title: Adaptive Role Playing Games: An Immersive Approach for Problem Based Learning

abstract: In this paper we present a general framework, called NUCLEO, for the application of socio-constructive educational approaches in higher education. The underlying pedagogical approach relies on an adaptation model in order to improve group dynamics, as this has been identified as one of the key features in the success of collaborative learning scenarios. Students’ learning strategies are analyzed considering a simplified version of Vermunt’s model for learning styles. The resulting profiles provide the basis to group students in teams where each student is assigned a role according to his/her learning strategies. The result is the formation of complementary and semi-autonomous learning teams that collaborate to achieve solutions to the problems provided by the instructor. The framework is instantiated through an online multiplayer role-playing game environment, which sets a stage for the underlying collaborative problem-based learning approach. The framework has been benchmarked in different programming courses at the Complutense University of Madrid during 2007-08 and 2008-09 academic years. This work also presents the results of these experiences.


title: Generating artificial errors for grammatical error correction

abstract: This paper explores the generation of artificial errors for correcting grammatical mistakes made by learners of English as a second language. Artificial errors are injected into a set of error-free sentences in a probabilistic manner using statistics from a corpus. Unlike previous approaches, we use linguistic information to derive error generation probabilities and build corpora to correct several error types, including open-class errors. In addition, we also analyse the variables involved in the selection of candidate sentences. Experiments using the NUCLE corpus from the CoNLL 2013 shared task reveal that: 1) training on artificially created errors improves precision at the expense of recall and 2) different types of linguistic information are better suited for correcting different error types.


title: A Verilog piecewise-linear analog behavior model for mixed-signal validation

abstract: Full chip mixed-signal validation requires simulating the entire design through a large number of test vectors, which makes fast, event-based Verilog models of analog circuits essential. We describe an extensible approach to creating these models that maps continuous signals into piecewise linear waveforms by creating analog events which contain a value and slope. By breaking analog circuits into sub-blocks with mostly unidirectional ports, we avoid explicit time integration, thus fitting well into an event-driven digital framework. The result is Verilog analog functional models that are pin-accurate, fast to simulate and capture the key dynamics in analog circuits. A 2.5V-1.8V buck converter and 1GHz PLL models are demonstrated.


title: Security Testing: A Survey

abstract: Identifying vulnerabilities and ensuring security functionality by security testing is a widely applied measure to evaluate and improve the security of software. Due to the openness of modern software-based systems, applying appropriate security testing techniques is of growing importance and essential to perform effective and efficient security testing. Therefore, an overview of actual security testing techniques is of high value both for researchers to evaluate and refine the techniques and for practitioners to apply and disseminate them. This chapter fulfills this need and provides an overview of recent security testing techniques. For this purpose, it first summarize the required background of testing and security engineering. Then, basics and recent developments of security testing techniques applied during the secure software development lifecycle, i.e., model-based security testing, code-based testing and static analysis, penetration testing and dynamic analysis, as well as security regression testing are discussed. Finally, the security testing techniques are illustrated by adopting them for an example three-tiered web-based business application.


title: Cloud security ecosystem for data security and privacy

abstract: In the past couple of years Cloud Computing has become an eminent part of the IT industry. As a result of its economic benefits more and more people are heading towards Cloud adoption. In present times there are numerous Cloud Service providers (CSP) allowing customers to host their applications and data onto Cloud. However Cloud Security continues to be the biggest obstacle in Cloud adoption and thereby prevents customers from accessing its services. Various techniques have been implemented by provides in order to mitigate risks pertaining to Cloud security. In this paper, we present a Hybrid Cryptographic System (HCS) that combines the benefits of both symmetric and asymmetric encryption thus resulting in a secure Cloud environment. The paper focuses on creating a secure Cloud ecosystem wherein we make use of multi-factor authentication along with multiple levels of hashing and encryption. The proposed system along with the algorithm are simulated using the CloudSim simulator. To this end, we illustrate the working of our proposed system along with the simulated results.


title: 0.18um BCD technology with best-in-class LDMOS from 6 V to 45 V

abstract: We propose a novel nLDMOS structure and a design concept in BCD technology with the best-in-class performance. The drift profile is optimized and the multi-oxide in the drift region is adopted to approach the RESURF limit of on-resistance vs. BVdss characteristic (i.e., 36V DMOS has a Ron_sp of 20mohm-mm2 with a BVdss of 50V; 45V DMOS has a Ron_sp of 28mohm-mm2 with a BVdss of 65V). Moreover, this modification requires merely three extra masks in the Non-Epitaxy LV process to achieve this improvement. Therefore it is not only a high performance but also a low cost solution.


title: Adaptive PID Controller based on Reinforcement Learning for Wind Turbine Control

abstract: A self tuning PID control strategy using reinforcement learning is proposed in this paper to deal with the control of wind energy conversion systems (WECS). Actor-Critic learning is used to tune PID parameters in an adaptive way by taking advantage of the model-free and on-line learning properties of reinforcement learning effectively. In order to reduce the demand of storage space and to improve the learning efficiency, a single RBF neural network is used to approximate the policy function of Actor and the value function of Critic simultaneously. The inputs of RBF network are the system error, as well as the first and the second-order differences of error. The Actor can realize the mapping from the system state to PID parameters, while the Critic evaluates the outputs of the Actor and produces TD error. Based on TD error performance index and gradient descent method, the updating rules of RBF kernel function and network weights were given. Simulation results show that the proposed controller is efficient for WECS and it is perfectly adaptable and strongly robust, which is better than that of a conventional PID controller.


title: The Uncertainty Bellman Equation and Exploration

abstract: We consider the exploration/exploitation problem in reinforcement learning. For exploitation, it is well known that the Bellman equation connects the value at any time-step to the expected value at subsequent time-steps. In this paper we consider a similar uncertainty Bellman equation (UBE), which connects the uncertainty at any time-step to the expected uncertainties at subsequent time-steps, thereby extending the potential exploratory benefit of a policy beyond individual time-steps. We prove that the unique fixed point of the UBE yields an upper bound on the variance of the estimated value of any fixed policy. This bound can be much tighter than traditional count-based bonuses that compound standard deviation rather than variance. Importantly, and unlike several existing approaches to optimism, this method scales naturally to large systems with complex generalization. Substituting our UBE-exploration strategy for $\epsilon$-greedy improves DQN performance on 51 out of 57 games in the Atari suite.


title: Can distributed software development be agile?

abstract: Three organizations studied here suggest the answer is "yes," when the unique characteristics of both environments are successfully blended.


title: ORIGINAL ARTICLE Usability and Design Guidelines of Smart Canes for Users with Visual Impairments

abstract: A white cane is one of the most common mobility aids for the visually impaired. However, it does not help users with visual impairments find obstacles at head- or knee-level, or at distances greater than 1 m. To overcome these difficulties, smart canes with vibration alerts and an extended obstacle detection range have been introduced. However, several usability problems mean that users with visual impairments rarely adopt a smart cane. The goal of this study was to understand the potential for using a smart cane, along with the existing usability problems and then to develop design guidelines for improved smart canes. Both quantitative and qualitative methods were used to address these goals. A quantitative evaluation was performed of users with visual impairments using a prototype smart cane. Then, in-depth interviews of cane users were conducted. The results showed that a smart cane was more effective in avoiding obstacles than a white cane, but there were several potential usability problems. On the basis of the results, design guidelines for a smart cane prototype were devised.


title: Doctrine for Cybersecurity

abstract: Abstract A succession of doctrines for enhancing cybersecurity has been advocated in the past, including prevention, risk management, and deterrence through accountability. None has proved effective. Proposals that are now being made view cybersecurity as a public good and adopt mechanisms inspired by those used for public health. This essay discusses the failings of previous doctrines and surveys the landscape of cybersecurity through the lens that a new doctrine, public cybersecurity, provides.


title: Subspace gradient domain mesh deformation

abstract: In this paper we present a general framework for performing constrained mesh deformation tasks with gradient domain techniques. We present a gradient domain technique that works well with a wide variety of linear and nonlinear constraints. The constraints we introduce include the nonlinear volume constraint for volume preservation, the nonlinear skeleton constraint for maintaining the rigidity of limb segments of articulated figures, and the projection constraint for easy manipulation of the mesh without having to frequently switch between multiple viewpoints. To handle nonlinear constraints, we cast mesh deformation as a nonlinear energy minimization problem and solve the problem using an iterative algorithm. The main challenges in solving this nonlinear problem are the slow convergence and numerical instability of the iterative solver. To address these issues, we develop a subspace technique that builds a coarse control mesh around the original mesh and projects the deformation energy and constraints onto the control mesh vertices using the mean value interpolation. The energy minimization is then carried out in the subspace formed by the control mesh vertices. Running in this subspace, our energy minimization solver is both fast and stable and it provides interactive responses. We demonstrate our deformation constraints and subspace deformation technique with a variety of constrained deformation examples.


title: Toward automatic program synthesis

abstract: An elementary outline of the theorem-proving approach to automatic program synthesis is given, without dwelling on technical details. The method is illustrated by the automatic construction of both recursive and iterative programs operating on natural numbers, lists, and trees. In order to construct a program satisfying certain specifications, a theorem induced by those specifications is proved, and the desired program is extracted from the proof. The same technique is applied to transform recursively defined functions into iterative programs, frequently with a major gain in efficiency. It is emphasized that in order to construct a program with loops or with recursion, the principle of mathematical induction must be applied. The relation between the version of the induction rule used and the form of the program constructed is explored in some detail.


title: Booby trapping software

abstract: Cyber warfare is asymmetric in the current paradigm, with attackers having the high ground over defenders. This asymmetry stems from the situation that attackers have the initiative, while defenders concentrate on passive fortifications. Defenders are constantly patching the newest hole in their defenses and creating taller and thicker walls, without placing guards on those walls to watch for the enemy and react to attacks. Current passive cyber security defenses such as intrusion detection, anti-virus, and hardened software are not sufficient to repel attackers. In fact, in conventional warfare this passivity would be entirely nonsensical, given the available active strategies, such as counterattacks and deception. Based on this observation, we have identified the technique of booby trapping software. This extends the arsenal of weaponry available to defenders with an active technique for directly reacting to attacks. Ultimately, we believe this approach will restore some of the much sought after equilibrium between attackers and defenders in the digital domain.


title: A brief survey of blackhole detection and avoidance for ZRP protocol in MANETs

abstract: Within Mobile Ad-Hoc Network(MANETs) unusual categories of routing protocols have been determined. It is a group of wireless system. MANET is other susceptible to a variety of attack than wired system. Black hole attack is further meticulous intimidation to MANETs. The routing protocols of MANET is less protected and therefore consequenced the system with malicious node. There are various routing protocols being used for MANETs. All routing protocols have been briefly discussed, however, Zone Routing Protocol (ZRP) is discussed in detail. Black hole is a major security threat for MANETs. Hence, in this paper, the various techniques used for detection and avoidance of Black hole attack in MANETs using ZRP routing protocol have been discussed.


title: Coda: A Highly Available File System for a Distributed Workstation Environment

abstract: The design and implementation of Coda, a file system for a large-scale distributed computing environment composed of Unix workstations, is described. It provides resiliency to server and network failures through the use of two distinct but complementary mechanisms. One mechanism, server replication, stores copies of a file at multiple servers. The other mechanism, disconnected operation, is a mode of execution in which a caching site temporarily assumes the role of a replication site. The design of Coda optimizes for availability and performance and strives to provide the highest degree of consistency attainable in the light of these objectives. Measurements from a prototype show that the performance cost of providing high availability in Coda is reasonable. >


title: BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation

abstract: Semantic segmentation requires both rich spatial information and sizeable receptive field. However, modern approaches usually compromise spatial resolution to achieve real-time inference speed, which leads to poor performance. In this paper, we address this dilemma with a novel Bilateral Segmentation Network (BiSeNet). We first design a Spatial Path with a small stride to preserve the spatial information and generate high-resolution features. Meanwhile, a Context Path with a fast downsampling strategy is employed to obtain sufficient receptive field. On top of the two paths, we introduce a new Feature Fusion Module to combine features efficiently. The proposed architecture makes a right balance between the speed and segmentation performance on Cityscapes, CamVid, and COCO-Stuff datasets. Specifically, for a 2048 \(\times \) 1024 input, we achieve 68.4% Mean IOU on the Cityscapes test dataset with speed of 105 FPS on one NVIDIA Titan XP card, which is significantly faster than the existing methods with comparable performance.


title: Genetic Operator PSO with Information Reused Scheme and its Application in VRP

abstract: For the sake of characters of logistics, Improved Genetic Operator based Particle Swarm Optimization (GOPSO) is proposed. An pseudo-continuous encoding algorithm are proposed to the GOPSO for the Vehicle routing problem(VRP), then information of all the particles is reserved and stored. The reserved information can be utilized to reform and update the particles, by which the efficiency of GOPSO is improved. The effectiveness and advantage of the proposed algorithm is demonstrated by the simulations.


title: Artificial intelligence in drug design

abstract: Thanks to the fast improvement of the computing power and the rapid development of the computational chemistry and biology, the computer-aided drug design techniques have been successfully applied in almost every stage of the drug discovery and development pipeline to speed up the process of research and reduce the cost and risk related to preclinical and clinical trials. Owing to the development of machine learning theory and the accumulation of pharmacological data, the artificial intelligence (AI) technology, as a powerful data mining tool, has cut a figure in various fields of the drug design, such as virtual screening, activity scoring, quantitative structure-activity relationship (QSAR) analysis, de novo drug design, and in silico evaluation of absorption, distribution, metabolism, excretion and toxicity (ADME/T) properties. Although it is still challenging to provide a physical explanation of the AI-based models, it indeed has been acting as a great power to help manipulating the drug discovery through the versatile frameworks. Recently, due to the strong generalization ability and powerful feature extraction capability, deep learning methods have been employed in predicting the molecular properties as well as generating the desired molecules, which will further promote the application of AI technologies in the field of drug design.


Design and Simulation of Wireless Sensor Network Topologies Using the ZigBee Standard


title: FGPGA: An efficient genetic approach for producing feasible graph partitions

abstract: Graph partitioning, a well studied problem of parallel computing has many applications in diversified fields such as distributed computing, social network analysis, data mining and many other domains. In this paper, we introduce FGPGA, an efficient genetic approach for producing feasible graph partitions. Our method takes into account the heterogeneity and capacity constraints of the partitions to ensure balanced partitioning. Such approach has various applications in mobile cloud computing that include feasible deployment of software applications on the more resourceful infrastructure in the cloud instead of mobile hand set. Our proposed approach is light weight and hence suitable for use in cloud architecture. We ensure feasibility of the partitions generated by not allowing over-sized partitions to be generated during the initialization and search. Our proposed method tested on standard benchmark datasets significantly outperforms the state-of-the-art methods in terms of quality of partitions and feasibility of the solutions.


title: Finexus: Tracking Precise Motions of Multiple Fingertips Using Magnetic Sensing

abstract: With the resurgence of head-mounted displays for virtual reality, users need new input devices that can accurately track their hands and fingers in motion. We introduce Finexus, a multipoint tracking system using magnetic field sensing. By instrumenting the fingertips with electromagnets, the system can track fine fingertip movements in real time using only four magnetic sensors. To keep the system robust to noise, we operate each electromagnet at a different frequency and leverage bandpass filters to distinguish signals attributed to individual sensing points. We develop a novel algorithm to efficiently calculate the 3D positions of multiple electromagnets from corresponding field strengths. In our evaluation, we report an average accuracy of 1.33 mm, as compared to results from an optical tracker. Our real-time implementation shows Finexus is applicable to a wide variety of human input tasks, such as writing in the air.


title: A Live-User Study of Opinionated Explanations for Recommender Systems

abstract: This paper describes an approach for generating rich and compelling explanations in recommender systems, based on opinions mined from user-generated reviews. The explanations highlight the features of a recommended item that matter most to the user and also relate them to other recommendation alternatives and the user's past activities to provide a context.


title: Improved Multimodal Deep Learning with Variation of Information

abstract: Deep learning has been successfully applied to multimodal representation learning problems, with a common strategy to learning joint representations that are shared across multiple modalities on top of layers of modality-specific networks. Nonetheless, there still remains a question how to learn a good association between data modalities; in particular, a good generative model of multimodal data should be able to reason about missing data modality given the rest of data modalities. In this paper, we propose a novel multimodal representation learning framework that explicitly aims this goal. Rather than learning with maximum likelihood, we train the model to minimize the variation of information. We provide a theoretical insight why the proposed learning objective is sufficient to estimate the data-generating joint distribution of multimodal data. We apply our method to restricted Boltzmann machines and introduce learning methods based on contrastive divergence and multi-prediction training. In addition, we extend to deep networks with recurrent encoding structure to finetune the whole network. In experiments, we demonstrate the state-of-the-art visual recognition performance on MIR-Flickr database and PASCAL VOC 2007 database with and without text features.


title: Zero-Shot Learning With Transferred Samples

abstract: By transferring knowledge from the abundant labeled samples of known source classes, zero-shot learning (ZSL) makes it possible to train recognition models for novel target classes that have no labeled samples. Conventional ZSL approaches usually adopt a two-step recognition strategy, in which the test sample is projected into an intermediary space in the first step, and then the recognition is carried out by considering the similarity between the sample and target classes in the intermediary space. Due to this redundant intermediate transformation, information loss is unavoidable, thus degrading the performance of overall system. Rather than adopting this two-step strategy, in this paper, we propose a novel one-step recognition framework that is able to perform recognition in the original feature space by using directly trained classifiers. To address the lack of labeled samples for training supervised classifiers for the target classes, we propose to transfer samples from source classes with pseudo labels assigned, in which the transferred samples are selected based on their transferability and diversity. Moreover, to account for the unreliability of pseudo labels of transferred samples, we modify the standard support vector machine formulation such that the unreliable positive samples can be recognized and suppressed in the training phase. The entire framework is fairly general with the possibility of further extensions to several common ZSL settings. Extensive experiments on four benchmark data sets demonstrate the superiority of the proposed framework, compared with the state-of-the-art approaches, in various settings.


Image Copy Move Forgery Detection using Block Representing Method


title: Reinventing germanium avalanche photodetector for nanophotonic on-chip optical interconnects

abstract: Integration of optical communication circuits directly into high-performance microprocessor chips can enable extremely powerful computer systems. A germanium photodetector that can be monolithically integrated with silicon transistor technology is viewed as a key element in connecting chip components with infrared optical signals. Such a device should have the capability to detect very-low-power optical signals at very high speed. Although germanium avalanche photodetectors (APD) using charge amplification close to avalanche breakdown can achieve high gain and thus detect low-power optical signals, they are universally considered to suffer from an intolerably high amplification noise characteristic of germanium. High gain with low excess noise has been demonstrated using a germanium layer only for detection of light signals, with amplification taking place in a separate silicon layer. However, the relatively thick semiconductor layers that are required in such structures limit APD speeds to about 10 GHz, and require excessively high bias voltages of around 25 V (ref. 12). Here we show how nanophotonic and nanoelectronic engineering aimed at shaping optical and electrical fields on the nanometre scale within a germanium amplification layer can overcome the otherwise intrinsically poor noise characteristics, achieving a dramatic reduction of amplification noise by over 70 per cent. By generating strongly non-uniform electric fields, the region of impact ionization in germanium is reduced to just 30 nm, allowing the device to benefit from the noise reduction effects that arise at these small distances. Furthermore, the smallness of the APDs means that a bias voltage of only 1.5 V is required to achieve an avalanche gain of over 10 dB with operational speeds exceeding 30 GHz. Monolithic integration of such a device into computer chips might enable applications beyond computer optical interconnects-in telecommunications, secure quantum key distribution, and subthreshold ultralow-power transistors.


title: Branding the brain: A critical review and outlook

abstract: Abstract The application of neuroscience to marketing, and in particular to the consumer psychology of brands, has gained popularity over the past decade in the academic and the corporate world. In this paper, we provide an overview of the current and previous research in this area and explain why researchers and practitioners alike are excited about applying neuroscience to the consumer psychology of brands. We identify critical issues of past research and discuss how to address these issues in future research. We conclude with our vision of the future potential of research at the intersection of neuroscience and consumer psychology.


title: A smart gate drive with self-diagnosis for power MOSFETs and IGBTs

abstract: This paper proposes a useful smart gate drive with self-diagnosis for power MOSFETs and IGBTs. This new method is based on monitoring of the gate charge and discharge currents. Two gate current monitoring circuits are proposed and validated with experimental results. The implemented smart functions include self-acknowledgement, gate drive connection verification, power device status diagnosis, gate dielectric wearout detection and prediction. The proposed method can detect abnormal conditions that occur either within the gate drive circuit itself or in the driven power device. It also features simple and low cost implementation and can be easily integrated into gate drive circuits or gate drive chips.


Sketching storyboards to illustrate interface behaviors


title: Optic Disc Boundary and Vessel Origin Segmentation of Fundus Images

abstract: This paper presents a novel classification-based optic disc (OD) segmentation algorithm that detects the OD boundary and the location of vessel origin (VO) pixel. First, the green plane of each fundus image is resized and morphologically reconstructed using a circular structuring element. Bright regions are then extracted from the morphologically reconstructed image that lie in close vicinity of the major blood vessels. Next, the bright regions are classified as bright probable OD regions and non-OD regions using six region-based features and a Gaussian mixture model classifier. The classified bright probable OD region with maximum Vessel-Sum and Solidity is detected as the best candidate region for the OD. Other bright probable OD regions within 1-disc diameter from the centroid of the best candidate OD region are then detected as remaining candidate regions for the OD. A convex hull containing all the candidate OD regions is then estimated, and a best-fit ellipse across the convex hull becomes the segmented OD boundary. Finally, the centroid of major blood vessels within the segmented OD boundary is detected as the VO pixel location. The proposed algorithm has low computation time complexity and it is robust to variations in image illumination, imaging angles, and retinal abnormalities. This algorithm achieves 98.8%–100% OD segmentation success and OD segmentation overlap score in the range of 72%–84% on images from the six public datasets of DRIVE, DIARETDB1, DIARETDB0, CHASE_DB1, MESSIDOR, and STARE in less than 2.14 s per image. Thus, the proposed algorithm can be used for automated detection of retinal pathologies, such as glaucoma, diabetic retinopathy, and maculopathy.


title: Sampling of graph signals with successive local aggregations

abstract: A new scheme to sample signals defined on the nodes of a graph is proposed. The underlying assumption is that such signals admit a sparse representation in a frequency domain related to the structure of the graph, which is captured by the so-called graph-shift operator. Instead of using the value of the signal observed at a subset of nodes to recover the signal in the entire graph, the sampling scheme proposed here uses as input observations taken at a single node. The observations correspond to sequential applications of the graph-shift operator, which are linear combinations of the information gathered by the neighbors of the node. When the graph corresponds to a directed cycle (which is the support of time-varying signals), our method is equivalent to the classical sampling in the time domain. When the graph is more general, we show that the Vandermonde structure of the sampling matrix, critical when sampling time-varying signals, is preserved. Sampling and interpolation are analyzed first in the absence of noise, and then noise is considered. We then study the recovery of the sampled signal when the specific set of frequencies that is active is not known. Moreover, we present a more general sampling scheme, under which, either our aggregation approach or the alternative approach of sampling a graph signal by observing the value of the signal at a subset of nodes can be both viewed as particular cases. Numerical experiments illustrating the results in both synthetic and real-world graphs close the paper.


title: Sharing features between objects and their attributes

abstract: Visual attributes expose human-defined semantics to object recognition models, but existing work largely restricts their influence to mid-level cues during classifier training. Rather than treat attributes as intermediate features, we consider how learning visual properties in concert with object categories can regularize the models for both. Given a low-level visual feature space together with attribute-and object-labeled image data, we learn a shared lower-dimensional representation by optimizing a joint loss function that favors common sparsity patterns across both types of prediction tasks. We adopt a recent kernelized formulation of convex multi-task feature learning, in which one alternates between learning the common features and learning task-specific classifier parameters on top of those features. In this way, our approach discovers any structure among the image descriptors that is relevant to both tasks, and allows the top-down semantics to restrict the hypothesis space of the ultimate object classifiers. We validate the approach on datasets of animals and outdoor scenes, and show significant improvements over traditional multi-class object classifiers and direct attribute prediction models.


title: A Local Contrast Method for Small Infrared Target Detection

abstract: Robust small target detection of low signal-to-noise ratio (SNR) is very important in infrared search and track applications for self-defense or attacks. Consequently, an effective small target detection algorithm inspired by the contrast mechanism of human vision system and derived kernel model is presented in this paper. At the first stage, the local contrast map of the input image is obtained using the proposed local contrast measure which measures the dissimilarity between the current location and its neighborhoods. In this way, target signal enhancement and background clutter suppression are achieved simultaneously. At the second stage, an adaptive threshold is adopted to segment the target. The experiments on two sequences have validated the detection capability of the proposed target detection method. Experimental evaluation results show that our method is simple and effective with respect to detection accuracy. In particular, the proposed method can improve the SNR of the image significantly.


title: A small-size internal dual-band metal-strip antenna for 2.4/5 GHz WLAN operation in the laptop computer

abstract: This paper presents a novel small-size dual-band metal-strip antenna promising to operate as an internal laptop antenna for WLAN operation in the 2.4 and 5 GHz bands has been proposed, fabricated and tested. The antenna is especially suited to be employed in the narrow spacing between the top edge of the display panel and the casing of the laptop computer. The antenna is formed by using two inverted-L metal strips, including one driven strip and one parasitic shorted strip, stacked together to achieve a compact configuration, which is different from the traditional shorted T-shaped monopole antenna or planar inverted-F antenna. This compact configuration leads to a small lateral length of 20 mm for the proposed antenna. The antennapsilas two resonant modes can be easily fine-adjusted to cover the desired operating bands. For the fabricated prototype studied in the paper, good radiation characteristics over the 2.4 and 5 GHz bands have been obtained. It is perspective that the proposed antenna will become a promising internal antenna for 2.4/5 GHz WLAN operation for laptop computer applications.


title: Intrusion Detection Techniques for Mobile Wireless Networks

abstract: The rapid proliferation of wireless networks and mobile computing applications has changed the landscape of network security. The traditional way of protecting networks with firewalls and encryption software is no longer sufficient and effective. We need to search for new architecture and mechanisms to protect the wireless networks and mobile computing application. In this paper, we examine the vulnerabilities of wireless networks and argue that we must include intrusion detection in the security architecture for mobile computing environment. We have developed such an architecture and evaluated a key mechanism in this architecture, anomaly detection for mobile ad-hoc network, through simulation experiments.


title: High-speed and low-power VLSI-architecture for inexact speculative adder

abstract: This paper presents carry-lookahead adder (CLA) based design of the contemporary inexact-speculative adder (ISA) which is fine grain pipelined to include few logic gates along its critical path and thereby, enhancing the frequency of operation. Additionally, various stages of the proposed ISA architecture has been clock gated to reduce the power consumed by this design. Functional verification and hardware implementation for various configurations of the suggested ISA is carried out on field-programmable gate-array (FPGA) platform. It could operate at a maximum clock frequency of 324 MHz which is 52% better then the conventional ISA. Thereafter, the synthesis and post-layout simulation of 32-bit proposed ISA is carried out using 90 nm complementary metal-oxide semiconductors (CMOS) technology node for power and area analysis. Our design occupied 5.11 mm2 of chip area and consumed 9.68 mW of total power at 400 MHz clock frequency. The proposed ISA burns 52.8% lesser power than the state-of-the-art work.


Message from the President and the Vice President CONTENT


title: A Review of Performance Evaluation Measures for Hierarchical Classifiers

abstract: Criteria for evaluating the performance of a classifier are an important part in its design. They allow to estimate the behavior of the generated classifier on unseen data and can be also used to compare its performance against the performance of classifiers generated by other classification algorithms. There are currently several performance measures for binary and flat classification problems. For hierarchical classification problems, where there are multiple classes which are hierarchically related, the evaluation step is more complex. This paper reviews the main evaluation metrics proposed in the literature to evaluate hierarchical classification models.


title: An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks

abstract: Deep learning is formulated as a discrete-time optimal control problem. This allows one to characterize necessary conditions for optimality and develop training algorithms that do not rely on gradients with respect to the trainable parameters. In particular, we introduce the discrete-time method of successive approximations (MSA), which is based on the Pontryagin's maximum principle, for training neural networks. A rigorous error estimate for the discrete MSA is obtained, which sheds light on its dynamics and the means to stabilize the algorithm. The developed methods are applied to train, in a rather principled way, neural networks with weights that are constrained to take values in a discrete set. We obtain competitive performance and interestingly, very sparse weights in the case of ternary networks, which may be useful in model deployment in low-memory devices.


title: Learning to lip read words by watching videos

abstract: Abstract Our aim is to recognise the words being spoken by a talking face, given only the video but not the audio. Existing works in this area have focussed on trying to recognise a small number of utterances in controlled environments ( e.g. digits and alphabets), partially due to the shortage of suitable datasets. We make three novel contributions: first, we develop a pipeline for fully automated data collection from TV broadcasts. With this we have generated a dataset with over a million word instances, spoken by over a thousand different people; second, we develop a two-stream convolutional neural network that learns a joint embedding between the sound and the mouth motions from unlabelled data. We apply this network to the tasks of audio-to-video synchronisation and active speaker detection; third, we train convolutional and recurrent networks that are able to effectively learn and recognize hundreds of words from this large-scale dataset. In lip reading and in speaker detection, we demonstrate results that exceed the current state-of-the-art on public benchmark datasets.


title: A Point-to-Ray Framework for Generating Smooth Parallel Parking Maneuvers

abstract: The problem of generating smooth parallel parking maneuver for autonomous car-like vehicles is considered, wherein the vehicle moves forward from the starting point to an intermediate point and then traces a path backward to the parking position. Any error in accurately reaching the intermediate point leads to maneuver replanning and hence puts extra computational burden on the planner. Addressing the issue, this letter formulates the problem in a point-to-ray framework connecting the parking position to all points in a neighborhood of the intermediate point. Exploiting its asymptotic convergence, four parameter logistic (4PL) curve based S-shaped path is proposed as a solution with a single maneuver connecting the intermediate point neighborhood to the parking position. Inherent to the 4PL curve, the maneuver presents a continuous steering profile with zero steering angle at the ends. Parking slot collision avoidance and steering limit of the vehicle are satisfied by deducing closed-form analytic conditions on the two design parameters. Together with the robustness to intermediate point variation, comparison with existing approaches highlights computational and design advantages. The proposed approach is validated using a double-tracked Ackermann steering vehicle model.


title: Automatic Instrument Segmentation in Robot-Assisted Surgery Using Deep Learning

abstract: Semantic segmentation of robotic instruments is an important problem for the robot-assisted surgery. One of the main challenges is to correctly detect an instrument's position for the tracking and pose estimation in the vicinity of surgical scenes. Accurate pixel-wise instrument segmentation is needed to address this challenge. In this paper we describe our deep learning-based approach for robotic instrument segmentation. Our approach demonstrates an improvement over the state-of-the-art results using several deep neural network architectures. It addressed the binary segmentation problem, where every pixel in an image is labeled as an instrument or background from the surgery video feed. In addition, we address a multi-class segmentation problem, in which we distinguish between different instruments or different parts of an instrument from the background. In this setting, our approach outperforms other methods for automatic instrument segmentation thereby providing state-of-the-art results for these problems. The source code for our solution is made publicly available at https://github.com/ternaus/robot-surgery-segmentation.


title: Expressing Robot Incapability

abstract: Our goal is to enable robots to express their incapability, and to do so in a way that communicates both what they are trying to accomplish and why they are unable to accomplish it. We frame this as a trajectory optimization problem: maximize the similarity between the motion expressing incapability and what would amount to successful task execution, while obeying the physical limits of the robot. We introduce and evaluate candidate similarity measures, and show that one in particular generalizes to a range of tasks, while producing expressive motions that are tailored to each task. Our user study supports that our approach automatically generates motions expressing incapability that communicate both what and why to end-users, and improve their overall perception of the robot and willingness to collaborate with it in the future.


title: Mixed Verbs in Code-Switching: The Syntax of Light Verbs

abstract: This study investigates word order variation in Korean-English and Japanese-English code-switching, with specific focus on the relative placement of the object and the verb in two contrasting word orders, Object-Verb (OV) vs. Verb-Object (VO). The results of an experiment eliciting code-switching judgment data provides strong evidence indicating that the distinction between heavy vs. light verbs plays a major role in deriving different word orders in mixed verb constructions in Korean-English and Japanese-English code-switching. In particular, an explanation pursued in this research supports the hypothesis that parametric variation is attributed to differences in the features of a functional category in the lexicon, as assumed in Minimalist Syntax.


title: Robust perceptual color image hashing using randomized hypercomplex matrix factorizations

abstract: Compact representations of color image and video content allow efficient search, retrieval and storage of this content over the Internet and online repositories. However, most of these representations neither take into account the inherent correlation nor the perceptual redundancy of the color information. In this paper, we propose a perceptual hash representation for color images using robust image features. These features, most dominant singular vectors extracted using the quaternion singular value decomposition (QSVD) of pseudorandomly selected overlapping image blocks, are efficiently used for color image search and retrieval applications. Their robustness is guaranteed by the underlying singular vectors. The motivation behind our work is twofold: 1) the ability of the QSVD algorithm to provide the best low-rank approximation of color images in the Frobenius norm sense and 2) compact representations to handle the color components as a single entity. The QSVD algorithm leads to proper modeling of possible geometric attacks as an independent and identically-distributed (i.i.d) quaternionic random noise on the singular vectors. Such modeling simplifies the hash code detector design. Hash code robustness against geometric attacks is evaluated over a large set of test color images where the proposed scheme outperforms existing factorization-based hashing algorithms in terms of lower miss and false alarm probabilities by orders of magnitude. Finally, the improved robustness and performance does not come at the expense of increased computational complexity which is another salient feature of the proposed hashing scheme.


title: Variable Stiffness Actuators: Review on Design and Components

abstract: Variable stiffness actuators (VSAs) are complex mechatronic devices that are developed to build passively compliant, robust, and dexterous robots. Numerous different hardware designs have been developed in the past two decades to address various demands on their functionality. This review paper gives a guide to the design process from the analysis of the desired tasks identifying the relevant attributes and their influence on the selection of different components such as motors, sensors, and springs. The influence on the performance of different principles to generate the passive compliance and the variation of the stiffness are investigated. Furthermore, the design contradictions during the engineering process are explained in order to find the best suiting solution for the given purpose. With this in mind, the topics of output power, potential energy capacity, stiffness range, efficiency, and accuracy are discussed. Finally, the dependencies of control, models, sensor setup, and sensor quality are addressed.


title: Research of a dual stage bending dexterous robotic hand with EMG control

abstract: This Paper presented a new dual stage bending dexterous robotic hand whose soft composite finger (SCF) was consisted of a soft composite structure and a solid hinge both driven by shape memory alloy (SMA) wires. There are three layers of the soft composite structure: an equivalent natural plane with elastic PVC plate, an actuation layer with SMA wires and the basal structure using soft material polydimethylsiloxane (PDMS). The designation of the SCF was inspired by human fingers whose dimension information and movement principle determined the parameters of the bionic finger. The bending principle of the SCF can be describe as below: the hinge provides relative rotation between phalanx and metacarpal bone; the soft composite structure bends on this basis and provides a soft touch. The dual stage bending structure enhances deformation and force output. Several SCFs of different length form the dexterous robotic hand. Various gestures are achieved by the dexterous robotic hand through controlling the kinestate of the actuators. Electromyography (EMG) was used to control the bionic hand.


title: Explicit Computation of Input Weights in Extreme Learning Machines

abstract: We present a closed form expression for initializing the input weights in a multilayer perceptron, which can be used as the first step in synthesis of an Extreme Learning Machine. The expression is based on the standard function for a separating hyperplane as computed in multilayer perceptrons and linear Support Vector Machines; that is, as a linear combination of input data samples. In the absence of supervised training for the input weights, random linear combinations of training data samples are used to project the input data to a higher dimensional hidden layer. The hidden layer weights are solved in the standard ELM fashion by computing the pseudoinverse of the hidden layer outputs and multiplying by the desired output values. All weights for this method can be computed in a single pass, and the resulting networks are more accurate and more consistent on some standard problems than regular ELM networks of the same size.


title: Smooth eye movement interaction using EOG glasses

abstract: Orbits combines a visual display and an eye motion sensor to allow a user to select between options by tracking a cursor with the eyes as the cursor travels in a circular path around each option. Using an off-the-shelf Jins MEME pair of eyeglasses, we present a pilot study that suggests that the eye movement required for Orbits can be sensed using three electrodes: one in the nose bridge and one in each nose pad. For forced choice binary selection, we achieve a 2.6 bits per second (bps) input rate at 250ms per input. We also inntroduce Head Orbits, where the user fixates the eyes on a target and moves the head in synchrony with the orbiting target. Measuring only the relative movement of the eyes in relation to the head, this method achieves a maximum rate of 2.0 bps at 500ms per input. Finally, we combine the two techniques together with a gyro to create an interface with a maximum input rate of 5.0 bps.


Improvement of Ad hoc ON Demand Distance Vector ( AODV ) Routing Algorithm in Wireless Sensor Network ( WSN )


title: An agent-based decision support system for wholesale electricity market,” Decision Support Systems

abstract: Application software has been developed for analyzing and understanding a dynamic price change in the US wholesale power market. Traders can use the software as an effective decision-making tool by modeling and simulating a power market. The software uses different features of a decision support system by creating a framework for assessing new trading strategies in a competitive electricity trading environment. The practicality of the software is confirmed by comparing its estimation accuracy with those of other methods (e.g., neural network and genetic algorithm). The software has been applied to a data set regarding the California electricity crisis in order to examine whether the learning (convergence) speed of traders is different between the two periods (before and during the crisis). Such an application confirms the validity of the proposed software.


title: Computer-Aided Decision-Making with Trust Relations and Trust Domains (Cryptographic Applications) ⋆

abstract: We propose generic declarative definitions of individual and collective trust relations between interacting agents and agent collections, and trust domains of trust-related agents in distributed systems. Our definitions yield (1) (in)compatibility, implicational, and transitivity results for trust relationships, including a Datalog-implementability result for their logical structure; (2) computational complexity results for deciding potential and actual trust relationships and membership in trust domains; (3) a positive (negative) compositionality result for strong (weak) trust domains; (4) a computational design pattern for building up strong trust domains; and (5) a negative scalability result for trust domains in general. We instantiate our generic trust concepts in five major cryptographic applications of trust, namely: Access Control, Trusted Third Parties, the Web of Trust, Public-Key Infrastructures, and Identity-Based Cryptography. We also show that accountability induces trust. Our defining principle for weak and strong trust (domains) is (common) belief in and (common) knowledge of agent correctness, respectively.


title: Spectral Signature of Alpine Snow Cover from the Landsat Thematic Mapper

abstract: Mapping of snow and estimation of snow characteristics from satellite remote sensing data require that we distinguish snow from other surface cover and from clouds and compensate for the effects of the atmosphere and rugged terrain. The spectral signature of the snowpack is calculated from a radiative transfer model, accounting for scattering and absorption by the ice grains, water inclusions, and particulates. In interpreting the spectral reflectance measured from the Thematic Mapper, or from any other satellite, we need to account for topographic effects without requiring that satellite data be precisely registered to digital elevation data, because the poor quality of most digital elevation data introduces considerable noise into calculations of slope and azimuth. For a range of snow types, atmospheric profiles, and topographic illumination conditions, I estimate typical spectral signatures for the Landsat Thematic Mapper. TM images of the southern Sierra Nevada are analyzed to distinguish several classes of snow from other surface covers. Snow can be reliably distinguished from other surfaces and from clouds at all sun angles encountered in the mid-latitudes. In TM Band 1 or 2, snow is brighter than other natural surfaces, and in Band 5 clouds are usually much more reflective than snow. Therefore, the intersection of the following criteria maps the snow cover: planetary reflectance Rp in TM Band 1 is greater than about 0.16; Rp (TM5) is less than about 0.2; the normalized Band 2 – 5 difference, [Rp(TM2) − Rp(TM5)] / [Rp(TM2) + Rp(TM5)], is greater than about 0.4. Large surface grain sizes can be distinguished from areas where the grain size is finer at the snow surface, using TM Bands 2, 4 and 5. Because of saturation in TM Band 1, estimation of the degree of contamination by absorbing aerosols is not feasible. [Rp(TM2) − Rp(TM5)]/[Rp(TM2) + Rp(TM5)], is


title: Substrate Integrated Waveguide Loaded by Complementary Split-Ring Resonators and Its Applications to Miniaturized Waveguide Filters

abstract: A substrate integrated waveguide with square complementary split-ring resonators (CSRRs) etched on the waveguide surface is investigated in this paper. The proposed structures allow the implementation of a forward-wave passband propagating below the characteristic cutoff frequency of the waveguide. By changing the orientations of the CSRRs, which are incorporated in the waveguide surface and can be interpreted in terms of electric dipoles, varied passband characteristics are observed. A detailed explanation for the generation and variations of the passbands has been illuminated. The application of this waveguide and CSRR combination technique to the design of miniaturized waveguide bandpass filters characterized by transmission zeros is then illustrated. Filter design methodology is examined. These proposed filters exhibit high selectivity and compact size due to the employment of the subwavelength resonators and an evanescent-wave transmission. By slightly altering the configuration of the CSRRs, we find that the propagation of the TE10 mode can be suppressed and filters with improved selectivity and stopband rejection can be obtained. To verify the presented concept, three different types of filters are fabricated based on the standard printed circuit board process. The measured results are in good agreement with the simulation.


title: Investigation of relation between speech perception and production based on EEG source reconstruction

abstract: Mirror neuron system has been investigated using the functional magnetic resonance imaging (fMRI) technique. Activation of the Broca's area and the premotor cortex (PMC), which related with speech production, were observed during speech perception, and seems to be a mirror. However, it is not clear how the mirror neurons function between speech production and perception. This study attempts to investigate the functions of the mirror neurons by utilizing the high temporal resolution of the Electroencephalography (EEG) system. The participants watched Chinese material from screen then heard the material reading from an earphone, finally made a judgement about the consistency of the two stimuli. The high-density EEG signal under source reconstruction revealed that the Wernicke's area activated before the Broca's area and PMC during the speech perception tasks. Results are also consistent with the mirror neuron system: the speech production related regions are working during the speech perception tasks.


title: Analysis of subway station capacity with the use of queueing theory

abstract: A new concept of subway station capacity (SSC) is defined according to the gathering and scattering process. A queuing network analytical model of station is created for calculating SSC, which is built by M/G/C/C state dependent queuing network and discrete time Markov chain (DTMC). Based on the definition and the analytical queuing network, a SSC optimization model is developed, whose objective function is to optimize SSC with a satisfactory rate of remaining passengers. Besides, a solution to the model is proposed integrating response surface methodology with iterative generalized expansion method (IGEM) and DTMC. A case study of Beijing Station in Beijing subway line 2 is implemented to verify the validity and practicability of the proposed methods by comparison with simulation model in different experiments. Finally, some sensitivity analysis results are provided to identify the nodes that have the greatest impact on SSC.


title: Design and implementation of smart home energy management systems based on zigbee

abstract: Today, organizations use IEEE802.15.4 and ZigBee to effectively deliver solutions for a variety of areas including consumer electronic device control, energy management and efficiency home and commercial building automation as well as industrial plant management. The Smart home energy network has gained widespread attentions due to its flexible integration into everyday life. This next generation green home system transparently unifies various home appliances, smart sensors and wireless communication technologies. The green home energy network gradually forms a complex system to process various tasks. Developing this trend, we suggest a new Smart Home Energy Management System (SHEMS) based on an IEEE802.15.4 and ZigBee (we call it as a "ZigBee sensor network"). The proposed smart home energy management system divides and assigns various home network tasks to appropriate components. It can integrate diversified physical sensing information and control various consumer home devices, with the support of active sensor networks having both sensor and actuator components. We develop a new routing protocol DMPR (Disjoint Multi Path based Routing) to improve the performance of our ZigBee sensor networks. This paper introduces the proposed home energy control system's design that provides intelligent services for users. We demonstrate its implementation using a real environment.


title: Rattle : A Data Mining GUI for R by

abstract: Data mining delivers insights, pat- terns, and descriptive and predictive models from the large amounts of data available today in many organisations. The data miner draws heavily on methodologies, techniques and al- gorithms from statistics, machine learning, and computer science. R increasingly provides a powerful platform for data mining. However, scripting and programming is sometimes a chal- lenge for data analysts moving into data mining. The Rattle package provides a graphical user in- terface specifically for data mining using R. It also provides a stepping stone toward using R as a programming language for data analysis.


title: Musicality-Novelty Generative Adversarial Nets for Algorithmic Composition

abstract: Algorithmic composition, which enables computer to generate music like human composers, has lasting charm because it intends to approximate artistic creation, most mysterious part of human intelligence. To deliver both melodious and refreshing music, this paper proposes the Musicality-Novelty Generative Adversarial Nets for algorithmic composition. With the same generator, two adversarial nets alternately optimize the musicality and novelty of the machine-composed music. A new model called novelty game is presented to maximize the minimal distance between the machine-composed music sample and any human-composed music sample in the novelty space, where all well-known human composed music products are far from each other. We implement the proposed framework using three supervised CNNs with one for generator, one for musicality critic and one for novelty critic on the time-pitch feature space. Specifically, the novelty critic is implemented by Siamese neural networks with temporal alignment using dynamic time warping. We provide empirical validations by generating the music samples under various scenarios.


title: Requirements engineering paper classification and evaluation criteria: a proposal and a discussion

abstract: In recent years, members of the steering committee of the IEEE Requirements Engineering (RE) Conference have discussed paper classification and evaluation criteria for RE papers. The immediate trigger for this discussion was our concern about differences in opinion that sometimes arise in program committees about the criteria to be used in evaluating papers. If program committee members do not all use the same criteria, or if they use criteria different from those used by authors, then papers might be rejected or accepted for the wrong reasons. Surely not all papers should be evaluated according to the same criteria. Some papers describe new techniques but do not report on empirical research; others describe new conceptual frameworks for investigating certain RE problems; others report on industrial experience with existing RE techniques. Other kinds of papers can also be easily recognized. All of these types of papers should be evaluated according to different criteria. But we are far from a consensus about what classes of paper we should distinguish, and what the criteria are for each of these classes.


title: Lecture Notes on Holographic Renormalization

abstract: We review the formalism of holographic renormalization. We start by discussing mathematical results on asymptotically anti-de Sitter (AdS) spacetimes. We then outline the general method of holographic renormalization. The method is illustrated by working all details in a simple example: a massive scalar field on anti-de Sitter spacetime. The discussion includes the derivation of the on-shell renormalized action, holographic Ward identities, anomalies and renormalization group (RG) equations, and the computation of renormalized one-, two- and four-point functions. We then discuss the application of the method to holographic RG flows. We also show that the results of the near-boundary analysis of asymptotically AdS spacetimes can be analytically continued to apply to asymptotically de Sitter spacetimes. In particular, it is shown that the Brown–York stress energy tensor of de Sitter spacetime is equal, up to a dimension-dependent sign, to the Brown–York stress energy tensor of an associated AdS spacetime.


title: RegTrack: a differential relative gps tracking solution

abstract: In many mobile wireless applications such as the automated driving of cars, formation flying of unmanned air vehicles, and source localization or target tracking with wireless sensor networks, it is more important to know the precise relative locations of nodes than their absolute coordinates. GPS, the most ubiquitous localization system available, generally provides only absolute coordinates. Furthermore, low-cost receivers can exhibit tens of meters of error or worse in challenging RF environments. This video demonstration presents an approach that uses GPS to derive relative location information for multiple receivers. Cooperating nodes in a network share their raw satellite measurements and use this data to track the relative motions of neighboring nodes as opposed to computing their own absolute coordinates. This is achieved via creation of a new error model that incorporates GPS localization errors specific to the multiple-receiver case, development of a new, highly accurate observation model that allows for the change in range between a single satellite and two receivers to be mapped through time, and the synthesis of these two models into a novel 3D pairwise tracking algorithm that uses the local GPS node as its own reference and requires only an approximate estimate of its own absolute position to achieve centimeter-scale relative tracking precision. The system just described was implemented on a network of Android smartphones equipped with a Bluetooth-enabled, custom GPS node to provide raw measurement data. Several experiments were carried out to test our proof of concept in various GPS environments and under different types of dynamic conditions. Our evaluation shows that centimeter-scale tracking accuracy at an update rate of 1 Hz is possible under various conditions with the presented technique. This is more than an order of magnitude more accurate than simply taking the difference of reported absolute node coordinates or other simplistic approaches due to uncorrelated measurement errors. The demo shown in this video represents one of the high-dynamic test cases in which we placed a stationary GPS receiver on a tripod in the middle of a parking lot and three additional nodes on top of an automobile. We proceeded to drive the automobile through various environments, including a multipath-rich alleyway, moderately benign suburban roads, and at high speeds on an interstate highway. The total length of the experiment was 15 minutes, spanning 12.2 km of terrain, with the baseline vector between the stationary node in the parking lot and the roving nodes on the automobile ranging from 0 to 3.5 km at any given time. The visualization of the experimental results is taken from the point of view of the stationary node, enabling us to map the relative tracks of the roving nodes in an absolute coordinate space using Google Earth. The results of this experiment showed centimeter-scale tracking accuracy - a level of precision that enables clear distinction of driving features such as lane shifts and velocity changes - under various driving conditions, with only one problematic loss of satellite locks due to signal obstruction by a wide overpass while the vehicle was changing directions. This momentary loss of lock caused the tracking algorithm to resume after approximately 2 meters of error had already been accrued, and this error remained present and constant throughout the remainder of the experiment. Future work involves solving for initialization and re-calibration of the tracking algorithm on the fly, such that losses of lock and visibility obstructions do not degrade the accuracy of the system in the long run. In its present form, RegTrack already provides an order of magnitude better precision than complementary methods using low-cost, single-frequency commercial receivers, and the stage is set for further research to increase its robustness and utility for high-precision, low-cost applications.


Recent advances in autonomic provisioning of big data applications on clouds


title: Speech Acts for Dialogue Agents

abstract: A dialogue agent is one that can interact and communicate with other agents, in a coherent manner, not just with one-shot messages, but with a sequence of related messages all on the same topic or in service of an overall goal. Following the basic insights of speech act theory, these communications are seen not just as transmitting information but as actions which change the state of the world. Most of these changes will be to the mental states of the agents involved in the conversation, as well as the state or context of the dialogue. As such, speech act theory allows an agent theorist or designer to place agent communication within the same general framework as agent action. In general, though, communicative action requires a more expressive logic of action than is required for something like the single-agent blocks world, familiar in classical AI planning. For one thing, there are multiple agents, and there is also a possibility of simultaneous and fallible action.


title: DRONE: Predicting Priority of Reported Bugs by Multi-factor Analysis

abstract: Bugs are prevalent. To improve software quality, developers often allow users to report bugs that they found using a bug tracking system such as Bugzilla. Users would specify among other things, a description of the bug, the component that is affected by the bug, and the severity of the bug. Based on this information, bug triagers would then assign a priority level to the reported bug. As resources are limited, bug reports would be investigated based on their priority levels. This priority assignment process however is a manual one. Could we do better? In this paper, we propose an automated approach based on machine learning that would recommend a priority level based on information available in bug reports. Our approach considers multiple factors, temporal, textual, author, related-report, severity, and product, that potentially affect the priority level of a bug report. These factors are extracted as features which are then used to train a discriminative model via a new classification algorithm that handles ordinal class labels and imbalanced data. Experiments on more than a hundred thousands bug reports from Eclipse show that we can outperform baseline approaches in terms of average F-measure by a relative improvement of 58.61%.


title: Using NaWmMan uage ~ Processing f m Pro e ? uce

abstract: Improved numerical weather prediction simulations have led weather services to examine how and where human forecasters add value to forecast production. The Forecast Production Assistant (FPA) was developed with that in mind. The authors discuss the Forecast Generator (FOG), the first application developed on the FPA. FOG is a bilingual report generator that produces routine and special purpose forecast directly from the FPA's graphical weather predictions. Using rules and a natural-language generator, FOG converts weather maps into forecast text. The natural-language issues involved are relevant to anyone designing a similar system. >


title: K-tree: Large Scale Document Clustering

abstract: We introduce K-tree in an information retrieval context. It is an efficient approximation of the k-means clustering algorithm. Unlike k-means it forms a hierarchy of clusters. It has been extended to address issues with sparse representations. We compare performance and quality to CLUTO using document collections. The K-tree has a low time complexity that is suitable for large document collections. This tree structure allows for efficient disk based implementations where space requirements exceed that of main memory.


title: Conical monopole type UWB antenna with SRR to tune out the suppressed dual bands

abstract: In this paper, conical monopole type UWB antenna which suppress dual bands is proposed. The SSRs were arranged in such a way that the interaction of the magnetic field with them enables the UWB antenna to reject the dual bands using the resonance of SRRs. The proposed conical monopole antenna has a return loss less than −10dB and antenna gain greater than 5dB at 2GHz∼11GHz frequency band, except the suppressed bands. The return loss and gain at WiMAX and WLAN bands is greater than −3dB and less than 0dB respectively.


title: Experiences with an Interactive Museum Tour-Guide Robot

abstract: This article describes the software architecture of an autonomous, interactive tour-guide robot. It presents a modular and distributed software architecture, which integrates localization, mapping, collision avoidance, planning, and various modules concerned with user interaction and Web-based telepresence. At its heart, the software approach relies on probabilistic computation, on-line learning, and any-time algorithms. It enables robots to operate safely, reliably, and at high speeds in highly dynamic environments, and does not require any modifications of the environment to aid the robot’s operation. Special emphasis is placed on the design of interactive capabilities that appeal to people’s intuition. The interface provides new means for human-robot interaction with crowds of people in public places, and it also provides people all around the world with the ability to establish a “virtual telepresence” using the Web. To illustrate our approach, results are reported obtained in mid-1997, when our robot “RHINO” was deployed for a period of six days in a densely populated museum. The empirical results demonstrate reliable operation in public environments. The robot successfully raised the museum’s attendance by more than 50%. In addition, thousands of people all over the world controlled the robot through the Web. We conjecture that these innovations transcend to a much larger range of application domains for service robots. © 1999 Elsevier Science B.V. All rights reserved.


TALE-SPIN, An Interactive Program that Writes Stories


title: Stochastic Training of Neural Networks via Successive Convex Approximations

abstract: This paper proposes a new family of algorithms for training neural networks (NNs). These are based on recent developments in the field of nonconvex optimization, going under the general name of successive convex approximation techniques. The basic idea is to iteratively replace the original (nonconvex, highly dimensional) learning problem with a sequence of (strongly convex) approximations, which are both accurate and simple to optimize. Different from similar ideas (e.g., quasi-Newton algorithms), the approximations can be constructed using only first-order information of the NN function, in a stochastic fashion, while exploiting the overall structure of the learning problem for a faster convergence. We discuss several use cases, based on different choices for the loss function (e.g., squared loss and cross-entropy loss), and for the regularization of the NN's weights. We experiment on several medium-sized benchmark problems and on a large-scale data set involving simulated physical data. The results show how the algorithm outperforms the state-of-the-art techniques, providing faster convergence to a better minimum. Additionally, we show how the algorithm can be easily parallelized over multiple computational units without hindering its performance. In particular, each computational unit can optimize a tailored surrogate function defined on a randomly assigned subset of the input variables, whose dimension can be selected depending entirely on the available computational power.


title: Analysis of GDI Technique for Digital Circuit Design

abstract: Power Dissipation of Digital circuits can be reduced by 15% 25% by using appropriate logic restructuring and also it can be reduced by 40% - 60% by lowering switching activity. Here, Gate Diffusion Input Technique which is based on a Shannon expansion is analyzed for minimizing the power consumption and delay of static digital circuits. This technique as compare to other currently used logic design style, allows less power consumption and reduced propagation delay for low-power design of combinatorial digital circuits with minimum number of transistors. In this paper, basic building blocks of digital system and few combinational circuits are analyzed using GDI and other CMOS techniques. All circuits are designed at 180nm technology in CADENCE and simulate using VIRTUOSO SPECTRE simulator at 100 MHz frequency. Comparative analysis has been done among GDI and other parallel design styles for designing ripple adder, CLA adder and bit magnitude comparator. Simulation result shows GDI technique saves 53.3%, 55.6% and 75.6% power in ripple adder, CLA adder and bit magnitude comparator respectively as compare to CMOS. Also delay is reduced with 25.2%, 3.4% and 6.9% as compare to CMOS. Analysis conclude that GDI is revolutionary high speed and low power consumption technique.


title: Skype-Hunter: A real-time system for the detection and classification of Skype traffic

abstract: In the previous years, Skype has gained more and more popularity, since it is seen as the best VoIP software with good quality of sound, ease of use and one that works everywhere and with every OS. Because of its great diffusion, both the operators and the users are, for different reasons, interested in detecting Skype traffic. ::: ::: In this paper we propose a real-time algorithm (named Skype-Hunter) to detect and classify Skype traffic. In more detail, this novel method, by means of both signature-based and statistical procedures, is able to correctly reveal and classify the signaling traffic as well as the data traffic (calls and file transfers). To assess the effectiveness of the algorithm, experimental tests have been performed with several traffic data sets, collected in different network scenarios. Our system outperforms the ‘classical’ statistical traffic classifiers as well as the state-of-the-art ad hoc Skype classifier. Copyright © 2011 John Wiley & Sons, Ltd.


title: Digital forensic framework using feedback and case history keeper

abstract: Cyber crime investigation is the integration of two technologies named theoretical methodology and second practical tools. First is the theoretical digital forensic methodology that encompasses the steps to investigate the cyber crime. And second technology is the practically development of the digital forensic tool which sequentially and systematically analyze digital devices to extract the evidence to prove the crime. This paper explores the development of digital forensic framework, combine the advantages of past twenty five forensic models and generate a algorithm to create a new digital forensic model. The proposed model provides the following advantages, a standardized method for investigation, the theory of model can be directly convert into tool, a history lookup facility, cost and time minimization, applicable to any type of digital crime investigation.


title: Comparing time and frequency domain for audio event recognition using deep learning

abstract: Recognizing acoustic events is an intricate problem for a machine and an emerging field of research. Deep neural networks achieve convincing results and are currently the state-of-the-art approach for many tasks. One advantage is their implicit feature learning, opposite to an explicit feature extraction of the input signal. In this work, we analyzed whether more discriminative features can be learned from either the time-domain or the frequency-domain representation of the audio signal. For this purpose, we trained multiple deep networks with different architectures on the Freiburg-106 and ESC-10 datasets. Our results show that feature learning from the frequency domain is superior to the time domain. Moreover, additionally using convolution and pooling layers, to explore local structures of the audio signal, significantly improves the recognition performance and achieves state-of-the-art results.


title: Lower-extremity resistance training on unstable surfaces improves proxies of muscle strength, power and balance in healthy older adults: a randomised control trial

abstract: It is well documented that both balance and resistance training have the potential to mitigate intrinsic fall risk factors in older adults. However, knowledge about the effects of simultaneously executed balance and resistance training (i.e., resistance training conducted on unstable surfaces [URT]) on lower-extremity muscle strength, power and balance in older adults is insufficient. The objective of the present study was to compare the effects of machine-based stable resistance training (M-SRT) and two types of URT, i.e., machine-based (M-URT) and free-weight URT (F-URT), on measures of lower-extremity muscle strength, power and balance in older adults. Seventy-five healthy community-dwelling older adults aged 65–80 years, were assigned to three intervention groups: M-SRT, M-URT and F-URT. Over a period of ten weeks, all participants exercised two times per week with each session lasting ~60 min. Tests included assessment of leg muscle strength (e.g., maximal isometric leg extension strength), power (e.g., chair rise test) and balance (e.g., functional reach test), carried out before and after the training period. Furthermore, maximal training load of the squat-movement was assessed during the last training week. Maximal training load of the squat-movement was significantly lower in F-URT in comparison to M-SRT and M-URT. However, lower-extremity resistance training conducted on even and uneven surfaces meaningfully improved proxies of strength, power and balance in all groups. M-URT produced the greatest improvements in leg extension strength and F-URT in the chair rise test and functional reach test. Aside from two interaction effects, overall improvements in measures of lower-extremity muscle strength, power and balance were similar across training groups. Importantly, F-URT produced similar results with considerably lower training load as compared to M-SRT and M-URT. Concluding, F-URT seems an effective and safe alternative training program to mitigate intrinsic fall risk factors in older adults. This trial has been registered with clinicaltrials.gov (NCT02555033) on 09/18/2015.


title: Personality and citizenship behavior: The mediating role of job satisfaction.

abstract: Using meta-analytic path analysis, the authors tested several structural models linking agreeableness and conscientiousness to organizational citizenship behavior (OCB). Results showed that the 2 personality traits had both direct effects and indirect effects-through job satisfaction-on overall OCB. Meta-analytic moderator analyses that distinguished between individual- and organization-targeted citizenship behaviors (OCB-I and OCB-O) showed that agreeableness was more closely related with OCB-I and conscientiousness with OCB-O. Finally, the path analyses predicting OCB-I and OCB-O offered further support for the general hypothesis that these 2 constructs are distinct. That is, the results of these analyses revealed that agreeableness had both direct and indirect effects on OCB-I but only indirect effects on OCB-O, and that for conscientiousness the pattern of direct and indirect effects was exactly opposite (direct and indirect effects on OCB-O but only indirect effects on OCB-I).


title: A review on Video Quality Assessment

abstract: With the advancement in technology, more and more video content are available on Internet. Video content such as news, sports, entertainment plays a very important role in everybody's life. But before reaching at the user end due to processing and transmission the videos may get distorted. In this paper various artifacts, distortions which gets introduced in videos are discussed. The assessment of quality of video depends upon the type of distortion. In this paper we also discussed two Video Quality Assessment techniques namely: Subjective and Objective assessment. These methods provide the users QoE (Quality of experience) and QoS (Quality of service). However, subjective assessment is considered to the easiest method for evaluation as the evaluation is done with the help of users. But it is very time consuming and tedious. So, most of modern applications are inclined to use the objective assessment. Different VQA performance metrics used for objective assessment are analyzed.


title: Imitating driver behavior with generative adversarial networks

abstract: The ability to accurately predict and simulate human driving behavior is critical for the development of intelligent transportation systems. Traditional modeling methods have employed simple parametric models and behavioral cloning. This paper adopts a method for overcoming the problem of cascading errors inherent in prior approaches, resulting in realistic behavior that is robust to trajectory perturbations. We extend Generative Adversarial Imitation Learning to the training of recurrent policies, and we demonstrate that our model rivals rule-based controllers and maximum likelihood models in realistic highway simulations. Our model both reproduces emergent behavior of human drivers, such as lane change rate, while maintaining realistic control over long time horizons.


title: A fast, high-endurance and scalable non-volatile memory device made from asymmetric Ta2O5−x/TaO2−x bilayer structures

abstract: Resistive switching is a promising technology to replace current non-volatile memory technologies such as flash. The demonstration of a fast, stable and highly scalable resistive-switching memory device represents a significant advance towards the practical implementation of this technology.


title: Balancing the power of multimedia information retrieval and usability in designing interactive tv

abstract: Steady progress in the field of multimedia information retrieval (MMIR) promises a useful set of tools that could provide new usage scenarios and features to enhance the user experience in today's digital media applications. In the interactive TV domain, the simplicity of interaction is more crucial than in any other digital media domain and ultimately determines the success or otherwise of any new applications. Thus when integrating emerging tools like MMIR into interactive TV, the increase in interface complexity and sophistication resulting from these features can easily reduce its actual usability. In this paper we describe a design strategy we developed as a result of our efforts in balancing the power of emerging multimedia information retrieval techniques and maintaining the simplicity of the interface in interactive TV. By providing multiple levels of interface sophistication in increasing order as a viewer repeatedly presses the same button on their remote control, we provide a layered interface that can accommodate viewers requiring varying degrees of power and simplicity. A series of screen shots from the system we have actually developed and built illustrates how this is achieved.


An Analysis of Encoder Representations in Transformer-Based Machine Translation


title: Imaging human EEG dynamics using independent component analysis

abstract: This review discusses the theory and practical application of independent component analysis (ICA) to multi-channel EEG data. We use examples from an audiovisual attention-shifting task performed by young and old subjects to illustrate the power of ICA to resolve subtle differences between evoked responses in the two age groups. Preliminary analysis of these data using ICA suggests a loss of task specificity in independent component (IC) processes in frontal and somatomotor cortex during post-response periods in older as compared to younger subjects, trends not detected during examination of scalp-channel event-related potential (ERP) averages. We discuss possible approaches to component clustering across subjects and new ways to visualize mean and trial-by-trial variations in the data, including ERP-image plots of dynamics within and across trials as well as plots of event-related spectral perturbations in component power, phase locking, and coherence. We believe that widespread application of these and related analysis methods should bring EEG once again to the forefront of brain imaging, merging its high time and frequency resolution with enhanced cm-scale spatial resolution of its cortical sources.


title: Anatomy of the Clitoris: Revision and Clarifications about the Anatomical Terms for the Clitoris Proposed (without Scientific Bases) by Helen O'Connell, Emmanuele Jannini, and Odile Buisson

abstract: The anatomy of the clitoris is described in human anatomy textbooks. Some researchers have proposal and divulged a new anatomical terminology for the clitoris. This paper is a revision of the anatomical terms proposed by Helen O'Connell, Emmanuele Jannini, and Odile Buisson. Gynecologists, sexual medicine experts, and sexologists should spread certainties for all women, not hypotheses or personal opinions, they should use scientific terminology: clitoral/vaginal/uterine orgasm, G/A/C/U spot orgasm, and female ejaculation, are terms that should not be used by sexologists, women, and mass media. Clitoral bulbs, clitoral or clitoris-urethrovaginal complex, urethrovaginal space, periurethral glans, Halban's fascia erogenous zone, vaginal anterior fornix erogenous zone, genitosensory component of the vagus nerve, and G-spot, are terms used by some sexologists, but they are not accepted or shared by experts in human anatomy. Sexologists should define have sex, make love, the situation in which the orgasm happens in both partners with or without a vaginal intercourse.


title: Usurp: distributed NAT traversal for overlay networks

abstract: Many existing overlay networks are not practical on the open Internet because of the presence of Network Address Translation (NAT) devices and firewalls. In this paper, we introduce Usurp, a message routing infrastructure that enables communication between private nodes (behind NATs or firewalls) either by direct connectivity or relaying messages via public nodes (nodes that support direct connectivity). Usurp provides fully distributed NAT-type identification and NAT traversal services using a structured overlay network (SON) built using the public nodes in the system. Private nodes do not join the SON, instead, each private node is assigned a key in the SON's address space and the public node(s) responsible for its key acts as both a rendezvous and relay server to the private node. Usurp is designed as a middleware that existing overlay networks can be built over, enabling them to function correctly in the presence of NATs. We evaluate Usurp using a gossip-based peer sampling service (PSS). Our results show that the PSS running over Usurp preserves its randomness properties and remains connected even in scenarios with high churn rates and where 80% of the nodes are behind NATs. We also show that Usurp only adds a low and manageable overhead to public nodes.


title: 3D Human pose and shape estimation from multi-view imagery

abstract: In this study we present robust solution for estimating 3D pose and shape of human targets from multiple, synchronized video streams. The objective is to automatically estimate physical attributes of the targets that would allow us to analyze its behavior non-intrusively. Proposed system estimates the anthropometric skeleton, pose and shape of the human target from the 3D visual hull reconstructed from multiple silhouettes of the target. Discriminative (bottom-up) method is used to first initialize 3D pose of the targets using low-level features extracted from the 2D image. The pose is refined using generative (top-down) method that also estimates the optimal skeleton of the target using anthropometric prior models learned from the CAESAR dataset. Statistical shape models are also learned from the CAESAR dataset and are used to model both global and local shape variability of human body parts. We also propose a novel optimization scheme to fit 3D shape by searching in the parametric space of local parts model and constraining the overall shape using a global shape model. The system provides a useful framework for automatically identifying dispropotionate body parts, estimating size of backpacks and inferring attributes like gender, age and ethnicity of the human target.


title: Structural inertia and organizational change revisited III: The evolution of organizational inertia. Research Paper 1734. Graduate

abstract: Building on a formal theory of the structural aspects of organizational change initiated in Hannan, Polos, and Carroll (2002a, 2002b), this paper focuses on structural inertia. We define inertia as a persistent organizational resistance to changing architectural features. We examine the evolutionary consequences of architectural inertia. The main theorem holds that selection favors architectural inertia in the sense that the median level of inertia in cohort of organizations presumably increases over time. A second theorem holds that the selection intensity favoring architectural inertia is greater when foresight about the consequences of changes is more limited. According to the prior theory of Hannan, Polos, and Carroll (2002a, 2002b), foresight is limited by complexity and opacity. Thus it follows that the selection intensity favoring architectural inertia is stronger in populations composed of complex and opaque organizations that in those composed of simple and transparent ones.


title: Design of VGA display controller based on FPGA and VHDL

abstract: The paper designs a VGA image and writing display controller with FPGA chip based on EDA technology. The system designs VGA display control module with VHDL, produces the image signal and the control signal with FPGA. In the process of designing the system deposited the color image and the writing information through the LPM module transfer, finally completes video data processing through the simulation and downloading only based on the single chip. This design has achieved a request in the area and speed easily.


title: The Eval that Men Do: A Large-scale Study of the Use of Eval

abstract: Transforming text into executable code with a function such as Java-Script's eval endows programmers with the ability to extend applications, at any time, and in almost any way they choose. But, this expressive power comes at a price: reasoning about the dynamic behavior of programs that use this feature becomes challenging. Any ahead-of-time analysis, to remain sound, is forced to make pessimistic assumptions about the impact of dynamically created code. This pessimism affects the optimizations that can be applied to programs and significantly limits the kinds of errors that can be caught statically and the security guarantees that can be enforced. A better understanding of how eval is used could lead to increased performance and security. This paper presents a large-scale study of the use of eval in JavaScript-based web applications. We have recorded the behavior of 337 MB of strings given as arguments to 550,358 calls to the eval function exercised in over 10,000 web sites. We provide statistics on the nature and content of strings used in eval expressions, as well as their provenance and data obtained by observing their dynamic behavior.


A flexible microcontroller architecture for fail-safe and fail-operational systems


title: Parallel Processing Systems for Big Data : A Survey This survey categorizes the existing parallel data processing systems for big data and introduces representative projects in each category . By

abstract: The volume, variety, and velocity properties of big data and the valuable information it contains have motivated the investigation of many new parallel data processing systems in addition to the approaches using traditional database management systems (DBMSs). MapReduce pioneered this paradigm change and rapidly became the primary big data processing system for its simplicity, scalability, and fine-grain fault tolerance. However, compared with DBMSs, MapReduce also arouses controversy in processing efficiency, low-level abstraction, and rigid dataflow. Inspired by MapReduce, nowadays the big data systems are blooming. Some of them follow MapReduce's idea, but with more flexible models for general-purpose usage. Some absorb the advantages of DBMSs with higher abstraction. There are also specific systems for certain applications, such as machine learning and stream data processing. To explore new research opportunities and assist users in selecting suitable processing systems for specific applications, this survey paper will give a high-level overview of the existing parallel data processing systems categorized by the data input as batch processing, stream processing, graph processing, and machine learning processing and introduce representative projects in each category. As the pioneer, the original MapReduce system, as well as its active variants and extensions on dataflow, data access, parameter tuning, communication, and energy optimizations will be discussed at first. System benchmarks and open issues for big data processing will also be studied in this survey.


title: Overlapping irritable bowel syndrome and inflammatory bowel disease: less to this than meets the eye?

abstract: Though distinct in terms of pathology, natural history and therapeutic approach, irritable bowel syndrome (IBS) and inflammatory bowel disease (IBD) have some features in common. These include shared symptomatology and largely similar demographics. However, in most instances, clinical presentation, together with laboratory, imaging and endoscopic findings will readily permit the differentiation of active IBD from IBS. More problematic is the situation where a subject with IBD, in apparent remission, continues to complain of symptoms which, in aggregate, satisfy commonly employed criteria for the diagnosis of IBS. Access to methodologies, such the assay for levels of calprotectin in feces, now allows identification of ongoing inflammation in some such individuals and prompts appropriate therapy. More challenging is the IBD patient with persisting symptoms and no detectable evidence of inflammation; is this coincident IBS, IBS triggered by IBD or an even more subtle level of IBD activity unrecognized by available laboratory or imaging methods? Arguments can be advanced for each of these proposals; lacking definitive data, this issue remains unresolved. The occurrence of IBS-type symptoms in the IBD patient, together with some data suggesting a very subtle level of 'inflammation' or 'immune activation' in IBS, raises other questions: is IBS a prodromal form of IBD; and are IBS and IBD part of the spectrum of the same disease? All of the available evidence indicates that the answer to both these questions should be a resounding 'no'. Indeed, the whole issue of overlap between IBS and IBD should be declared moot given their differing pathophysiologies, contrasting natural histories and divergent treatment paths. The limited symptom repertoire of the gastrointestinal tract may well be fundamental to the apparent confusion that has, of late, bedeviled this area.