title: Robust human action recognition via long short-term memory

abstract: The long short-term memory (LSTM) neural network utilizes specialized modulation mechanisms to store information for extended periods of time. It is thus potentially well-suited for complex visual processing, where the current video frame must be considered in the context of past frames. Recent studies have indeed shown that LSTM can effectively recognize and classify human actions (e.g., running, hand waving) in video data; however, these results were achieved under somewhat restricted settings. In this effort, we seek to demonstrate that LSTM's performance remains robust even as experimental conditions deteriorate. Specifically, we show that classification accuracy exhibits graceful degradation when the LSTM network is faced with (a) lower quantities of available training data, (b) tighter deadlines for decision making (i.e., shorter available input data sequences) and (c) poorer video quality (resulting from noise, dropped frames or reduced resolution). We also clearly demonstrate the benefits of memory for video processing, particularly, under high noise or frame drop rates. Our study is thus an initial step towards demonstrating LSTM's potential for robust action recognition in real-world scenarios.


title: Soricid Response to Canopy Gaps Created by Wind Disturbance in the Southern Appalachians

abstract: Abstract We used drift fences with pitfall traps to compare soricid abundance, richness, and demographic parameters among intact multiple-tree windthrow gaps, salvaged gaps, and mature forest in a xeric southern Appalachian forest type during 1997–1999. We also tested whether capture rates were correlated with rainfall, and whether similar-sized species did not co-occur as predicted by multi-species assemblage rules. We captured six species: Blarina brevicauda (Say) (northern short-tailed shrew), Cryptotis parva (Say) (least shrew), Sorex cinereus Kerr (masked shrew), S. fumeus G.M. Miller (smoky shrew), S. hoyi Baird (pygmy shrew), and S. longirostris Bachman (southeastern shrew). Tree basal area, forest structural features, and arthropod abundance differed among treatments, but species richness and abundance of most shrews did not. Captures during June–October were higher in 1998 than in 1997, although 1998 summer rainfall was low. Rainfall was correlated with shrew activity, but explained little of the...


Linguistic Relativism Variants and Misconceptions


title: Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks

abstract: Abstract: Recognizing arbitrary multi-character text in unconstrained natural photographs is a hard problem. In this paper, we address an equally hard sub-problem in this domain viz. recognizing arbitrary multi-digit numbers from Street View imagery. Traditional approaches to solve this problem typically separate out the localization, segmentation, and recognition steps. In this paper we propose a unified approach that integrates these three steps via the use of a deep convolutional neural network that operates directly on the image pixels. We employ the DistBelief implementation of deep neural networks in order to train large, distributed neural networks on high quality images. We find that the performance of this approach increases with the depth of the convolutional network, with the best performance occurring in the deepest architecture we trained, with eleven hidden layers. We evaluate this approach on the publicly available SVHN dataset and achieve over $96\%$ accuracy in recognizing complete street numbers. We show that on a per-digit recognition task, we improve upon the state-of-the-art, achieving $97.84\%$ accuracy. We also evaluate this approach on an even more challenging dataset generated from Street View imagery containing several tens of millions of street number annotations and achieve over $90\%$ accuracy. To further explore the applicability of the proposed system to broader text recognition tasks, we apply it to synthetic distorted text from reCAPTCHA. reCAPTCHA is one of the most secure reverse turing tests that uses distorted text to distinguish humans from bots. We report a $99.8\%$ accuracy on the hardest category of reCAPTCHA. Our evaluations on both tasks indicate that at specific operating thresholds, the performance of the proposed system is comparable to, and in some cases exceeds, that of human operators.


title: Survivor guilt, submissive behaviour and evolutionary theory: The down-side of winning in social comparison

abstract: In prior research submissive behaviour has been studied in relation to social comparison. Evolutionary theory conceptualized submissive behaviour as a fear-based self-protective strategy when in a subordinate position. In this study we hypothesized that survivor guilt, the type of guilt associated with feeling better off than others, is also linked to submissive behaviour. The Interpersonal Guilt Questionnaire, the Submissive Behaviour Inventory, the Eysenck Personality Questionnaire-Revised and the Automatic Thoughts Questionnaire were administered to a sample of 199 college students. Submissive behaviour was found to be significantly correlated with survivor guilt. Introversion, used as an indirect measure of the fear of being put down, was also correlated with submissive behaviour. A principal components analysis found two components: the first was composed of high loadings of submissiveness, survivor guilt and omnipotent responsibility guilt; the second was composed of high loadings of submissiveness and introversion. This supports the hypothesis that there may be two motivational states related to submissive behaviour, the fear of harm to the self, as described in prior studies, and the fear of harm to another or guilt-based submissive behaviour. We propose that survivor guilt has been selected by evolution as a psychological mechanism supporting group living, and that it may be considered from the perspective of inclusive fitness, reciprocal altruism, and multilevel selection theory.


title: On the Convergence and Robustness of Training GANs with Regularized Optimal Transport

abstract: Generative Adversarial Networks (GANs) are one of the most practical methods for learning data distributions. A popular GAN formulation is based on the use of Wasserstein distance as a metric between probability distributions. Unfortunately, minimizing the Wasserstein distance between the data distribution and the generative model distribution is a computationally challenging problem as its objective is non-convex, non-smooth, and even hard to compute. In this work, we show that obtaining gradient information of the smoothed Wasserstein GAN formulation, which is based on regularized Optimal Transport (OT), is computationally effortless and hence one can apply first order optimization methods to minimize this objective. Consequently, we establish theoretical convergence guarantee to stationarity for a proposed class of GAN optimization algorithms. Unlike the original non-smooth formulation, our algorithm only requires solving the discriminator to approximate optimality. We apply our method to learning MNIST digits as well as CIFAR-10 images. Our experiments show that our method is computationally efficient and generates images comparable to the state of the art algorithms given the same architecture and computational power.


title: Blockchain and Trusted Computing: Problems, Pitfalls, and a Solution for Hyperledger Fabric

abstract: A smart contract on a blockchain cannot keep a secret because its data is replicated on all nodes in a network. To remedy this problem, it has been suggested to combine blockchains with trusted execution environments (TEEs), such as Intel SGX, for executing applications that demand privacy. Untrusted blockchain nodes cannot get access to the data and computations inside the TEE. This paper first explores some pitfalls that arise from the combination of TEEs with blockchains. Since TEEs are, in principle, stateless they are susceptible to rollback attacks, which should be prevented to maintain privacy for the application. However, in blockchains with non-final consensus protocols, such as the proof-of-work in Ethereum and others, the contract execution must handle rollbacks by design. This implies that TEEs for securing blockchain execution cannot be directly used for such blockchains; this approach works only when the consensus decisions are final. Second, this work introduces an architecture and a prototype for smart-contract execution within Intel SGX technology for Hyperledger Fabric, a prominent platform for enterprise blockchain applications. Our system resolves difficulties posed by the execute-order-validate architecture of Fabric and prevents rollback attacks on TEE-based execution as far as possible. For increasing security, our design encapsulates each application on the blockchain within its own enclave that shields it from the host system. An evaluation shows that the overhead moving execution into SGX is within 10%-20% for a sealed-bid auction application.


A Factored Neural Network Model for Characterizing Online Discussions in Vector Space


©1999 I NSNA Building a Network Theory of Social Capital'


title: iHandRehab: An interactive hand exoskeleton for active and passive rehabilitation

abstract: This paper presents an interactive exoskeleton device for hand rehabilitation, iHandRehab, which aims to satisfy the essential requirements for both active and passive rehabilitation motions. iHandRehab is comprised of exoskeletons for the thumb and index finger. These exoskeletons are driven by distant actuation modules through a cable/sheath transmission mechanism. The exoskeleton for each finger has 4 degrees of freedom (DOF), providing independent control for all finger joints. The joint motion is accomplished by a parallelogram mechanism so that the joints of the device and their corresponding finger joints have the same angular displacement when they rotate. Thanks to this design, the joint angles can be measured by sensors real time and high level motion control is therefore made very simple without the need of complicated kinematics. The paper also discusses important issues when the device is used by different patients, including its adjustable joint range of motion (ROM) and adjustable range of phalanx length (ROPL). Experimentally collected data show that the achieved ROM is close to that of a healthy hand and the ROPL covers the size of a typical hand, satisfying the size need of regular hand rehabilitation. In order to evaluate the performance when it works as a haptic device in active mode, the equivalent moment of inertia (MOI) of the device is calculated. The results prove that the device has low inertia which is critical in order to obtain good backdrivability. Experimental analysis shows that the influence of friction accounts for a large portion of the driving torque and warrants future investigation.


title: The Best Friendships of Shy/Withdrawn Children: Prevalence, Stability, and Relationship Quality.

abstract: The mutual best friendships of shy/withdrawn and control children were examined for prevalence, stability, best friend’s characteristics, and friendship quality. Using peer nominations of shy/socially withdrawn and aggressive behaviors, two groups of children were identified from a normative sample of fifth-grade children: shy/withdrawn (n = 169) and control (nonaggressive/nonwithdrawn; n = 163). Friendship nominations, teacher reports, and friendship quality data were gathered. Results revealed that shy/withdrawn children were as likely as control children to have mutual stable best friendships. Withdrawn children’s friends were more withdrawn and victimized than were the control children’s best friends; further, similarities in social withdrawal and peer victimization were revealed for withdrawn children and their friends. Withdrawn children and their friends reported lower friendship quality than did control children. Results highlight the importance of both quantitative and qualitative measures of friendship when considering relationships as risk and/or protective factors.


title: IOTSim: a Simulator for Analysing IoT Applications

abstract: A disruptive technology that is influencing not only computing paradigm but every other business is the rise of big data. Internet of Things (IoT) applications are considered to be a major source of big data. Such IoT applications are in general supported through clouds where data is stored and processed by big data processing systems. In order to improve the efficiency of cloud infrastructure so that they can efficiently support IoT big data applications, it is important to understand how these applications and the corresponding big data processing systems will perform in cloud computing environments. However, given the scalability and complex requirements of big data processing systems, an empirical evaluation on actual cloud infrastructure can hinder the development of timely and cost effective IoT solutions. Therefore, a simulator supporting IoT applications in cloud environment is highly demanded, but such work is still in its infancy. To fill this gap, we have designed and implemented IOTSim which supports and enables simulation of IoT big data processing using MapReduce model in cloud computing environment. A real case study validates the efficacy of the simulator.


Evaluating Word Embeddings Using a Representative Suite of Practical Tasks


title: The truck backer-upper: an example of self-learning in neural networks

abstract: Neural networks can be used to solve highly nonlinear control problems. A two-layer neural network containing 26 adaptive neural elements has learned to back up a computer-simulated trailer truck to a loading dock, even when initially jackknifed. It is not yet known how to design a controller to perform this steering task. Nevertheless, the neural net was able to learn of its own accord to do this, regardless of initial conditions. Experience gained with the truck backer-upper should be applicable to a wide variety of nonlinear control problems. >


title: Defect detection and identification in textile fabrics using Multi Resolution Combined Statistical and Spatial Frequency Method

abstract: In textile industry, reliable and accurate quality control and inspection becomes an important element. Presently, this is still accomplished by human experience, which is more time consuming and is also prone to errors. Hence automated visual inspection systems become mandatory in textile industries. This Paper presents a novel algorithm of fabric defect detection by making use of Multi Resolution Combined Statistical and Spatial Frequency Method. Defect detection consists of two phases, first is the training and next is the testing phase. In the training phase, the reference fabric images are cropped into non-overlapping sub-windows. By applying MRCSF the features of the textile fabrics are extracted and stored in the database. During the testing phase the same procedure is applied for test fabric and the features are compared with database information. Based on the comparison results, each sub-window is categorized as defective or non-defective. The classification rate obtained by the process of simulation using MATLAB was found to be 99%.


title: An effort to design a power efficient, long reach WDM-FSO system

abstract: Free Space Optics offers many advantages over different communication systems like higher bandwidth, etc. FSO is being used to overcome the drawbacks of other systems like optical and radio communication systems. FSO requires free space as the medium of communication. Attenuation caused by the medium can degrade a system performance. To overcome the effect of these losses due to attenuation, either the data rate is decreased or the laser power is increased. In this article, concept of laser power is analyzed by changing the power level and the performance is compared with that of system with constant power. It has been found that for higher attenuation, there is no significant decrease in link distance where as the power is reduced from 40dBm to 10dBm.


title: Adaptive Feature Abstraction for Translating Video to Language

abstract: Previous models for video captioning often use the output from a specific layer of a Convolutional Neural Network (CNN) as video representations, preventing them from modeling rich, varying context-dependent semantics in video descriptions. In this paper, we propose a new approach to generating adaptive spatiotemporal representations of videos for a captioning task. For this purpose, novel attention mechanisms with spatiotemporal alignment is employed to adaptively and sequentially focus on different layers of CNN features (levels of feature ``abstraction''), as well as local spatiotemporal regions of the feature maps at each layer. Our approach is evaluated on three benchmark datasets: YouTube2Text, M-VAD and MSR-VTT. Along with visualizing the results and how the model works, these experiments quantitatively demonstrate the effectiveness of the proposed adaptive spatiotemporal feature abstraction for translating videos to sentences with rich semantics.


title: Automatic Traffic Density Estimation and Vehicle Classification for Traffic Surveillance Systems Using Neural Networks

abstract: It is important to know the road traffic density real time especially in mega cities for signal control and effective traffic management. In recent years, video monitoring and surveillance systems have been widely used in traffic management. Hence, traffic density estimation and vehicle classification can be achieved using video monitoring systems. In most vehicle detection methods in the literature, only the detection of vehicles in frames of the given video is emphesized. However, further analysis is needed in order to obtain the useful information for traffic management such as real time traffic density and number of vehicle types passing these roads. This paper presents vehicle classification and traffic density calculation methods using neural networks. The paper also reports results from real traffic videos obtained from Istanbul Traffic Management Company (ISBAK).


title: Context-Aware User Authentication – Supporting Proximity-Based Login in Pervasive Computing

abstract: This paper explores computer security in pervasive computing with focus on user authentication. We present the concept of Proximity-Based User Authentication, as a usability-wise ideal for UbiComp systems. We present a context-aware user authentication protocol, which (1) uses a JavaCard for identification and cryptographic calculations, (2) uses a context-awareness system for verifying the user’s location, and (3) implements a security fall-back strategy. We analyze the security of this protocol and discuss the tradeoff between usability and security. We also present our current implementation of the protocol and discuss future work.


title: Life beyond Distributed Transactions: an Apostate's Opinion

abstract: Many decades of work have been invested in the area of distributed transactions including protocols such as 2PC, Paxos, and various approaches to quorum. These protocols provide the application programmer a facade of global serializability. Personally, I have invested a nontrivial portion of my career as a strong advocate for the implementation and use of platforms providing guarantees of global serializability. My experience over the last decade has led me to liken these platforms to the Maginot Line. In general, application developers simply do not implement large scalable applications assuming distributed transactions. When they attempt to use distributed transactions, the projects founder because the performance costs and fragility make them impractical. Natural selection kicks in... 1 The Maginot Line was a huge fortress that ran the length of the Franco-German border and was constructed at great expense between World War I and World War II. It successfully kept the German army from directly crossing the border between France and Germany. It was quickly bypassed by the Germans in 1940 who invaded through Belgium. This article is published under a Creative Commons License Agreement (http://creativecommons.org/licenses/by/2.5/). You may copy, distribute, display, and perform the work, make derivative works and make commercial use of the work, but you must attribute the work to the author and CIDR 2007. 3 Biennial Conference on Innovative DataSystems Research (CIDR) January 7-10, Asilomar, California USA. Instead, applications are built using different techniques which do not provide the same transactional guarantees but still meet the needs of their businesses. This paper explores and names some of the practical approaches used in the implementations of large-scale mission-critical applications in a world which rejects distributed transactions. We discuss the management of fine-grained pieces of application data which may be repartitioned over time as the application grows. We also discuss the design patterns used in sending messages between these repartitionable pieces of data. The reason for starting this discussion is to raise awareness of new patterns for two reasons. First, it is my belief that this awareness can ease the challenges of people hand-crafting very large scalable applications. Second, by observing the patterns, hopefully the industry can work towards the creation of platforms that make it easier to build these very large applications.


title: Efficient FIR Filter Design Using Modified Carry Select Adder & Wallace Tree Multiplier

abstract: An area-power-delay efficient design of FIR filter is described in this paper. In proposed multiplier unit high speed is achieved using XOR-XNOR column by column reduction compressors instead of compressors using full adder. The carry propagation delay and area of carry select adder is reduced by splitting carry select adder into equal bit groups. The proposed carry select adder unit consumes less power than conventional carry select adder unit. With proposed multiplier unit and carry select adder unit, the designed FIR Filter consumes 55% power less than the conventional filter without significant increase in area. The power & delay comparison is performed for both existing and proposed method of FIR filter. The design is implemented using 0.18μm technology.


title: Ketamine in Chronic Pain Management: An Evidence-Based Review

abstract: Ketamine has diverse effects that may be of relevance to chronic pain including: N-methyl-D-aspartic acid, alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid, kainate, gamma-aminobutyric acid(A) receptors; inhibition of voltage gated Na(+) and K(+) channels and serotonin, dopamine re-uptake. Ketamine has been in clinical practice for over 30 yr; however, there has been little formal research on the effectiveness of ketamine for chronic pain management. In this review we evaluate the available clinical data as a basis for defining the potential use of ketamine for chronic pain. Literature referenced in this review was obtained from a computer search of EMBASE and MEDLINE from 1966 through August, 2002. Search terms included ketamine, ketalar, pain, painful, analgesic, and analgesia. Abstracts were screened for relevance and publications relating to chronic pain use were obtained. Levels of evidence were stratified according to accepted guidelines (level I-IV). For central pain, there is level II and level IV evidence of efficacy for parenteral and oral ketamine. For complex regional pain syndromes, there is only level IV evidence of efficacy of epidural ketamine. For fibromyalgia, there is level II evidence of pain relief, reduced tenderness at trigger points, and increased endurance. For ischemic pain, a level II study reported a potent dose-dependent analgesic effect, but with a narrow therapeutic window. For nonspecific neuropathic pain, level II and level IV studies reported divergent results with questionable long-term effects on pain. For phantom limb pain and postherpetic neuralgia, level II and level II studies provided objective evidence of reduced hyperpathia and pain relief was usually substantial either after parenteral or oral ketamine. Acute on chronic episodes of severe neuropathic pain represented the most frequent use of ketamine as a "third line analgesic," often by IV or subcutaneous infusion (level IV). In conclusion, the evidence for efficacy of ketamine for treatment of chronic pain is moderate to weak. However, in situations where standard analgesic options have failed ketamine is a reasonable "third line" option. Further controlled studies are needed.


title: Insights into the Sensitivity of the BRAIN (Braided Ring Availability Integrity Network)--On Platform Robustness in Extended Operation

abstract: Low-cost fault-tolerant systems design presents a continual trade-off between improving fault-tolerant properties and accommodating cost constraints. With limited hardware options and to justify the system design rationale, it is necessary to formulate a fault hypothesis to bound failure assumptions. The system must be built on a foundation of real-world relevance and the assumption of coverage of the fault hypothesis. This paper discusses a study that examines the sensitivity of a BRAIN (braided ring availability integrity network) design to different fault types and failure rates in a safety-relevant application. It presents a Markov-based model (using ASSIST, SURE, and STEM analysis tools) and a series of experiments that were run to analyze the overall dependability of the BRAIN approach. The study evaluates the mission reliability and safety in the context of a hypothetical automotive integrated x-by-wire architecture on top of the BRAIN. Drawing from experience in the aerospace domain, the authors investigate the possibility of continued operation for a limited period after a detected critical electronic failure. Continued operation would allow a driver to reach repair facilities rather than stopping the vehicle to call for roadside assistance or "limping home."


title: Using latent dirichlet allocation for topic modelling in twitter

abstract: Due to its predictive nature, Social Media has proved to be an important resource in support of the identification of trends. In Customer Relationship Management there is a need beyond trend identification which includes understanding the topics propagated through Social Networks. In this paper, we explore topic modeling by considering the techniques of Latent Dirichlet Allocation which is a generative probabilistic model for a collection of discrete data. We evaluate this technique from the perspective of classification as well as identification of noteworthy topics as it is applied to a filtered collection of Twitter messages. Experiments show that these methods are effective for the identification of sub-topics as well as to support classification within large-scale corpora.


title: ADVANCES IN MATHEMATICAL PROGRAMMING MODELS FOR ENTERPRISE-WIDE OPTIMIZATION

abstract: Abstract Enterprise-wide Optimization (EWO) has become a major goal in the process industries due to the increasing pressures for remaining competitive in the global marketplace. EWO involves optimizing the supply, manufacturing and distribution activities of a company to reduce costs, inventories and environmental impact, and to maximize profits and responsiveness. Major operational items include planning, scheduling, real-time optimization and control. We provide an overview of EWO in terms of a mathematical programming framework. We first provide a brief overview of mathematical programming techniques (mixed-integer linear and nonlinear optimization methods), as well as decomposition methods, stochastic programming and modeling systems. We then address some of the major issues involved in the modeling and solution of these problems. Finally, based on the EWO program at the Center of Advanced Process Decision-making at Carnegie Mellon, we describe several applications to show the potential of this area.


title: A 16 Gb/s receiver with DC wander compensated rail-to-rail AC coupling and passive linear-equalizer in 22 nm CMOS

abstract: A 16 Gb/s receiver implemented in 22 nm SOI CMOS technology is reported. The analog frontend accepts a rail-to-rail input common-mode imposed from the transmitter side. It consists of a baseline wander compensated passive linear equalizer that AC-couples the received signal to the subsequent active CTLE with a regulated common-mode level. The programmable passive linear equalizer features a frequency response suitable for low-frequency equalization such as for skin-effect losses. When its zero is programmed at 200 MHz minimum frequency, the measured maximum mid-band peaking is 7 dB. The receiver architecture is half-rate and comprises an 8-tap DFE and a baud-rate CDR. With no FFE at the transmitter, 0.9 Vppd PRBS31 NRZ data are recovered error-free (BER<;10 ::: -12 ::: ) across a copper channel with 34 dB attenuation at 8 GHz.


title: G-SPARQL: a hybrid engine for querying large attributed graphs

abstract: We propose a SPARQL-like language, G-SPARQL, for querying attributed graphs. The language expresses types of queries which of large interest for applications which model their data as large graphs such as: pattern matching, reachability and shortest path queries. Each query can combine both of structural predicates and value-based predicates (on the attributes of the graph nodes and edges). We describe an algebraic compilation mechanism for our proposed query language which is extended from the relational algebra and based on the basic construct of building SPARQL queries, the Triple Pattern. We describe a hybrid Memory/Disk representation of large attributed graphs where only the topology of the graph is maintained in memory while the data of the graph is stored in a relational database. The execution engine of our proposed query language splits parts of the query plan to be pushed inside the relational database while the execution of other parts of the query plan are processed using memory-based algorithms, as necessary. Experimental results on real datasets demonstrate the efficiency and the scalability of our approach and show that our approach outperforms native graph databases by several factors.


title: Convolutional Neural Networks for Position Estimation in TDoA-Based Locating Systems

abstract: Object localization and tracking is essential for many applications including logistics and industry. Many local Time-of-Flight (ToF)-based locating systems use synchronized antennas to receive radio signals emitted by mobile tags. They detect the Time-of-Arrival (TOA) of the signal at each antenna and trilaterate the position from the Time Difference-of-Arrival (TDoA) between antennas. However, in multipath scenarios it is difficult to extract the correct ToA. This causes wrong positions. This paper proposes a signal processing method that uses deep learning to estimate the absolute tag position directly from the raw channel impulse response (CIR) data. We use the CIR together with ground truth positional data to train a convolutional neural network (CNN) that not only estimates non-linearities in the signal propagation space but also analyzes the signal for multipath effects. Our evaluation shows that our position estimation works in multipath environments and also outperforms classical signal processing in line-of-sight situations.


title: A Review of Galvanically Isolated Impedance-Source DC–DC Converters

abstract: Impedance-source converters, an emerging technology in electric energy conversion, overcome limitations of conventional solutions by the use of specific impedance-source networks. Focus of this paper is on the topologies of galvanically isolated impedance-source dc–dc converters. These converters are particularly appropriate for distributed generation systems with renewable or alternative energy sources, which require input voltage and load regulation in a wide range. We review here the basic topologies for researchers and engineers, and classify all the topologies of the impedance-source galvanically isolated dc–dc converters according to the element that transfers energy from the input to the output: a transformer, a coupled inductor, or their combination. This classification reveals advantages and disadvantages, as well as a wide space for further research. This paper also outlines the most promising research directions in this field.


title: Personalized Multitask Learning for Predicting Tomorrow's Mood, Stress, and Health

abstract: While accurately predicting mood and wellbeing could have a number of important clinical benefits, traditional machine learning (ML) methods frequently yield low performance in this domain. We posit that this is because a one-size-fits-all machine learning model is inherently ill-suited to predicting outcomes like mood and stress, which vary greatly due to individual differences. Therefore, we employ Multitask Learning (MTL) techniques to train personalized ML models which are customized to the needs of each individual, but still leverage data from across the population. Three formulations of MTL are compared: i) MTL deep neural networks, which share several hidden layers but have final layers unique to each task; ii) Multi-task Multi-Kernel learning, which feeds information across tasks through kernel weights on feature types; and iii) a Hierarchical Bayesian model in which tasks share a common Dirichlet Process prior. We offer the code for this work in open source. These techniques are investigated in the context of predicting future mood, stress, and health using data collected from surveys, wearable sensors, smartphone logs, and the weather. Empirical results demonstrate that using MTL to account for individual differences provides large performance improvements over traditional machine learning methods and provides personalized, actionable insights.


title: Teachers' Content Knowledge and Pedagogical Content Knowledge: The Role of Structural Differences in Teacher Education

abstract: Pedagogical content knowledge (PCK) and content knowledge (CK) are key components of teacher competence that affect student progress. However, little is known about how teacher education affects the development of CK and PCK. To address this question, our research group constructed tests to directly assess mathematics teachers’ CK and PCK. Based on these tests, we compared the PCK and CK of four groups of mathematics teachers at different points in their teaching careers in Germany. Confirmatory factor analyses showed that PCK and CK measurement was satisfactorily invariant across the teacher populations considered. As expected, the largest differences in CK and PCK were found between the beginning and the end of initial teacher education. Differences in the structures of teacher education were reasonably well reflected in participants’ CK and PCK.


title: Generative Adversarial Network Architectures For Image Synthesis Using Capsule Networks

abstract: In this paper, we propose Generative Adversarial Network (GAN) architectures that use Capsule Networks for image-synthesis. Based on the principal of positional-equivariance of features, Capsule Network's ability to encode spatial relationships between the features of the image helps it become a more powerful critic in comparison to Convolutional Neural Networks (CNNs) used in current architectures for image synthesis. Our proposed GAN architectures learn the data manifold much faster and therefore, synthesize visually accurate images in significantly lesser number of training samples and training epochs in comparison to GANs and its variants that use CNNs. Apart from analyzing the quantitative results corresponding the images generated by different architectures, we also explore the reasons for the lower coverage and diversity explored by the GAN architectures that use CNN critics.


Worthiness of Sentences in Scientific Reports


title: Comparative Effectiveness of Biologic Therapy Regimens for Ankylosing Spondylitis: A Systematic Review and a Network Meta-Analysis

abstract: AbstractTo establish the comparative effectiveness of all available biologic therapy regimens for ankylosing spondylitis, we performed a systematic review and a Bayesian network meta-analysis of randomized controlled trials.PubMed, Medline, Embase, Cochrane library, and ClinicalTrials.gov were searched from the inception of each database to June 2015. Systematic review and network meta-analysis was reported according to the Preferred Reporting Items of Systematic Reviews and Meta-Analyses Extension Statement for Reporting of Systematic Reviews Incorporating Network Meta-analyses. The primary outcome was 20% improvement of Assessments in SpondyloArthritis International Society Response Criteria (ASAS20) at Week 12 or 14; secondary outcomes were ASAS40, ASAS5/6, ASAS partial remission and 50% improvement in baseline Bath ankylosing spondylitis (AS) disease activity index. We reported relative risks and 95% confidence intervals from direct meta-analysis and 95% credible intervals from Bayesian network meta-analysis, and ranked the treatment for outcomes. We also used Grading of Recommendations Assessment, Development and Evaluation criteria to appraise quality of evidence.Fourteen RCTs comprising 2672 active AS patients were included in the network meta-analysis. Most biologic therapy regimens were more effective than placebo regarding all the outcomes assessed, except for secukinumab and tocilizumab. No differences between biologic therapies in the treatment of AS could be found, except for the finding that infliximab 5 mg was superior to tocilizumab. Infliximab 5 mg/kg had the highest probability of being ranked the best for achieving ASAS20, whereas notably, secukinumab had the highest probability of being ranked the second best.Our study suggests that no differences between biologic therapies in the treatment of AS could be found except that infliximab 5 mg was superior to tocilizumab. Infliximab 5 mg/kg seems to be the better biologic therapy regimen for AS. Secukinumab appears promising, though additional data is warranted. Nevertheless, these interpretations should be accepted very cautiously.


title: A Smart City Data Model based on Semantics Best Practice and Principles

abstract: Data management is crucial in modern smart cities. A good data model for smart cities has to be able to describe and integrate data from multiple domains, such as geographic information, public transportation, road maintenance, waste collection, and urban faults management. We describe our approach for creating a semantic platform for the Municipality of Catania, one of the main cities in Southern Italy. The ultimate goal is to boost the metropolis towards the route of a modern smart city and improve urban life. Our platform exhibits a consistent, minimal and comprehensive semantic data model for the city based on the Linked Open Data paradigm. Both the model and the data are publically accessible thorough dedicated user-friendly services, which allow citizens to observe and interact with the work of the public administration. Our platform also enables interested businesses and programmers to develop front-end services on the top of it. We describe the methodology used to extract data from sources, enrich them, building an ontology that describes them and publish them under the Linked Open Data paradigm. We include in our description employed tools and technologies. Our methodology is based on the standards of the W3C, on good practices of ontology design, on the guidelines issued by the Agency for Digital Italy and the Italian Index of Public Administration, as well as on the in-depth experience of the researchers in this field.


title: Building Dynamic Software Product Lines

abstract: Dynamic software product lines extend existing product line engineering approaches by moving their capabilities to runtime, helping to ensure that system adaptations lead to desirable properties.


title: Efficient marginal likelihood optimization in blind deconvolution

abstract: In blind deconvolution one aims to estimate from an input blurred image y a sharp image x and an unknown blur kernel k. Recent research shows that a key to success is to consider the overall shape of the posterior distribution p(x, k\y) and not only its mode. This leads to a distinction between MAP x, k strategies which estimate the mode pair x, k and often lead to undesired results, and MAP k strategies which select the best k while marginalizing over all possible x images. The MAP k principle is significantly more robust than the MAP x, k one, yet, it involves a challenging marginalization over latent images. As a result, MAP k techniques are considered complicated, and have not been widely exploited. This paper derives a simple approximated MAP k algorithm which involves only a modest modification of common MAP x, k algorithms. We show that MAP k can, in fact, be optimized easily, with no additional computational complexity.


title: TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension

abstract: We present TriviaQA, a challenging reading comprehension dataset containing over 650K question-answer-evidence triples. TriviaQA includes 95K question-answer pairs authored by trivia enthusiasts and independently gathered evidence documents, six per question on average, that provide high quality distant supervision for answering the questions. We show that, in comparison to other recently introduced large-scale datasets, TriviaQA (1) has relatively complex, compositional questions, (2) has considerable syntactic and lexical variability between questions and corresponding answer-evidence sentences, and (3) requires more cross sentence reasoning to find answers. We also present two baseline algorithms: a feature-based classifier and a state-of-the-art neural network, that performs well on SQuAD reading comprehension. Neither approach comes close to human performance (23% and 40% vs. 80%), suggesting that TriviaQA is a challenging testbed that is worth significant future study. Data and code available at -- this http URL


title: Manhattan-world stereo

abstract: Multi-view stereo (MVS) algorithms now produce reconstructions that rival laser range scanner accuracy. However, stereo algorithms require textured surfaces, and therefore work poorly for many architectural scenes (e.g., building interiors with textureless, painted walls). This paper presents a novel MVS approach to overcome these limitations for Manhattan World scenes, i.e., scenes that consists of piece-wise planar surfaces with dominant directions. Given a set of calibrated photographs, we first reconstruct textured regions using an existing MVS algorithm, then extract dominant plane directions, generate plane hypotheses, and recover per-view depth maps using Markov random fields. We have tested our algorithm on several datasets ranging from office interiors to outdoor buildings, and demonstrate results that outperform the current state of the art for such texture-poor scenes.


Reactor -- An Object Behavioral Pattern for Demultiplexing and Dispatching Handlers for Synchronous Events


title: A decision procedure for bit-vectors and arrays

abstract: STP is a decision procedure for the satisfiability of quantifier-free formulas in the theory of bit-vectors and arrays that has been optimized for large problems encountered in software analysis applications. The basic architecture of the procedure consists of word-level pre-processing algorithms followed by translation to SAT. The primary bottlenecks in software verification and bug finding applications are large arrays and linear bit-vector arithmetic. New algorithms based on the abstraction-refinement paradigm are presented for reasoning about large arrays. A solver for bit-vector linear arithmetic is presented that eliminates variables and parts of variables to enable other transformations, and reduce the size of the problem that is eventually received by the SAT solver. ::: ::: These and other algorithms have been implemented in STP, which has been heavily tested over thousands of examples obtained from several real-world applications. Experimental results indicate that the above mix of algorithms along with the overall architecture is far more effective, for a variety of applications, than a direct translation of the original formula to SAT or other comparable decision procedures.


title: Electric Vehicle Traction Based on Synchronous Reluctance Motors

abstract: It was recently demonstrated that the synchronous reluctance motor is well suited for electric as well as for hybrid electric vehicles. This paper deeply investigates the capabilities of a synchronous reluctance motor and compares them with those of a permanent-magnet-assisted synchronous reluctance motor, according to the typical requirement of a traction application. A proper rotor design is necessary. The average torque is due to the rotor anisotropy. The permeance difference between the direct- and the quadrature-axis is achieved by means of a high number of flux barriers. The position of the flux barrier ends and proper rotor asymmetries are chosen so as to reduce the torque ripple, mainly due to the slot harmonics. The impact of the rotor design on the motor performance is presented deeply, showing several simulation and experimental results, carried out on synchronous reluctance motors with different rotor geometries. Permanent magnets can be inset in the flux barriers to assist the synchronous reluctance motor improving its capabilities, but avoiding to use rare-earth permanent magnets. The main advantages of the permanent magnet assistance is an increase of the main torque density and of the power factor. They are evaluated experimentally. However, the drawback of adopting permanent magnets is the possible demagnetization of the magnets themselves. This can greatly limit the maximum overload capability of the motor, which is a salient requirement of a traction motor.


title: Amotivation in Schizophrenia: Integrated Assessment With Behavioral, Clinical, and Imaging Measures

abstract: Motivational deficits play a central role in disability caused by schizophrenia and constitute a major unmet therapeutic need. Negative symptoms have previously been linked to hypofunction in ventral striatum (VS), a core component of brain motivation circuitry. However, it remains unclear to what extent this relationship holds for specific negative symptoms such as amotivation, and this question has not been addressed with integrated behavioral, clinical, and imaging measures. Here, 41 individuals with schizophrenia and 37 controls performed a brief, computerized progressive ratio task (PRT) that quantifies effort exerted in pursuit of monetary reward. Clinical amotivation was assessed using the recently validated Clinical Assessment Interview for Negative Symptoms (CAINS). VS function was probed during functional magnetic resonance imaging using a monetary guessing paradigm. We found that individuals with schizophrenia had diminished motivation as measured by the PRT, which significantly and selectively related to clinical amotivation as measured by the CAINS. Critically, lower PRT motivation in schizophrenia was also dimensionally related to VS hypofunction. Our results demonstrate robust dimensional associations between behavioral amotivation, clinical amotivation, and VS hypofunction in schizophrenia. Integrating behavioral measures such as the PRT will facilitate translational efforts to identify biomarkers of amotivation and to assess response to novel therapeutic interventions.


title: Evolution, brain, and the nature of language

abstract: Language serves as a cornerstone for human cognition, yet much about its evolution remains puzzling. Recent research on this question parallels Darwin's attempt to explain both the unity of all species and their diversity. What has emerged from this research is that the unified nature of human language arises from a shared, species-specific computational ability. This ability has identifiable correlates in the brain and has remained fixed since the origin of language approximately 100 thousand years ago. Although songbirds share with humans a vocal imitation learning ability, with a similar underlying neural organization, language is uniquely human.


title: Default Prediction for Real Estate Companies with Imbalanced Dataset

abstract: When analyzing default predictions in real estate companies, the number of non-defaulted cases always greatly exceeds the defaulted ones, which creates the two- class imbalance problem. This lowers the ability of prediction models to distinguish the default sample. In order to avoid this sample selection bias and to improve the prediction model, this paper applies a minority sample generation approach to create new minority samples. The logistic regression, support vector machine (SVM) classification, and neural network (NN) classification use an imbalanced dataset. They were used as benchmarks with a single prediction model that used a balanced dataset corrected by the minority samples generation approach. Instead of using prediction- oriented tests and the overall accuracy, the true positive rate (TPR), the true negative rate (TNR), G-mean, and F-score are used to measure the performance of default prediction models for imbalanced dataset. In this paper, we describe an empirical experiment that used a sampling of 14 default and 315 non-default listed real estate companies in China and report that most results using single prediction models with a balanced dataset generated better results than an imbalanced dataset. Keywords—Default prediction, Imbalanced dataset, Real estate listed companies, Minority- sample generation approach


title: Control applications of TORO — A Torque controlled humanoid robot

abstract: This video presents an overview of the hardware and control applications of the TOrque-controlled humanoid RObot TORO developed at DLR. The self-contained robot was developed as a research platform for walking and whole-body control. It is based on the drive units of the DLR-KUKA Lightweight Robot Arm (LBR) III, has a total of 39 DoF, and it can be operated both in position and torque control modes. The internal state of the robot can be measured by the motor position and torque sensors, plus two IMUs in the trunk and head. Force/torque sensors at the ankles provide measurements to evaluate the stability of the robot, and stereo cameras plus one RGB-D sensor are integrated in the head for research on autonomous navigation and manipulation.


title: Action Recognition From Video Using Feature Covariance Matrices

abstract: We propose a general framework for fast and accurate recognition of actions in video using empirical covariance matrices of features. A dense set of spatio-temporal feature vectors are computed from video to provide a localized description of the action, and subsequently aggregated in an empirical covariance matrix to compactly represent the action. Two supervised learning methods for action recognition are developed using feature covariance matrices. Common to both methods is the transformation of the classification problem in the closed convex cone of covariance matrices into an equivalent problem in the vector space of symmetric matrices via the matrix logarithm. The first method applies nearest-neighbor classification using a suitable Riemannian metric for covariance matrices. The second method approximates the logarithm of a query covariance matrix by a sparse linear combination of the logarithms of training covariance matrices. The action label is then determined from the sparse coefficients. Both methods achieve state-of-the-art classification performance on several datasets, and are robust to action variability, viewpoint changes, and low object resolution. The proposed framework is conceptually simple and has low storage and computational requirements making it attractive for real-time implementation.


Management Information Systems : Managing The Digital


title: Two Decades of Research and Development in Transformational Leadership

abstract: The interests of the organization and its members need to be aligned. Such is a task for the transformational leader. In contrast to the transactional leader who practises contingent reinforcement of followers, the transformational leader inspires, intellectually stimulates, and is individually considerate of them. Transformational leadership may be directive or participative. Requiring higher moral development, transformational leadership is recognized universally as a concept. Furthermore, contrary to earlier expectations, women leaders tend to be more transformational than their male counterparts. Although a six-factor model of transformational/ transactional leadership best fits a diversity of samples according to confirmatory factor analyses, whether fewer factors are necessary remains an open question. Another important research question that has only been partially answered is why transformational leadership is more effective than transactional leadership in a wide variety of business, military, industrial, hospital, and educational circumstances.


title: Comparison of Requirement Prioritization Techniques to Find Best Prioritization Technique

abstract: This paper describes an assessment of different requirement prioritization techniques (binary search tree, AHP, hierarchy AHP, spanning tree matrix, priority group/Numerical Analysis, bubble sort, MoSoW, simple ranking and Planning Game) on the basis of previous literature. Five research papers and thesis are critically reviewed, in order to select best requirement prioritization method. The study of literature shows that AHP is the best requirements prioritization technique amongst all the requirements prioritization techniques. It provides the most efficient and reliable results which are on ratio scale. It is faulttolerant and provides a consistency check.


title: Cognitive Radio-based Wireless Sensor Networks: Conceptual design and open issues

abstract: Traditional static spectrum allocation policies have been to grant each wireless service exclusive usage of certain frequency bands, leaving several spectrum bands unlicensed for industrial, scientific and medical purposes. The rapid proliferation of low-cost wireless applications in unlicensed spectrum bands has resulted in spectrum scarcity among those bands. Since most applications in Wireless Sensor Networks (WSNs) utilize the unlicensed spectrum, network-wide performance of WSNs will inevitably degrade as their popularity increases. Sharing of under-utilized licensed spectrum among unlicensed devices is a promising solution to the spectrum scarcity issue. Cognitive Radio (CR) is a new paradigm in wireless communication that allows sensor nodes as the unlicensed users or Secondary Users (SUs) to detect and use the under-utilized licensed spectrum temporarily. Given that the licensed or Primary Users (PUs) are oblivious to the presence of SUs, the SUs access the licensed spectrum opportunistically without interfering the PUs, while improving their own performance. In this paper, we propose an approach to build Cognitive Radio-based Wireless Sensor Networks (CR-WSNs). We believe that CR-WSN is the next-generation WSN. Realizing that both WSNs and CR present unique challenges to the design of CR-WSNs, we provide an overview and conceptual design of WSNs from the perspective of CR. The open issues are discussed to motivate new research interests in this field. We also present our method to achieving context-awareness and intelligence, which are the key components in CR networks, to address an open issue in CR-WSN.


title: A Scale Independent Selection Process for 3D Object Recognition in Cluttered Scenes

abstract: During the last years a wide range of algorithms and devices have been made available to easily acquire range images. The increasing abundance of depth data boosts the need for reliable and unsupervised analysis techniques, spanning from part registration to automated segmentation. In this context, we focus on the recognition of known objects in cluttered and incomplete 3D scans. Locating and fitting a model to a scene are very important tasks in many scenarios such as industrial inspection, scene understanding, medical imaging and even gaming. For this reason, these problems have been addressed extensively in the literature. Several of the proposed methods adopt local descriptor-based approaches, while a number of hurdles still hinder the use of global techniques. In this paper we offer a different perspective on the topic: We adopt an evolutionary selection algorithm that seeks global agreement among surface points, while operating at a local level. The approach effectively extends the scope of local descriptors by actively selecting correspondences that satisfy global consistency constraints, allowing us to attack a more challenging scenario where model and scene have different, unknown scales. This leads to a novel and very effective pipeline for 3D object recognition, which is validated with an extensive set of experiments and comparisons with recent techniques at the state of the art.


title: HDR image reconstruction from a single exposure using deep CNNs

abstract: Camera sensors can only capture a limited range of luminance simultaneously, and in order to create high dynamic range (HDR) images a set of different exposures are typically combined. In this paper we address the problem of predicting information that have been lost in saturated image areas, in order to enable HDR reconstruction from a single exposure. We show that this problem is well-suited for deep learning algorithms, and propose a deep convolutional neural network (CNN) that is specifically designed taking into account the challenges in predicting HDR values. To train the CNN we gather a large dataset of HDR images, which we augment by simulating sensor saturation for a range of cameras. To further boost robustness, we pre-train the CNN on a simulated HDR dataset created from a subset of the MIT Places database. We demonstrate that our approach can reconstruct high-resolution visually convincing HDR results in a wide range of situations, and that it generalizes well to reconstruction of images captured with arbitrary and low-end cameras that use unknown camera response functions and post-processing. Furthermore, we compare to existing methods for HDR expansion, and show high quality results also for image based lighting. Finally, we evaluate the results in a subjective experiment performed on an HDR display. This shows that the reconstructed HDR images are visually convincing, with large improvements as compared to existing methods.


title: Fuzzy portfolio optimization under downside risk measures *

abstract: This paper presents two fuzzy portfolio selection models where the objective is to minimize the downside risk constrained by a given expected return. We assume that the rates of returns on securities are approximated as LR-fuzzy numbers of the same shape, and that the expected return and risk are evaluated by interval-valued means. We establish the relationship between those mean-interval definitions for a given fuzzy portfolio by using suitable ordering relations. Finally, we formulate the portfolio selection problem as a linear program when the returns on the assets are of trapezoidal form.


title: A Sequential Embedding Approach for Item Recommendation with Heterogeneous Attributes

abstract: Attributes, such as metadata and profile, carry useful information which in principle can help improve accuracy in recommender systems. However, existing approaches have difficulty in fully leveraging attribute information due to practical challenges such as heterogeneity and sparseness. These approaches also fail to combine recurrent neural networks which have recently shown effectiveness in item recommendations in applications such as video and music browsing. To overcome the challenges and to harvest the advantages of sequence models, we present a novel approach, Heterogeneous Attribute Recurrent Neural Networks (HA-RNN), which incorporates heterogeneous attributes and captures sequential dependencies in \textit{both} items and attributes. HA-RNN extends recurrent neural networks with 1) a hierarchical attribute combination input layer and 2) an output attribute embedding layer. We conduct extensive experiments on two large-scale datasets. The new approach show significant improvements over the state-of-the-art models. Our ablation experiments demonstrate the effectiveness of the two components to address heterogeneous attribute challenges including variable lengths and attribute sparseness. We further investigate why sequence modeling works well by conducting exploratory studies and show sequence models are more effective when data scale increases.


title: A hybrid local and distributed sketching design for accurate and scalable heavy key detection in network data streams

abstract: Real-time characterization of network traffic anomalies, such as heavy hitters and heavy changers, is critical for the robustness of operational networks, but its accuracy and scalability are challenged by the ever-increasing volume and diversity of network traffic. We address this problem by leveraging parallelization. We propose LD-Sketch, a data structure designed for accurate and scalable traffic anomaly detection using distributed architectures. LD-Sketch combines the classical counter-based and sketch-based techniques, and performs detection in two phases: local detection, which guarantees zero false negatives, and distributed detection, which reduces false positives by aggregating multiple detection results. We derive the error bounds and the space and time complexity for LD-Sketch. We further analyze the impact of ordering of data items on the memory usage and accuracy of LD-Sketch. We compare LD-Sketch with state-of-the-art sketch-based techniques by conducting experiments on traffic traces from a real-life 3G cellular data network. Our results demonstrate the accuracy and scalability of LD-Sketch over prior approaches.


title: Aqua-Sim Next Generation: An NS-3 Based Underwater Sensor Network Simulator

abstract: Through the use of network simulators we are able to test and examine different combinations of protocols in low cost and controlled environments. To ensure the accuracy of these simulators it is crucial that they consistently expand and enhance their modules to offer extensive support. In this paper, we introduce Aqua-Sim Next Generation, an NS-3 based underwater sensor network simulator. This work is an expansion of Aqua-Sim, transferring to the newer core simulator, NS-3, for enhanced memory management and performance. Additionally, we revitalize legacy supported protocols through better packet handling and header usage. Aqua-Sim Next Generation offers real-world features to close the gap between simulation and real-system tests while providing additional modules for easier user development. With the goal to create a more efficient and functional simulator, we improved Aqua-Sim's architecture. Furthermore, we integrate a specialized underwater information-centric module to assist in simulating innovative techniques in underwater sensor networks. Experimental results show that Aqua-Sim Next Generation runs twice as fast compared with Aqua-Sim while showing large increases in memory management.


title: Optimization of the curing process of epoxy resins using methods of structural analysis

abstract: Epoxy resins belong among materials frequently used for the production of impregnating composites especially for the construction of motors, generators, and transformers. During production, great emphasis must be focused on the proper curing process of these materials. Otherwise, it is not possible to guarantee the satisfactory properties of the final product. The paper is therefore focused on the analysis of the level of curing of epoxy resin by methods of structural analyses. The Fourier Transform Infrared Spectroscopy (FT-IR) and glass transition temperature evaluated from a reversible signal of heat flow measured by modulated differential scanning calorimetry (MDSC) were used. Curing process was performed in a laboratory oven. Partly and fully cured samples were analyzed during two independent experiments. These two experiments differ to each other especially by different times of sampling. The trend of the changes of infrared spectra measured on samples during the curing process corresponds well with the results of the glass transitions temperature measurements obtained by MDSC. Insufficient curing state is relatively well detectable by the presence of the spectral bands corresponding to the vibration of epoxy and anhydride groups (901, 1875, 1786 cm-1). Both methods are acceptable to detection of the curing level but with different sensitivity. While infrared spectroscopy is possible to use for prediction especially short times of curing, analysis of the glass transition is sensitive during the whole curing process.


title: Automatic inference of code transforms for patch generation

abstract: We present a new system, Genesis, that processes human patches to automatically infer code transforms for automatic patch generation. We present results that characterize the effectiveness of the Genesis inference algorithms and the complete Genesis patch generation system working with real-world patches and defects collected from 372 Java projects. To the best of our knowledge, Genesis is the first system to automatically infer patch generation transforms or candidate patch search spaces from previous successful patches.


title: Anti Plagiarism Application with Algorithm Karp-Rabin at Thesis in Gunadarma University

abstract: Plagiarism that is plagiarizing or composition retrieval, opinion, etcetera from other people and makes it is likely composition and opinion him-self. Plagiarism can be considered to be crime because stealing others copyrights. Like action a student copying some part of writings without valid permission from the original writer. In education world, plagiarism perpetrator can get the devil to pay from school/university. Plagiarism perpetrator conceived of plagiator. This thing is possible unable to be paid attention by the side of campus because of limitation from some interconnected factors for example student amounts Gunadarma University reaching thousands and incommensurate to tester amounts or lecturer the side of campus in charge directs problem thesis. In this paper, an application have been developed in order to check and look for 5 type percentage similarity from a thesis with other one at certain part or chapters. Percentage got that is 0%, under 15%, between 15-50%, up to 50% and 100%. So it should be expected that the results could be used by thesis advisor and also thesis examiner from the Student at Gunadarma University.


title: Electric Field Effect in Atomically Thin Carbon Films

abstract: We describe monocrystalline graphitic films, which are a few atoms thick but are nonetheless stable under ambient conditions, metallic, and of remarkably high quality. The films are found to be a two-dimensional semimetal with a tiny overlap between valence and conductance bands, and they exhibit a strong ambipolar electric field effect such that electrons and holes in concentrations up to 10(13) per square centimeter and with room-temperature mobilities of approximately 10,000 square centimeters per volt-second can be induced by applying gate voltage.


title: A Multilevel Class-D CMOS Power Amplifier for an Out-Phasing Transmitter With a Nonisolated Power Combiner

abstract: This brief presents a nonisolated multilevel linear amplifier with nonlinear component (LINC) power amplifier (PA) implemented in a standard 0.18- $\mu\text{m}$ complementary metal–oxide–semiconductor process. Using a nonisolated power combiner, the overall power efficiency is increased by reducing the wasted power at the combined out-phased signal; however, the efficiency at low power still needs to be improved. To further improve the efficiency of the low-power (LP) mode, we propose a multiple-outputpower-level LINC PA, with load modulation implemented by switches. In addition, analysis of the proposed design on the system level as well as the circuit level was performed to optimize its performance. The measurement results demonstrate that the proposed technique maintains more than 45% power-added efficiency (PAE) for peak power at 21 dB for the high-power mode and 17 dBm for the LP mode at 600 MHz. The PAE for a 6-dB peak-to-average ratio orthogonal frequency-division multiplexing modulated signal is higher than 24% PAE in both power modes. To the authors' knowledge, the proposed output-phasing PA is the first implemented multilevel LINC PA that uses quarter-wave lines without multiple power supply sources.


Time-release Protocol from Bitcoin and Witness Encryption for SAT


title: A practical guide to the management of anophthalmia and microphthalmia

abstract: Congenital anophthalmia and microphthalmia are rare developmental defects of the globe. They often arise in conjunction with other ocular defects such as coloboma and orbital cyst. They may also be part of more generalised syndromes, such as CHARGE syndrome. Anophthalmia, microphthalmia, and coloboma are likely to be caused by disturbances of the morphogenetic pathway that controls eye development, either as a result of primary genetic defect, or external gestational factors, including infection or drugs that can influence the smooth processes of morphogenesis. The ophthalmologist is often the primary carer for children with anophthalmia and microphthalmia, and as such can coordinate the multidisciplinary input needed to offer optimal care for these individuals, including vision and family support services. They are able to assess the vision and maximise the visual potential of the child and they can also ensure that the cosmetic and social impact of anophthalmia or microphthalmia is minimised by starting socket expansion or referring to a specialist oculoplastics and prosthetics unit. A coordinated approach with paediatrics is necessary to manage any associated conditions. Genetic diagnosis and investigations can greatly assist in providing a diagnosis and informed genetic counselling.


title: The QR-code reorganization in illegible snapshots taken by mobile phones

abstract: In this paper, we give an algorithm for the analysis and correction of the distorted QR barcode (QR-code) image. The introduced algorithm is based on the code area finding by four corners detection for 2D barcode. We combine Canny edge detection with contours finding algorithms to erase noises and reduce computation and utilize two tangents to approximate the right-bottom point. Then, we give a detail description on how to use inverse perspective transformation in rebuilding a QR-code image from a distorted one. We test our algorithm on images taken by mobile phones. The experiment shows that our algorithm is effective.


title: Scripting smart contracts for distributed ledger technology

abstract: We give an overview of the scripting languages used in existing cryptocurrencies, and in particular we review in some detail the scripting languages of Bitcoin, Nxt and Ethereum, in the context of a high-level overview of Distributed Ledger Technology and cryptocurrencies. We survey different approaches, and give an overview of critiques of existing languages. We also cover technologies that might be used to underpin extensions and innovations in scripting and contracts, including technologies for verification, such as zero knowledge proofs, proof-carrying code and static analysis, as well as approaches to making systems more efficient, e.g. Merkelized Abstract Syntax Trees.


title: Extreme learning machine as a generalizable classification engine

abstract: Extreme learning machine is an emerging neural network architecture that offers fast learning and generalization for multiple tasks. In this work, a scalable digital architecture for multi-classifier extreme learning machine (MT-ELM) is proposed. The proposed architecture performs multiple classification tasks without reconfiguring the network. The design is validated with MNIST dataset and it is shown that the proposed model achieves an accuracy of 91.7% for classifying numbers in the MNIST dataset and an accuracy of 90.35% for categorizing number parity. The design is synthesized on a TSMC-65nm technology node and the power dissipation is 13.6 mW for MT-ELM network with 80 hidden neurons and 12 output neurons.


title: IEEE 802.11ad-Based Radar: An Approach to Joint Vehicular Communication-Radar System

abstract: Millimeter-wave (mmWave) radar is widely used in vehicles for applications such as adaptive cruise control and collision avoidance. In this paper, we propose an IEEE 802.11ad-based radar for long-range radar (LRR) applications at the 60 GHz unlicensed band. We exploit the preamble of a single-carrier physical layer frame, which consists of Golay complementary sequences with good correlation properties that make it suitable for radar. This system enables a joint waveform for automotive radar and a potential mmWave vehicular communication system based on the mmWave consumer wireless local area network standard, allowing hardware reuse. To formulate an integrated framework of vehicle-to-vehicle communication and LRR, we make typical assumptions for LRR applications, incorporating the full duplex radar operation. This new feature is motivated by the recent development of systems with sufficient isolation and self-interference cancellation. We develop single- and multi-frame radar receiver algorithms for target detection as well as range and velocity estimation for both single- and multi-target scenarios. Our proposed radar processing algorithms leverage channel estimation and time–frequency synchronization techniques used in a conventional IEEE 802.11ad receiver with minimal modifications. Analysis and simulations show that in a single-target scenario, a gigabits-per-second data rate is achieved simultaneously with cm-level range accuracy and cm/s-level velocity accuracy. The target vehicle is detected with a high probability (above 99.99 $\%$ ) at a low false alarm rate of 10 $^{-6}$ for an equivalent isotropically radiated power of 40 dBm up to a vehicle separation distance of about 200 m. The proposed IEEE 802.11ad-based radar meets the minimum accuracy/resolution requirement of range and velocity estimates for LRR applications.


title: Mindfulness group therapy in primary care patients with depression, anxiety and stress and adjustment disorders: randomised controlled trial

abstract: Background ::: ::: Individual-based cognitive–behavioural therapy (CBT) is in short supply and expensive. ::: ::: Aims ::: ::: The aim of this randomised controlled trial (RCT) was to compare mindfulness-based group therapy with treatment as usual (primarily individual-based CBT) in primary care patients with depressive, anxiety or stress and adjustment disorders. ::: ::: Method ::: ::: This 8-week RCT ([ClinicalTrials.gov][1] ID: [NCT01476371][2]) was conducted during spring 2012 at 16 general practices in Southern Sweden. Eligible patients (aged 20–64 years) scored ⩾10 on the Patient Health Questionnaire-9, ⩾7 on the Hospital Anxiety and Depression Scale or 13–34 on the Montgomery–Asberg Depression Rating Scale (self-rated version). The power calculations were based on non-inferiority. In total, 215 patients were randomised. Ordinal mixed models were used for the analysis. ::: ::: Results ::: ::: For all scales and in both groups, the scores decreased significantly. There were no significant differences between the mindfulness and control groups. ::: ::: Conclusions ::: ::: Mindfulness-based group therapy was non-inferior to treatment as usual for patients with depressive, anxiety or stress and adjustment disorders. ::: ::: [1]: http://ClinicalTrials.gov ::: [2]: /lookup/external-ref?link_type=CLINTRIALGOV&access_num=NCT01476371&atom=%2Fbjprcpsych%2Fearly%2F2014%2F11%2F11%2Fbjp.bp.114.150243.atom


title: A Survey of Image-Based Techniques for Hair Modeling

abstract: With the tremendous performance increase of today’s graphics technologies, visual details of digital humans in games, online virtual worlds, and virtual reality applications are becoming significantly more demanding. Hair is a vital component of a person’s identity and can provide strong cues about age, background, and even personality. More and more researchers focus on hair modeling in the fields of computer graphics and virtual reality. Traditional methods are physics-based simulation by setting different parameters. The computation is expensive, and the constructing process is non-intuitive, difficult to control. Conversely, image-based methods have the advantages of fast modeling and high fidelity. This paper surveys the state of the art in the major topics of image-based techniques for hair modeling, including single-view hair modeling, static hair modeling from multiple images, video-based dynamic hair modeling, and the editing and reusing of hair modeling results. We first summarize the single-view approaches, which can be divided into the orientation-field and data-driven-based methods. The static methods from multiple images and dynamic methods are then reviewed in Sections III and IV . In Section V , we also review the editing and reusing of hair modeling results. The future development trends and challenges of image-based methods are proposed in the end.


title: Modeling Attention-Deficit/Hyperactivity Disorder: A Critical Appraisal of the Cognitive-Energetic Model

abstract: A number of theoretical models of attention-deficit/hyperactivity disorder (ADHD) have emerged in recent years that may be used as systematic guides for clinical research. The cognitive-energetic model (CEM) proposes that the overall efficiency of information processing is determined by the interplay of three levels: computational mechanisms of attention, state factors, and management/executive function (EF). The CEM encompasses both top-down and bottom-up processes and draws attention to the fact that ADHD causes defects at all three levels. These include cognitive mechanisms, such as response output; energetic mechanisms, such as activation and effort; and management/EF deficits. Increasing evidence suggests that inhibition deficits associated with ADHD may, at least in part, be explained in terms of an energetic dysfunction. The activation and effort energetic pools appear most relevant to ADHD, being directly related to response organization; however, further testing of CEM is critically dependent on the development of direct measures of these energetic pools. The CEM is a comprehensive model of ADHD but is not without limitations. In particular, further research is required to define more specifically the relationship between process dysfunction and state dysregulation in ADHD.


title: Complementary Split-Ring Resonators for Measuring Dielectric Constants and Loss Tangents

abstract: A noninvasive planar complementary split-ring resonator (CSRR) for measuring the dielectric constants and loss tangents of material is presented in this letter. Measurements were developed to extract the complex permittivity of the material under test (MUT) using the equivalent RLC resonator circuit model. This technique enables a single-step measurement of the MUT without the need for machining or reshaping. The method requires calculating only two parameters, the resonant frequency (fr) and the magnitude (dB) response, thus achieving a substantial reduction in cost and computation time. A CSRR sensor operating in the 1.8 to 2.8 GHz band is fabricated and tested for verification. The measurement errors in the dielectric constant were less than 7.6%. An efficient technique of CSRR measuring complex permittivity is experimentally verified.


title: Advantage of CNTFET characteristics over MOSFET to reduce leakage power

abstract: In this paper we compare and justify the advantage of CNTFET devices over MOSFET devices in nanometer regime. Thereafter we have analyzed the effect of chiral vector, and temperature on threshold voltage of CNTFET device. After simulation on HSPICE tool we observed that the high threshold voltage can be achieved at low chiral vector pair. It is also observed that the effect of temperature on threshold voltage of CNTFET is negligibly small. After analysis of channel length variation and their impact on threshold voltage of CNTFET as well as MOSFET devices, we found an anomalous result that the threshold voltage increases with decreasing channel length in CNTFET devices, this is quite contrary to the well known short channel effect. It is observed that at below 10 nm channel length the threshold voltage is increased rapidly in case of CNTFET device whereas in case of MOSFET device the threshold voltage decreases drastically below 10 nm channel length.


title: 6.5kV RCDC: For increased power density in IGBT-modules

abstract: A reverse conducting IGBT in trench technology is presented. By this approach no carrier life time means are necessary to balance static and dynamic losses of the diode. The diode's p-emitter efficiency can be dynamically tailored by the applied gate voltage due to inversion charge carriers in the vicinity of the gate trench. This opens up the opportunity for a variety of gate control schemes with the aim of a charge carrier reduction before commutation thus reducing the recovery and the corresponding IGBT turn-on losses while the on-state diode losses remain low. A simple substitution of the IGBT and diode dies by the RCDC chips in the industry standard packages enables a significant increase of the power density (e.g. up to 30% for typical traction application) due to a thermal benefit given by lower thermal resistances as well as by a reduction of the dynamic losses due to special gate control.


title: Low-complexity image denoising based on statistical modeling of wavelet coefficients

abstract: We introduce a simple spatially adaptive statistical model for wavelet image coefficients and apply it to image denoising. Our model is inspired by a recent wavelet image compression algorithm, the estimation-quantization (EQ) coder. We model wavelet image coefficients as zero-mean Gaussian random variables with high local correlation. We assume a marginal prior distribution on wavelet coefficients variances and estimate them using an approximate maximum a posteriori probability rule. Then we apply an approximate minimum mean squared error estimation procedure to restore the noisy wavelet image coefficients. Despite the simplicity of our method, both in its concept and implementation, our denoising results are among the best reported in the literature.


Neural Cryptanalysis of Classical Ciphers


title: Adversarial Resilience Learning - Towards Systemic Vulnerability Analysis for Large and Complex Systems

abstract: This paper introduces Adversarial Resilience Learning (ARL), a concept to model, train, and analyze artificial neural networks as representations of competitive agents in highly complex systems. In our examples, the agents normally take the roles of attackers or defenders that aim at worsening or improving-or keeping, respectively-defined performance indicators of the system. Our concept provides adaptive, repeatable, actor-based testing with a chance of detecting previously unknown attack vectors. We provide the constitutive nomenclature of ARL and, based on it, the description of experimental setups and results of a preliminary implementation of ARL in simulated power systems.


title: Multi-dimensional storage virtualization

abstract: Most state-of-the-art commercial storage virtualization systems focus only on one particular storage attribute, capacity. This paper describes the design, implementation and evaluation of a multi-dimensional storage virtualization system called Stonehenge, which is able to virtualize a cluster-based physical storage system along multiple dimensions, including bandwidth, capacity, and latency. As a result, Stonehenge is able to multiplex multiple virtual disks, each with a distinct bandwidth, capacity, and latency attribute, on a single physical storage system as if they are separate physical disks. A key enabling technology for Stonehenge is an efficiency-aware real-time disk scheduling algorithm called dual-queue disk scheduling, which maximizes disk utilization efficiency while providing Quality of Service (QoS) guarantees. To optimize disk utilization efficiency, Stonehenge exploits run-time measurements extensively, for admission control, computing latency-derived bandwidth requirement, and predicting disk service time.


title: The Sleeping Brain's Influence on Verbal Memory: Boosting Resistance to Interference

abstract: Memories evolve. After learning something new, the brain initiates a complex set of post-learning processing that facilitates recall (i.e., consolidation). Evidence points to sleep as one of the determinants of that change. But whenever a behavioral study of episodic memory shows a benefit of sleep, critics assert that sleep only leads to a temporary shelter from the damaging effects of interference that would otherwise accrue during wakefulness. To evaluate the potentially active role of sleep for verbal memory, we compared memory recall after sleep, with and without interference before testing. We demonstrated that recall performance for verbal memory was greater after sleep than after wakefulness. And when using interference testing, that difference was even more pronounced. By introducing interference after sleep, this study confirms an experimental paradigm that demonstrates the active role of sleep in consolidating memory, and unmasks the large magnitude of that benefit.


title: Using mathematical morphology for document skew estimation

abstract: We propose a concise definition of the skew angle of document, based on mathematical morphology. This definition has the advantages to be applicable both for binary and grey-scale images. We then discuss various possible implementations of this definition, and show that results we obtain are comparable to those of other existing algorithms.


title: A Real-Time Driver Visual Attention Monitoring System

abstract: This paper describes a framework for analyzing video sequences of a driver and determining his level of attention. The proposed system deals with the computation of eyelid movement parameters and head (face) orientation estimation. The system relies on pupil detection to robustly track the driver's head pose and monitoring its level of fatigue. Visual information is acquired using a specially designed solution combining a CCD video camera with an NIR illumination system. The system is fully automatic and classifies rotation in all-view direction, detects eye blinking and eye closure and recovers the gaze of the eyes. Experimental results using real images demonstrates the accuracy and robustness of the proposed solution.


title: EXTENDING GENERALIZED HOUGH TRANSFORM TO DETECT 3 D OBJECTS IN LASER RANGE DATA

abstract: Automated detection and 3D modelling of objects in laser range data is of great importance in many applications. Existing approaches to object detection in range data are limited to either 2.5D data (e.g. range images) or simple objects with a parametric form (e.g. spheres). This paper describes a new approach to the detection of 3D objects with arbitrary shapes in a point cloud. We present an extension of the generalized Hough transform to 3D data, which can be used to detect instances of an object model in laser range data, independent of the scale and orientation of the object. We also discuss the computational complexity of the method and provide cost-reduction strategies that can be employed to improve the efficiency of the method.


title: Rotationally Invariant Descriptors Using Intensity Order Pooling

abstract: This paper proposes a novel method for interest region description which pools local features based on their intensity orders in multiple support regions. Pooling by intensity orders is not only invariant to rotation and monotonic intensity changes, but also encodes ordinal information into a descriptor. Two kinds of local features are used in this paper, one based on gradients and the other on intensities; hence, two descriptors are obtained: the Multisupport Region Order-Based Gradient Histogram (MROGH) and the Multisupport Region Rotation and Intensity Monotonic Invariant Descriptor (MRRID). Thanks to the intensity order pooling scheme, the two descriptors are rotation invariant without estimating a reference orientation, which appears to be a major error source for most of the existing methods, such as Scale Invariant Feature Transform (SIFT), SURF, and DAISY. Promising experimental results on image matching and object recognition demonstrate the effectiveness of the proposed descriptors compared to state-of-the-art descriptors.


title: A fuzzy cognitive map approach for effect-based operations: An illustrative case

abstract: This paper proposes the use of fuzzy cognitive maps (FCMs) as a technique for supporting the decision-making process in effect-based planning. The goal is to determine alternative courses of action to realize the aims of an operation, and choose the best option among them. With adequate consideration of the problem features and the constraints governing the method used, an FCM is developed to model effect-based operations (EBOs). In this study, certain features that do not exist in the classical FCM method were added to our FCM concept value calculation algorithm; these include influence possibility, influence duration, dynamic influence value-changing, and influence permanence. The model developed was applied to an illustrative scenario involving military planning, and we comment on the usefulness of the proposed methodology.


title: Ball On The Plate Model based on PID tuning methods

abstract: Controller design is one of the important steps during the modelling of various systems, starting with constructing an electric kettle to an aircraft system. To achieve a good controller it is needed to define suitable values for the controller coefficients. This paper describes the research practices of the tuning technique by using MATLAB/Simulink compared with manually PID tuning based on the trial and error process, as well as, PID tuning methods from Ziegler-Nichols and Tyreus-Luyben (closed-loop proportional gain control or P-Control tests). The paper can be useful for readers who want to have a basic knowledge of research assignments based on PID tuning.


title: FPGA implementation of a large-number multiplier for fully homomorphic encryption

abstract: The first plausible scheme of fully homomorphic encryption (FHE), introduced by Gentry in 2009, was considered a major breakthrough in the field of information security. FHE allows the evaluation of arbitrary functions directly on encrypted data on untrusted servers. However, previous implementations of FHE on general-purpose processors had very long latency, which makes it impractical for cloud computing. The most computationally intensive components in the Gentry-Halevi FHE primitives are the large-number modular multiplications and additions. In this paper, we attempt to use customized circuits to speedup the large number multiplication. Strassen's algorithm is employed in the design of an efficient, high-speed large-number multiplier. In particular, we propose an architecture design of an 768K-bit multiplier. As a key compoment, an 64K-point finite-field fast Fourier transform (FFT) processor is designed and prototyped on the Stratix-V FPGA. At 100 MHz, the FPGA implementation is about twice as fast as the same FFT algorithm executed on the NVIDA C2050 GPU which has 448 cores running at 1.15 GHz but at much lower power consumption.


title: Integrated WiFi/PDR/Smartphone Using an Adaptive System Noise Extended Kalman Filter Algorithm for Indoor Localization

abstract: Wireless signal strength is susceptible to the phenomena of interference, jumping, and instability, which often appear in the positioning results based on Wi-Fi field strength fingerprint database technology for indoor positioning. Therefore, a Wi-Fi and PDR (pedestrian dead reckoning) real-time fusion scheme is proposed in this paper to perform fusing calculation by adaptively determining the dynamic noise of a filtering system according to pedestrian movement (straight or turning), which can effectively restrain the jumping or accumulation phenomena of wireless positioning and the PDR error accumulation problem. Wi-Fi fingerprint matching typically requires a quite high computational burden: To reduce the computational complexity of this step, the affinity propagation clustering algorithm is adopted to cluster the fingerprint database and integrate the information of the position domain and signal domain of respective points. An experiment performed in a fourth-floor corridor at the School of Environment and Spatial Informatics, China University of Mining and Technology, shows that the traverse points of the clustered positioning system decrease by 65%–80%, which greatly improves the time efficiency. In terms of positioning accuracy, the average error is 4.09 m through the Wi-Fi positioning method. However, the positioning error can be reduced to 2.32 m after integration of the PDR algorithm with the adaptive noise extended Kalman filter (EKF).


title: Clinical translation of a PSMA inhibitor for 99mTc-based SPECT

abstract: Abstract Background Prostate-specific membrane antigen (PSMA) is highly over-expressed in advanced prostate cancers. 68 Ga-labeled PSMA inhibitors (iPSMA) are currently used for prostate cancer detection by PET imaging. The availability of simple, efficient and reproducible radiolabeling procedures is essential for developing new SPECT radiopharmaceuticals for clinical translation. The aim of this research was to prepare 99m Tc-EDDA/HYNIC-Lys(Nal)-Urea-Glu ( 99m Tc-EDDA/HYNIC-iPSMA) obtained from lyophilized kit formulations and evaluate the in vitro and in vivo radiopharmaceutical binding to prostate cancer cells over-expressing PSMA, as well as the 99m Tc-EDDA/HYNIC-iPSMA normal biodistribution in humans and the preliminary uptake in patients with prostate cancer. Methods 99m Tc labeling was performed by adding sodium pertechnetate solution and a 0.2M phosphate buffer (pH 7.0) to a lyophilized formulation containing HYNIC-iPSMA, EDDA, tricine, mannitol and stannous chloride. The radiochemical purity was evaluated by reversed-phase HPLC and ITLC-SG analyses. Stability studies in human serum were performed by size-exclusion HPLC. In vitro cell uptake was tested using prostate cancer cells (LNCaP) with blocked and non-blocked receptors. Biodistribution and tumor uptake were determined in LNCaP tumor-bearing nude mice with blocked and non-blocked receptors, and images were obtained using a micro-SPECT/CT. Whole-body images from three healthy men and two patients with histologically-confirmed prostate cancer (one of them with a previous 68 Ga-PSMA-617scan) were acquired at 1h and 3h after 99m Tc-EDDA/HYNIC-iPSMA administration with radiochemical purities of >98%. Results In vitro and in vivo studies showed high radiopharmaceutical stability in human serum, specific recognition for PSMA, high tumor uptake (10.22±2.96% ID/g at 1h) with rapid blood clearance and mainly kidney elimination. Preliminary images in patients demonstrated the ability of 99m Tc-EDDA/HYNIC-iPSMA to detect tumors and metastases of prostate cancer as well as 68 Ga-PSMA-617 does. Conclusions The results obtained in this study warrant further dosimetry and clinical studies to determine the specificity and sensitivity of 99m Tc-EDDA/HYNIC-iPSMA.


Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues Knowledge Management and Knowledge Management Systems: Conceptual Foundations and Research Issues Knowle


title: An offset-canceling low-noise lock-in architecture for capacitive sensing

abstract: We describe an offset-canceling low-noise lock-in architecture for capacitive sensing. We take advantage of the properties of modulation and demodulation to separate the signal from the DC offset and use nonlinear multiplicative feedback to cancel the offset. The feedback also attenuates out-of-band noise and further enhances the power of a lock-in technique. Experimentally, in a 1.5-/spl mu/m BiCMOS chip, a fabrication DC offset of 2 mV and an intentional offset of 100 mV were attenuated to 9 /spl mu/V. Our offset-canceling technique could also be useful for practical multipliers that need tolerance to fabrication errors. We present a detailed theoretical noise analysis of our architecture that is confirmed by experiment. As an example application, we demonstrate the use of our architecture in a simple capacitive surface-microelectromechanical-system vibration sensor where the performance is limited by mechanical Brownian noise. However, we show that our electronics limits us to 30 /spl mu/g//spl radic/Hz, which is at least six times lower than the noise floor of commercial state-of-the-art surface-micromachined inertial sensors. Our architecture could, thus, be useful in high-performance inertial sensors with low mechanical noise. In a 1-100-Hz bandwidth, our electronic detection threshold corresponds to a one-part-per-eight-million change in capacitance.


title: Eye Tracking in User Experience Design

abstract: Eye Tracking for User Experience Design explores the many applications of eye tracking to better understand how users view and interact with technology. Ten leading experts in eye tracking discuss how they have taken advantage of this new technology to understand, design, and evaluate user experience. Real-world stories are included from these experts who have used eye tracking during the design and development of products ranging from information websites to immersive games. They also explore recent advances in the technologywhich tracks how users interact with mobile devices, large-screen displays and video game consoles. Methods for combining eye tracking with other research techniques for a more holistic understanding of the user experience are discussed. This is an invaluable resource to those who want to learn how eye tracking can be used to better understand and design for their users. Includes highly relevant examples and information for those who perform user research and design interactive experiences Written by numerous experts in user experience and eye tracking. Highly relevant to anyone interested in eye tracking & UX design Features contemporary eye tracking research emphasizing the latest uses of eye tracking technology in the user experience industry.