title: Software development in startup companies: A systematic mapping study

abstract: Context: Software startups are newly created companies with no operating history and fast in producing cutting-edge technologies. These companies develop software under highly uncertain conditions, tackling fast-growing markets under severe lack of resources. Therefore, software startups present a unique combination of characteristics which pose several challenges to software development activities. Objective: This study aims to structure and analyze the literature on software development in startup companies, determining thereby the potential for technology transfer and identifying software development work practices reported by practitioners and researchers. Method: We conducted a systematic mapping study, developing a classification schema, ranking the selected primary studies according their rigor and relevance, and analyzing reported software development work practices in startups. Results: A total of 43 primary studies were identified and mapped, synthesizing the available evidence on software development in startups. Only 16 studies are entirely dedicated to software development in startups, of which 10 result in a weak contribution (advice and implications (6); lesson learned (3); tool (1)). Nineteen studies focus on managerial and organizational factors. Moreover, only 9 studies exhibit high scientific rigor and relevance. From the reviewed primary studies, 213 software engineering work practices were extracted, categorized and analyzed. Conclusion: This mapping study provides the first systematic exploration of the state-of-art on software startup research. The existing body of knowledge is limited to a few high quality studies. Furthermore, the results indicate that software engineering work practices are chosen opportunistically, adapted and configured to provide value under the constrains imposed by the startup context.


title: Fabrication of ZnO nanoparticles in SiO2 by ion implantation combined with thermal oxidation

abstract: Zinc-oxide (ZnO) nanoparticles (NPs) are fabricated in silica glasses (SiO2) by implantation of Zn+ ions of 60 keV up to 1.0×1017ions∕cm2 and following thermal oxidation. After the oxidation at 700 °C for 1 h, the absorption in the visible region due to Zn metallic NPs disappears and a new absorption edge due to ZnO appears at ∼3.25eV. Cross-sectional transmission electron microscopy confirms the formation of ZnO NPs of 5–10 nm in diameter within the near-surface region of ∼80nm thick and larger ZnO NPs on the surface. Under He–Cd laser excitation at λ=325nm, an exciton luminescence peak centered at 375 nm with FWHM of 113 meV was observed at room temperature.


title: Three-Switch Active-Clamp Forward Converter With Low Switch Voltage Stress and Wide ZVS Range for High-Input-Voltage Applications

abstract: In low-to-medium power applications, an active-clamp forward (ACF) converter is popular for its simple structure and zero-voltage switching (ZVS) capability. However, the high voltage stress on switches can be a burden in high-input-voltage applications. To relieve this, a three-switch ACF (TS-ACF) converter, which employs two main switches for a forward converter and one auxiliary switch for an active-clamp circuit (ACC), is proposed in this paper. With a repositioned ACC, all the switches of TS-ACF converter have relatively low voltage stress compared to the conventional one. In addition, one of the main switches always ensures ZVS during the transformer reset. Consequently, the TS-ACF converter can be promising for high-input-voltage applications with high efficiency.


Person Re-Identification by Localizing Discriminative Regions


title: Learning Social Relation Traits from Face Images

abstract: Social relation defines the association, e.g, warm, friendliness, and dominance, between two or more people. Motivated by psychological studies, we investigate if such fine-grained and high-level relation traits can be characterised and quantified from face images in the wild. To address this challenging problem we propose a deep model that learns a rich face representation to capture gender, expression, head pose, and age-related attributes, and then performs pairwise-face reasoning for relation prediction. To learn from heterogeneous attribute sources, we formulate a new network architecture with a bridging layer to leverage the inherent correspondences among these datasets. It can also cope with missing target attribute labels. Extensive experiments show that our approach is effective for fine-grained social relation learning in images and videos.


title: Discrete Fourier transform–based watermarking method with an optimal implementation radius

abstract: In this paper, we evaluate the degradation of an image due to the implementation of a watermark in the frequency domain of the image. As a result, a watermarking method, which minimizes the impact of the watermark implementation on the overall quality of an image, is developed. The watermark is embedded in magnitudes of the Fourier transform. A peak signal-to-noise ratio is used to evaluate quality degradation. The obtained results were used to develop a watermarking strategy that chooses the optimal radius of the implementation to minimize quality degradation. The robustness of the proposed method was evaluated on the dataset of 1000 images. Detection rates and receiver operating characteristic performance showed considerable robustness against the print-scan process, print-cam process, amplitude modulated, halftoning, and attacks from the StirMark benchmark software.


title: Ontology learning from text: A look back and into the future

abstract: Ontologies are often viewed as the answer to the need for interoperable semantics in modern information systems. The explosion of textual information on the Read/Write Web coupled with the increasing demand for ontologies to power the Semantic Web have made (semi-)automatic ontology learning from text a very promising research area. This together with the advanced state in related areas, such as natural language processing, have fueled research into ontology learning over the past decade. This survey looks at how far we have come since the turn of the millennium and discusses the remaining challenges that will define the research directions in this area in the near future.


title: Multimodal Networks: Structure and Operations

abstract: A multimodal network (MMN) is a novel graph-theoretic formalism designed to capture the structure of biological networks and to represent relationships derived from multiple biological databases. MMNs generalize the standard notions of graphs and hypergraphs, which are the bases of current diagrammatic representations of biological phenomena and incorporate the concept of mode. Each vertex of an MMN is a biological entity, a biot, while each modal hyperedge is a typed relationship, where the type is given by the mode of the hyperedge. The current paper defines MMNs and concentrates on the structural aspects of MMNs. A companion paper develops MMNs as a representation of the semantics of biological networks and discusses applications of the MMNs in managing complex biological data. The MMN model has been implemented in a database system containing multiple kinds of biological networks.


Simplified Kinetic Modelling of Alkaline Delignification of Hemp Woody Core


title: aueb.twitter.sentiment at SemEval-2016 Task 4: A Weighted Ensemble of SVMs for Twitter Sentiment Analysis

abstract: This paper describes the system with which we participated in SemEval-2016 Task 4 (Sentiment Analysis in Twitter) and specifically the Message Polarity Classification subtask. Our system is a weighted ensemble of two systems. The first one is based on a previous sentiment analysis system and uses manually crafted features. The second system of our ensemble uses features based on word embeddings. Our ensemble was ranked 5th among 34 teams. The source code of our system is publicly available.


A Survey of the Stream Processing Landscape


title: Advances in Automatic Tuberculosis Detection in Chest X-Ray Images

abstract: Tuberculosis (TB) is very dangerous and rapidly spread disease in the world. In the investigating cases for suspected tuberculosis (TB), chest radiography is not only the key techniques of diagnosis based on the medical imaging but also the diagnostic radiology. So, Computer aided diagnosis (CAD) has been popular and many researchers are interested in this research areas and different approaches have been proposed for the TB detection and lung decease classification. In this paper, the medical background history of TB decease in chest X-rays and a survey of the various approaches in TB detection and classification are presented. The literature in the related methods is surveyed papers in this research area until now 2014.


title: 3D reconstruction and enrichment of broadcast soccer video

abstract: Recently, it has become a new trend to reconstruct sports video for various purposes. This paper presents a 3D reconstruction and enrichment system that not only reconstructs broadcast soccer video but also enriches reconstructed video with music and illustrations of the video contents. The system can reconstruct not only the goalmouth scene but also the midfield scene, which cannot be reconstructed by the existing systems. To quickly find the feature points for calibrating the camera, we propose a fast algorithm to detect the lines in the goalmouth scene and use the algorithm proposed in our previous papers to detect the partial ellipses in the midfield scene. The reconstruction is conducted on several video sequences of two scenes. The reconstructed videos eliminate the ball deformation and unnecessary camera changes through smoothing the camera parameters. This system also serves as an experimental system for our project that reconstructs the on-going soccer game in real time.


title: Asymmetric Programming: A Highly Reliable Metadata Allocation Strategy for MLC NAND Flash Memory-Based Sensor Systems

abstract: While the NAND flash memory is widely used as the storage medium in modern sensor systems, the aggressive shrinking of process geometry and an increase in the number of bits stored in each memory cell will inevitably degrade the reliability of NAND flash memory. In particular, it's critical to enhance metadata reliability, which occupies only a small portion of the storage space, but maintains the critical information of the file system and the address translations of the storage system. Metadata damage will cause the system to crash or a large amount of data to be lost. This paper presents Asymmetric Programming, a highly reliable metadata allocation strategy for MLC NAND flash memory storage systems. Our technique exploits for the first time the property of the multi-page architecture of MLC NAND flash memory to improve the reliability of metadata. The basic idea is to keep metadata in most significant bit (MSB) pages which are more reliable than least significant bit (LSB) pages. Thus, we can achieve relatively low bit error rates for metadata. Based on this idea, we propose two strategies to optimize address mapping and garbage collection. We have implemented Asymmetric Programming on a real hardware platform. The experimental results show that Asymmetric Programming can achieve a reduction in the number of page errors of up to 99.05% with the baseline error correction scheme.


title: Reflex Profile of Children with Down Syndrome Improvement of Neurosensorimotor Development Using the MNRIÃÂ® Reflex Integration Program

abstract: A reflex profile of children with Down Syndrome (n=48) has been created based on an Assessment of their reflex patterns. This profile and its analysis demonstrate that dysfunctional patterns intrude upon the neurodevelopment of children in this group significantly, and cause more delays in their neurosensorimotor integration, motor coordination, and other areas. The MNRI® (Masgutova Neurosensorimotor Reflex Integration) therapy modality which comprises of techniques and exercises of repatterning, was used for children participating in this research. It has proved its optimizing effect on sensory (tactile, visual-auditory) perception, motor programming and control, and proprioceptive awareness. The MNRI® concept of reflex integration differs from other traditional theories of reflex inhibition/extinction of retained reflexes. The MNRI® approach with immature or dysfunctional reflex circuits is based on activating the sensory-motor patterns encoded in a human nervous system on the genetic level. Non-invasive exercises and techniques in the MNRI® Program are aimed at the development of proper connectivity between sensory and motor neurons in neurophysiological circuits, and at strengthening and coordinating the links between different reflex patterns. The MNRI® process proposes exercises that remind the body-brain system of reflex patterns in a delicate and safe way, sometimes through the use of games and play. These techniques can be easily used by parents, caregivers, and specialists working with Down syndrome children. Statistic analysis of five paramers of a reflex pattern: sensory-motor coordination, direction of a response, intensity (muscle tone regulation), latency/dynamics, and symmetry before and after the MNRI® therapy process (based on synthesized Z function; A. Krefft algorythm) allowed for an objective scientific approach of the effectiveness of the MNRI® processes. The therapy program and evaluations were conducted during 11 day therapy-rehabilitation camps with 48 children from different countries (Poland, USA, Canada, and Russia). The feedback reports by parents and specialists on the motor and cognitive function changes in children with Down syndrome after the MNRI® program show certain improvements.


title: Cybersickness: Perception of Self-Motion in Virtual Environments

abstract: Human perceptual systems have evolved to provide accurate information about orientation and movement through the environment. However, these systems have been challenged in the past century by modern transportation devices and will be further challenged by virtual environments VEs and teleoperator systems. Illusory self-motion within a VE “cyberspace” will be entertaining and instructive, but for many users it will result in motion sickness “cybersickness”. Sensory conflict theory and the poison hypothesis provide an unproven theoretical foundation for understanding the phenomenon. Although no single engineering solution is likely, the problem can be contained by a combination of engineering design, equipment calibration, and exposure management.


title: Major depression in stroke patients. A 3-year longitudinal study

abstract: BACKGROUND AND PURPOSE: This prospective study was designed to examine the contributions of neurobiological, functional, and psychosocial factors to major depression after stroke. In addition, the ...


title: SiC vs. Si - Evaluation of Potentials for Performance Improvement of Power Electronics Converter Systems by SiC Power Semiconductors

abstract: Switching devices based on wide band gap materials as SiC oer a signicant perfor- mance improvement on the switch level compared to Si devices. A well known example are SiC diodes employed e.g. in PFC converters. In this paper, the impact on the system level perfor- mance, i.e. eciency/power density, of a PFC and of a DC-DC converter resulting with the new SiC devices is evaluated based on analytical optimisation procedures and prototype systems. There, normally-on JFETs by SiCED and normally-off JFETs by SemiSouth are considered.


title: A Survey of Communication Sub-systems for Intersatellite Linked Systems and CubeSat Missions

abstract: Intersatellite links or crosslinks provide direct connectivity between two or more satellites, thus eliminating the need for intermediate ground stations when sending data. Intersatellite links have been considered for satellite constellation missions involving earth observation and communications. Historically, a large satellite system has needed an extremely high financial budget. However, the advent of the successful CubeSat platform allows for small satellites of less than one kilogram. This low-mass pico-satellite class platform could provide financially feasible support for large platform satellite constellations. This article surveys past and planned large intersatellite linking systems. Then, the article chronicles CubeSat communication subsystems used historically and in the near future. Finally, we examine the history of inter-networking protocols in space and open research issues with the goal of moving towards the next generation intersatellite-linking constellation supported by CubeSat platform satellites.


title: The electrically noncontacting ECG measurement on the toilet seat using the capacitively-coupled insulated electrodes

abstract: The studies of ECG measurement on the toilet seat have been performed specifically for the ubiquitous health care. Instead of the mainly used dry electrodes having several problems such as the electrical safety or the environmental stability, we used the capacitively-coupled insulated electrodes, which were composed of the Cu plate and the PTFE film for the measurement. The biosignal sensed with the insulated electrodes was measured through the ultra-high input impedance system including OPA111 having the common mode impedance of 10/sup 14/ /spl Omega/ ft. As the result of measuring the signal, with the electrical ground on the neck or the hand, the R-peaks were detected very positively. However, without the electrical ground on the body, we could detect the heartbeat signal, a land of the motion artifacts by the variation of the blood vessel volume. It seems that this heartbeat signal can be also used as the important parameter like the R-peaks for the HRV (heart rate variability) analysis. In addition, it is thought that, without the directly electrical ground, the R-peak detection will be possible by the improvement of the SNR with the active common canceling system.


title: Cover Fe ature

abstract: Finding data governance practices that maintain a balance between value creation and risk exposure is the new organizational imperative for unlocking competitive advantage and maximizing value from the application of big data. The first Web extra at http://youtu.be/B2RlkoNjrzA is a video in which author Paul Tallon expands on his article "Corporate Governance of Big Data: Perspectives on Value, Risk, and Cost" and discusses how finding data governance practices that maintain a balance between value creation and risk exposure is the new organizational imperative for unlocking competitive advantage and maximizing value from the application of big data. The second Web extra at http://youtu.be/g0RFa4swaf4 is a video in which author Paul Tallon discusses the supplementary material to his article "Corporate Governance of Big Data: Perspectives on Value, Risk, and Cost" and how projection models can help individuals responsible for data handling plan for and understand big data storage issues.


title: Pyridoxine-induced sensory ataxic neuronopathy and neuropathy: revisited

abstract: High dose pyridoxine is neurotoxic. Previous case reports were sparse and little is known about the clinical and electrodiagnostic findings. Three patients with pyridoxine-induced sensory ataxic neuropathy were studied and a review of the involved literature was performed. Three patients, aged 80, 83 and 83 years old, presented with sensory ataxia for 3–8 months. Examination showed signs of polyneuropathy and sensory ataxia. Six hundred milligrams of pyridoxine was consumed each day for 3–10 years, in the form of vitamin B1-6-12 combination tablet. Investigations for other causes of neuropathy were unremarkable. Blood levels of vitamin B6 were markedly elevated at 104.6, 81.4 and 66.9 times of upper normal limits. Electrodiagnostic tests showed symmetric axonal sensory polyneuropathy in two patients. Two years after vitamin discontinuation, all patients showed no significant improvement in the neuropathy and gait. In conclusion, consumption of high dose pyridoxine can cause sensory neuronopathy and axonal sensorimotor polyneuropathy, leading to sensory ataxia which may not be reversible.


title: Do Supervised Distributional Methods Really Learn Lexical Inference Relations?

abstract: Distributional representations of words have been recently used in supervised settings for recognizing lexical inference relations between word pairs, such as hypernymy and entailment. We investigate a collection of these state-of-the-art methods, and show that they do not actually learn a relation between two words. Instead, they learn an independent property of a single word in the pair: whether that word is a “prototypical hypernym”.


title: From research to practice: experiences engineering a production metadata database for a scale out file system

abstract: HP's Store All with Express Query is a scalable commercial file archiving product that offers sophisticated file metadata management and search capabilities [3]. A new REST API enables fast, efficient searching to find all files that meet a given set of metadata criteria and the ability to tag files with custom metadata fields. The product brings together two significant systems: a scale out file system and a metadata database based on LazyBase [10]. In designing and building the combined product, we identified several real-world issues in using a pipelined database system in a distributed environment, and overcame several interesting design challenges that were not contemplated by the original research prototype. This paper highlights our experiences.


Earning the Grade or Just Window Dressing ? The Effects of Information Disclosure on Restaurant Hygiene Quality A Study of New York City Restaurant Hygiene Inspection


title: Pedestrian detection based on faster R-CNN in nighttime by fusing deep convolutional features of successive images

abstract: Abstract Existing studies using visible-light cameras have mainly focused on methods of pedestrian detection during daytime. However, these studies found it difficult to detect pedestrians during nighttime with low external light. The NIR illuminator has limitations in terms of illumination angle and distance, and the illuminator's power needs to be adjusted depending on whether an object is near or distant. Although, thermal cameras were used for nighttime pedestrian detection, thermal cameras are currently expensive and thus difficult to install in many places. To solve these problems, attempts have been made to use visible-light cameras for nighttime pedestrian detection. However, most of these attempts considered an indoor environment where the distance to the object was short. This study proposes a method of pedestrian detection at nighttime using a visible-light camera and faster region-based convolutional neural network (R-CNN). In addition, as pedestrians cannot be reliably detected from a single nighttime image, we combined deep convolutional features in successive frames. Using Korea advanced institute of science and technology (KAIST) open database, we conducted experiments and observed that the proposed method performed better than the baseline methods at all times (day and night). In addition, through the experiments with national ICT Australia Ltd. (NICTA) open database, we confirm that the proposed method is effective for pedestrian detection at all times. Finally, we present theoretical grounds for the proposed fusion.


title: Real-time indoor localization of service robots using fisheye camera and laser pointers

abstract: This paper presents a new real-time localization method for indoor service robots based on camera and laser. First of all, it is proved that planar motion of an upward camera in the floor plane can be solved by only two matching points with equal depth. Then, a real-time indoor localization method is proposed accordingly using an upward fisheye camera mounted on the robot and two fixed laser pointers which project red and green laser spots onto the ceiling. In addition, to improve the localization accuracy, an Extended Kalman Filter is employed with active matching to speed up laser spots recognition process. Finally, the proposed method is implemented on a mobile developer board, and tested with P3-DX platform in indoor environment. Experimental results show the excellent performance of the given method.


title: PICCIL: Interactive Learning to Support Log File Categorization

abstract: Motivated by the real-world application of categorizing system log messages into defined situation categories, this paper describes an interactive text categorization method, PICCIL, that leverages supervised machine learning to reduce the burden of assigning categories to documents in large finite data sets but, by coupling human expertise to the machine learning, does so without sacrificing accuracy. PICCIL uses keywords and keyword rules both to preclassify documents and to assist in the manual process of grouping and reviewing documents. The reviewed documents, in turn, are used to refine the keyword rules iteratively to improve subsequent grouping and document review. We apply PICCIL to the problem of assigning semantic situation labels to the entries of a catalog of log events to support on-line labeling of log events


Digital Signature Schemes Based on Hash Functions


title: Facilitating success for people with mental health issues in a college through cognitive remediation therapy and social and emotional learning

abstract: Purpose ::: ::: ::: ::: ::: The purpose of this paper is to describe the components, structure and theoretical underpinnings of a cognitive remediation intervention that was delivered within a supported education program for mental health survivors. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: In total, 21 participants enrolled in the course Strengthening Memory, Concentration and Learning (PREP 1033 at George Brown College (GBC)) with the diagnosis of depression, anxiety, PTSD, ED and substance use disorder were included in the research. After a baseline assessment, participants completed 14 week cognitive remediation training (CRT) protocol that included six essential components that were integrated and implemented within the course structure of the supported education program at GBC. This was followed by a post-training assessment. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: Analysis of the participants’ performance on CRT protocol using computerized games showed little significant progress. However, the research found a positive change in the self-esteem of the participants that was statistically significant and the findings also aligned with the social and emotional learning framework. ::: ::: ::: ::: ::: Research limitations/implications ::: ::: ::: ::: ::: One of the limitations in the research was the use of computer-assisted cognitive remediation in the form of the HappyNeuron software. The value and relevance of computer assisted needs are to be further examined. It seems that the implementation of the course that explicitly address cognitive challenges creates a supportive environment can be helpful. ::: ::: ::: ::: ::: Practical implications ::: ::: ::: ::: ::: Despite the mixed results and the few limitations associated with the CRT intervention reported in the research, the study offers reminders of the complexity of cognitive remediation and all the factors involved that need to be taken into consideration. ::: ::: ::: ::: ::: Social implications ::: ::: ::: ::: ::: This research created explicit space for addressing some of the implicit assumptions about the cognitive abilities when in post-secondary education. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: This work is based on author’s previous work on cognitive remediation research within the supported education setting.


Security Solutions and Applied Cryptography in Smart Grid Communications


title: CS10K Teachers by 2017?: Try CS1K+ students NOW! Coping with the Largest CS1 Courses in History

abstract: "Be careful what you wish for, you just might get it." - Proverb In 2005, computing education was experiencing a crisis. Enrollments had "fallen to such an extent that some academic computing programs were facing significant reductions in staffing levels or even elimination". The community responded, with panels to investigate and highlight ways to infuse "passion, beauty, joy and awe" into the introductory experiences, the CS10K project to bring computing to 10,000 teachers and 100,000 students, and better messaging of career opportunities, to name a few of the initiatives to bring students back into our seats. Well, by golly, it worked! It certainly didn't hurt our cause that Wall Street almost collapsed, young whiz kids were becoming TECH billionaires, an inspiring video and an interactive website led millions of people to code for an hour every December, or smart devices put computing into the hands of young people, and social media became the killer app. Whatever it was, CS became hot again. And we mean HOT. There are now several institutions around the world that have well over a thousand students taking CS1 in the Fall of 2015. There's just so much lemonade one can make before the seams start to burst, and the wheels come off the bus, as many shared at SIGCSE 2015 at the Birds of the Feather session. The goal of this panel is to bring together educators who were charged with delivering face-to-face CS1 on the grandest scale the field has ever seen. How did they cope? Does it become all people management with an army of Teaching Assistants? What were the differences and common themes in their survival plans? What is working? What mistakes were made? How are they supporting differential learning for the students who don't have the same experience as others? How is diversity being affected? Finally, what advice would they have for others interested in venturing into the tsunami, and broaden participation at a massive scale?


title: Content-Based Routing: Different Plans for Different Data

abstract: Query optimizers in current database systems are designed to pick a single efficient plan for a given query based on current statistical properties of the data. However, different subsets of the data can sometimes have very different statistical properties. In such scenarios it can be more efficient to process different subsets of the data for a query using different plans. We propose a new query processing technique called content-based routing (CBR) that eliminates the single-plan restriction in current systems. We present low-overhead adaptive algorithms that partition input data based on statistical properties relevant to query execution strategies, and efficiently route individual tuples through customized plans based on their partition. We have implemented CBR as an extension to the Eddies query processor in the TelegraphCQ system, and we present an extensive experimental evaluation showing the significant performance benefits of CBR.


title: Bacterium-inspired robots for environmental monitoring

abstract: Locating gradient sources and tracking them over time has important applications to environmental monitoring and studies of the ecosystem. We present an approach, inspired by bacterial chemotaxis, for robots to navigate to sources using gradient measurements and a simple actuation strategy (biasing a random walk). Extensive simulations show the efficacy of the approach in varied conditions including multiple sources, dissipative sources, and noisy sensors and actuators. We also show how such an approach could be used for boundary finding. We validate our approach by testing it on a small robot (the robomote) in a phototaxis experiment. A comparison of our approach with gradient descent shows that while gradient descent is faster, our approach is better suited for boundary coverage, and performs better in the presence of multiple and dissipative sources.


Abstractive Summarization with Global Importance Scores


title: Implementation of Flight Control Computer Hardware Using the C 6701 Processor

abstract: Flying Test Bed (FTB) program is to establish the in-house capability of digital flight control computer (DFLCC) development which will be installed on the existing Korean Supersonic Aircraft (KSA). A hardware manufacturing technology of DFLCC mainly lies in constitution and layout of central processing unit (CPU), and input output processor (IOP) board which includes core processor, with those of other boards. The FTB DFLCC has triplex digital redundancy architecture. We use to DFLCC rapid prototyping (RP) to help the participants perform design review and function analysis easily. In this paper, we present the establishment and implementation of FTB DFLCC hardware using the SMJ320C6701 processor and RP. Also, channel valid logic and cross channel data link (CCDL) are described for redundancy management.


title: Effect of the Phase-Error of Local Oscillators in Digital Back-Propagation

abstract: The effect of the phase-error of local oscillators (LOs) on the performance of wavelength-division multiplexing (WDM) optical transmission systems employing digital back-propagation (DBP) is investigated. A simple model describing the performance degradation due to the phase-error is developed, and the validity of it is demonstrated by numerical simulations of 20-GHz-spaced-nine-channel orthogonal frequency division multiplexing (OFDM)-modulated transmissions. It is also shown that, whereas the variance of the phase-error should be less than 5° for the full benefit of the DBP, the performance is significantly improved by the DBP even when there is no control on the phase of LOs.


title: Multicore bundle adjustment

abstract: We present the design and implementation of new inexact Newton type Bundle Adjustment algorithms that exploit hardware parallelism for efficiently solving large scale 3D scene reconstruction problems. We explore the use of multicore CPU as well as multicore GPUs for this purpose. We show that overcoming the severe memory and bandwidth limitations of current generation GPUs not only leads to more space efficient algorithms, but also to surprising savings in runtime. Our CPU based system is up to ten times and our GPU based system is up to thirty times faster than the current state of the art methods [1], while maintaining comparable convergence behavior. The code and additional results are available at http://grail.cs. washington.edu/projects/mcba.


title: Analyzing the Relationship Between Organic and Sponsored Search Advertising : Positive , Negative , or Zero Interdependence ?

abstract: The phenomenon of paid search advertising has now become the most predominant form of online advertising in the marketing world. However, we have little understanding of the impact of search engine advertising on consumers' responses in the presence of organic listings of the same firms. In this paper, we model and estimate the interrelationship between organic search listings and paid search advertisements. We use a unique panel data set based on aggregate consumer response to several hundred keywords over a three-month period collected from a major nationwide retailer store chain that advertises on Google. In particular, we focus on understanding whether the presence of organic listings on a search engine is associated with a positive, a negative, or no effect on the click-through rates of paid search advertisements, and vice versa for a given firm. We first build an integrated model to estimate the relationship between different metrics such as search volume, click-through rates, conversion rates, cost per click, and keyword ranks. A hierarchical Bayesian modeling framework is used and the model is estimated using Markov chain Monte Carlo methods. Our empirical findings suggest that click-throughs on organic listings have a positive interdependence with click-throughs on paid listings, and vice versa. We also find that this positive interdependence is asymmetric such that the impact of organic clicks on increases in utility from paid clicks is 3.5 times stronger than the impact of paid clicks on increases in utility from organic clicks. Using counterfactual experiments, we show that on an average this positive interdependence leads to an increase in expected profits for the firm ranging from 4.2% to 6.15% when compared to profits in the absence of this interdependence. To further validate our empirical results, we also conduct and present the results from a controlled field experiment. This experiment shows that total click-through rates, conversions rates, and revenues in the presence of both paid and organic search listings are significantly higher than those in the absence of paid search advertisements. The results predicted by the econometric model are also corroborated in this field experiment, which suggests a causal interpretation to the positive interdependence between paid and organic search listings. Given the increased spending on search engine-based advertising, our analysis provides critical insights to managers in both traditional and Internet firms.


title: Online Video Object Detection Using Association LSTM

abstract: Video object detection is a fundamental tool for many applications. Since direct application of image-based object detection cannot leverage the rich temporal information inherent in video data, we advocate to the detection of long-range video object pattern. While the Long Short-Term Memory (LSTM) has been the de facto choice for such detection, currently LSTM cannot fundamentally model object association between consecutive frames. In this paper, we propose the association LSTM to address this fundamental association problem. Association LSTM not only regresses and classifiy directly on object locations and categories but also associates features to represent each output object. By minimizing the matching error between these features, we learn how to associate objects in two consecutive frames. Additionally, our method works in an online manner, which is important for most video tasks. Compared to the traditional video object detection methods, our approach outperforms them on standard video datasets.


title: Efficacy of antibiotic therapy for peritoneal dialysis-associated peritonitis: a proportional meta-analysis

abstract: The choice of antimicrobials for initial treatment of peritoneal dialysis (PD)-related peritonitis is crucial for a favorable outcome. There is no consensus about the best therapy; few prospective controlled studies have been published, and the only published systematic reviews did not report superiority of any class of antimicrobials. The objective of this review was to analyze the results of PD peritonitis treatment in adult patients by employing a new methodology, the proportional meta-analysis. A review of the literature was conducted. There was no language restriction. Studies were obtained from MEDLINE, EMBASE, and LILACS. The inclusion criteria were: (a) case series and RCTs with the number of reported patients in each study greater than five, (b) use of any antibiotic therapy for initial treatment (e.g., cefazolin plus gentamicin or vancomycin plus gentamicin), for Gram-positive (e.g., vancomycin or a first generation cephalosporin), or for Gram-negative rods (e.g., gentamicin, ceftazidime, and fluoroquinolone), (c) patients with PD-related peritonitis, and (d) studies specifying the rates of resolution. A proportional meta-analysis was performed on outcomes using a random-effects model, and the pooled resolution rates were calculated. A total of 64 studies (32 for initial treatment and negative culture, 28 reporting treatment for Gram-positive rods and 24 reporting treatment for Gram-negative rods) and 21 RCTs met all inclusion criteria (14 for initial treatment and negative culture, 8 reporting treatment for Gram-positive rods and 8 reporting treatment for Gram-negative rods). The pooled resolution rate of ceftazidime plus glycopeptide as initial treatment (pooled proportion = 86% [95% CI 0.82–0.89]) was significantly higher than first generation cephalosporin plus aminoglycosides (pooled proportion = 66% [95% CI 0.57–0.75]) and significantly higher than glycopeptides plus aminoglycosides (pooled proportion = 75% [95% CI 0.69–0.80]. Other comparisons of regimens used for either initial treatment, treatment for Gram-positive rods or Gram-negative rods did not show statistically significant differences. We showed that the association of a glycopeptide plus ceftazidime is superior to other regimens for initial treatment of PD peritonitis. This result should be carefully analyzed and does not exclude the necessity of monitoring the local microbiologic profile in each dialysis center to choice the initial therapeutic protocol.


title: SiGe BiCMOS processes for commercial RF front-end-module applications

abstract: Over the past decade, SiGe BiCMOS processes have become a mainstay in the Front-End-Module (FEM) of commercial radio products. SiGe BiCMOS processes offer an excellent compromise between the low cost of commodity CMOS and the high performance of III-V based technologies. This allows them to address many of the difficult specification challenges of FEM components in cellular phones and other complex radio systems at a cost level that is acceptable for very high volume products. In this paper several examples of applications of SiGe BiCMOS processes in FEMs are given, including power amplifiers, low-noise amplifiers, RF switches and combinations thereof. Further, the utility of SiGe BiCMOS to address emerging commercial applications at higher frequencies is discussed.


title: Structured feature selection using coordinate descent optimization

abstract: BackgroundExisting feature selection methods typically do not consider prior knowledge in the form of structural relationships among features. In this study, the features are structured based on prior knowledge into groups. The problem addressed in this article is how to select one representative feature from each group such that the selected features are jointly discriminating the classes.The problem is formulated as a binary constrained optimization and the combinatorial optimization is relaxed as a convex-concave problem, which is then transformed into a sequence of convex optimization problems so that the problem can be solved by any standard optimization algorithm. Moreover, a block coordinate gradient descent optimization algorithm is proposed for high dimensional feature selection, which in our experiments was four times faster than using a standard optimization algorithm.ResultsIn order to test the effectiveness of the proposed formulation, we used microarray analysis as a case study, where genes with similar expressions or similar molecular functions were grouped together. In particular, the proposed block coordinate gradient descent feature selection method is evaluated on five benchmark microarray gene expression datasets and evidence is provided that the proposed method gives more accurate results than the state-of-the-art gene selection methods. Out of 25 experiments, the proposed method achieved the highest average AUC in 13 experiments while the other methods achieved higher average AUC in no more than 6 experiments.ConclusionA method is developed to select a feature from each group. When the features are grouped based on similarity in gene expression, we showed that the proposed algorithm is more accurate than state-of-the-art gene selection methods that are particularly developed to select highly discriminative and less redundant genes. In addition, the proposed method can exploit any grouping structure among features, while alternative methods are restricted to using similarity based grouping.


title: Augmented Reality Technology and Art: The Analysis and Visualization of Evolving Conceptual Models

abstract: This paper provides a logical and methodological reconstruction of the evolving concept of Augmented Reality (AR) and also of the paradigmatic shift in art, caused by this emerging technology. It starts with an analysis of the notion of Augmented Reality, that leads to the construction of a conceptual model and a definition which together capture the nature of present AR applications. This is followed by a detailed conceptual analysis of major types of Augmented Reality that contribute to an understanding of current concepts. Finally, the resulting conceptual models are applied to the newly emerging field of Augmented Reality Art in order to assess the paradigmatic potential of AR as a new artistic medium. The paper puts a strong emphasis on the effective and adequate visualisation of the analysed conceptual frameworks, in order to promote a better comprehension of the logical structures underlying the notions of Augmented Reality and AR Art. This paper was intended to be presented at the opening of the First International Symposium on Augmented Reality Visualisation and Art, proposed and chaired by the author as part of the 16th International Conference on Information Visualization, IV2012.


title: A software-based fault detection scheme for wireless sensor networks

abstract: Recently, wireless sensor networks are being increasingly integrated with consumer electronic devices to deliver more intelligent services. Usually, the wireless sensor networks are expected to provide continuous, unattended service for months or even years. However, hardware reliability poses a major challenge to this expectation. To address this issue, this paper designs and implements a software-based minimal-overhead fault detection method to detect failures in various hardware components. The fault detection scheme has been implemented in the SOS kernel on the sensor nodes. Experimental results indicate that except for the memory access trap, most failures can be successfully detected through low-cost software detectors.


title: Model Based Separation of Overlapping Latent Fingerprints

abstract: Latent fingerprints lifted from crime scenes often contain overlapping prints, which are difficult to separate and match by state-of-the-art fingerprint matchers. A few methods have been proposed to separate overlapping fingerprints to enable fingerprint matchers to successfully match the component fingerprints. These methods are limited by the accuracy of the estimated orientation field, which is not reliable for poor quality overlapping latent fingerprints. In this paper, we improve the robustness of overlapping fingerprints separation, particularly for low quality images. Our algorithm reconstructs the orientation fields of component prints by modeling fingerprint orientation fields. In order to facilitate this, we utilize the orientation cues of component fingerprints, which are manually marked by fingerprint examiners. This additional markup is acceptable in forensics, where the first priority is to improve the latent matching accuracy. The effectiveness of the proposed method has been evaluated not only on simulated overlapping prints, but also on real overlapped latent fingerprint images. Compared with available methods, the proposed algorithm is more effective in separating poor quality overlapping fingerprints and enhancing the matching accuracy of overlapping fingerprints.


title: A Novel Feature Selection Based on One-Way ANOVA F-Test for E-Mail Spam Classification

abstract: Spam is commonly defined as unwanted e-mails and it became a global threat against e-mail users. Although, Support Vector Machine (SVM) has been commonly used in e-mail spam classification, yet the problem of high data dimensionality of the feature space due to the massive number of e-mail dataset and features still exist. To improve the limitation of SVM, reduce the computational complexity (efficiency) and enhancing the classification accuracy (effectiveness). In this study, feature selection based on one-way ANOVA F-test statistics scheme was applied to determine the most important features contributing to e-mail spam classification. This feature selection based on one-way ANOVA F-test is used to reduce the high data dimensionality of the feature space before the classification process. The experiment of the proposed scheme was carried out using spam base well- known benchmarking dataset to evaluate the feasibility of the proposed method. The comparison is achieved for different datasets, categorization algorithm and success measures. In addition, experimental results on spam base English datasets showed that the enhanced SVM (FSSVM) significantly outperforms SVM and many other recent spam classification methods for English dataset in terms of computational complexity and dimension reduction.


title: Machine Comprehension with Syntax, Frames, and Semantics

abstract: We demonstrate significant improvement on the MCTest question answering task (Richardson et al., 2013) by augmenting baseline features with features based on syntax, frame semantics, coreference, and word embeddings, and combining them in a max-margin learning framework. We achieve the best results we are aware of on this dataset, outperforming concurrentlypublished results. These results demonstrate a significant performance gradient for the use of linguistic structure in machine comprehension.


title: Why mate choices are not as reciprocal as we assume: The role of personality, flirting, and physical attractiveness

abstract: Based on a social relations perspective on mating, the actual and assumed reciprocity of mate choices was studied in a real-life speed-dating context. A community sample involving 382 singles aged 18-54 years filled out a questionnaire for the measurement of self-perceived mate value, sociosexuality, extraversion, and shyness and participated in free speed-dating sessions. Immediately after each date, choices and assumed choices were recorded. Measures of physical attractiveness and flirting behaviour were obtained by independent observers. Results show that actual mate choices are not reciprocal although people strongly expect their choices to be reciprocated and flirting behaviour is indeed strongly reciprocal. This interesting pattern of results was explained by investigating individual and dyadic effects of flirting, self-perceived mate value and physical attractiveness on mate choices. Results have important implications for understanding mating behaviour, sex differences and the (in)accuracies of mating decisions. Copyright # 2010 John Wiley & Sons, Ltd.


title: Statistical methods for linguistic research: Foundational Ideas - Part I

abstract: We present the fundamental ideas underlying statistical hypothesis testing using the frequentist framework. We start with a simple example that builds up the one-sample t-test from the beginning, explaining important concepts such as the sampling distribution of the sample mean, and the iid assumption. Then, we examine the meaning of the p-value in detail and discuss several important misconceptions about what a p-value does and does not tell us. This leads to a discussion of Type I, II error and power, and Type S and M error. An important conclusion from this discussion is that one should aim to carry out appropriately powered studies. Next, we discuss two common issues that we have encountered in psycholinguistics and linguistics: running experiments until significance is reached and the ‘garden-of-forking-paths’ problem discussed by Gelman and others. The best way to use frequentist methods is to run appropriately powered studies, check model assumptions, clearly separate exploratory data analysis from planned comparisons decided upon before the study was run, and always attempt to replicate results.


title: Using the FEniCS Package for FEM Solutions in Electromagnetics

abstract: FEniCS is a set of software tools that allows for rapid implementation of expressions associated with finite-element analysis. The main interface for FEniCS is DOLFIN, which provides both C++ and Python front ends to the software tools. The use of the Python front end, PyDOLFIN, to model various electromagnetic (EM) problems is investigated. Some elementary problems implemented in FEniCS include the scalar potential solution to closed- and open-boundary electrostatic problems, as well as the cutoff and dispersion analysis of a hollow rectangular waveguide. More advanced topics considered include the implementation of radiation from an infinitesimal dipole, and near-field-to-far-field transformations.


title: Accidental Childhood Death and the Role of the Pathologist

abstract: The following study provides an overview of accidental childhood death. This study is based on a review of 369 cases of fatal childhood accidents taken from the records of the Department of Histopathology, Women’s and Children’s Hospital, Adelaide, Australia, over a 34-year period from 1963 to 1996. Data provide information on deaths due to motor vehicle accidents, drownings, accidental asphyxia, burns, poisonings, electrocution, and miscellaneous trauma. In addition, certain categories have undergone further examination, including asphyxial deaths due to unsafe sleeping environments and unsafe eating practices, drowning deaths, and deaths on farms, following identification of significant child safety problems in these areas as part of the “Keeping Your Baby and Child Safe” program. Previously unrecognized dangers to children detected through this program include mesh-sided cots, V-shaped pillows, and certain types of stroller-prams. The production of information pamphlets and packages for parents and the recall of certain dangerous products following recommendations made by pathologists demonstrate that pediatric and forensic pathologists have an important role to play in preventive medicine issues and in formulating public health strategies.


title: How Far are We from Solving Pedestrian Detection?

abstract: Encouraged by the recent progress in pedestrian detection, we investigate the gap between current state-of-the-art methods and the "perfect single frame detector". We enable our analysis by creating a human baseline for pedestrian detection (over the Caltech dataset), and by manually clustering the recurrent errors of a top detector. Our results characterize both localization and background-versus-foreground errors. To address localization errors we study the impact of training annotation noise on the detector performance, and show that we can improve even with a small portion of sanitized training data. To address background/foreground discrimination, we study convnets for pedestrian detection, and discuss which factors affect their performance. Other than our in-depth analysis, we report top performance on the Caltech dataset, and provide a new sanitized set of training and test annotations.


title: Data Selection Strategies for Multi-Domain Sentiment Analysis

abstract: Domain adaptation is important in sentiment analysis as sentiment-indicating words vary between domains. Recently, multi-domain adaptation has become more pervasive, but existing approaches train on all available source domains including dissimilar ones. However, the selection of appropriate training data is as important as the choice of algorithm. We undertake -- to our knowledge for the first time -- an extensive study of domain similarity metrics in the context of sentiment analysis and propose novel representations, metrics, and a new scope for data selection. We evaluate the proposed methods on two large-scale multi-domain adaptation settings on tweets and reviews and demonstrate that they consistently outperform strong random and balanced baselines, while our proposed selection strategy outperforms instance-level selection and yields the best score on a large reviews corpus.


title: Findings of the Second Workshop on Neural Machine Translation and Generation

abstract: This document describes the findings of the Second Workshop on Neural Machine Translation and Generation, held in concert with the annual conference of the Association for Computational Linguistics (ACL 2018). First, we summarize the research trends of papers presented in the proceedings, and note that there is particular interest in linguistic structure, domain adaptation, data augmentation, handling inadequate resources, and analysis of models. Second, we describe the results of the workshop's shared task on efficient neural machine translation, where participants were tasked with creating MT systems that are both accurate and efficient.


title: Instance Search via Instance Level Segmentation and Feature Representation

abstract: Instance search is an interesting task as well as a challenging issue due to the lack of effective feature representation. In this paper, an instance level feature representation built upon recent fully convolutional instance-aware segmentation is proposed. The feature is ROI-pooled based on the segmented instance region. So that instances in different sizes and layouts are represented by deep feature in uniform length. This representation is further enhanced by the use of deformable ResNeXt blocks. Superior performance in terms of its distinctiveness and scalability is observed on a challenging evaluation dataset built by ourselves.


title: Wireless Sensor Network Operating System Design Rules Based on Real-World Deployment Survey

abstract: Wireless sensor networks (WSNs) have been a widely researched field since the beginning of the 21st century. The field is already maturing, and TinyOS has established itself as the de facto standard WSN Operating System (OS). However, the WSN researcher community is still active in building more flexible, efficient and user-friendly WSN operating systems. Often, WSN OS design is based either on practical requirements of a particular research project or research group's needs or on theoretical assumptions spread in the WSN community. The goal of this paper is to propose WSN OS design rules that are based on a thorough survey of 40 WSN deployments. The survey unveils trends of WSN applications and provides empirical substantiation to support widely usable and flexible WSN operating system design.


title: The Cross-Entropy Method for Optimization

abstract: The cross-entropy method is a versatile heuristic tool for solving difficult estimation and optimization problems, based on Kullback–Leibler (or cross-entropy) minimization. As an optimization method it unifies many existing population-based optimization heuristics. In this chapter we show how the cross-entropy method can be applied to a diverse range of combinatorial, continuous, and noisy optimization problems.


title: A clinical decision support system for aiding diagnosis of Alzheimer's disease and related disorders in mobile devices

abstract: The worldwide aging phenomenon is a growing concern. Alzheimer's disease (AD) has a high prevalence in the elderly. In this paper, we present a clinical decision support system for aiding the diagnosis of AD and related disorders. We describe system's main components and architecture, which is based on a mobile web-based platform. Its predictive model is based on Bayesian networks designed considering AD diagnosis criteria, trained and tested with the patient database of the Center for Alzheimer's Disease and Related Disorder at the Institute of Psychiatry of the Federal University of Rio de Janeiro, Brazil. Patient database attributes are composed by predisposal factors, demographic data, assessment scales, symptoms and signs. When the system indicates a patient diagnosis, it provides: the most probable diagnosis, health data that lead to such diagnosis and, in case of low certainty factor, unobserved health data that should be collected to confirm or refuse the initial diagnostic hypothesis. Preliminary usability tests indicate potential use of the system in clinical practice.


title: A Recurrent Latent Variable Model for Sequential Data

abstract: In this paper, we explore the inclusion of latent random variables into the hidden state of a recurrent neural network (RNN) by combining the elements of the variational autoencoder. We argue that through the use of high-level latent random variables, the variational RNN (VRNN)1 can model the kind of variability observed in highly structured sequential data such as natural speech. We empirically evaluate the proposed model against other related sequential models on four speech datasets and one handwriting dataset. Our results show the important roles that latent random variables can play in the RNN dynamics.


title: Microcontroller based Closed Loop Speed Control of DC Motor using PWM Technique

abstract: Speed control of DC motor is very important especially in the fields including industrial applications, robotics and so on. So, speed control of DC motors at different load conditions is a prominent field of research in order to achieve a robust system. This paper presents the design and implementation of microcontroller based speed control of DC motors which emphasize the design of PWM control using ICs like SG3524, TL494 in the field of motor speed control. For PWM generation microchip PIC16F877A microcontroller is used. The feedback loop is created by using ADC which is available in the microcontroller IC. This ADC reads the voltage level of the motor and correspondingly the voltage level of the motor can be maintained at a fixed level. A driver circuit is used to drive the motor. Hence, a closed loop motor speed control circuit is designed and the total amount of power delivered to the motor is varied depending on load conditions. In this technique, the regulation of motor’s speed is realized by changing the voltage of the motor which is adjusted by the duty ratio of PWM. General Terms DC motor, speed control.


title: Keeping the Bad Guys Out: Protecting and Vaccinating Deep Learning with JPEG Compression

abstract: Deep neural networks (DNNs) have achieved great success in solving a variety of machine learning (ML) problems, especially in the domain of image recognition. However, recent research showed that DNNs can be highly vulnerable to adversarially generated instances, which look seemingly normal to human observers, but completely confuse DNNs. These adversarial samples are crafted by adding small perturbations to normal, benign images. Such perturbations, while imperceptible to the human eye, are picked up by DNNs and cause them to misclassify the manipulated instances with high confidence. In this work, we explore and demonstrate how systematic JPEG compression can work as an effective pre-processing step in the classification pipeline to counter adversarial attacks and dramatically reduce their effects (e.g., Fast Gradient Sign Method, DeepFool). An important component of JPEG compression is its ability to remove high frequency signal components, inside square blocks of an image. Such an operation is equivalent to selective blurring of the image, helping remove additive perturbations. Further, we propose an ensemble-based technique that can be constructed quickly from a given well-performing DNN, and empirically show how such an ensemble that leverages JPEG compression can protect a model from multiple types of adversarial attacks, without requiring knowledge about the model.


title: Learning latent engagement patterns of students in online courses

abstract: Maintaining and cultivating student engagement is critical for learning. Understanding factors affecting student engagement will help in designing better courses and improving student retention. The large number of participants in massive open online courses (MOOCs) and data collected from their interaction with the MOOC open up avenues for studying student engagement at scale. In this work, we develop a framework for modeling and understanding student engagement in online courses based on student behavioral cues. Our first contribution is the abstraction of student engagement types using latent representations. We use that abstraction in a probabilistic model to connect student behavior with course completion. We demonstrate that the latent formulation for engagement helps in predicting student survival across three MOOCs. Next, in order to initiate better instructor interventions, we need to be able to predict student survival early in the course. We demonstrate that we can predict student survival early in the course reliably using the latent model. Finally, we perform a closer quantitative analysis of user interaction with the MOOC and identify student activities that are good indicators for survival at different points in the course.


title: Application of the Generalized Differential Quadrature Method to the Study of Pull-In Phenomena of MEMS Switches

abstract: This paper reports on the pull-in behavior of nonlinear microelectromechanical coupled systems. The generalized differential quadrature method has been used as a high-order approximation to discretize the governing nonlinear integro-differential equation, yielding more accurate results with a considerably smaller number of grid points. Various electrostatically actuated microstructures such as cantilever beam-type and fixed-fixed beam-type microelectromechanical systems (MEMS) switches are studied. The proposed models capture the following effects: (1) the intrinsic residual stress from fabrication processes; (2) the fringing effects of the electrical field; and (3) the nonlinear stiffening or axial stress due to beam stretching. The effects of important parameters on the mechanical performance have been studied in detail. These results are expected to be useful in the optimum design of MEMS switches or other actuators. Further, the results obtained are summarized and compared with other existing empirical and analytical models.


title: Blockchain-based electricity trading with Digitalgrid router

abstract: As decentralized renewable energy has been installed into the present grid, millions of consumers and prosumers interacting each other over power grid. The variety of needs for exchanging energy would arise based on each customers' situation. The future electricity grid should be a bi-directional system with interconnected using distributed energy management software. Being able to provide a secure and decentralized control to these autonomous, peer-to-peer exchanges is one of the biggest challenges. We propose to use Blockchain-based electricity trading system with Digitalgrid router as an underlying platform because it could fit these requirements. Digitalgrid router, which consists back-to-back bi-directional digital inverters with software-based control, enables us to realize power exchange.


title: A novel high-gain quasi-Yagi antenna with a parabolic reflector

abstract: The simplicity and intuitive design of traditional planar printed quasi-Yagi antennas has led to its widespread popularity for its good directivity. In this paper, a novel quasi-Yagi antenna with a single director and a concave parabolic reflector, operating in S-band, is proposed. The impedance characteristic and radiation characteristic are simulated with CST-Microwave Studio, and the antenna is fabricated and measured. The measured results indicate that the antenna which can operate at 2.28–2.63GHz can achieve an average gain of 6.5dBi within the operating frequency range, especially a highest gain of 7.5dBi at 2.5GHz. The proposed antenna can be widely used in WLAN/TD-LTE/BD1 and so on.


title: Real-Time Epileptic Seizure Detection from EEG Signals via Random Subspace Ensemble Learning

abstract: Real-time detection of seizure activities in epileptic patients is crucial and can help improve patients' quality of life. Accurate evaluation, pre-surgery assessments, seizure prevention, and emergency alerts for medical aid all depend on the rapid detection of the onset of seizures. A new method of feature selection and classification for rapid and precise epileptic seizure detection is discussed. In this solution, informative components of Electroencephalogram~(EEG) data are extracted and an automatic method is presented using Infinite Independent Component Analysis~(I-ICA) to select efficiently independent features. The feature space is divided into subspaces via random selection, and multi-channel Support Vector Machines~(SVMs) are used to classify the subspaces, then, the result of each classifier is combined by majority voting to find the final output. To evaluate the solution, a benchmark clinical intracranial EEG~(iEEG) of eight patients with temporal and extratemporal lobe epilepsy has been considered in a multi-tier cloud-computing architecture. Via the leave-one-out cross-validation, accuracy, sensitivity, specificity, and false positive and false negative ratios of the proposed method are 0.95, 0.96, 0.94, 0.06, and 0.04, respectively, which confirm the effectiveness of the proposed solution.


title: Expression of matrix metalloproteinase in the fluids of renal cystic lesions.

abstract: PURPOSE ::: Cystic lesions of the kidney are common conditions usually diagnosed by imaging. Although simple cysts are easy to diagnose, preoperative diagnosis of a complicated cystic lesion can be difficult. There is little information available on the biological activity of cystic fluid and associations with clinicopathological findings. We analyzed the expression of matrix metalloproteinase (MMP) in the fluids of benign and malignant renal cystic lesions to clarify matriolytic activities in the cyst. ::: ::: ::: MATERIALS AND METHODS ::: Included in this study were 22 samples of cystic fluids from renal cystic lesions, including 14 benign cysts and 8 cystic renal cell carcinomas. MMP-2 and 9 was determined in fluids using gelatin zymography and enzyme-linked immunosorbent assay. ::: ::: ::: RESULTS ::: MMP-2 expression was ubiquitously observed on zymography except for 2 benign cysts associated with acquired cystic disease of the kidney. MMP-9 was detected in 7 of 8 carcinomas but in only 2 of 14 benign cysts (p <0.01). The concentration of MMP-2 and 9 was significantly higher in cystic carcinomas than in benign cysts (p <0.01). ::: ::: ::: CONCLUSIONS ::: Our data show that MMPs were detectable in cystic fluids in the presence of renal cystic changes. MMP-2 and 9 are more abundant in cystic carcinoma fluids than in benign cystic fluids. These observations suggest that matriolytic enzymes in renal cystic fluid reflect biological aggressiveness and in part explain the pathogenesis of renal cystic lesions.


title: Emotion recognition using mobile phones

abstract: The availability of built-in sensors in mobile phones has enabled a host of innovative applications. One class of applications deals with detecting a user's emotions. Previous applications have primarily relied on recording and displaying self-reported emotions. This paper presents an intelligent emotion detection system for mobile phones implemented as a smart keyboard. The smart keyboard independently infers a user's emotional state using machine learning techniques. The system uses accelerometer readings and various aspect of typing behavior like speed, number of backspaces, and time delay between letters to train a classifier to predict emotions. Naive Bayes, J48, IBK, Multi-response linear regression and SVM were evaluated and J48 was found to be the best classifier with over 90% accuracy and precision. In addition to providing emotive feedback to individual users, the system also uses geo-tagged data to collect and display emotional states of regions or countries through a website.


title: A 220-mV input, 8.6 step-up voltage conversion ratio, 10.45-μW output power, fully integrated switched-capacitor converter for energy harvesting

abstract: This paper presents a fully integrated step-up switched-capacitor DC-DC converter for energy harvesting applications. The proposed solution exhibits both a high voltage-conversion ratio and a high power-conversion efficiency from a low-voltage source such as a thermoelectric generator or an indoor solar cell. The circuit has been implemented in TSMC 55nm CMOS process. Measurements demonstrate that it can properly operate with a 220 mV input, providing a 1.9 V output voltage at 10.45 μW output power, with 37.4% power conversion efficiency. We demonstrate that it can be used as a power supply for a Bluetooth Low Energy beacon with a thermoelectric generator subjected to a temperature difference of only 3.5°C.


title: Attachment across the life span: Factors that contribute to stability and change

abstract: The purpose of this paper is to provide a comprehensive review of the empirical findings on the stability of attachment from infancy through adulthood. More specifically, the paper discusses the longitudinal research concerning the continuity of attachment from infancy to adulthood as well as those studies that have assessed stability within a shorter time frame, across the various developmental periods (infancy, early childhood, adolescence and adulthood). This paper provides a unique contribution in examining developmental issues surrounding attachment stability and change. Additionally, since few studies have examined the factors that contribute to stable security and change from insecurity to security, this review highlights the variables that are influential in predicting attachment security.


A Theory and Tools for Applying Sandboxes Effectively


title: Towards personalised, gamified systems: an investigation into game design, personality and player typologies

abstract: With the rise of Gamification, the boundaries between play and games on the one hand, and everyday life on the other are being challenged, and as a result game play is entering the realm of everyday life. We believe that with the breakdown of this dichotomy and with the increasing presence of game elements in everyday life in the form of Gamification, there are more factors such as users intrinsic motivation, agenda, learning preferences and personality that should be considered in the design of gamified systems. In this paper, we investigate the relationship between player types, and personality types and traits. By way of investigating pre-existing player type models as well as personality traits and types models, we have identified possible relationships between these two areas of research, and in that, between the realm of games, and the realm of the everyday. As a result, we propose a table identifying these possible relationships between player types, personality types and traits, and game elements and game mechanics and discuss how this connection may impact the design of gamified systems and offer insight towards more user orientated design objectives.


title: Estimating 3D point-of-regard and visualizing gaze trajectories under natural head movements

abstract: The portability of an eye tracking system encourages us to develop a technique for estimating 3D point-of-regard. Unlike conventional methods, which estimate the position in the 2D image coordinates of the mounted camera, such a technique can represent richer gaze information of the human moving in the larger area. In this paper, we propose a method for estimating the 3D point-of-regard and a visualization technique of gaze trajectories under natural head movements for the head-mounted device. We employ visual SLAM technique to estimate head configuration and extract environmental information. Even in cases where the head moves dynamically, the proposed method could obtain 3D point-of-regard. Additionally, gaze trajectories are appropriately overlaid on the scene camera image.


title: Factors Influencing The Quality of Accounting Information System And Its Implications on The Quality of Accounting Information

abstract: The purpose of this study was to determine the influence of organizational factors (management commitment, organizational culture and organizational structure) to the quality of the accounting information system and its implications on the quality of accounting information. The unit of analysis in this study is the accounting staff of 33 cooperatives in Bandung-Indonesia. The results showed that the commitment of management, organizational culture and organizational structure have a significant effect on the quality of accounting information systems. Furthermore it was found that the quality of accounting information system has implications for the quality of accounting information. Keywords: management commitment, organizational culture and organizational structure, accounting information system, accounting information


title: Anomaly detection in categorical datasets using bayesian networks

abstract: In this paper we present a method for finding anomalous records in categorical or mixed datasets in an unsupervised fashion. Since the data in many problems consist of normal records with a small minority of anomalies, many approaches build a model from the training data and compare the test records against it. But instead of building a model, we keep track of the number of occurrences of different attribute value combinations. We also consider a more meaningful definition of anomalies and incorporate the Bayesian network structure in it. A scoring technique is defined for each test record. In this procedure we combine supports of different rules according to the Bayesian network structure in order to determine the label of the test instances. As it is shown in the results, our proposed method has a higher or similar f-measure and precision compared to a Bayesian network based approach in all cases.


title: A simple sizing algorithm for stand-alone PV/Wind/Battery hybrid microgrids

abstract: In this paper, we develop a simple algorithm to determine the required number of generating units of wind-turbine generator and photovoltaic array, and the associated storage capacity for stand-alone hybrid microgrid. The algorithm is based on the observation that the state of charge of battery should be periodically invariant. The optimal sizing of hybrid microgrid is given in the sense that the life cycle cost of system is minimized while the given load power demand can be satisfied without load rejection. We also report a case study to show the efficacy of the developed algorithm.


title: Advances and Challenges in Super-Resolution

abstract: Super-Resolution reconstruction produces one or a set of high-resolution images from a sequence of low-resolution frames. This article reviews a variety of Super-Resolution methods proposed in the last 20 years, and provides some insight into, and a summary of, our recent contributions to the general Super-Resolution problem. In the process, a detailed study of several very important aspects of Super-Resolution, often ignored in the literature, is presented. Spe- cifically, we discuss robustness, treatment of color, and dynamic operation modes. Novel methods for addressing these issues are accompanied by experimental results on simulated and real data. Finally, some future challenges in Super-Resolution are outlined and discussed. © 2004 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 14, 47-57, 2004; Published online in Wiley InterScience (www.interscience.wiley. com). DOI 10.1002/ima.20007


title: A Tele-Presence Wheelchair for Elderly People.

abstract: This paper presents the architecture and implementation of a tele-presence wheelchair based on tele-presence robot, intelligent wheelchair, and smart pad TIUI technologies. The tele-presence wheelchair is just a commercial electric wheelchair equipped with a tele-presence interaction system. The tele-presence interaction system is an add-on module which consists of three parts: tele-presence, tele-operation imaging, and user interface. The tele-presence part is mounted on the front of the wheelchair for an elderly person to communicate with family members through video and voice. The tele-operation imaging part captures the live video for tele-operation and semi-autonomous navigation. The user interface developed in our lab for a smart pad [12] is the TIUI, which allows an operator to directly touch and push the wheelchair image in the live video of a remote environment as him/her to do in the presence. This paper also discusses the preliminary evaluation of the user experience.


title: Differences in physical fitness among indoor and outdoor elite male soccer players

abstract: This study compared anthropometric (body height, body mass, percent body fat, fat-free body mass) and physical fitness characteristics (vertical jump height, power-load curve of the leg, 5 and 15 m sprint running time and blood lactate concentrations ([La]b) at submaximal running velocities) among 15 elite male indoor soccer (IS) and 25 elite male outdoor soccer (OS) players. IS players had similar values in body height, body mass, fat-free body mass and endurance running than OS players. However, the IS group showed higher (P < 0.05–0.01) values in percent body fat (28%) and sprint running time (2%) but lower values in vertical jump (15%) and half-squat power (20%) than the OS group. Significant negative correlations (P < 0.05–0.01) were observed between maximal sprint running time, power production during half-squat actions, as well as [La]b at submaximal running velocities. Percent body fat correlated positively with maximal sprint time and [La]b, but correlated negatively with vertical jump height. The present results show that compared to elite OS players, elite IS players present clearly lower physical fitness (lower maximal leg extension power production) characteristics associated with higher values of percent body fat. This should give IS players a disadvantage during soccer game actions.


title: Green-Marl: a DSL for easy and efficient graph analysis

abstract: The increasing importance of graph-data based applications is fueling the need for highly efficient and parallel implementations of graph analysis software. In this paper we describe Green-Marl, a domain-specific language (DSL) whose high level language constructs allow developers to describe their graph analysis algorithms intuitively, but expose the data-level parallelism inherent in the algorithms. We also present our Green-Marl compiler which translates high-level algorithmic description written in Green-Marl into an efficient C++ implementation by exploiting this exposed data-level parallelism. Furthermore, our Green-Marl compiler applies a set of optimizations that take advantage of the high-level semantic knowledge encoded in the Green-Marl DSL. We demonstrate that graph analysis algorithms can be written very intuitively with Green-Marl through some examples, and our experimental results show that the compiler-generated implementation out of such descriptions performs as well as or better than highly-tuned hand-coded implementations.


title: Dynamic tracking of page miss ratio curve for memory management

abstract: Memory can be efficiently utilized if the dynamic memory demands of applications can be determined and analyzed at run-time. The page miss ratio curve(MRC), i.e. page miss rate vs. memory size curve, is a good performance-directed metric to serve this purpose. However, dynamically tracking MRC at run time is challenging in systems with virtual memory because not every memory reference passes through the operating system (OS).This paper proposes two methods to dynamically track MRC of applications at run time. The first method is using a hardware MRC monitor that can track MRC at fine time granularity. Our simulation results show that this monitor has negligible performance and energy overheads. The second method is an OS-only implementation that can track MRC at coarse time granularity. Our implementation results on Linux show that it adds only 7--10% overhead.We have also used the dynamic MRC to guide both memory allocation for multiprogramming systems and memory energy management. Our real system experiments on Linux with applications including Apache Web Server show that the MRC-directed memory allocation can speed up the applications' execution/response time by up to a factor of 5.86 and reduce the number of page faults by up to 63.1%. Our execution-driven simulation results with SPEC2000 benchmarks show that the MRC-directed memory energy management can improve the Energy * Delay metric by 27--58% over previously proposed static and dynamic schemes.


title: Estimating the aspect layout of object categories

abstract: In this work we seek to move away from the traditional paradigm for 2D object recognition whereby objects are identified in the image as 2D bounding boxes. We focus instead on: i) detecting objects; ii) identifying their 3D poses; iii) characterizing the geometrical and topological properties of the objects in terms of their aspect configurations in 3D. We call such characterization an object's aspect layout (see Fig. 1). We propose a new model for solving these problems in a joint fashion from a single image for object categories. Our model is constructed upon a novel framework based on conditional random fields with maximal margin parameter estimation. Extensive experiments are conducted to evaluate our model's performance in determining object pose and layout from images. We achieve superior viewpoint accuracy results on three public datasets and show extensive quantitative analysis to demonstrate the ability of accurately recovering the aspect layout of objects.


The Application of Link Mining in Social Network Analysis


title: Setae from the pine processionary moth (Thaumetopoea pityocampa) contain several relevant allergens.

abstract: BACKGROUND ::: Pine processionary larvae produce urticating hairs (setae) that serve for protection against predators. Setae induce cutaneous reactions in animals and humans. The presence of toxic or allergic mechanisms is a matter of debate. ::: ::: ::: OBJECTIVES ::: To detect the presence of allergens in setae and to characterize them. ::: ::: ::: MATERIALS AND METHODS ::: Setae extracts were characterized by gel staining and immunoblot, with sera from patients with immediate reactions and positive prick test reactions, as well as a rabbit antiserum raised against setae. Setae proteins were fractionated by high-performance liquid chromatography. The most relevant allergen was analysed by matrix-assisted laser desorption/ionization (MALDI) mass spectrometry (MS), and its sequence was deduced from an expressed sequence tag bank. Results. Setae contained at least seven different allergens. The most intense detection corresponded to a protein of MW ~ 14,000 that was similar to thaumetopoein, a previously described protein with mast cell-degranulating properties. MALDI-MS-based de novo sequencing provided a partial amino acid sequence different from that of the previously described allergen Tha p 1, and it was named Tha p 2. This allergen was detected in 61% of patients, and it is therefore a new major caterpillar allergen. ::: ::: ::: CONCLUSIONS ::: Penetration of the setae from the pine processionary caterpillar delivers their allergenic content in addition to causing mechanical or toxic injury.


title: A magnetic integration half-turn planar transformer for LLC resonant DC-DC converters

abstract: This paper presents a new half-turn planar transformer with integrated leakage inductance for LLC resonant DC-DC converters. A simple EILP32 ferrite core is adopted in the proposed transformer. The primary side windings are placed at both the center and side legs of the ferrite core. By adjusting the distribution of the windings on the center and side legs, the leakage inductance of the transformer can be changed. In order to balance the core loss and the copper loss, the turn number of the secondary side winding is designed to 0.5. Two configurations of secondary winding and circuit structure are given and compared. The characteristics of the proposed transformer are simulated using Maxwell and Simplorer. Finally, a 380V / 12V, 500 kHz, 750W LLC converter was built to evaluate the performance of the proposed transformer. The converter can reach 12V stable output when the input voltage range is from 360V to 400V, and realize the highest efficiency of 97 % at half load condition.


title: Mechanisms of hypoosmotic volume regulation in glioma cells

abstract: Abstract Rat C6 glioma cells undergo regulatory volume decrease (RVD) following hypoosmotic exposure. RVD was inhibited by the K + channel blockers barium (10 mM) and quinine (1 mM). The mechanism of activation of the volume regulatory process was studied. Volume regulation was not observed following incubation of cells in Ca 2+ -free medium. Fluorescent measurement of intracellular free Ca 2+ revealed no change following hypoosmotic exposure. Okadaic acid, an inhibitor of protein phosphatase type 1 and 2A inhibited VRD in C6 glioma cells. These results suggest that hypoosmotic RVD in C6 glioma cells involves a loss of K + (and anion) from the cell. The activation of K + loss is dependent on the presence of extracellular calcium (but not an increase in intracellular free calcium); and on protein dephosphorylation, either of a transport protein or another protein in the signalling pathway.


title: Rapid Prototyping Technologies and their Applications in Prosthodontics, a Review of Literature

abstract: The early computer-aided design/computer-aided manufacturing (CAD/CAM) systems were relied exclusively on subtractive methods. In recent years, additive methods by employing rapid prototyping (RP) have progressed rapidly in various fields of dentistry as they have the potential to overcome known drawbacks of subtractive techniques such as fit problems. RP techniques have been exploited to build complex 3D models in medicine since the 1990s. RP has recently proposed successful applications in various dental fields, such as fabrication of implant surgical guides, frameworks for fixed and removable partial dentures, wax patterns for the dental prosthesis, zirconia prosthesis and molds for metal castings, and maxillofacial prosthesis and finally, complete dentures. This paper aimed to offer a comprehensive literature review of various RP methods, particularly in dentistry, that are expected to bring many improvements to the field. A search was made through MEDLINE database and Google scholar search engine. The keywords; ‘rapid prototyping’ and ‘dentistry’ were searched in title/abstract of publications; limited to 2003 to 2013, concerning past decade. The inclusion criterion was the technical researches that predominately included laboratory procedures. The exclusion criterion was meticulous clinical and excessive technical procedures. A total of 106 articles were retrieved, recited by authors and only 50 met the specified inclusion criteria for this review. Selected articles had used rapid prototyping techniques in various fields in dentistry through different techniques. This review depicted the different laboratory procedures employed in this method and confirmed that RP technique have been substantially feasible in dentistry. With advancement in various RP systems, it is possible to benefit from this technique in different dental practices, particularly in implementing dental prostheses for different applications.


title: Metasurface-Based Tunable Reflector for Independent Control of Orthogonal Polarizations

abstract: This paper presents a metasurface reflector, characterized with a 0° reflection phase, which can be tuned to operate at any frequency within the band [1.1 – 1.7] GHz and in two orthogonal polarizations independently. The proposed metasurface, based on a double layer of periodic metallic patches and varactors, behaves like an artificial magnetic conductor (AMC). By varying the capacitance values of the varactors, the positions of the resonant frequencies of the AMC can be tuned in real time. The proposed solution offers the possibility to overcome the narrow band characteristics of AMC structures and to control the two orthogonal polarizations independently.


title: Fake News Identification Characteristics Using Named Entity Recognition and Phrase Detection

abstract: Information explosion that can be generated by anyone may lead to the spread of fake news not only at the news channel, but also at social media, and so forth. Detection of fake news has become an urgent need on the society because of fake news spread of unrest in the society. Several related studies have been conducted in the news classification with the aim of providing a decision whether a news is included in fake news or original news. In the related research, a vector representation of documents is used. This vector representation is then given to the algorithm for further processing. This study aims to model vectors that can accommodate the characteristics of fake news before further processed by language algorithms using the Indonesian language. In this research, fake news and original news are represented according to the vector space model. Vector model combination of frequency term, inverse document frequency and frequency reversed with 10-fold cross validation using support vector machine algorithm classifier. Variations of phrase detection as well as name recognition entities (entity recognition names) are also used in vector representation. A vector representation that uses the term frequency shows promising performance. It can recognize news characteristics correctly 96.74% of 2516 documents across phrase detection and named entity recognition process.


title: Pedestrian intention recognition by means of a Hidden Markov Model and body language

abstract: According to several reports published by world-wide organisations, thousands of pedestrians die on road accidents every year. Due to this fact, vehicular technologies have been evolving with the intent of reducing these fatalities. Improving these technological advances is crucial since an early recognition of pedestrian intentions can lead to much more accurate active interventions in last second automatic manoeuvres. This paper proposes a method based on a Hidden Markov Model that recognises intentions by means of 3D positions and displacements of 11 joints located along the pedestrian body. The method is able to recognise the intention with an accuracy of 95.13%. It recognises starting intentions 125 ms after gait initiation with an accuracy of 80% and stopping intentions 291.67 and 58.33 ms before the event with an accuracy of 50% and 70% respectively. In addition, an approach based on point clouds and anthropometric constraints to extract the joints in realistic environments is proposed.


title: Image denoising via group sparsity residual constraint

abstract: Group sparsity has shown great potential in various low-level vision tasks (e.g, image denoising, deblurring and inpainting). In this paper, we propose a new prior model for image denoising via group sparsity residual constraint (GSRC). To enhance the performance of group sparse-based image denoising, the concept of group sparsity residual is proposed, and thus, the problem of image denoising is translated into one that reduces the group sparsity residual. To reduce the residual, we first obtain some good estimation of the group sparse coefficients of the original image by the first-pass estimation of noisy image, and then centralize the group sparse coefficients of noisy image to the estimation. Experimental results have demonstrated that the proposed method not only outperforms many state-of-the-art denoising methods such as BM3D and WNNM, but results in a faster speed.


title: Learning Software Maintenance Tasks in Offshoring Projects: A Cognitive-Load Perspective

abstract: Individual learning is central to the success of the transition phase in software mainte-nance offshoring projects. However, little is known on how learning activities, such as on-the-job training and formal presentations, are effectively combined during the tran-sition phase. In this study, we present and test propositions derived from cognitive load theory. The results of a multiple-case study suggest that learning effectiveness was highest when learning tasks such as authentic maintenance requests were used. Con-sistent with cognitive load theory, learning tasks were most effective when they imposed moderate cognitive load. Our data indicate that cognitive load was influenced by the expertise of the onsite coordinator, by intrinsic task complexity, by the degree of specifi-cation of tasks, and by supportive information. Cultural and semantic distances may in-fluence learning by inhibiting supportive information, specification, and the assignment of learning tasks.


title: A New Method of Region Embedding for Text Classification

abstract: To represent a text as a bag of properly identified “phrases” and use the representation for processing the text is proved to be useful. The key question here is how to identify the phrases and represent them. The traditional method of utilizing n-grams can be regarded as an approximation of the approach. Such a method can suffer from data sparsity, however, particularly when the length of n-gram is large. In this paper, we propose a new method of learning and utilizing task-specific distributed representations of n-grams, referred to as “region embeddings”. Without loss of generality we address text classification. We specifically propose two models for region embeddings. In our models, the representation of a word has two parts, the embedding of the word itself, and a weighting matrix to interact with the local context, referred to as local context unit. The region embeddings are learned and used in the classification task, as parameters of the neural network classifier. Experimental results show that our proposed method outperforms existing methods in text classification on several benchmark datasets. The results also indicate that our method can indeed capture the salient phrasal expressions in the texts.


title: Performance assessment of a pushrim-activated power-assisted wheelchair control system

abstract: Wheelchairs are an important form of mobility for people with disabilities. For many years, there have only been three wheelchair varieties: electric-powered wheelchairs, scooters and manual wheelchairs. Recently, wheelchairs have been developed that use a combination of human power and electric power. The human power is delivered by the arms through the pushrims while the electric power is delivered by a battery through two electric motors. The shared control system for a pushrim-activated power-assisted wheelchair (PAPAW) must account for the human behavior and the interaction with the device. The PAPAW uses a form of gain scheduling based upon events recorded from the pushrim torque. The control system significantly altered (p<0.05) selected variables from pushrim torque curves for manual wheelchair propulsion and PAPAW operation as desired. The peak torque was reduced by over 50%, and the time on the rim was nearly doubled with the PAPAW. The PAPAW provided intuitive control and was capable of significantly reducing the strain on the upper extremities commonly associated with secondary disabling conditions among manual wheelchair users.