title: Ultrasound based gesture recognition

abstract: In this study, we explore the possibility of recognizing hand gestures using ultrasonic depth imaging. The ultrasonic device consists of a single piezoelectric transducer and an 8 - element microphone array. Using carefully designed transmit pulse, and a combination of beamforming, matched filtering, and cross-correlation methods, we construct ultrasound images with depth and intensity pixels. Thereafter, we use a combined Convolutional (CNN) and Long Short-Term Memory (LSTM) network to recognize gestures from the ultrasound images. We report gesture recognition accuracies in the range 64.5–96.9%, based on the number of gestures to be recognized, and show that ultrasound sensors have the potential to become low power, low computation, and low cost alternatives to existing optical sensors.


title: A Compact UWB Antenna Design for Breast Cancer Detection

abstract: Near fleld imaging using microwave in medical applications has gain much attention recently as various researchers show its high ability and accuracy in illuminating object compares to well known screening tools. This paper documents the development of new compact ultra wide bandwidth antenna designed primarily for microwave imaging application such as breast cancer detection. A parametric study is carried out using two well known softwares packages to achieve optimum antenna performances. The performance of the Prototype antenna is tested and analyzed experimentally and exhibit reasonable agreement with the simulations.


title: Service and Utility Oriented Distributed Computing Systems: Challenges and Opportunities for Modeling and Simulation Communities

abstract: Grids and peer-to-peer (P2P) networks have emerged as popular platforms for the next generation parallel and distributed computing. In these environments, resources are geographically distributed, managed and owned by various organizations with different policies, and interconnected by wide-area networks or the Internet. This introduces a number of resource management and application scheduling challenges in the domain of security, resource and policy heterogeneity, fault tolerance, and dynamic resource conditions. In these dynamic distributed computing environments, it is hard and challenging to carry out resource management design studies in a repeatable and controlled manner as resources and users are autonomous and distributed across multiple organizations with their own policies. Therefore, simulations have emerged as the most feasible technique for analyzing policies for resource allocation. This paper presents emerging trends in distributed computing and their promises for revolutionizing the computing field, and identifies distinct characteristics and challenges in building them. We motivate opportunities for modeling and simulation communities and present our discrete-event grid simulation toolkit, called GridSim, used by researchers world-wide for investigating the design of utility-oriented computing systems such as Data Centers and Grids. We present various case studies on the use of GridSim in modeling and simulation of Business Grids, parallel applications scheduling, workflow scheduling, and service pricing and revenue management.


title: CNN-based pattern recognition on nonvolatile IoT platform for smart ultraviolet monitoring: (Invited paper)

abstract: Intelligent computing and maintenance-free powering are two desirable characteristics of wearable IoT devices. Energy harvesting nonvolatile intelligent processor (NIP) with neural network computation capability has the potential to advance these goals. Individual ultraviolet (UV) exposure monitoring progressively becomes one conspicuous application of wearable devices. In resource constrained wearable sensor nodes, we can alleviate the data transmission burden via convolutional neural networks (CNNs) based pattern recognition. Nevertheless, in spite of the substantially improved computing capability of NIP, typically computational and memory intensive CNNs are still too bulky for on-node implementation. We develop an CNN-based pattern recognition system for nonvolatile IoT platform for smart UV monitoring, and propose a optimization method to achieve extremely tiny and efficient CNNs. Experimental results show that the offline-trained CNN can recognize individual UV exposure patterns with accuracy of 85%, and the simplified on-node CNN can achieve 93.2% parameters reduction with only 5% accuracy loss.


title: A semi-Lagrangian contouring method for fluid simulation

abstract: In this article, we present a semi-Lagrangian surface tracking method for use with fluid simulations. Our method maintains an explicit polygonal mesh that defines the surface, and an octree data structure that provides both a spatial index for the mesh and a means for efficiently approximating the signed distance to the surface. At each timestep, a new surface is constructed by extracting the zero set of an advected signed-distance function. Semi-Lagrangian backward path tracing is used to advect the signed-distance function. One of the primary advantages of this formulation is that it enables tracking of surface characteristics, such as color or texture coordinates, at negligible additional cost. We include several examples demonstrating that the method can be effectively used as part of a fluid simulation to animate complex and interesting fluid behaviors.


title: A Microprocessor-based Hypercube Supercomputer

abstract: Each node in the NCUBE/ten parallel processor is organized around a custom, VAX-like, 32-bit CPU chip. With 1024 nodes, the NCUBE/ten provides a throughput of 500 MELOPS.


title: A novel design method for blass matrix beam-forming networks

abstract: A novel design method for lossy Blass matrix beam-forming networks is presented. Compared to those formerly developed (W.R. Jones and E.C. DuFort, 1971), (S. Mosca et al., 2002), the new method allows the design of such circuits to generate an arbitrary number of simultaneous beams oriented in arbitrary positions. This is obtained by means of an equivalent circuit approach to compute the transmission parameters of the Blass matrix. The method has been applied to the design of a specific multi-beam waveguide application, which required the development and characterization of a novel waveguide cross coupler. Experimental results confirm the accuracy of the method showing high performance for all beams in terms of beam position, side lobe level and efficiency.


title: A history of the workplace : environment and health at stake

abstract: 1. History of the workplace: Environment and health at stake - An introduction Judith Rainhorn and Lars Bluma 2. The hygienic movement and German mining 1890 - 1914 Lars Bluma 3. The banning of white lead: French and American experiences in a comparative perspective (early twentieth century) Judith Rainhorn 4. Aluminium in health and food: a gradual global approach Florence Hachez-Leroy 5. Fiddling, drinking and stealing: moral code in the Soviet Estonian mining industry Eeva Keskula 6. Hygienists, workers' bodies and machines in nineteenth-century France Thomas Le Roux 7. The factory as environment: social engineering and the ecology of industrial workplaces in inter-war Germany Timo Luks 8. The ideal of Lebensraum and the spatial order of power at German factories, 1900 - 45 Karsten Uhl


title: Answering Yes/No Questions via Question Inversion

abstract: This paper investigates a solution to yes/no question answering, which can be mapped to the task of determining the correctness of a given proposition. Generally it is hard to obtain explicit evidence to conclude a proposition is false from an information source, so we convert this task to a set of factoid-style questions and use an existing question answering system as a subsystem. By aggregating the answers and confidence values from a factoid-style question answering system we can determine the correctness of the entire proposition or the substitutions that make the proposition false. We evaluated the system on multiple-choice questions from a university admission test on world history, and found it to be highly accurate.


title: A 0.5–16.3 Gbps Multi-Standard Serial Transceiver With 219 mW/Channel in 16-nm FinFET

abstract: This paper presents a flexible-reach 0.5–16.3 Gb/s serial transceiver which is integrated into a field-programmable gate array (FPGA) and fabricated in 16-nm FinFET CMOS. The transceiver is fully adaptive to cover the FPGA requirement to support a multitude of combinations of data-rates and standards such as 10 G-KR, PCIe Gen3/4, and SFP+ across a wide range of channel loss profiles. High-performance techniques employed include a fully adaptive continuous-time linear equalizer, automatic gain control, an 11-tap decision feedback equalizer, wideband LC phase-locked loops, and a high-tracking-bandwidth clock and data recovery loop with low latency. Low-power techniques such as half-rate clocking, active inductors and data-rate programmability are employed to meet stringent power budgets at the different rates. At 16.3 Gb/s, the receiver has a jitter tolerance of >0.3UI at 100 MHz. The transceiver achieves a bit error rate < 10e − 15 with up to 28-dB loss at Nyquist. It consumes 219 mW/channel at 16.3 Gb/s. The design has robust performance across process, voltage, and temperature, and the architecture allows for a high degree of tuning to handle different system environments in deployment.


title: An Estimation Technology of Temperature Rise in GIS Bus Bar using Three-Dimensional Coupled-Field Multiphysics

abstract: This paper shows the temperature rise of the high voltage GIS bus bar. The temperature rise in GIS bus bar is due to Joule's losses in the conductor and the induced eddy current in the tank. The power losses of a bus bar calculated by the magnetic field analysis are used as the input data rise for the thermal analysis to predict the temperature. The required analysis is a couple-field Multiphysics that accounts for the interactions between three-dimensional AC harmonic magnetic and fluid fields. The heat transfer calculation using the fluid analysis is done by considering the natural convection and the radiation from the tank to the atmosphere. Consequently, because temperature distributions by couple-field Multiphysics (coupled magnetic-fluid) have good agreement with results of temperature rise test, the proposed couple-field Multiphysics technique is likely to be used in a conduction design of the single-pole and three pole-encapsulated bus bar in GIS.


title: Page segmentation of historical document images with convolutional autoencoders

abstract: In this paper, we present an unsupervised feature learning method for page segmentation of historical handwritten documents available as color images. We consider page segmentation as a pixel labeling problem, i.e., each pixel is classified as either periphery, background, text block, or decoration. Traditional methods in this area rely on carefully hand-crafted features or large amounts of prior knowledge. In contrast, we apply convolutional autoencoders to learn features directly from pixel intensity values. Then, using these features to train an SVM, we achieve high quality segmentation without any assumption of specific topologies and shapes. Experiments on three public datasets demonstrate the effectiveness and superiority of the proposed approach.


title: Android smartphone vulnerabilities: A survey

abstract: Round the globe mobile devices like Smartphone, PDAs & tablets are playing an essential role in every person day to day lives. Various operating systems such as android, ios, blackberry etc provides a platform for smart devices. Google's Android is a one of the most popular and user friendly open source software platform for mobile devices. Along with its convenience people are likely to download and install malicious applications developed to misguide the user which will create a security gap to penetrate for the attacker. Hackers are inclined to discover and exploit the new vulnerabilities which will bring forth with the latest version of Android. In this paper, we concentrate on examining and understanding the vulnerabilities exist in android operating system. We will also suggest a metadata model which gives the information of all the related terms required for vulnerability assessment. Here, analyzing data is extracted from Open Source Vulnerability Database (OSVDB) and National Vulnerability Database (NVD).


title: ARAPrototyper: Enabling Rapid Prototyping and Evaluation for Accelerator-Rich Architectures

abstract: Compared to conventional general-purpose processors, accelerator-rich architectures (ARAs) can provide orders-of-magnitude performance and energy gains and are emerging as one of the most promising solutions in the age of dark silicon. However, many design issues related to the complex interaction between general-purpose cores, accelerators, customized on-chip interconnects, and memory systems remain unclear and difficult to evaluate. ::: In this paper we design and implement the ARAPrototyper to enable rapid design space explorations for ARAs in real silicons and reduce the tedious prototyping efforts far down to manageable efforts. First, ARAPrototyper provides a reusable baseline prototype with a highly customizable memory system, including interconnect between accelerators and buffers, interconnect between buffers and last-level cache (LLC) or DRAM, coherency choice at LLC or DRAM, and address translation support. Second, ARAPrototyper provides a clean interface to quickly integrate users' own accelerators written in high-level synthesis (HLS) code. The whole design flow is highly automated to generate a prototype of ARA on an FPGA system-on-chip (SoC). Third, to quickly develop applications that run seamlessly on the ARA prototype, ARAPrototyper provides a system software stack, abstracts the accelerators as software libraries, and provides APIs for software developers. Our experimental results demonstrate that ARAPrototyper enables a wide range of design space explorations for ARAs at manageable prototyping efforts, which has 4,000X to 10,000X faster evaluation time than full-system simulations. We believe that ARAPrototyper can be an attractive alternative for ARA design and evaluation.


title: Convolutional auto-encoder for image denoising of ultra-low-dose CT

abstract: Abstract Objectives The purpose of this study was to validate a patch-based image denoising method for ultra-low-dose CT images. Neural network with convolutional auto-encoder and pairs of standard-dose CT and ultra-low-dose CT image patches were used for image denoising. The performance of the proposed method was measured by using a chest phantom. Materials and methods Standard-dose and ultra-low-dose CT images of the chest phantom were acquired. The tube currents for standard-dose and ultra-low-dose CT were 300 and 10 mA, respectively. Ultra-low-dose CT images were denoised with our proposed method using neural network, large-scale nonlocal mean, and block-matching and 3D filtering. Five radiologists and three technologists assessed the denoised ultra-low-dose CT images visually and recorded their subjective impressions of streak artifacts, noise other than streak artifacts, visualization of pulmonary vessels, and overall image quality. Results For the streak artifacts, noise other than streak artifacts, and visualization of pulmonary vessels, the results of our proposed method were statistically better than those of block-matching and 3D filtering (p-values Conclusion Neural network with convolutional auto-encoder could be trained using pairs of standard-dose and ultra-low-dose CT image patches. According to the visual assessment by radiologists and technologists, the performance of our proposed method was superior to that of large-scale nonlocal mean and block-matching and 3D filtering.


title: Primitive activity recognition from short sequences of sensory data

abstract: Activity recognition from mobile device sensors and wearables is attracting more attention from the research community due to the widespread adoption of these devices and the unique opportunity they provide for understanding user’s behavior leading to novel services and improvements in the delivery of existing ones. Approaches to tackle this problem either rely on predefined statistical features of sensor data streams or feature learning with the latter providing higher accuracies in most cases. Deep learning methods proved more effective than traditional approaches to feature learning in multiple studies. This paper presents a novel end-to-end trainable deep architecture that utilizes multiple convolutional neural networks (CNN), late fusion and extensive layer bypassing. The proposed method can easily accommodate multiple sensors and signal representations. The proposed approach is validated on eight publicly available datasets using a variety of evaluation conditions showing that it outperforms state-of-the-art methods in six of them.


title: Endoscopic endonasal surgery of the midline skull base: anatomical study and clinical considerations.

abstract: OBJECT ::: The midline skull base is an anatomical area that extends from the anterior limit of the cranial fossa down to the anterior border of the foramen magnum. Resection of lesions involving this area requires a variety of innovative skull base approaches. These include anterior, anterolateral, and posterolateral routes, performed either alone or in combination, and resection via these routes often requires extensive neurovascular manipulation. The goals in this study were to define the application of the endoscopic endonasal approach and to become more familiar with the views and skills associated with the technique by using cadaveric specimens. ::: ::: ::: METHODS ::: To assess the feasibility of the endonasal route for the surgical management of lesions in the midline skull base, five fresh cadaver heads injected with colored latex were dissected using a modified endoscopic endonasal approach. Full access to the skull base and the cisternal space around it is possible with this route. From the crista galli to the spinomedullary junction, with incision of the dura mater, a complete visualization of the carotid and vertebrobasilar arterial systems and of all 12 of the cranial nerves is obtainable. ::: ::: ::: CONCLUSIONS ::: The major potential advantage of the endoscopic endonasal approach to the skull base is that it provides a direct anatomical route to the lesion without traversing any major neurovascular structures, obviating brain retraction. Many tumors grow in a medial-to-lateral direction, displacing structures laterally as they expand, creating natural corridors for their resection via an anteromedial approach. Potential disadvantages of this procedure include the relatively restricted working space and the danger of an inadequate dural repair with cerebrospinal fluid (CSF) leakage and potential for meningitis resulting. These approaches often require a large opening of the dura mater over the tuberculum sellae and posterior planum sphenoidale, or retroclival space. In addition, they typically involve large intraoperative CSF leaks, which necessitate precise and effective dural closure.


title: Cryptographic Currencies Crash Course (C4): Tutorial

abstract: "Bitcoin is a rare case where practice seems to be ahead of theory." Joseph Bonneau et al.[15] ::: ::: This tutorial aims to further close the gap between IT security research and the area of cryptographic currencies and block chains. We will describe and refer to Bitcoin as an example throughout the tutorial, as it is the most prominent representative of a such a system. It also is a good reference to discuss the underlying block chain mechanics which are the foundation of various altcoins (e.g. Namecoin) and other derived systems. In this tutorial, the topic of cryptographic currencies is solely addressed from a technical IT security point-of-view. Therefore we do not cover any legal, sociological, financial and economical aspects. ::: ::: The tutorial is designed for participants with a solid IT security background but will not assume any prior knowledge on cryptographic currencies. Thus, we will quickly advance our discussion into core aspects of this field.


title: Juvenile idiopathic scoliosis.

abstract: A series of 98 patients with juvenile idiopathic scoliosis have been analysed. This showed that between the ages of four and six there was a higher incidence in boys whereas between seven and nine years of age, the proportion of girls was higher. Regardless of sex and age the majority of the curves were convex to the right and the single thoracic curve was the commonest pattern. Spontaneous resolution occurred in seven patients: in four the curves resolved within two years; in the three others the curves resolved in three, four and five years respectively. Forty-four per cent of all patients were managed conservatively and in 56 per cent spinal fusion was carried out.


title: A survey on Data Mining approaches for Healthcare

abstract: Data Mining is one of the most motivating area of research that is become increasingly popular in health organization. Data Mining plays an important role for uncovering new trends in healthcare organization which in turn helpful for all the parties associated with this field. This survey explores the utility of various Data Mining techniques such as classification, clustering, association, regression in health domain. In this paper, we present a brief introduction of these techniques and their advantages and disadvantages. This survey also highlights applications, challenges and future issues of Data Mining in healthcare. Recommendation regarding the suitable choice of available Data Mining technique is also discussed in this paper.


title: Big data and sentiment analysis using KNIME: Online reviews vs. social media

abstract: Text analytics and sentiment analysis can help an organization derive potentially valuable business insights from text-based content such as word documents, email and postings on social media streams like Facebook, Twitter and LinkedIn. The system described here analyses opinions about various gadgets collected from two different sources and in two different forms; online reviews and Twitter posts (tweets). Sentiment analysis can be applied to online reviews in easier and more detailed way than to the tweets. Namely, online reviews are written in clear and grammatically more accurate form, while in tweets, internet slang, sarcasm and allegory are often used. System described here explains methods of data collection, sentiment analysis process for online reviews and tweets using KNIME, gives an overview of differences and analysis possibilities in sentiment analysis for both data sources.


title: Trends and lessons from three years fighting malicious extensions

abstract: In this work we expose wide-spread efforts by criminals to abuse the Chrome Web Store as a platform for distributing malicious extensions. A central component of our study is the design and implementation of WebEval, the first system that broadly identifies malicious extensions with a concrete, measurable detection rate of 96.5%. Over the last three years we detected 9,523 malicious extensions: nearly 10% of every extension submitted to the store. Despite a short window of operation--we removed 50% of malware within 25 minutes of creation--a handful of under 100 extensions escaped immediate detection and infected over 50 million Chrome users. Our results highlight that the extension abuse ecosystem is drastically different from malicious binaries: miscreants profit from web traffic and user tracking rather than email spam or banking theft.


title: A Gauge Field Model of Modal Completion

abstract: Perceptual completion of figures is a basic process revealing the deep architecture of low level vision. In this paper a complete gauge field Lagrangian is proposed allowing to couple the retinex equation with neurogeometrical models and to solve the problem of modal completion, i.e. the pop up of the Kanizsa triangle. Euler-Lagrange equations are derived by variational calculus and numerically solved. Plausible neurophysiological implementations of the particle and field equations are discussed and a model of the interaction between LGN and visual cortex is proposed.


title: Planning by Prioritized Sweeping with Small Backups

abstract: Efficient planning plays a crucial role in model-based reinforcement learning. Traditionally, the main planning operation is a full backup based on the current estimates of the successor states. Consequently, its computation time is proportional to the number of successor states. In this paper, we introduce a new planning backup that uses only the current value of a single successor state and has a computation time independent of the number of successor states. This new backup, which we call a small backup, opens the door to a new class of model-based reinforcement learning methods that exhibit much finer control over their planning process than traditional methods. We empirically demonstrate that this increased flexibility allows for more efficient planning by showing that an implementation of prioritized sweeping based on small backups achieves a substantial performance improvement over classical implementations.


title: A prototype sensor node for footstep detection

abstract: Persons moving over ground can be detected from vibrations induced to soil in the form of seismic waves which are measured by geophones or expensive MEMS accelerometers. We are proposing a sensor node that uses a low-cost bending mode piezoelectric accelerometer, operating near its resonant frequency, specially designed for footstep detection.


title: Data Analytics based Deep Mayo Predictor for IPL-9

abstract: This paper presents a Deep Mayo Predictor model for predicting the outcomes of the matches in IPL 9 being played in April – May, 2016. The model has three components which are based on multifarious considerations emerging out of a deeper analysis of T20 cricket. The models are created using Data Analytics methods from machine learning domain. The prediction accuracy obtained is high as the Mayo Predictor Model is able to correctly predict the outcomes of 39 matches out of the 56 matches played in the league stage of the IPL IX tournament. Further improvement in the model can be attempted by using a larger training data set than the one that has been utilized in this work. No such effort at creating predictor models for cricket matches has been reported in the literature.


title: Consequentialist conditional cooperation in social dilemmas with imperfect information

abstract: Social dilemmas, where mutual cooperation can lead to high payoffs but participants face incentives to cheat, are ubiquitous in multi-agent interaction. We wish to construct agents that cooperate with pure cooperators, avoid exploitation by pure defectors, and incentivize cooperation from the rest. However, often the actions taken by a partner are (partially) unobserved or the consequences of individual actions are hard to predict. We show that in a large class of games good strategies can be constructed by conditioning one's behavior solely on outcomes (ie. one's past rewards). We call this consequentialist conditional cooperation. We show how to construct such strategies using deep reinforcement learning techniques and demonstrate, both analytically and experimentally, that they are effective in social dilemmas beyond simple matrix games. We also show the limitations of relying purely on consequences and discuss the need for understanding both the consequences of and the intentions behind an action.


title: A 1000-word vocabulary, speaker-independent, continuous live-mode speech recognizer implemented in a single FPGA

abstract: The Carnegie Mellon In Silico Vox project seeks to move best-quality speech recognition technology from its current software-only form into a range of efficient all-hardware implementations. The central thesis is that, like graphics chips, the application is simply too performance hungry, and too power sensitive, to stay as a large software application. As a first step in this direction, we describe the design and implementation of a fully functional speech-to-text recognizer on a single Xilinx XUP platform. The design recognizes a 1000 word vocabulary, is speaker-independent, recognizes continuous (connected) speech, and is a "live mode" engine, wherein recognition can start as soon as speech input appears. To the best of our knowledge, this is the most complex recognizer architecture ever fully committed to a hardware-only form. The implementation is extraordinarily small, and achieves the same accuracy as state-of-the-art software recognizers, while running at a fraction of the clock speed.


title: Gated Bayesian networks for algorithmic trading

abstract: This paper introduces a new probabilistic graphical model called gated Bayesian network (GBN). This model evolved from the need to represent processes that include several distinct phases. In essence, a GBN is a model that combines several Bayesian networks (BNs) in such a manner that they may be active or inactive during queries to the model. We use objects called gates to combine BNs, and to activate and deactivate them when predefined logical statements are satisfied. In this paper we also present an algorithm for semi-automatic learning of GBNs. We use the algorithm to learn GBNs that output buy and sell decisions for use in algorithmic trading systems. We show how the learnt GBNs can substantially lower risk towards invested capital, while they at the same time generate similar or better rewards, compared to the benchmark investment strategy buy-and-hold. We also explore some differences and similarities between GBNs and other related formalisms. We present a new probabilistic graphical model which we call gated Bayesian networks.A semi-automatic algorithm is presented that can learn a gated Bayesian network.Gated Bayesian networks are applied to the domain of algorithmic trading.We show that gated Bayesian networks can improve results over a standard benchmark.We compare gated Bayesian networks to other common models and formalisms.


title: Municipal Shared Services Cloud

abstract: In essence, governments are information-intensive service enterprises. Electronic government (e-government also known as digital government, online government, among others) is an effort to address the issues in operations, information and IT. It aims to create open and efficient interactions among many stakeholders, e.g. governments, citizens, and business enterprises. In this paper, we detail the importance of information sharing in support of citizen-based service delivery and e-government initiatives. We also describe a study of 12 New York State municipalities and their specific needs and challenges relating to service delivery. We then present our research work on the Municipal Shared Services Cloud, and describe how this technology can be used to deliver municipal government IT services more efficiently and at a lower cost while providing enhanced and customizable information sharing capabilities.


title: Design high power and high efficiency inverter operating at 13.56MHz for wireless power transfer systems

abstract: This paper presents the proposed design of high power and high efficiency inverter for wireless power transfer systems operating at 13.56 MHz using multiphase resonant inverter and GaN HEMT devices. The high efficiency and the stable of inverter are the main targets of the design. The module design, the power loss analysis and the drive circuit design have been addressed. In experiment, a 3 kW inverter with the efficiency of 96.1% is achieved that significantly improves the efficiency of 13.56 MHz inverter. In near future, a 10 kW inverter with the efficiency of over 95% can be realizable by following this design concept.


title: PHONEME-TO-VISEME MAPPINGFOR VISUAL SPEECH RECOGNITION

abstract: Phonemes are the standard modelling unit in HMM-based continuous speech recognition systems. Visemes are the equivalent unit in the visual domain, but there is less agreement on precisely what visemes are, or how many to model on the visual side in audio-visual speech recognition systems. This paper compares the use of 5 viseme maps in a continuous speech recognition task. The focus of the study is visual-only recognition to examine the choice of viseme map. All the maps are based on the phoneme-to-viseme approach, created either using a linguistic method or a data driven method. DCT, PCA and optical flow are used as the visual features. The best visual-only recognition on the VidTIMIT database is achieved using a linguistically motivated viseme set. These initial experiments demonstrate that the choice of visual unit requires more careful attention in audio-visual speech recognition system development.


Graph Representation Learning and Graph Classification by


title: On Type-Aware Entity Retrieval

abstract: Today, the practice of returning entities from a knowledge base in response to search queries has become widespread. One of the distinctive characteristics of entities is that they are typed, i.e., assigned to some hierarchically organized type system (type taxonomy). The primary objective of this paper is to gain a better understanding of how entity type information can be utilized in entity retrieval. We perform this investigation in an idealized "oracle" setting, assuming that we know the distribution of target types of the relevant entities for a given query. We perform a thorough analysis of three main aspects: (i) the choice of type taxonomy, (ii) the representation of hierarchical type information, and (iii) the combination of type-based and term-based similarity in the retrieval model. Using a standard entity search test collection based on DBpedia, we find that type information proves most useful when using large type taxonomies that provide very specific types. We provide further insights on the extensional coverage of entities and on the utility of target types.


title: Multi-document Summarization via Budgeted Maximization of Submodular Functions

abstract: We treat the text summarization problem as maximizing a submodular function under a budget constraint. We show, both theoretically and empirically, a modified greedy algorithm can efficiently solve the budgeted submodular maximization problem near-optimally, and we derive new approximation bounds in doing so. Experiments on DUC'04 task show that our approach is superior to the best-performing method from the DUC'04 evaluation on ROUGE-1 scores.


title: A Formal Study of Shot Boundary Detection

abstract: This paper conducts a formal study of the shot boundary detection problem. First, a general formal framework of shot boundary detection techniques is proposed. Three critical techniques, i.e., the representation of visual content, the construction of continuity signal and the classification of continuity values, are identified and formulated in the perspective of pattern recognition. Meanwhile, the major challenges to the framework are identified. Second, a comprehensive review of the existing approaches is conducted. The representative approaches are categorized and compared according to their roles in the formal framework. Based on the comparison of the existing approaches, optimal criteria for each module of the framework are discussed, which will provide practical guide for developing novel methods. Third, with all the above issues considered, we present a unified shot boundary detection system based on graph partition model. Extensive experiments are carried out on the platform of TRECVID. The experiments not only verify the optimal criteria discussed above, but also show that the proposed approach is among the best in the evaluation of TRECVID 2005. Finally, we conclude the paper and present some further discussions on what shot boundary detection can learn from other related fields


title: Classification of human emotion from EEG using discrete wavelet transform

abstract: In this paper, we summarize the human emotion recognition using different set of electroencephalogram (EEG) channels using discrete wavelet transform. An audio-visual induction based protocol has been designed with more dynamic emotional content for inducing discrete emotions (disgust, happy, surprise, fear and neutral). EEG signals are collected using 64 electrodes from 20 subjects and are placed over the entire scalp using International 10-10 system. The raw EEG signals are preprocessed using Surface Laplacian (SL) filtering method and decomposed into three different frequency bands (alpha, beta and gamma) using Discrete Wavelet Transform (DWT). We have used “db4” wavelet function for deriving a set of conventional and modified energy based features from the EEG signals for classifying emotions. Two simple pattern classification methods, K Nearest Neighbor (KNN) and Linear Discriminant Analysis (LDA) methods are used and their performances are compared for emotional states classification. The experimental results indicate that, one of the proposed features (ALREE) gives the maximum average classification rate of 83.26% using KNN and 75.21% using LDA compared to those of conventional features. Finally, we present the average classification rate and subsets of emotions classification rate of these two different classifiers for justifying the performance of our emotion recognition system.


title: Measuring Invariances in Deep Networks

abstract: For many pattern recognition tasks, the ideal input feature would be invariant to multiple confounding properties (such as illumination and viewing angle, in computer vision applications). Recently, deep architectures trained in an unsupervised manner have been proposed as an automatic method for extracting useful features. However, it is difficult to evaluate the learned features by any means other than using them in a classifier. In this paper, we propose a number of empirical tests that directly measure the degree to which these learned features are invariant to different input transformations. We find that stacked autoencoders learn modestly increasingly invariant features with depth when trained on natural images. We find that convolutional deep belief networks learn substantially more invariant features in each layer. These results further justify the use of "deep" vs. "shallower" representations, but suggest that mechanisms beyond merely stacking one autoencoder on top of another may be important for achieving invariance. Our evaluation metrics can also be used to evaluate future work in deep learning, and thus help the development of future algorithms.


title: Personalized online document, image and video recommendation via commodity eye-tracking

abstract: We propose a new recommendation algorithm for online documents, images and videos, which is personalized. Our idea is to rely on the attention time of individual users captured through commodity eye-tracking as the essential clue. The prediction of user interest over a certain online item (a document, image or video) is based on the user's attention time acquired using vision-based commodity eye-tracking during his previous reading, browsing or video watching sessions over the same type of online materials. After acquiring a user's attention times over a collection of online materials, our algorithm can predict the user's probable attention time over a new online item through data mining. Based on our proposed algorithm, we have developed a new online content recommender system for documents, images and videos. The recommendation results produced by our algorithm are evaluated by comparing with those manually labeled by users as well as by commercial search engines including Google (Web) Search, Google Image Search and YouTube.


title: Behavior change types with Pokémon GO

abstract: Digital games are one of the most popular entertainment media in the world. Their allure and widespread popularity makes them an interesting and highly potential platform for behavior change attempts. In this paper, we investigate what types of behavior changes Pokemon GO has promoted or induced among its players. The study is based on an online survey sample of 262 Pokemon GO players, collected using the critical incident technique and analyzed using qualitative methods. The analysis shows that the behavior changes induced by Pokemon GO are not just restricted to increased physical activity or social behavior but are actually much more multifaceted: players were more social, found their routines more meaningful, expressed more positive emotions, and were more motivated to explore their surroundings.


title: Situating Learning in the Workplace – Having Another Look at Apprenticeships

abstract: Examines the acquisition of vocational skills through apprenticeship‐type situated learning. Presents findings from studies of skilled workers revealing that learning processes that were consonant with the apprenticeship model of learning were highly valued as a means of acquiring and maintaining vocational skills. Supported by current research and theorizing, describes some conditions by which situated learning through apprenticeship can be utilized to develop vocational skills. These conditixons include the nature of the activities learners engage in, the agency of the learning environment and mentoring role of experts. Addresses conditions which may inhibit the effectiveness of an apprenticeship approach to learning. Concludes by suggesting that situated approaches to learning, such as the apprenticeship model, may address problems of access to effective vocational skill development within the workforces.


title: Understanding Hand-Object Manipulation with Grasp Types and Object Attributes

abstract: Our goal is to automate the understanding of natural hand-object manipulation by developing computer visionbased techniques. Our hypothesis is that it is necessary to model the grasp types of hands and the attributes of manipulated objects in order to accurately recognize manipulation actions. Specifically, we focus on recognizing hand grasp types, object attributes and actions from a single image within an unified model. First, we explore the contextual relationship between grasp types and object attributes, and show how that context can be used to boost the recognition of both grasp types and object attributes. Second, we propose to model actions with grasp types and object attributes based on the hypothesis that grasp types and object attributes contain complementary information for characterizing different actions. Our proposed action model outperforms traditional appearance-based models which are not designed to take into account semantic constraints such as grasp types or object attributes. Experiment results on public egocentric activities datasets strongly support our hypothesis.


title: Article Structure and Anonymity of the Bitcoin Transaction Graph

abstract: The Bitcoin network of decentralized payment transactions has attracted a lot of attention from both Internet users and researchers in recent years. Bitcoin utilizes a peer-to-peer network to issue anonymous payment transactions between different users. In the currently used Bitcoin clients, the full transaction history is available at each node of the network to prevent double spending without the need for a central authority, forming a valuable source for empirical research on network structure, network dynamics, and the implied anonymity challenges, as well as guidance on the future evolution of complex payment systems. We found dynamical effects of which some increase anonymity while others decrease it. Most importantly, several parameters of the Bitcoin transaction graph seem to have become stationary over the last 12–18 months. We discuss the implications.


title: Convolutional Neural Networks using Logarithmic Data Representation

abstract: Recent advances in convolutional neural networks have considered model complexity and hardware efficiency to enable deployment onto embedded systems and mobile devices. For example, it is now well-known that the arithmetic operations of deep networks can be encoded down to 8-bit fixed-point without significant deterioration in performance. However, further reduction in precision down to as low as 3-bit fixed-point results in significant losses in performance. In this paper we propose a new data representation that enables state-of-the-art networks to be encoded to 3 bits with negligible loss in classification performance. To perform this, we take advantage of the fact that the weights and activations in a trained network naturally have non-uniform distributions. Using non-uniform, base-2 logarithmic representation to encode weights, communicate activations, and perform dot-products enables networks to 1) achieve higher classification accuracies than fixed-point at the same resolution and 2) eliminate bulky digital multipliers. Finally, we propose an end-to-end training procedure that uses log representation at 5-bits, which achieves higher final test accuracy than linear at 5-bits.


title: 2D Line Matching Using Geometric and Intensity Data

abstract: We present a stereo matching method of 2D line segments for the detection of 3D line segment. Stereo line matching is a difficult task, since it relies on incomplete 2D line data. The proposed matching method is based on geometric and intensity information of 2D line segment. A set of line attributes is used to match a pair of 2D lines. Line attributes consist of intensity information and geometric information including length, orientation, and endpoint position. Multi-threshold technique has been employed in the implementation of the proposed method so that flexible and efficient matching capability can be attained. Aerial and satellite images are used for the experiments of the proposed method. The experimental results indicate that the proposed method generates accurate 3D line segments from aerial and satellite images.


title: Vehicular platooning experiments with LEGO MINDSTORMS NXT

abstract: The paper reports on experimenting with a popular introductory robotics toolkit LEGO® MINDSTORMS® NXT that was aimed at demonstrating a few algorithms for distributed control of a long platoon of autonomous vehicles. The configuration used in this report measures the distance to the vehicle ahead and the velocity of the vehicle itself. The acquired technical experience with this platform is shared in the report, including recommendation towards improvements.


title: A Compact Dual-Polarized Printed Dipole Antenna With High Isolation for Wideband Base Station Applications

abstract: A compact dual-polarized printed dipole antenna for wideband base station applications is presented in this communication. The proposed dipole antenna is etched on three assembled substrates. Four horizontal triangular patches are introduced to form two dipoles in two orthogonal polarizations. Two integrated baluns connected with 50 Ω SMA launchers are used to excite the dipole antenna. The proposed dipole antenna achieves a more compact size than many reported wideband printed dipole and magneto-electric dipole antennas. Both simulated and measured results show that the proposed antenna has a port isolation higher than 35 dB over 52% impendence bandwidth (VSWR <; 1.5). Moreover, stable radiation pattern with a peak gain of 7 dBi - 8.6 dBi is obtained within the operating band. The proposed dipole antenna is suitable as an array element and can be used for wideband base station antennas in the next generation IMT-advanced communications.


title: Dynamic tunnel switching for SDN-based cellular core networks

abstract: Future cellular networks are faced with the challenge of coping with significant traffic growth without increasing operating costs. Network virtualization and Software Defined Networking (SDN) are emerging solutions for fine-grained control and management of networks. In this article, we present a new dynamic tunnel switching technique for SDN-based cellular core networks. The technique introduces a virtualized Evolved Packet Core (EPC) gateway with the capability to select and dynamically switch the user plane processing element for each user. Dynamic GPRS Tunneling Protocol (GTP) termination enables switching the mobility anchor of an active session between a cloud environment, where general purpose hardware is in use, and a fast path implemented with dedicated hardware. We describe a prototype implementation of the technique based on an OpenStack cloud, an OpenFlow controller with GTP tunnel switching, and a dedicated fast path element.


title: Cu-Sn based joint material having IMC forming control capabilities

abstract: This paper describes development of joint materials using only base metals (Cu and Sn) for power semiconductor assembly. The optimum composition at this moment is Cu8wt%Sn92wt% (8Cu92Sn hereafter) particles: pure Cu (100Cu hereafter) particles = 20:80 (wt% ratio), which indicates good stability under Thermal Cycling Test (TCT, −55°C∼+200°C, 20cycles). The composition indicated to be effective to eliminate voids and chip cracks. As an initial choice of joint material using TLPS (Transient Liquid Phase Sintering), we considered SAC305 might have good role as TLPS trigger. But, actual TCT results indicated that existence of Ag must have negative effect to eliminate voids from the joint region. Tentative behavior model using 8Cu92Sn and 100Cu joint material is proposed. Optimized composition indicated shear force 40MPa at 300°C. Re-melting point of the composition is 409°C after TLPS when there is additional Cu supply from substrate and terminal of mounted die.


title: A Tutorial on Support Vector Machines for Pattern Recognition

abstract: The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail. We describe a mechanical analogy, and discuss when SVM solutions are unique and when they are global. We describe how support vector training can be practically implemented, and discuss in detail the kernel mapping technique which is used to construct SVM solutions which are nonlinear in the data. We show how Support Vector machines can have very large (even infinite) VC dimension by computing the VC dimension for homogeneous polynomial and Gaussian radial basis function kernels. While very high VC dimension would normally bode ill for generalization performance, and while at present there exists no theory which shows that good generalization performance is guaranteed for SVMs, there are several arguments which support the observed high accuracy of SVMs, which we review. Results of some experiments which were inspired by these arguments are also presented. We give numerous examples and proofs of most of the key theorems. There is new material, and I hope that the reader will find that even old material is cast in a fresh light.


title: A Variational Model for Joint Motion Estimation and Image Reconstruction

abstract: The aim of this paper is to derive and analyze a variational model for the joint estimation of motion and reconstruction of image sequences, which is based on a time-continuous Eulerian motion model. The model can be set up in terms of the continuity equation or the brightness constancy equation. The analysis in this paper focuses on the latter for robust motion estimation on sequences of two-dimensional images. We rigorously prove the existence of a minimizer in a suitable function space setting. Moreover, we discuss the numerical solution of the model based on primal-dual algorithms and investigate several examples. Finally, the benefits of our model compared to existing techniques, such as sequential image reconstruction and motion estimation, are shown.


title: Computational Intelligence Techniques in Smart grid planning and operation: A Survey

abstract: The Smart Grids are the future vision of the electric power system with integrated communication, protection, control and sensing technologies. With the introduction of new technologies which constitute the smart grid like demand response, demand side management, electric vehicles, energy storage systems, distributed energy resources, integration of renewable energy resources, and forecasting methods like artificial neural networks, deep learning methods etc, the scope of planning and operation of a smart grid has broadened. The new technologies bring in the need for better tools for solving the planning and operation problems. This paper aims to provide a survey of the works related to some of the smart grid components and classifies the works based on the computational intelligence tools used in solving the planning or operation problem.


title: Construction of state graphs with PVS

abstract: In this paper, we propose a method for the automatic construction of an abstract state graph of an arbitrary system using the Pvs theorem prover.


title: Flexible unit A-star trajectory planning for autonomous vehicles on structured road maps

abstract: In this paper we propose a trajectory planning approach for autonomous vehicles on structured road maps. Therefore we are using the well-known A∗ optimal path planning algorithm. We generate a safe optimal trajectory through a three-dimensional graph, considering the two-dimensional position and time. (1) The graph is generated dynamically with fixed time differences and flexible distances between nodes, based on the vehicle's velocity, using a structured road map. (2) Furthermore the position of dynamic obstacles is predicted over time along the road lanes. The proposed Flexible Unit A∗ (FU-A∗) algorithm was tested for real-time applications with execution times of less than 50 ms on the car's main computer. The feasibility and reliability of FU-A∗ is validated by implementing on simulated autonomous car of Freie university “MadeInGermany” using the roadmap of Tempelhof, Berlin.


title: A BoW-equivalent Recurrent Neural Network for Action Recognition.

abstract: Bag-of-words (BoW) models are widely used in the field of computer vision. A BoW model consists of a visual vocabulary that is generated by unsupervised clustering the features of the training data, e.g., by using kMeans. The clustering methods, however, struggle with large amounts of data, in particular, in the context of action recognition. In this paper, we propose a transformation of the standard BoW model into a neural network, enabling discriminative training of the visual vocabulary on large action recognition datasets. We show that our model is equivalent to the original BoW model but allows for the application of supervised neural network training. Our model outperforms the conventional BoW model and sparse coding methods on recent action recognition benchmarks.


title: An Anomaly Detection Fabric for Clouds Based on Collaborative VM Communities

abstract: The vast attack surface of clouds presents a challenge in deploying scalable and effective defenses. Traditional security mechanisms, which work from inside the VM fail to provide strong protection as attackers can bypass them easily. The only available option is to provide security from the layer below the VM i.e., the hypervisor. Previous works that attempt to secure VMs from "outside" either incur substantial space or compute overheads making them slow and impractical or require modifications to the OS or the application codebase. To address these issues, we propose an anomaly detection fabric for clouds based on system call monitoring, which compresses the stream of system calls at their source making the system scalable and near real-time. Our system requires no modifications to the guest OS or the application making it ideal for the data center setting. Additionally, for robust and early detection of threats, we leverage the notion of VM/container communities that share information about attacks in their early stages to provide immunity to the entire deployment. We make certain aspects of the system flexible so that vendors can tune metrics to offer customized protection to clients based on their workload types. Detailed evaluation on a prototype implementation on KVM substantiates our claims.


title: Linking online news and social media

abstract: Much of what is discussed in social media is inspired by events in the news and, vice versa, social media provide us with a handle on the impact of news events. We address the following linking task: given a news article, find social media utterances that implicitly reference it. We follow a three-step approach: we derive multiple query models from a given source news article, which are then used to retrieve utterances from a target social media index, resulting in multiple ranked lists that we then merge using data fusion techniques. Query models are created by exploiting the structure of the source article and by using explicitly linked social media utterances that discuss the source article. To combat query drift resulting from the large volume of text, either in the source news article itself or in social media utterances explicitly linked to it, we introduce a graph-based method for selecting discriminative terms. For our experimental evaluation, we use data from Twitter, Digg, Delicious, the New York Times Community, Wikipedia, and the blogosphere to generate query models. We show that different query models, based on different data sources, provide complementary information and manage to retrieve different social media utterances from our target index. As a consequence, data fusion methods manage to significantly boost retrieval performance over individual approaches. Our graph-based term selection method is shown to help improve both effectiveness and efficiency.


title: Environmental perception and multi-sensor data fusion for off-road autonomous vehicles

abstract: Environmental perception is one of the most difficult problems for off-road autonomous vehicles. Due to the variety and complexity of off-road environments, the information from any single sensor is not enough for safe and efficient vehicle navigation. Employing more sensors can greatly improve the vehicle's perceptive capability. This paper describes a multi-sensor data fusion system for off-road autonomous vehicles. The system acquires data from one camera, four laser range finders, one radar, and several ultrasonic sensors. A hierarchical structure is used to organize the sensors from feature level to high fusion level. Dempster-Shafer evidence theory is adopted to decide the classification of each grid in the fusion map. A weighted evidence combination rule is proposed and implemented to improve the decision results under high conflicting circumstance. The experimental results showed the validity of our method.


title: From torque feedback-controlled lightweight robots to intrinsically compliant systems

abstract: After decades of intensive research, it seems that we get closer to the time when robots will finally leave the cages of industrial robotic workcells and start working in the vicinity of and together with humans. This is not only believed by robotics researchers, but meanwhile also by leading automotive and IT companies, and of course by some clear-sighted industrial robot manufacturers. Several technologies required for this new kind of robots reached the necessary level of performance, e.g. computing power, communication technologies, sensors, and electronics integration. However, it is clear that these human friendly robots will look very different from today’s industrial robots. Rich sensory information, light-weight design and soft robotics features are required in order to reach the expected performance and safety during interaction with unknown environments or with humans. In this paper we will present and compare two approaches for reaching the aforementioned soft robotics features. The first one is the meanwhile mature technology of torque controlled light-weight robots developed during the past decade at DLR (arms, hands, a humanoid upper body, a crawler). Several products resulted from this research and are currently being commercialized through cooperations with different industrial partners (DLR-KUKA Lightweight Robot, DLR-HIT-Schunk Hand, DLR-Brainlab-KUKA medical robot). The second technology, still a topic of worldwide ongoing research, is variable compliance actuation which implements the soft robotics features mainly in hardware. We will start by reviewing the main design and control ideas of actively controlled compliant systems using as examples the DLR arms, hands, and the humanoid manipulator Justin. We are taking these robots as a performance reference, which we are currently trying to outperform with new variable stiffness actuators. This will lead us to the motivation of the variable stiffness actuator design. We will present the main design ideas and our first results with the new actuator prototypes. Some experimental examples providing first validation of the performance and safety gain of this design approach finally will be presented.


title: Impact Of Employee Participation On Job Satisfaction, Employee Commitment And Employee Productivity

abstract: It is widely believed that the employee participation may affect employee’s job satisfaction; employee productivity, employee commitment and they all can create comparative advantage for the organization. The main intention of this study was to find out relationship among employee participation, job satisfaction, employee productivity and employee commitment. For the matter 34 organizations from Oil & Gas, Banking and Telecommunication sectors were contacted, of which 15 responded back. The findings of this study are that employee participation not only an important determinant of job satisfaction components. Increasing employee participation will have a positive effect on employee’s job satisfaction, employee commitment and employee productivity. Naturally increasing employee participation is a long-term process, which demands both attention from management side and initiative from the employee side.


title: Series feeding rectangular microstrip patch array antenna for 77 GHz automotive radar

abstract: This paper presents the design of a 1×10 series feeding rectangular microstrip patch array antenna for 77 GHz automotive radar. The proposed antenna is designed based on the transmission line model analysis method. To satisfy the sidelobe level of −20 dB in E-plane, 20 dB Taylor distribution is applied to the proposed antenna. Considering the measurement environment, waveguide to microstrip transition is designed and combined with the antenna. To evaluate the antenna performance, a prototype of the proposed antenna is fabricated and tested. The measured −10 dB reflection coefficient bandwidth is 3.8% (75.04–77.95 GHz). The measured broadside gain and sidelobe level in E-plane at 76.5 GHz are 16.56 dBi and −17.2 dB. The simulated and measured results are in good agreement.


Topical 15% resorcinol for hidradenitis suppurativa: An uncontrolled prospective trial with clinical and ultrasonographic follow‐up


The padres distributed publish/subscribe system


title: A 143$\,\times\,$ 81 Mutual-Capacitance Touch-Sensing Analog Front-End With Parallel Drive and Differential Sensing Architecture

abstract: This paper presents an analog front-end (AFE) IC for mutual capacitance touch sensing with 224 sensor channels in 0.18 $~\mu\hbox{m}$ CMOS with 3.3 V drive voltage. A 32-in touch sensing system and a 70-in one having 37 dB SNR for 1 mm diameter stylus at 240 Hz reporting rate are realized with the AFE. The AFE adopts a parallel drive method to achieve the large format and the high SNR simultaneously. With the parallel drive method, the measured SNRs of the AFE stay almost constant at a higher level regardless of the number of sensor channels, which was impossible by conventional sequential drive methods. A novel differential sensing scheme which enhances the immunity against the noise from a display device is also realized in the AFE. While the coupled LCD is on and off, the differences between the measured SNRs are less than 2 dB.


title: Dynamic radiation pattern diversity (DRPD) MIMO using CRLH leaky-wave antennas

abstract: This paper presents a dynamic radiation pattern diversity (DRPD) MIMO wireless system where the antennas radiation patterns are electronically controlled in real-time. The adopted antenna elements are composite right/left-handed (CRLH) microstrip leaky-wave (LW) antennas for which the beam direction can be electronically scanned in a continuous manner from backfire to endfire and the beam shape can be controlled. This system offers a tremendous amount of flexibility in tuning the channel transfer matrix to increase the system performance. Based on this architecture, we propose two simple radiation pattern control algorithms. The first one requires no feedback and averages out the channel fades while the second one requires a simple feedback at the receiver to select the beam pattern maximizing the performance metric. The capacity of both algorithms is derived and analyzed using Monte Carlo simulations. The results show that these algorithms significantly improve the wireless link outage performance in a slow fading environment and increase the diversity gain of a MIMO system for a fixed multiplexing gain.


title: A layered, heterogeneous reflectance model for acquiring and rendering human skin

abstract: We introduce a layered, heterogeneous spectral reflectance model for human skin. The model captures the inter-scattering of light among layers, each of which may have an independent set of spatially-varying absorption and scattering parameters. For greater physical accuracy and control, we introduce an infinitesimally thin absorbing layer between scattering layers. To obtain parameters for our model, we use a novel acquisition method that begins with multi-spectral photographs. By using an inverse rendering technique, along with known chromophore spectra, we optimize for the best set of parameters for each pixel of a patch. Our method finds close matches to a wide variety of inputs with low residual error. ::: We apply our model to faithfully reproduce the complex variations in skin pigmentation. This is in contrast to most previous work, which assumes that skin is homogeneous or composed of homogeneous layers. We demonstrate the accuracy and flexibility of our model by creating complex skin visual effects such as veins, tattoos, rashes, and freckles, which would be difficult to author using only albedo textures at the skin's outer surface. Also, by varying the parameters to our model, we simulate effects from external forces, such as visible changes in blood flow within the skin due to external pressure.


title: Boundary Negotiating Artifacts in Personal Informatics: Patient-Provider Collaboration with Patient-Generated Data

abstract: Patient-generated data is increasingly common in chronic disease care management. Smartphone applications and wearable sensors help patients more easily collect health information. However, current commercial tools often do not effectively support patients and providers in collaboration surrounding these data. This paper examines patient expectations and current collaboration practices around patient-generated data. We survey 211 patients, interview 18 patients, and re-analyze a dataset of 21 provider interviews. We find that collaboration occurs in every stage of self- tracking and that patients and providers create boundary negotiating artifacts to support the collaboration. Building upon current practices with patient-generated data, we use these theories of patient and provider collaboration to analyze misunderstandings and privacy concerns as well as identify opportunities to better support these collaborations. We reflect on the social nature of patient-provider collaboration to suggest future development of the stage-based model of personal informatics and the theory of boundary negotiating artifacts.


title: A Stochastic Gradient Method with an Exponential Convergence Rate for Finite Training Sets

abstract: We propose a new stochastic gradient method for optimizing the sum of a finite set of smooth functions, where the sum is strongly convex. While standard stochastic gradient methods converge at sublinear rates for this problem, the proposed method incorporates a memory of previous gradient values in order to achieve a linear convergence rate. In a machine learning context, numerical experiments indicate that the new algorithm can dramatically outperform standard algorithms, both in terms of optimizing the training error and reducing the test error quickly.


title: Switching Convolutional Neural Network for Crowd Counting

abstract: We propose a novel crowd counting model that maps a given crowd scene to its density. Crowd analysis is compounded by myriad of factors like inter-occlusion between people due to extreme crowding, high similarity of appearance between people and background elements, and large variability of camera view-points. Current state-of-the art approaches tackle these factors by using multi-scale CNN architectures, recurrent networks and late fusion of features from multi-column CNN with different receptive fields. We propose switching convolutional neural network that leverages variation of crowd density within an image to improve the accuracy and localization of the predicted crowd count. Patches from a grid within a crowd scene are relayed to independent CNN regressors based on crowd count prediction quality of the CNN established during training. The independent CNN regressors are designed to have different receptive fields and a switch classifier is trained to relay the crowd scene patch to the best CNN regressor. We perform extensive experiments on all major crowd counting datasets and evidence better performance compared to current state-of-the-art methods. We provide interpretable representations of the multichotomy of space of crowd scene patches inferred from the switch. It is observed that the switch relays an image patch to a particular CNN column based on density of crowd.


title: Machine learning approaches for the discovery of gene-gene interactions in disease data

abstract: Because of the complexity of gene-phenotype relationships machine learning approaches have considerable appeal as a strategy for modelling interactions. A number of such methods have been developed and applied in recent years with some modest success. Progress is hampered by the challenges presented by the complexity of the disease genetic data, including phenotypic and genetic heterogeneity, polygenic forms of inheritance and variable penetrance, combined with the analytical and computational issues arising from the enormous number of potential interactions. We review here recent and current approaches focusing, wherever possible, on applications to real data (particularly in the context of genome-wide association studies) and looking ahead to the further challenges posed by next generation sequencing data.


title: CHARISMATIC AND TRANSFORMATIONAL LEADERSHIP: APPROACHES FOR EFFECTING CHANGE

abstract: The theory and practice show that transactional leadership is a necessary, evolutionary path to transformational and charismatic leadership of the time in a relatively stable business environment, time for operating in a turbulent environment with many unknowns. Transformation phase and charismatic leadership are characterized by different interactions of social actors, initiative, efficiency and effectiveness, readiness for change and variety of strategic choices in accordance with the requirements of the environment and the perception of new vision and business goals. It is an evolutionary path that coexists with the changes in the environment and the inevitability of the appearance of transformational and charismatic leadership as a complex process based on the individual vision, courage and willingness to learn, openness to followers and values that include better and more efficiently, based on a radical change in the organization and environment.


APPLE TREE GROWTH RESPONSE IN GREENHOUSE POT TESTS USING HEAT-TREATED REPLANT SOIL VERSUS ORCHARD REPLANTED TREES WITH IN SITU HEATED SOIL


title: Road surface crack detection: Improved segmentation with pixel-based refinement

abstract: Cracks are among the most commonly found road surface degradations, requiring periodical road surveys for monitoring pavement quality. Images of road pavement surface can be automatically processed, typically employing segmentation algorithms to identify cracks. However, a set of distinct connected components often result, leading to the detection of several independent crack segments, although they may belong to the same pavement surface defect. This is often observed for cracks that exhibit a longer linear development or present several branches. This paper presents a new strategy to identify cracks on images captured during road pavement surveys, even when those cracks appear with a complex shape. The proposed crack segmentation algorithm includes two stages: (i) selection of prominent "crack seeds", adopting an efficient segmentation procedure, after appropriate image smoothing, minimizing the detection of false positives; (ii) iterative binary pixel classification, into the crack or non-crack classes, extending the "seeds" to identify the complete crack shape. The paper also tests the combination of the proposed two stage crack segmentation with three smoothing techniques, to evaluate their suitability for crack detection. As a final step the system classifies the identified cracks as longitudinal, transversal or miscellaneous types. Tests performed with images acquired from different types of sensors (active and non-active), show improved crack segmentation results.


title: Interactive region segmentation for manga

abstract: Manga (Japanese comics) are popular all over the world, and are created digitally. In this paper, we propose an interactive segmentation method tailored for manga. The proposed method enables annotators to select areas in manga efficiently. Our experimental results showed that the proposed framework works better than Adobe Photoshop CC, which is the most widely used commercial image editing software.


title: Multimodal 'eyes-free' interaction techniques for wearable devices

abstract: Mobile and wearable computers present input/output prob-lems due to limited screen space and interaction techniques. When mobile, users typically focus their visual attention on navigating their environment - making visually demanding interface designs hard to operate. This paper presents two multimodal interaction techniques designed to overcome these problems and allow truly mobile, 'eyes-free' device use. The first is a 3D audio radial pie menu that uses head gestures for selecting items. An evaluation of a range of different audio designs showed that egocentric sounds re-duced task completion time, perceived annoyance, and al-lowed users to walk closer to their preferred walking speed. The second is a sonically enhanced 2D gesture recognition system for use on a belt-mounted PDA. An evaluation of the system with and without audio feedback showed users' ges-tures were more accurate when dynamically guided by au-dio-feedback. These novel interaction techniques demon-strate effective alternatives to visual-centric interface de-signs on mobile devices.


title: Mobile Monitoring and Inquiry System Using Fingerprint Biometrics and SMS Technology

abstract: This paper described a mobile monitoring and inquiry system using the fingerprint biometrics and Short Message Service (SMS) technology. It was specifically developed for the monitoring of preschoolers’ attendance during their schooling, which is beneficial to the working parents. The system adopted the Rational Unified Process (RUP), Visual C#.Net 2008 and MySQL Server 5.1 database and software development kit (SDK). The system was designed to automatically sends an alert SMS to the parents/guardians whenever the pupils loggedin/logged-out in the system. Likewise, it could receive an SMS inquiry and sent an SMS reply to the parents/guardians when no alert message was received after the expected time of loggedin/logged-out of the pupil. All the text messages used a predefined content of 160 characters or less, without graphics and are local texting only. Results of the system evaluation showed that the fingerprint scanning records an average of 93.48 % and 93.81 % accuracy for the login and logout respectively. The average time of sending an alert message to the parents/guardians is 27.20 s while 23.08 s for the reply of an SMS inquiry. The mobile monitoring and inquiry system using the fingerprint biometrics and SMS technology is convenient, economical and reliable method of monitoring, identifying and/or verifying users since it no longer requires identity cards or passwords memorization. Thus, the system could be used by the other levels of pupils depending on their monitoring needs. However, there is a need for some innovation like designing a multiple detection and recognition using a high resolution of camera for easy use of login and logout in the system.


Communication and Memory Efficient Parallel Decision Tree Construction


title: Individual Differences in Cyber Security Behaviors: An Examination of Who Is Sharing Passwords

abstract: Abstract In spite of the number of public advice campaigns, researchers have found that individuals still engage in risky password practices. There is a dearth of research available on individual differences in cyber security behaviors. This study focused on the risky practice of sharing passwords. As predicted, we found that individuals who scored high on a lack of perseverance were more likely to share passwords. Contrary to our hypotheses, we found younger people and individuals who score high on self-monitoring were more likely to share passwords. We speculate on the reasons behind these findings, and examine how they might be considered in future cyber security educational campaigns.


title: Deep Neural Networks in Fully Connected CRF for Image Labeling with Social Network Metadata

abstract: We propose a novel method for predicting image labels by fusing image content descriptors with the social media context of each image. An image uploaded to a social media site such as Flickr often has meaningful, associated information, such as comments and other images the user has uploaded, that is complementary to pixel content and helpful in predicting labels. Prediction challenges such as ImageNet [6]and MSCOCO [19] use only pixels, while other methods make predictions purely from social media context [21]. Our method is based on a novel fully connected Conditional Random Field (CRF) framework, where each node is an image, and consists of two deep Convolutional Neural Networks (CNN) and one Recurrent Neural Network (RNN) that model both textual and visual node/image information. The edge weights of the CRF graph represent textual similarity and link-based metadata such as user sets and image groups. We model the CRF as an RNN for both learning and inference, and incorporate the weighted ranking loss and cross entropy loss into the CRF parameter optimization to handle the training data imbalance issue. Our proposed approach is evaluated on the MIR-9K dataset and experimentally outperforms current state-of-the-art approaches.


title: Mesh adaptive direct search algorithms for constrained optimization

abstract: This paper addresses the problem of minimization of a nonsmooth function under general nonsmooth constraints when no derivatives of the objective or constraint functions are available. We introduce the mesh adaptive direct search (MADS) class of algorithms which extends the generalized pattern search (GPS) class by allowing local exploration, called polling, in an asymptotically dense set of directions in the space of optimization variables. This means that under certain hypotheses, including a weak constraint qualification due to Rockafellar, MADS can treat constraints by the extreme barrier approach of setting the objective to infinity for infeasible points and treating the problem as unconstrained. ::: ::: The main GPS convergence result is to identify limit points $\hat{x}$, where the Clarke generalized derivatives are nonnegative in a finite set of directions, called refining directions. Although in the unconstrained case, nonnegative combinations of these directions span the whole space, the fact that there can only be finitely many GPS refining directions limits rigorous justification of the barrier approach to finitely many linear constraints for GPS. The main result of this paper is that the general MADS framework is flexible enough to allow the generation of an asymptotically dense set of refining directions along which the Clarke derivatives are nonnegative. ::: ::: We propose an instance of MADS for which the refining directions are dense in the hypertangent cone at $\hat{x}$ with probability 1 whenever the iterates associated with the refining directions converge to a single $\hat{x}$. The instance of MADS is compared to versions of GPS on some test problems. We also illustrate the limitation of our results with examples.


title: Parallel Selective Algorithms for Big Data Optimization

abstract: We propose a decomposition framework for the parallel optimization of the sum of a differentiable (possibly nonconvex) function and a (block) separable nonsmooth, convex one. The latter term is usually employed to enforce structure in the solution, typically sparsity. Our framework is very flexible and includes both fully parallel Jacobi schemes and Gauss- Seidel (i.e., sequential) ones, as well as virtually all possibilities "in between" with only a subset of variables updated at each iteration. Our theoretical convergence results improve on existing ones, and numerical results on LASSO, logistic regression, and some nonconvex quadratic problems show that the new method consistently outperforms existing algorithms.


title: Poisson Surface Reconstruction

abstract: We show that surface reconstruction from oriented points can be cast as a spatial Poisson problem. This Poisson formulation considers all the points at once, without resorting to heuristic spatial partitioning or blending, and is therefore highly resilient to data noise. Unlike radial basis function schemes, our Poisson approach allows a hierarchy of locally supported basis functions, and therefore the solution reduces to a well conditioned sparse linear system. We describe a spatially adaptive multiscale algorithm whose time and space complexities are proportional to the size of the reconstructed model. Experimenting with publicly available scan data, we demonstrate reconstruction of surfaces with greater detail than previously achievable.


title: Multi-Objective Vehicle Routing Problem Applied to Large Scale Post Office Deliveries

abstract: The number of optimization techniques in the combinatorial domain is large and diversified. Nevertheless, real-world based benchmarks for testing algorithms are few. This work creates an extensible real-world mail delivery benchmark to the Vehicle Routing Problem (VRP) in a planar graph embedded in the 2D Euclidean space. Such problem is multi-objective on a roadmap with up to 25 vehicles and 30,000 deliveries per day. Each instance models one generic day of mail delivery, allowing both comparison and validation of optimization algorithms for routing problems. The benchmark may be extended to model other scenarios.


title: A star pattern recognition algorithm for satellite attitude determination

abstract: To determine the satellite attitude in space, various attitude sensors are used. Among them, the most accurate sensor is the star tracker. In this paper, a novel star pattern recognition algorithm in the “lost in space” mode is presented. The proposed method improves the searching speed by arranging the large star catalogue using a search tree data structure. To further improve the processing speed, the star image is processed at the grid level instead of pixel level. The results from the study demonstrate that the proposed approach has significantly reduced the average run-time by 50% as compared to the conventional methods while still achieve slightly better star recognition accuracy at 95.07%.


title: Real-time deformable models for surgery simulation: a survey

abstract: Simulating the behaviour of elastic objects in real time is one of the current objectives of computer graphics. One of its fields of application lies in virtual reality, mainly in surgery simulation systems. In computer graphics, the models used for the construction of objects with deformable behaviour are known as deformable models. These have two conflicting characteristics: interactivity and motion realism. The different deformable models developed to date have promoted only one of these (usually interactivity) to the detriment of the other (biomechanical realism). In this paper, we present a classification of the different deformable models that have been developed. We present the advantages and disadvantages of each one. Finally, we make a comparison of deformable models and perform an evaluation of the state of the art and the future of deformable models.


title: Novel High Step-Up DC–DC Converter With Coupled-Inductor and Voltage-Doubler Circuits

abstract: In this paper, a novel high step-up dc-dc converter with coupled-inductor and voltage-doubler circuits is proposed. The converter achieves high step-up voltage gain with appropriate duty ratio and low voltage stress on the power switches. Also, the energy stored in the leakage inductor of the coupled inductor can be recycled to the output. The operating principles and the steady-state analyses of the proposed converter are discussed in detail. Finally, a prototype circuit of the proposed converter is implemented in the laboratory to verify the performance of the proposed converter.


title: Classification Restricted Boltzmann Machine for comprehensible credit scoring model

abstract: We propose a comprehensible model for credit risk assessment using a scoring table.We use Restricted Boltzmann Machine to determine scoring points in a scoring table.We deal with the imbalanced data by applying the geometric mean criterion.The quality of the presented method is evaluated on four credit scoring datasets. Credit scoring is the assessment of the risk associated with a consumer (an organization or an individual) that apply for the credit. Therefore, the problem of credit scoring can be stated as a discrimination between those applicants whom the lender is confident will repay credit and those applicants who are considered by the lender as insufficiently reliable. In this work we propose a novel method for constructing comprehensible scoring model by applying Classification Restricted Boltzmann Machines (ClassRBM). In the first step we train the ClassRBM as a standalone classifier that has ability to predict credit status but does not contain interpretable structure. In order to obtain comprehensible model, first we evaluate the relevancy of each of binary features using ClassRBM and further we use these values to create the scoring table (scorecard). Additionally, we deal with the imbalanced data issue by proposing a procedure for determining the cutting point using the geometric mean of specificity and sensitivity. We evaluate our approach by comparing its performance with the results gained by other methods using four datasets from the credit scoring domain.


title: Communication of Social Agents and the Digital City - A Semiotic Perspective

abstract: This paper investigates the concept of digital city. First, a functional analysis of a digital city is made in the light of the modern study of urbanism; similarities between the virtual and urban constructions are pointed out. Next, a semiotic perspective on the subject matter is elaborated, and a terminological basis is introduced to treat a digital city as a self-organizing meaning-producing system intended to support social or spatial navigation. An explicit definition of a digital city is formulated. Finally, the proposed approach is discussed, conclusions are given, and future work is outlined.


title: Learning Localized Geometric Features Using 3D-CNN: An Application to Manufacturability Analysis of Drilled Holes

abstract: 3D convolutional neural networks (3D-CNN) have been used for object recognition based on the voxelized shape of an object. In this paper, we present a 3D-CNN based method to learn distinct local geometric features of interest within an object. In this context, the voxelized representation may not be sufficient to capture the distinguishing information about such local features. To enable efficient learning, we augment the voxel data with surface normals of the object boundary. We then train a 3D-CNN with this augmented data and identify the local features critical for decision-making using 3D gradient-weighted class activation maps. An application of this feature identification framework is to recognize difficult-to-manufacture drilled hole features in a complex CAD geometry. The framework can be extended to identify difficult-to-manufacture features at multiple spatial scales leading to a real-time decision support system for design for manufacturability.


title: FaceVR: Real-Time Gaze-Aware Facial Reenactment in Virtual Reality

abstract: FaceVR virtually removes VR-goggles using facial reenactment, which is of paramount importance in a VR-teleconferencing scenario. To this end, we transfer expressions as well as eye motions to a stereo target video.


title: Quantum Random Self-Modifiable Computation

abstract: Among the fundamental questions in computer science, at least two have a deep impact on mathematics. What can computation compute? How many steps does a computation require to solve an instance of the 3-SAT problem? Our work addresses the first question, by introducing a new model called the ex-machine. The ex-machine executes Turing machine instructions and two special types of instructions. Quantum random instructions are physically realizable with a quantum random number generator. Meta instructions can add new states and add new instructions to the ex-machine. A countable set of ex-machines is constructed, each with a finite number of states and instructions; each ex-machine can compute a Turing incomputable language, whenever the quantum randomness measurements behave like unbiased Bernoulli trials. In 1936, Alan Turing posed the halting problem for Turing machines and proved that this problem is unsolvable for Turing machines. Consider an enumeration E_a(i) = (M_i, T_i) of all Turing machines M_i and initial tapes T_i. Does there exist an ex-machine X that has at least one evolutionary path X -->X_1 -->X_2 -->. . . -->X_m, so at the mth stage ex-machine X_m can correctly determine for 0<= i<= m whether M_i's execution on tape T_i eventually halts? We demonstrate an ex-machine Q(x) that has one such evolutionary path. The existence of this evolutionary path suggests that David Hilbert was not misguided to propose in 1900 that mathematicians search for finite processes to help construct mathematical proofs. Our refinement is that we cannot use a fixed computer program that behaves according to a fixed set of mechanical rules. We must pursue methods that exploit randomness and self-modification so that the complexity of the program can increase as it computes.


title: Quantum circuits for floating-point arithmetic

abstract: Quantum algorithms to solve practical problems in quantum chemistry, materials science, and matrix inversion often involve a significant amount of arithmetic operations which act on a superposition of inputs. These have to be compiled to a set of fault-tolerant low-level operations and throughout this translation process, the compiler aims to come close to the Pareto-optimal front between the number of required qubits and the depth of the resulting circuit. In this paper, we provide quantum circuits for floating-point addition and multiplication which we find using two vastly different approaches. The first approach is to automatically generate circuits from classical Verilog implementations using synthesis tools and the second is to generate and optimize these circuits by hand. We compare our two approaches and provide evidence that floating-point arithmetic is a viable candidate for use in quantum computing, at least for typical scientific applications, where addition operations usually do not dominate the computation. All our circuits were constructed and tested using the software tools LIQ$Ui|\rangle{}$ and RevKit.


title: Kinematic-free position control of a 2-DOF planar robot arm

abstract: This paper challenges the well-established assumption in robotics that in order to control a robot it is necessary to know its kinematic information, that is, the arrangement of links and joints, the link dimensions and the joint positions. We propose a kinematic-free robot control concept that does not require any prior kinematic knowledge. The concept is based on our hypothesis that it is possible to control a robot without explicitly measuring its joint angles, by measuring instead the effects of the actuation on its end-effector. We implement a proof-of-concept encoderless robot controller and apply it for the position control of a physical 2-DOF planar robot arm. The prototype controller is able to successfully control the robot to reach a reference position, as well as to track a continuous reference trajectory. Notably, we demonstrate how this novel controller can cope with something that traditional control approaches fail to do: adapt to drastic kinematic changes such as 100% elongation of a link, 35-degree angular offset of a joint, and even a complete overhaul of the kinematics involving the addition of new joints and links.


title: OsiriX: An Open-Source Software for Navigating in Multidimensional DICOM Images

abstract: A multidimensional image navigation and display software was designed for display and interpretation of large sets of multidimensional and multimodality images such as combined PET-CT studies. The software is developed in Objective-C on a Macintosh platform under the MacOS X operating system using the GNUstep development environment. It also benefits from the extremely fast and optimized 3D graphic capabilities of the OpenGL graphic standard widely used for computer games optimized for taking advantage of any hardware graphic accelerator boards available. In the design of the software special attention was given to adapt the user interface to the specific and complex tasks of navigating through large sets of image data. An interactive jog-wheel device widely used in the video and movie industry was implemented to allow users to navigate in the different dimensions of an image set much faster than with a traditional mouse or on-screen cursors and sliders. The program can easily be adapted for very specific tasks that require a limited number of functions, by adding and removing tools from the program’s toolbar and avoiding an overwhelming number of unnecessary tools and functions. The processing and image rendering tools of the software are based on the open-source libraries ITK and VTK. This ensures that all new developments in image processing that could emerge from other academic institutions using these libraries can be directly ported to the OsiriX program. OsiriX is provided free of charge under the GNU open-source licensing agreement at http://homepage.mac.com/rossetantoine/osirix.


MERGING PLATFORM ECOSYSTEMS IN TECHNOLOGY ACQUISITIONS: A GOVERNANCE PERSPECTIVE


title: Author Name Disambiguation for PubMed

abstract: Log analysis shows that PubMed users frequently use author names in queries for retrieving scientific literature. However, author name ambiguity may lead to irrelevant retrieval results. To improve the PubMed user experience with author name queries, we designed an author name disambiguation system consisting of similarity estimation and agglomerative clustering. A machine-learning method was employed to score the features for disambiguating a pair of papers with ambiguous names. These features enable the computation of pairwise similarity scores to estimate the probability of a pair of papers belonging to the same author, which drives an agglomerative clustering algorithm regulated by 2 factors: name compatibility and probability level. With transitivity violation correction, high precision author clustering is achieved by focusing on minimizing false-positive pairing. Disambiguation performance is evaluated with manual verification of random samples of pairs from clustering results. When compared with a state-of-the-art system, our evaluation shows that among all the pairs the lumping error rate drops from 10.1% to 2.2% for our system, while the splitting error rises from 1.8% to 7.7%. This results in an overall error rate of 9.9%, compared with 11.9% for the state-of-the-art method. Other evaluations based on gold standard data also show the increase in accuracy of our clustering. We attribute the performance improvement to the machine-learning method driven by a large-scale training set and the clustering algorithm regulated by a name compatibility scheme preferring precision. With integration of the author name disambiguation system into the PubMed search engine, the overall click-through-rate of PubMed users on author name query results improved from 34.9% to 36.9%.