthropomorphically-inspired hybrid model of a bipedal robot with locking knees and feet in order to develop a control law that results in human-like walking. The presence of feet results in periods of full actuation and periods of underactuation during the course of a step. Properties of each of these phases of walking are utilized in order to achieve a stable walking gait. In particular, we will show that using controlled symmetries in the fully-actuated domains coupled with “partial” controlled symmetries and local ankle control laws in the underactuated domains yields stable walking; this result is possible due to the amount of time which the biped spends in the fully-actuated domains. The paper concludes with simulation results along with a comparison of these results to human walking data.


title: An extremely small transmission line antenna for DAB VHF band

abstract: This paper is to present a design method for an extremely small antenna, which is based on the traditional concept of transmission line antenna. Physically, it is composed of a short section of planar transmission line antenna and a long and slender cable. Since this slender cable can be wound and attached on the planar section, the whole apparent size is small even it is operating for DAB (Digital Audio Broadcasting) VHF band. Special techniques are applied for the purpose of impedance matching.


title: Dual-band circularly polarized microstrip antenna for satellite applications

abstract: This letter presents a dual-band circularly polarized patch antenna dedicated to satellite communications. The dual-band behavior is obtained by inserting a small X-band microstrip patch antenna into a large L-band one. Both patches are printed on the same substrate and fed by electromagnetic coupling through two perpendicular slots etched in their ground planes. These slots are fed by two different 90/spl deg/ microstrip branch-line couplers printed on a stacked lower substrate. A prototype of the antenna was realized with a 1.5-mm-thick upper layer substrate and a 0.758-mm-thick feed layer substrate, both of the same dielectric material with a relative permittivity of /spl epsiv//sub r/=2.22. Simulation and measurement results are presented, showing this compact dual-band antenna achieves the required Meteosat specifications in terms of frequency bandwidth, circular polarization bandwidth, and isolation between the two communication bands.


title: Residential Demand Response Scheduling With Multiclass Appliances in the Smart Grid

abstract: In this paper, we study an electricity load scheduling problem in a residence. Compared with previous works in which only limited sets of appliances are considered, we classify various appliances into five sets considering their different energy consumption and operation characteristics, and provide mathematical models for them. With these appliance models, we propose an electricity load scheduling algorithm that controls the operation time and energy consumption level of each appliance adapting to time-of-use pricing in order to maximize the overall net utility of the residence while satisfying its budget limit. The optimization problem is formulated as a mixed integer nonlinear programming (MINLP) problem, which is in general, difficult to solve. In order to solve the problem, we use the generalized Benders decomposition approach with which we can solve the MINLP problem easily with low computational complexity. By solving the problem, we provide an algorithm to obtain the optimal electricity load scheduling of various appliances with different energy consumption and operation characteristics in a unified way.


title: Investigation of High-Electric-Field Degradation Effects in AlGaN/GaN HEMTs

abstract: High-electric-field degradation phenomena are investigated in GaN-capped AlGaN/GaN HEMTs by comparing experimental data with numerical device simulations. Under power- and OFF-state conditions, 150-h DC stresses were carried out. Degradation effects characterizing both stress experiments were as follows: a drop in the dc drain current, the amplification of gate-lag effects, and a decrease in the reverse gate leakage current. Numerical simulations indicate that the simultaneous generation of surface (and/or barrier) and buffer traps can account for all of the aforementioned degradation modes. Experiments also showed that the power-state stress induced a drop in the transconductance at high gate-source voltages only, whereas the OFF-state stress led to a uniform transconductance drop over the entire gate-source-voltage range. This behavior can be reproduced by simulations provided that, under the power-state stress, traps are assumed to accumulate over a wide region extending laterally from the gate edge toward the drain contact, whereas, under the OFF-state stress, trap generation is supposed to take place in a narrower portion of the drain-access region close to the gate edge and to be accompanied by a significant degradation of the channel transport parameters.


title: PhaseLift: Exact and Stable Signal Recovery from Magnitude Measurements via Convex Programming

abstract: Suppose we wish to recover a signal x in C^n from m intensity measurements of the form | |^2, i = 1, 2,..., m; that is, from data in which phase information is missing. We prove that if the vectors z_i are sampled independently and uniformly at random on the unit sphere, then the signal x can be recovered exactly (up to a global phase factor) by solving a convenient semidefinite program---a trace-norm minimization problem; this holds with large probability provided that m is on the order of n log n, and without any assumption about the signal whatsoever. This novel result demonstrates that in some instances, the combinatorial phase retrieval problem can be solved by convex programming techniques. Finally, we also prove that our methodology is robust vis a vis additive noise.


title: Coflow: a networking abstraction for cluster applications

abstract: Cluster computing applications -- frameworks like MapReduce and user-facing applications like search platforms -- have application-level requirements and higher-level abstractions to express them. However, there exists no networking abstraction that can take advantage of the rich semantics readily available from these data parallel applications. We propose coflow, a networking abstraction to express the communication requirements of prevalent data parallel programming paradigms. Coflows make it easier for the applications to convey their communication semantics to the network, which in turn enables the network to better optimize common communication patterns.


title: An improvement on the Euler number computing algorithm used in MATLAB

abstract: Computation of the Euler number of a binary image is often necessary in image matching, image database retrieval, image analysis, pattern recognition, and computer vision. This paper proposes an improvement on the Euler number computing algorithm used in the famous image processing tool MATLAB. By use of the information obtained during processing the previous pixel, the number of times of checking the neighbor pixels for processing a pixel decrease from 4 to 2. Our method is very simple in principle, and easily implemented. The experimental results demonstrated that our method outperforms significantly conventional Euler number computing algorithms.


title: Signals of Genetic Quality and Maternal Investment Capacity: The Dynamic Effects of Fluctuating Asymmetry and Waist-to-Hip Ratio on Men’s Ratings of Women’s Attractiveness

abstract: Fluctuating asymmetry (FA) and waist-to-hip ratio (WHR) are frequently studied physical attractiveness variables in social and evolutionary psychology. FA represents deviations in bilateral symmetry—differences between left and right body parts. WHR is the ratio of the smallest part of the waist to the largest part of the hips. Although FA and WHR are important mate preference criteria, research has not examined their joint influence on attraction. Thus, 140 heterosexual male undergraduates ranked—and 118 rated—the attractiveness of 10 photographs of rear-facing nude women. Women’s FA and WHR were negatively related to attractiveness separately, after controlling for each other and after controlling for body mass index (BMI). An FA × WHR interaction emerged, such that men’s preferences for lower WHRs increased as FA decreased, even after controlling for BMI. FA and WHR affected attractiveness in ways consistent with the information they carry and its likely effects on offspring quality.


title: Target Estimation Using Sparse Modeling for Distributed MIMO Radar

abstract: Multiple-input multiple-output (MIMO) radar systems with widely separated antennas provide spatial diversity by viewing the targets from different angles. In this paper, we use a novel approach to accurately estimate properties (position, velocity) of multiple targets using such systems by employing sparse modeling. We also introduce a new metric to analyze the performance of the radar system. We propose an adaptive mechanism for optimal energy allocation at the different transmit antennas. We show that this adaptive energy allocation mechanism significantly improves in performance over MIMO radar systems that transmit fixed equal energy across all the antennas. We also demonstrate accurate reconstruction from very few samples by using compressive sensing at the receivers.


title: Automatic Labeling of Topics

abstract: An algorithm for the automatic labeling of topics accordingly to a hierarchy is presented. Its main ingredients are a set of similarity measures and a set of topic labeling rules. The labeling rules are specifically designed to find the most agreed labels between the given topic and the hierarchy. The hierarchy is obtained from the Google Directory service, extracted via an ad-hoc developed software procedure and expanded through the use of the OpenOffice English Thesaurus. The performance of the proposed algorithm is investigated by using a document corpus consisting of 33,801 documents and a dictionary consisting of 111,795 words. The results are encouraging, while particularly interesting and significant labeling cases emerged


title: Collecting a Large-scale Dataset of Fine-grained Cars

abstract: In this work we introduce a large-scale, fine-grained dataset of cars. This dataset, consisting of 197 classes and 16,185 images, represents an order of magnitude increase in size over the only existing fine-grained car dataset [7] (14 classes, 1,904 images) and is comparable in size to the largest fine-grained datasets publicly available [9, 3]. The goals of this work are twofold: 1) to describe the difficulties encountered when collecting such a dataset and 2) to present baseline performance for two state-of-the-art methods.


title: Towards Accurate Relation Extraction from Wikipedia

abstract: Enormous efforts of human volunteers have made Wikipedia become a treasure of textual knowledge. Relation extraction that aims at extracting structured knowledge in the unstructured texts in Wikipedia is an appealing but quite challenging problem because it's hard for machines to understand plain texts. Existing methods are not effective enough because they understand relation types in textual level without exploiting knowledge behind plain texts. In this paper, we propose a novel framework called Athena 2.0 leveraging Semantic Patterns which are patterns that can understand relation types in semantic level to solve this problem. Extensive experiments show that Athena 2.0 significantly outperforms existing methods.


Technology ( CSIT ) Identity : An Integrative Theory to Explain Gender Gap in IT Research-in-Progress


title: The neural correlates of person familiarity A functional magnetic resonance imaging study with clinical implications

abstract: Neural activity was measured in 10 healthy volunteers by functional MRI while they viewed familiar and unfamiliar faces and listened to familiar and unfamiliar voices. The familiar faces and voices were those of people personally known to the subjects; they were not people who are more widely famous in the media. Changes in neural activity associated with stimulus modality irrespective of familiarity were observed in modules previously demonstrated to be activated by faces (fusiform gyrus bilaterally) and voices (superior temporal gyrus bilaterally). Irrespective of stimulus modality, familiarity of faces and voices (relative to unfamiliar faces and voices) was associated with increased neural activity in the posterior cingulate cortex, including the retrosplenial cortex. Our results suggest that recognizing a person involves information flow from modality-specific modules in the temporal cortex to the retrosplenial cortex. The latter area has recently been implicated in episodic memory and emotional salience, and now seems to be a key area involved in assessing the familiarity of a person. We propose that disturbances in the information flow described may underlie neurological and psychiatric disorders of the recognition of familiar faces, voices and persons (prosopagnosia, phonagnosia and Capgras delusion, respectively).


Coupled Generative Adversarial Nets


title: Development status of photoresist as mask material for Injection Molded Solder (IMS) technique

abstract: Novel bumping technology that can realize high density assembly of IC chips and packages with a high number of I/O is required in the field of electronic packaging. Currently, the electroplating method or the solder ball placement method have been widely adopted for the fabrication of solder bumps down to sub-hundred microns in diameter. However, there are some limitations with these current bumping methods. For example, in the case of an electroplating method, the solder bump composition fabricated in this method is limited. In addition, careful maintenance of the plating solution is necessary in order to prevent a gradual compositional shift of the plating solution. Recently, a novel bumping process called Injection Molded Solder (IMS) was proposed, which enabled direct injection of molten solder into the holes of a photoresist patterned array. In this paper, the current status of photoresist development and recent achievements for obtaining excellent solder filling rates are described. One of the important factors to obtain a high solder filling rate is reducing the amount of outgases from the photoresists at high temperature, as they prevented smooth solder filling into the photoresist holes. In order to solve such an issue, novel photoresists with high thermal stability have been designed and developed. The number of defects was drastically decreased by applying the improved photoresist to the IMS process. Currently, solder bump arrays with 50 μm diameter were perfectly formed with SAC305 as a solder on a 6 inch wafer with no defects. Also, excellent solder filling was observed at holes down to 20 μm in diameter. Some other advantages of IMS, such as solder height uniformity and thermal reflow-free processes, are also described. Even though there is some significant progress in materials and processing, we still have room to modify our material design to establish a much more stable and reliable IMS process. As example parameters, the effects of the elastic modulus and molten solder wettability are also described. Our goal is to realize perfect solder filling with 12 inch wafers in the near future.


title: Face Parsing via a Fully-Convolutional Continuous CRF Neural Network

abstract: In this work, we address the face parsing task with a Fully-Convolutional continuous CRF Neural Network (FC-CNN) architecture. In contrast to previous face parsing methods that apply region-based subnetwork hundreds of times, our FC-CNN is fully convolutional with high segmentation accuracy. To achieve this goal, FC-CNN integrates three subnetworks, a unary network, a pairwise network and a continuous Conditional Random Field (C-CRF) network into a unified framework. The high-level semantic information and low-level details across different convolutional layers are captured by the convolutional and deconvolutional structures in the unary network. The semantic edge context is learnt by the pairwise network branch to construct pixel-wise affinity. Based on a differentiable superpixel pooling layer and a differentiable C-CRF layer, the unary network and pairwise network are combined via a novel continuous CRF network to achieve spatial consistency in both training and test procedure of a deep neural network. Comprehensive evaluations on LFW-PL and HELEN datasets demonstrate that FC-CNN achieves better performance over the other state-of-arts for accurate face labeling on challenging images.


title: Face poser: Interactive modeling of 3D facial expressions using facial priors

abstract: This article presents an intuitive and easy-to-use system for interactively posing 3D facial expressions. The user can model and edit facial expressions by drawing freeform strokes, by specifying distances between facial points, by incrementally editing curves on the face, or by directly dragging facial points in 2D screen space. Designing such an interface for 3D facial modeling and editing is challenging because many unnatural facial expressions might be consistent with the user's input. We formulate the problem in a maximum a posteriori framework by combining the user's input with priors embedded in a large set of facial expression data. Maximizing the posteriori allows us to generate an optimal and natural facial expression that achieves the goal specified by the user. We evaluate the performance of our system by conducting a thorough comparison of our method with alternative facial modeling techniques. To demonstrate the usability of our system, we also perform a user study of our system and compare with state-of-the-art facial expression modeling software (Poser 7).


title: Curious model-building control systems

abstract: A novel curious model-building control system is described which actively tries to provoke situations for which it learned to expect to learn something about the environment. Such a system has been implemented as a four-network system based on Watkins' Q-learning algorithm which can be used to maximize the expectation of the temporal derivative of the adaptive assumed reliability of future predictions. An experiment with an artificial nondeterministic environment demonstrates that the system can be superior to previous model-building control systems, which do not address the problem of modeling the reliability of the world model's predictions in uncertain environments and use ad-hoc methods (like random search) to train the world model. >


title: Enhanced Topic-based Vector Space Model for semantics-aware spam filtering

abstract: Spam has become a major issue in computer security because it is a channel for threats such as computer viruses, worms and phishing. More than 85% of received e-mails are spam. Historical approaches to combat these messages including simple techniques such as sender blacklisting or the use of e-mail signatures, are no longer completely reliable. Currently, many solutions feature machine-learning algorithms trained using statistical representations of the terms that usually appear in the e-mails. Still, these methods are merely syntactic and are unable to account for the underlying semantics of terms within the messages. In this paper, we explore the use of semantics in spam filtering by representing e-mails with a recently introduced Information Retrieval model: the enhanced Topic-based Vector Space Model (eTVSM). This model is capable of representing linguistic phenomena using a semantic ontology. Based upon this representation, we apply several well-known machine-learning models and show that the proposed method can detect the internal semantics of spam messages.


title: Primed for Violence: The Role of Gender Inequality in Predicting Internal Conflict

abstract: We know, most notably through Ted Gurr's research, that ethnic discrimination can lead to ethnopolitical rebellion–intrastate conflict. I seek to discover what impact, if any, gender inequality has on intrastate conflict. Although democratic peace scholars and others highlight the role of peaceful domestic behavior in predicting state behavior, many scholars have argued that a domestic environment of inequality and violence—structural and cultural violence—results in a greater likelihood of violence at the state and the international level. This project contributes to this line of inquiry and further tests the grievance theory of intrastate conflict by examining the norms of violence that facilitate a call to arms. And in many ways, I provide an alternative explanation for the significance of some of the typical economic measures—the greed theory—based on the link between discrimination, inequality, and violence. I test whether states characterized by higher levels of gender inequality are more likely to experience intrastate conflict. Ultimately, the basic link between gender inequality and intrastate conflict is confirmed—states characterized by gender inequality are more likely to experience intrastate conflict, 1960–2001.


title: ICDAR 2011 Document Image Binarization Contest (DIBCO 2011)

abstract: DIBCO 2011 is the International Document Image Binarization Contest organized in the context of ICDAR 2011 conference. The general objective of the contest is to identify current advances in document image binarization for both machine-printed and handwritten document images using evaluation performance measures that conform to document image analysis and recognition. This paper describes the contest details including the evaluation measures used as well as the performance of the 18 submitted methods along with a short description of each method.


EPITOMIC VARIATIONAL AUTOENCODER


Perceiving threat and feeling responsible. How severity of hate speech, number of bystanders, and prior reactions of others affect bystanders’ intention to counterargue against hate speech on Facebook


title: Word-Sense Disambiguation For Machine Translation

abstract: In word sense disambiguation, a system attempts to determine the sense of a word from contextual features. Major barriers to building a high-performing word sense disambiguation system include the difficulty of labeling data for this task and of predicting fine-grained sense distinctions. These issues stem partly from the fact that the task is being treated in isolation from possible uses of automatically disambiguated data. In this paper, we consider the related task of word translation, where we wish to determine the correct translation of a word from context. We can use parallel language corpora as a large supply of partially labeled data for this task. We present algorithms for solving the word translation problem and demonstrate a significant improvement over a baseline system. We then show that the word-translation system can be used to improve performance on a simplified machine-translation task and can effectively and accurately prune the set of candidate translations for a word.


CAMPVis – A Game Engine-inspired Research Framework for Medical Imaging and Visualization


title: Streaming submodular maximization: massive data summarization on the fly

abstract: How can one summarize a massive data set "on the fly", i.e., without even having seen it in its entirety? In this paper, we address the problem of extracting representative elements from a large stream of data. I.e., we would like to select a subset of say k data points from the stream that are most representative according to some objective function. Many natural notions of "representativeness" satisfy submodularity, an intuitive notion of diminishing returns. Thus, such problems can be reduced to maximizing a submodular set function subject to a cardinality constraint. Classical approaches to submodular maximization require full access to the data set. We develop the first efficient streaming algorithm with constant factor 1/2-e approximation guarantee to the optimum solution, requiring only a single pass through the data, and memory independent of data size. In our experiments, we extensively evaluate the effectiveness of our approach on several applications, including training large-scale kernel methods and exemplar-based clustering, on millions of data points. We observe that our streaming method, while achieving practically the same utility value, runs about 100 times faster than previous work.


Flexible Metric Nearest Neighbor Classification


Chapter 11. Facial Expression Analysis


title: Ethical leadership and trust: It's all about meeting expectations

abstract: In this study, we examine the effects of different ethical leadership behaviors, as perceived by followers, on the trust that those followers have in their leader. In line with the ethical leadership theory of Brown, Trevino, and Harrison (2005) we explore the following ethical leadership behaviors: role modeling, demonstrating morality, securing ethical behavior, contextualizing success, transmitting organizational values, and encouraging transparency. Using a web-based survey with nearly 500 respondents from European business corporations, we find that most ethical behaviors are positively related to trust. We also find that the more a leader acts in a way that followers feel is the appropriate ethical leader behavior, the more a leader will be trusted. Thus, the relationship between ethical leader behavior and trust is (partly) moderated by the consistency between desired and observed behavior of a leader, as perceived by their followers.


title: DeepSD: Supply-Demand Prediction for Online Car-Hailing Services Using Deep Neural Networks

abstract: The online car-hailing service has gained great popularity all over the world. As more passengers and more drivers use the service, it becomes increasingly more important for the the car-hailing service providers to effectively schedule the drivers to minimize the waiting time of passengers and maximize the driver utilization, thus to improve the overall user experience. In this paper, we study the problem of predicting the real-time car-hailing supply-demand, which is one of the most important component of an effective scheduling system. Our objective is to predict the gap between the car-hailing supply and demand in a certain area in the next few minutes. Based on the prediction, we can balance the supply-demands by scheduling the drivers in advance. We present an end-to-end framework called Deep Supply-Demand (DeepSD) using a novel deep neural network structure. Our approach can automatically discover complicated supply-demand patterns from the car-hailing service data while only requires a minimal amount hand-crafted features. Moreover, our framework is highly flexible and extendable. Based on our framework, it is very easy to utilize multiple data sources (e.g., car-hailing orders, weather and traffic data) to achieve a high accuracy. We conduct extensive experimental evaluations, which show that our framework provides more accurate prediction results than the existing methods.


title: Selective Transfer Learning for EEG-Based Drowsiness Detection

abstract: On the pathway from laboratory settings to real world environment, a major challenge on the development of a robust electroencephalogram (EEG)-based brain-computer interface (BCI) is to collect a significant amount of informative training data from each individual, which is labor intensive and time-consuming and thereby significantly hinders the applications of BCIs in real-world settings. A possible remedy for this problem is to leverage existing data from other subjects. However, substantial inter-subject variability of human EEG data could deteriorate more than improve the BCI performance. This study proposes a new transfer learning (TL)-based method that exploits a subject's pilot data to select auxiliary data from other subjects to enhance the performance of an EEG-based BCI for drowsiness detection. This method is based on our previous findings that the EEG correlates of drowsiness were stable within individuals across sessions and an individual's pilot data could be used as calibration/training data to build a robust drowsiness detector. Empirical results of this study suggested that the feasibility of leveraging existing BCI models built by other subjects' data and a relatively small amount of subject-specific pilot data to develop a BCI that can outperform the BCI based solely on the pilot data of the subject.


title: Routing Algorithms for Network Function Virtualization Enabled Multicast Topology on SDN

abstract: Many multicast services such as live multimedia distribution and real-time event monitoring require multicast mechanisms that involve network functions (e.g., firewall and video transcoding). Network function virtualization (NFV) is a concept that proposes using virtualization to implement network functions on infrastructure building block (such as high volume servers and virtual machines), where software provides the functionality of existing purpose-built network equipment. We present an approach for building the multicast mechanism whereby multicast flows are processed by NFV before reaching their end users. We propose a routing algorithm and a method for building an appropriate multicast topology.


title: A 79-GHz Radar Sensor in LTCC Technology Using Grid Array Antennas

abstract: Concept, design, and measurement results of a frequency-modulated continuous-wave radar sensor in low-temperature co-fired ceramics (LTCC) technology is presented in this paper. The sensor operates in the frequency band between 77–81 GHz. As a key component of the system, wideband microstrip grid array antennas with a broadside beam are presented and discussed. The combination with a highly integrated feeding network and a four-channel transceiver chip based on SiGe technology results in a very compact LTCC RF frontend (23 mm $\times$ 23 mm). To verify the feasibility of the concept, first radar measurement results are presented.


title: Performance analysis of faster than symbol rate sampling in 1-bit massive MIMO systems

abstract: Low resolution analog-to-digital converters (ADC) attracted much attention lately for massive multiple-input multiple-output (MIMO) communication and systems with high bandwidth. Especially, 1-bit ADCs are suitable for such systems due to their low power consumption and cost. In this study, we illustrate the benefits of using faster than symbol rate (FTSR) sampling in an uplink massive MIMO system with 1-bit ADCs in terms of symbol error rate (SER). We show that FTSR sampling provides about 4 dB signal-to-noise ratio (SNR) advantage in terms of SER with a linear low complexity zero-forcing type receiver. We also obtain an analytical bound for the SER performance of uplink massive MIMO structures with 1-bit quantization for the FTSR sampling scenario for low, medium and high SNR regimes. The proposed analytical bound is also applicable to no FTSR sampling case and shown to yield more accurate results compared to some other analytical expressions in the literature. Our results establish a tradeoff between temporal oversampling and the number of receive antennas.


title: Learnability and the Vapnik-Chervonenkis dimension

abstract: Valiant's learnability model is extended to learning classes of concepts defined by regions in Euclidean space En. The methods in this paper lead to a unified treatment of some of Valiant's results, along with previous results on distribution-free convergence of certain pattern recognition algorithms. It is shown that the essential condition for distribution-free learnability is finiteness of the Vapnik-Chervonenkis dimension, a simple combinatorial parameter of the class of concepts to be learned. Using this parameter, the complexity and closure properties of learnable classes are analyzed, and the necessary and sufficient conditions are provided for feasible learnability.


title: Financial Cryptography in 7 Layers

abstract: Financial Cryptography is substantially complex, requiring skills drawn from diverse and incompatible, or at least, unfriendly, disciplines. Caught between Central Banking and Cryptography, or between accountants and programmers, there is a grave danger that efforts to construct Financial Cryptography systems will simplify or omit critical disciplines.This paper presents a model that seeks to encompass the breadth of Financial Cryptography (at the clear expense of the depth of each area). By placing each discipline into a seven layer model of introductory nature, where the relationship between each adjacent layer is clear, this model should assist project, managerial and requirements people.Whilst this model is presented as efficacious, there are limits to any model. This one does not propose a methodology for design, nor a checklist for protocols. Further, given the young heritage of the model, and of the field itself, it should be taken as a hint of complexity rather than a defining guide.


title: Piecewise smooth surface reconstruction

abstract: We present a general method for automatic reconstruction of accurate, concise, piecewise smooth surface models from scattered range data. The method can be used in a variety of applications such as reverse engineering—the automatic generation of CAD models from physical objects. Novel aspects of the method are its ability to model surfaces of arbitrary topological type and to recover sharp features such as creases and corners. The method has proven to be effective, as demonstrated by a number of examples using both simulated and real data. A key ingredient in the method, and a principal contribution of this paper, is the introduction of a new class of piecewise smooth surface representations based on subdivision. These surfaces have a number of properties that make them ideal for use in surface reconstruction: they are simple to implement, they can model sharp features concisely, and they can be fit to scattered range data using an unconstrained optimization procedure.


title: Underwater Acoustic Navigation Using a Beacon With a Spiral Wave Front

abstract: In this paper, a method for performing underwater acoustic navigation using a spiral wave-front beacon is examined. A transducer designed to emit a signal whose phase changes by 360 $^{\circ}$ in one revolution can be used in conjunction with a reference signal to determine the aspect of a remote receiver relative to the beacon. Experiments are conducted comparing spiral wave-front beacon navigation to Global Positioning System (GPS) onboard an unmanned surface vehicle. The advantages and disadvantages of several outgoing signals and processing techniques are compared. The most successful technique involves the use of a phased array projector utilizing a broadband signal. Aspect is determined by using a weighted mean over frequencies. Sources of error for each of the techniques are also examined.


title: Cerebral Autoregulation Under Moderate Hypothermia in Patients With Acute Stroke

abstract: Background and Purpose— We undertook this study to examine the integrity of cerebral autoregulation in patients with acute ischemic stroke treated with moderate hypothermia (33°C). Methods— Fourteen patients, aged 58±11 years, with an acute anterior circulation infarction and National Institutes of Health Stroke Scale score >15 were evaluated. Patients received catecholamines (norepinephrine) via continuous intravenous infusion and were mechanically ventilated. Alpha-stat was used for pH maintenance. Arterial pressure (AP) and intracranial pressure (ICP) were invasively monitored. Flow velocity in the middle cerebral artery (MCA) supplying the unaffected hemisphere was continuously monitored. Instantaneous maximum flow velocity (Vmax MCA), ICP, and AP were simultaneously recorded in real time. Mean values of Vmax MCA (Vmean MCA) and AP (MAP) were calculated over 1 minute. Static cerebral autoregulation (sCA) was calculated as sCA=(%ΔCVR/%ΔMAP)×100% (where %ΔCVR is an estimate of percent change in cerebrov...


title: Trajectory Triangulation : 3 D Reconstruction of Moving Points from a Monocular Image Sequence

abstract: We consider the problem of reconstructing the 3D coordinates of a moving point seen from a monocular moving camera, i.e., to reconstruct moving objects from line-of-sight measurements only. The task is feasible only when some constraints are placed on the shape of the trajectory of the moving point. We coin the family of such tasks as "trajectory triangulation." We investigate the solutions for points moving along a straight-line and along conic-section trajectories, We show that if the point is moving along a straight line, then the parameters of the line (and, hence, the 3D position of the point at each time instant) can be uniquely recovered, and by linear methods, from at least five views. For the case of conic-shaped trajectory, we show that generally nine views are sufficient for a unique reconstruction of the moving point and fewer views when the conic is of a known type (like a circle in 3D Euclidean space for which seven views are sufficient). The paradigm of trajectory triangulation, in general, pushes the envelope of processing dynamic scenes forward. Thus static scenes become a particular case of a more general task of reconstructing scenes rich with moving objects (where an object could be a single point).