title: Recognizing and Encoding Discorder Concepts in Clinical Text using Machine Learning and Vector Space Model

abstract: The ShARe/CLEF eHealth Evaluation Lab (SHEL) organized a chal- lenge on natural language processing (NLP) and information retrieval (IR) in the medical domain in 2013. The first task of the 2013 ShARe/CLEF challenge was to extract disorder mention spans and their associated UMLS (Unified Medical Language System) concept unique identifiers (CUIs). We participated in Task 1 and developed a clinical disorder recognition and encoding system. The proposed system consists of two components: a machine learning-based approach to recognize disorder entities and a vector space model-based method to encode disorders to UMLS CUIs. The challenge organizers manually anno- tated disorder entities and corresponding UMLS CUIs in 298 clinical notes, of which 199 notes were used for training and 99 were for testing. Evaluation on the test data set showed that our system achieved the best F-measure of 0.750 for entity recognition (ranked first) and the highest F-measure of 0.514 for UMLS CUI encoding (ranked third), indicating the promise of the proposed ap- proaches.


title: MagneBike- Toward multi climbing robots for power plant inspection

abstract: An ever-growing infrastructure, including existing and newly built power plants, as well as a rising environmental awareness in society call for inspection and maintenance systems of high efficiency. A solution can be found in the development of mobile agents to provide assistive inspection tools with improved autonomy. In collaboration with industry the MagneBike robot for power plant inspection has been developed. The robot has been tested in a specific real field environment showing critical issues but inspiring future guidelines. This paper proposes to turn the semi-autonomous MagneBike robot into a multi-agent inspection system with clear benefits in speed, robustness and flexibility of task execution. The inspection task is approached by a hybrid coverage method that combines the concepts of blanket and sweep coverage. Three algorithms implementing hybrid coverage are presented and evaluated in simulations.


title: Few-Shot Learning with Graph Neural Networks

abstract: We propose to study the problem of few-shot learning with the prism of inference on a partially observed graphical model, constructed from a collection of input images whose label can be either observed or not. By assimilating generic message-passing inference algorithms with their neural-network counterparts, we define a graph neural network architecture that generalizes several of the recently proposed few-shot learning models. Besides providing improved numerical performance, our framework is easily extended to variants of few-shot learning, such as semi-supervised or active learning, demonstrating the ability of graph-based models to operate well on 'relational' tasks.


title: PESTO: Parameter EStimation TOolbox

abstract: PESTO is a widely applicable and highly customizable toolbox for parameter estimation in MathWorks MATLAB. It offers scalable algorithms for optimization, uncertainty and identifiability analysis, which work in a very generic manner, treating the objective function as a black box. Hence, PESTO can be used for any parameter estimation problem, for which the user can provide a deterministic objective function in MATLAB. PESTO is a MATLAB toolbox, freely available under the BSD license. The source code, along with extensive documentation and example code, can be downloaded from https://github.com/ICB-DCM/PESTO/. jan.hasenauer@helmholtz-muenchen.de Supplementary data are available at Bioinformatics online.


title: A wireless smart-shoe system for gait assistance

abstract: Methodical studies on Smart-shoe-based gait detection systems have become an influential constituent in decreasing elderly injuries due to fall. This paper proposes smartphone-based system for analyzing characteristics of gait by using a wireless Smart-shoe. The system employs four force sensitive resistors (FSR) to measure the pressure distribution underneath a foot. Data is collected via a Wi-Fi communication network between the Smart-shoe and smartphone for further processing in the phone. Experimentation and verification is conducted on 10 subjects with different gait including free gait. The sensor outputs, with gait analysis acquired from the experiment, are presented in this paper.


title: A Low-Power Forward and Reverse Body Bias Generator in CMOS 40 nm

abstract: This brief presents a low-power forward and reverse body bias (FRBB) generator with body bias (BB) switches to dynamically set BB voltage. The reverse BB (RBB) P-well generator uses pulse frequency modulation (PFM)-based switching capacitor power converter to achieve low power consumption in a wide load current range of 1–30 $\mu ~\text{A}$ . The FBB adopts class-AB output stage and an over-current indicator to detect large body diode forward conduction leakage current. BB switch is introduced to dynamically configure the FRBB generated voltage. The proposed FRBB with BB switch is fabricated in a CMOS 40-nm triple well process, occupying an area of around 0.1 mm2. The 300k digital gates are used as the load for test. The measured active current for FBB and RBB are 13 and $5~\mu \text{A}$ , respectively.


title: Packet Inspection for Unauthorized OS Detection in Enterprises

abstract: Many recent malware implementations employ virtual machines to carry out their malicious activities. These are hard to detect because their states can't be accessed by antivirus software running in the native OS. An approach for OS fingerprinting using TCP SYN packets in an enterprise environment can detect the presence of unauthorized OSs.


title: Efficient adverse drug event extraction using Twitter sentiment analysis

abstract: Extensive clinical trials are required before a drug is placed on the market; yet it is difficult to discover all the side effects for any approved drugs. The United States Food and Drug Administration actively monitors approved medications to identify adverse events. The FDA Adverse Event Reporting System contains a database of adverse drug events (ADE) reported by the healthcare providers and consumers. The pervasive online social networks, such as Twitter, can provide additional information ADE. Concurrently, advancements in social media technology have resulted in the booming of massive public data; the availability of these huge datasets offers numerous research opportunities for extracting ADEs. Towards this purpose, in this paper a simple, effective computation pipeline is proposed, which uses simple drug-related classification and sentiment analysis to extract ADEs on Twitter. The pipeline is described in detail, and is implemented into an automatic process. Experiments are carried out based on 4-months of Twitter data collected. Comparing with an existing pipeline, the new design is able to successfully capture 5 times more valid ADEs, among them 20% are new ADEs. The proposed method may be applied to other areas such as food, beverages, and other daily consumer products for identifying side effects and user opinions.


title: Small-signal modeling of multipulse rectifiers for more-electric aircraft applications

abstract: This paper presents small-signal input impedance models for multipulse rectifiers that are widely used on next-generation more-electric aircraft (MEA). The basic modeling method is a generalization of the impedance mapping method that has been previously applied to single- phase and three-phase six-pulse rectifiers. Mapping functions are first defined for general TV-phase rectifiers and used to calculate their input impedances by assuming symmetrical TV-phase input voltages and ideal operation. The resulting impedance models are then transformed to the primary of the phase-shifting transformer which is modeled by a transformation matrix. Magnetizing inductance and phase commutation due to transformer leakage inductance are also considered. Details of the modeling method are presented, and its application is demonstrated based on an 18-pulse rectifier. The developed models are validated by experimental measurements from an 18-pulse ATRU.


title: Wideband Planar Folded Dipole Antenna With Self-balanced Impedance Property

abstract: A planar folded dipole antenna that exhibits wideband characteristics is proposed. The antenna has simple planar construction without a ground plane and is easy to be assembled. Parameter values are adjusted in order to obtain wideband properties and compactness by using an electromagnetic simulator based on the method of moments. An experimental result centered at 1.7 GHz for 50 impedance matching shows that the antenna has bandwidth over 55% . The gains of the antenna are almost constant (2 dBi) in this frequency band and the radiation patterns are very similar to those of a normal dipole antenna. It is also shown that the antenna has a self-balanced impedance property in this frequency band.


deepViz : Visualizing Convolutional Neural Networks for Image Classification


title: What You Should Know about the Vehicle Routing Problem

abstract: In the Vehicle Routing Problem (VRP), the aim is to design a set of m minimum cost vehicle routes through n customer locations, so that each route starts and ends at a common location and some side constraints are satisfied. Common applications arise in newspaper and food delivery, and in milk collection. This article summarizes the main known results for the classical VRP in which only vehicle capacity constraints are present. The article is structured around three main headings: exact algorithms, classical heuristics, and metaheuristics. © 2007 Wiley Periodicals, Inc. Naval Research Logistics, 2007


title: Copy-move forgery detection using lexicographical sort

abstract: Copy-move forgery is one of the widely used forgery technique to alter digital image to conceal its originality. To testify the originality of an image, many detection techniques have been proposed and it is a very popular research field these days. Here we have presented a detection method based on discrete cosine transform (DCT) using lexicographical sorting. This is a block based copy-move forgery detection technique. In this method, initially the image is divided into 8×8 overlapping sub-blocks. Then we apply DCT on each sub-block to get the DCT coefficients of the corresponding blocks. Now to reduce the dimensionality of the DCT coefficients we take DCT coefficients in zig-zag manner according to their frequency property. These DCT coefficients are considered as the eigenvectors and the distance is calculated between the eigen vectors to get the matching blocks. In experimental results it can be seen that the detection ability is maintained and false matching ratio is reduced.


title: Chronic recurrent multifocal osteomyelitis (CRMO): a longitudinal case series review

abstract: BACKGROUND ::: Chronic recurrent multifocal osteomyelitis (CRMO) is an autoinflammatory disorder that is currently diagnosed based on clinical, radiologic, pathological and longitudinal findings. ::: ::: ::: OBJECTIVE ::: To provide detailed descriptions of CRMO lesion patterns seen on radiographs and MRI and to suggest clinical use of whole-body MRI and propose noninvasive diagnostic strategy. ::: ::: ::: MATERIALS AND METHODS ::: Retrospective longitudinal study (1989-2010) of 31 children (22 girls, 9 boys) diagnosed with CRMO. Imaging data were evaluated by two pediatric radiologists. ::: ::: ::: RESULTS ::: Mean age at diagnosis was 11 years (3-17). A total of 108 lesions were investigated. The most common sites were the long bone metaphyses (56 lesions in 24 children) especially femoral and tibial (20/24); pelvis (10/31); spine (9/31); clavicle (6/31) and mandible (3/31). In long bones, the radiologic appearance was normal (22/56), mixed lytic and sclerotic (20/56), sclerotic (8/56) or lytic (6/56) often juxtaphyseal (36/56), with hyperostosis or periosteal thickening (10/56). Vertebral involvement was often multifocal (6/9). Medullary edema was seen on MRI (42) with epiphyseal (23/42) or soft-tissue (22/42) inflammation and juxtaphyseal nodule-like appearance (7/42). Whole-body MRI (15/31) was key in detecting subclinical lesions. ::: ::: ::: CONCLUSION ::: CRMO is a polymorphous disorder in which whole-body MRI is extremely useful for showing subclinical edema. Vertebral collapse requires long-term monitoring.


title: Microwave Devices Simulation with Ansoft HFSS

abstract: Some microwave passive devices for applications in microwave moisture content measurement, including waveguide absorption type variable attenuator, optimum H surface horn antenna, optimum pyramidal horn antenna, were simulated by Ansoft's HFSS. Certain targets have been achieved, and it is a new attempt to apply the microwave simulation technology to instructing practice. The dielectric slab painted absorbing material (graphite powder) method or both sides of it inflicted resistance boundary condition method was separately used for the attenuator models. The latter method may achieve the big attenuation and satisfy the need of microwave moisture content measurement according to the simulation results. Optimum gain H surface horn antenna and pyramidal horn antenna with E and H 3dB lobe width equal were designed. The pyramidal horn antenna is better, which can obtain good antenna pattern, big gain and small VSWR according to the simulation results; therefore, it has been used widely in the microwave moisture content measurement.


title: An Empirical Study of Cross-Lingual Transfer Learning Techniques for Small-Footprint Keyword Spotting

abstract: This paper presents our work on building a small-footprint keyword spotting system for a resource-limited language, which requires low CPU, memory and latency. Our keyword spotting system consists of deep neural network (DNN) and hidden Markov model (HMM), which is a hybrid DNN-HMM decoder. We investigate different transfer learning techniques to leverage knowledge and data from a resource-abundant source language to improve the keyword DNN training for a target language which has limited in-domain data. The approaches employed in this paper include training a DNN using source language data to initialize the target language DNN training, mixing data from source and target languages together in a multi-task DNN training setup, using logits computed from a DNN trained on the source language data to regularize the keyword DNN training in the target language, as well as combinations of these techniques. Given different amounts of target language training data, our experimental results show that these transfer learning techniques successfully improve keyword spotting performance for the target language, measured by the area under the curve (AUC) of DNN-HMM decoding detection error tradeoff (DET) curves using a large in-house far-field test set.


Tell me who you are and I will tell you where to go - Use of travel personalities in destination recommendation systems


title: A Time-Reversal Paradigm for Indoor Positioning System

abstract: In an indoor environment, there commonly exist a large number of multipaths due to rich scatterers. These multipaths make the indoor positioning problem very challenging. The main reason is that most of the transmitted signals are significantly distorted by the multipaths before arriving at the receiver, which causes inaccuracies in the estimation of the positioning features such as the time of arrival (TOA) and the angle of arrival (AOA). On the other hand, the multipath effect can be very constructive when employed in the time-reversal (TR) radio transmission. By utilizing the uniqueness of the multipath profile at each location, TR can create a resonating effect of focusing the energy of the transmitted signal only onto the intended location, which is known as the spatial focusing effect. In this paper, we propose exploiting such a high-resolution focusing effect in the indoor positioning problem. Specifically, we propose a TR indoor positioning system (TRIPS) by utilizing the location-specific characteristic of multipaths. By doing so, we decompose the ill-posed single-access-point (AP) indoor positioning problem into two well-defined subproblems. The first subproblem is to create a database by mapping the physical geographical location with the logical location in the channel impulse response (CIR) space, whereas the second subproblem is to determine the real physical location by matching the estimated CIR with those in the database. To evaluate the performance of our proposed TRIPS, we build a prototype to conduct real experiments. The experimental results show that, with a single AP working in the 5.4-GHz band under the non-line-of-sight (NLOS) condition, our proposed TRIPS can achieve perfect 10-cm localization accuracy with zero-error rate within a 0.9 m by 1 m area of interest.


title: What do prototypes prototype

abstract: Publisher Summary Prototypes are widely recognized to be a core means of exploring and expressing designs for interactive computer artifacts. Choosing the right kind of more focused prototype to build is an art in itself, and communicating its limited purposes to its various audiences is a critical aspect of its use. Current terminology for describing prototypes centers on attributes of prototypes themselves that can be distracting. Tools can be used in many different ways, and detail is not a sure indicator of completion. This chapter proposes a change in the language used to talk about prototypes, to focus more on fundamental questions about the interactive system being designed. The goal of this chapter is to establish a model that describes any prototype in terms of the artifact being designed rather than the prototype's incidental attributes. By focusing on the purpose of the prototype—that is, what it prototypes—better decisions can be made about the kinds of prototypes to build. With a clear purpose for each prototype, prototypes can be better used to think and communicate about design. This chapter begins by describing current difficulties in communicating about prototypes: the complexity of interactive systems; issues of multi-disciplinary teamwork; and the audiences of prototypes. The chapter introduces the model and illustrates it with some initial examples of prototypes from real projects. The chapter concludes with a summary of the main implications of the model for prototyping practice.


title: Developmental phenotypes and causal pathways in attention deficit/hyperactivity disorder: potential targets for early intervention?

abstract: Early intervention approaches have rarely been implemented for the prevention of attention deficit/hyperactivity disorder (ADHD). In this paper we explore whether such an approach may represent an important new direction for therapeutic innovation. We propose that such an approach is most likely to be of value when grounded in and informed by developmental models of the dynamic, complex and heterogeneous nature of the condition. First, we set out a rationale for early intervention grounded in the science of ADHD viewed through developmental models. Second, we re-examine the concept of disorder-onset from the perspective of developmental trajectories and phenotypes. Third, we examine potential causal pathways to ADHD with regard to originating risk, pathophysiological mediators, environmental moderators and developmental continuities. Finally, we explore the potential value of strategies for identifying young children at risk for ADHD, and implementing interventions in ways that can target these underlying pathogenic processes. The utility of such an approach represents an important area for future research but still requires 'proof of concept'. Therefore prior to widespread clinical implementation, far greater knowledge is required of (i) developmental pathways into ADHD, (ii) the value of identifying neuropsychological mediators of these pathways, and (iii) the extent to which targeting mediating mechanisms will improve treatment outcomes for children with ADHD.


title: (in press). In H. Cohen & C. Lefebvre (Eds.), Categorization in cognitive science. New York: Elsevier. Multiple Systems of Perceptual Category Learning: Theory and Cognitive Tests

abstract: The first neurobiologically detailed theory of multiple systems in category learning, called COVIS, was originally conceived in 1998. COVIS, which is now well established, postulates two systems that compete throughout learning—a frontal-based declarative system that uses logical reasoning and depends on working memory and executive attention, and a basal ganglia-mediated system that uses procedural-learning. The procedural system can learn a wide variety of category structures, but it learns in a slow incremental fashion and is highly dependent on reliable and immediate feedback. In contrast, the declarative rule-based (RB) system can learn a fairly small set of category structures quickly—specifically, those structures that can be learned via a logical reasoning process. These two systems learn simultaneously, but as long as RB strategies lead to successful performance, the declarative system inhibits the procedural system. This theory is described in detail and a variety of cognitive behavioral and cognitive neuroscience experiments are reviewed that test some parameter-free a priori predictions made by COVIS.


title: A high accuracy solver for RTE in underwater optical communication path loss prediction

abstract: In this paper, we present a new improved numerical framework to evaluate the time-dependent radiative transfer equation (RTE) for underwater optical wireless communication (UOWC) systems. The RTE predicts the optical path-loss of light in an underwater channel, as a function of the inherent optical properties (IOPs) related to the water type, namely the absorption and scattering coefficients as well as the phase scattering function (PSF). We reach the simulation performance based on an improvement of the finite difference scheme proposed in [1] as well as an enhancement of the quadrature method aiming to calculate the integral term of the RTE [2]. Additionally, we evaluate the received power at the receiver plane in three dimensions by considering a given receiver aperture and a field of view (FOV). Finally, we evaluate the UOWC system's bit error rate performance metric as a function of the propagation distance, and time.


title: Detecting Offensive Language in Tweets Using Deep Learning

abstract: This paper addresses the important problem of discerning hateful content in social media. We propose a detection scheme that is an ensemble of Recurrent Neural Network (RNN) classifiers, and it incorporates various features associated with user-related information, such as the users' tendency towards racism or sexism. These data are fed as input to the above classifiers along with the word frequency vectors derived from the textual content. Our approach has been evaluated on a publicly available corpus of 16k tweets, and the results demonstrate its effectiveness in comparison to existing state of the art solutions. More specifically, our scheme can successfully distinguish racism and sexism messages from normal text, and achieve higher classification quality than current state-of-the-art algorithms.


title: Detection of soybean rust using a multispectral image sensor

abstract: Soybean rust, caused by Phakopsora pachyrhizi, is one of the most destructive diseases for soybean production. It often causes significant yield loss and may rapidly spread from field to field through airborne urediniospores. In order to implement timely fungicide treatments for the most effective control of the disease, it is essential to detect the infection and severity of soybean rust. This research explored feasible methods for detecting soybean rust and quantifying severity. In this study, images of soybean leaves with different rust severity were collected using both a portable spectroradiometer and a multispectral CDD camera. Different forms of vegetation indices were used to investigate the possibility of detecting rust infection. Results indicated that both leaf development stage and rust infection severity changed the surface reflectance within a wide band of spectrum. In general, old leaves with most severe rust infection resulted in lowest reflectance. A difference vegetation index (DVI) showed a positive correlation with reflectance differences. However, it lacks solid evidence to identify such reflectance change was solely caused by rust. As an alternative, three parameters, i.e. ratio of infected area (RIA), lesion color index (LCI) and rust severity index (RSI), were extracted from the multispectral images and used to detect leaf infection and severity of infection. The preliminary results obtained from this laboratory-scale research demonstrated that this multispectral imaging method could quantitatively detect soybean rust. Further tests of field scale are needed to verify the effectiveness and reliability of this sensing method to detect and quantify soybean rust infection in real time field scouting.


title: Fibonacci heaps and their uses in improved network optimization algorithms

abstract: In this paper we develop a new data structure for implementing heaps (priority queues). Our structure, <italic>Fibonacci heaps</italic> (abbreviated <italic>F-heaps</italic>), extends the binomial queues proposed by Vuillemin and studied further by Brown. F-heaps support arbitrary deletion from an <italic>n</italic>-item heap in <italic>O</italic>(log <italic>n</italic>) amortized time and all other standard heap operations in <italic>O</italic>(1) amortized time. Using F-heaps we are able to obtain improved running times for several network optimization algorithms. In particular, we obtain the following worst-case bounds, where <italic>n</italic> is the number of vertices and <italic>m</italic> the number of edges in the problem graph:<list><item><italic>O</italic>(<italic>n</italic> log <italic>n</italic> + <italic>m</italic>) for the single-source shortest path problem with nonnegative edge lengths, improved from <italic>O</italic>(<italic>m</italic>log<subscrpt>(<italic>m/n</italic>+2)</subscrpt><italic>n</italic>); ::: </item><item><italic>O</italic>(<italic>n</italic><supscrpt>2</supscrpt>log <italic>n</italic> + <italic>nm</italic>) for the all-pairs shortest path problem, improved from <italic>O</italic>(<italic>nm</italic> log<subscrpt>(<italic>m/n</italic>+2)</subscrpt><italic>n</italic>); ::: </item><item><italic>O</italic>(<italic>n</italic><supscrpt>2</supscrpt>log <italic>n</italic> + <italic>nm</italic>) for the assignment problem (weighted bipartite matching), improved from <italic>O</italic>(<italic>nm</italic>log<subscrpt>(<italic>m/n</italic>+2)</subscrpt><italic>n</italic>); ::: </item><item><italic>O</italic>(<italic>mβ</italic>(<italic>m, n</italic>)) for the minimum spanning tree problem, improved from <italic>O</italic>(<italic>m</italic>log log<subscrpt>(<italic>m/n</italic>+2)</subscrpt><italic>n</italic>); where <italic>β</italic>(<italic>m, n</italic>) = min {<italic>i</italic> ↿ log<supscrpt>(<italic>i</italic>)</supscrpt><italic>n</italic> ≤ <italic>m/n</italic>}. Note that <italic>β</italic>(<italic>m, n</italic>) ≤ log<supscrpt>*</supscrpt><italic>n</italic> if <italic>m</italic> ≥ <italic>n</italic>. ::: </item></list>Of these results, the improved bound for minimum spanning trees is the most striking, although all the results give asymptotic improvements for graphs of appropriate densities.


title: Mimic of manganese superoxide dismutase to induce apoptosis of human non-Hodgkin lymphoma Raji cells through mitochondrial pathways.

abstract: Increased reactive oxygen species (ROS) such as superoxide have been implicated as causal elements of oncogenesis. A variety of cancers have displayed changes in steady-state levels of key antioxidant enzymes, with the mitochondrial form of superoxide dismutase (MnSOD) being commonly implicated. Increasing MnSOD expression suppresses the malignant phenotype in various cancer cell lines and suppresses tumor formation in xenograft and transgenic mouse models. In this study, we examined the anti-proliferation effect of mimic of manganese superoxide dismutase (MnSODm) on human non-Hodgkin lymphoma Raji cells. The results showed that MnSODm significantly reduced the proliferation of Raji cells in a concentration and a time-dependent manner. By flow cytometric analysis, we found that MnSODm treatment resulted in an increased apoptosis in Raji cells. MnSODm also increased the production of ROS and the expression levels of cleaved caspase-9, caspase-3, poly (ADP-ribose) polymerase (PARP) and Bax in Raji cells. Moreover, the expression of Bcl-2 protein showed down-regulation in the MnSODm treatment group. In addition, MnSODm significantly elevated the level of cytochrome c in cytosol. These findings suggest that the activation of the mitochondrial pathway is involved in MnSODm-induced apoptosis in Raji cells.


title: Accelerating Asynchronous Stochastic Gradient Descent for Neural Machine Translation

abstract: In order to extract the best possible performance from asynchronous stochastic gradient descent one must increase the mini-batch size and scale the learning rate accordingly. In order to achieve further speedup we introduce a technique that delays gradient updates effectively increasing the mini-batch size. Unfortunately with the increase of mini-batch size we worsen the stale gradient problem in asynchronous stochastic gradient descent (SGD) which makes the model convergence poor. We introduce local optimizers which mitigate the stale gradient problem and together with fine tuning our momentum we are able to train a shallow machine translation system 27% faster than an optimized baseline with negligible penalty in BLEU.


title: Multi-channel surface EMG classification using support vector machines and signal-based wavelet optimization

abstract: Abstract The study proposes a method for supervised classification of multi-channel surface electromyographic signals with the aim of controlling myoelectric prostheses. The representation space is based on the discrete wavelet transform (DWT) of each recorded EMG signal using unconstrained parameterization of the mother wavelet. The classification is performed with a support vector machine (SVM) approach in a multi-channel representation space. The mother wavelet is optimized with the criterion of minimum classification error, as estimated from the learning signal set. The method was applied to the classification of six hand movements with recording of the surface EMG from eight locations over the forearm. Misclassification rate in six subjects using the eight channels was (mean ± S.D.) 4.7 ± 3.7% with the proposed approach while it was 11.1 ± 10.0% without wavelet optimization (Daubechies wavelet). The DWT and SVM can be implemented with fast algorithms, thus, the method is suitable for real-time implementation.


title: Academic procrastination in college students: The role of self-reported executive function

abstract: Procrastination, or the intentional delay of due tasks, is a widespread phenomenon in college settings. Because procrastination can negatively impact learning, achievement, academic self-efficacy, and quality of life, research has sought to understand the factors that produce and maintain this troublesome behavior. Procrastination is increasingly viewed as involving failures in self-regulation and volition, processes commonly regarded as executive functions. The present study was the first to investigate subcomponents of self-reported executive functioning associated with academic procrastination in a demographically diverse sample of college students aged 30 years and below (n = 212). We included each of nine aspects of executive functioning in multiple regression models that also included various demographic and medical/psychiatric characteristics, estimated IQ, depression, anxiety, neuroticism, and conscientiousness. The executive function domains of initiation, plan/organize, inhibit, self-monitor, wo...


title: Validation of heart rate extraction using video imaging on a built-in camera system of a smartphone

abstract: As a smartphone is becoming very popular and its performance is being improved fast, a smartphone shows its potential as a low-cost physiological measurement solution which is accurate and can be used beyond the clinical environment. Because cardiac pulse leads the subtle color change of a skin, a pulsatile signal which can be described as photoplethysmographic (PPG) signal can be measured through recording facial video using a digital camera. In this paper, we explore the potential that the reliable heart rate can be measured remotely by the facial video recorded using smartphone camera. First, using the front facing-camera of a smartphone, facial video was recorded. We detected facial region on the image of each frame using face detection, and yielded the raw trace signal from the green channel of the image. To extract more accurate cardiac pulse signal, we applied independent component analysis (ICA) to the raw trace signal. The heart rate was extracted using frequency analysis of the raw trace signal and the analyzed signal from ICA. The accuracy of the estimated heart rate was evaluated by comparing with the heart rate from reference electrocardiogram (ECG) signal. Finally, we developed FaceBEAT, an iPhone application for remote heart rate measurement, based on this study.


title: Energy Management of Fuel Cell/Battery/Supercapacitor Hybrid Power Sources Using Model Predictive Control

abstract: Well known as an efficient and eco-friendly power source, fuel cell, unfortunately, offers slow dynamics. When attached as primary energy source in a vehicle, fuel cell would not be able to respond to abrupt load variations. Supplementing battery and/or supercapacitor to the system will provide a solution to this shortcoming. On the other hand, a current regulation that is vital for lengthening time span of the energy storage system is needed. This can be accomplished by keeping fuel cell's and batteries' current slope in reference to certain values, as well as attaining a stable dc output voltage. For that purpose, a feedback control system for regulating the hybrid of fuel cell, batteries, and supercapacitor was constructed for this study. Output voltage of the studied hybrid power sources (HPS) was administered by assembling three dc-dc converters comprising two bidirectional converters and one boost converter. Current/voltage output of fuel cell was regulated by boost converter, whereas the bidirectional converters regulated battery and supercapacitor. Reference current for each converter was produced using Model Predictive Control (MPC) and subsequently tracked using hysteresis control. These functions were done on a controller board of a dSPACE DS1104. Subsequently, on a test bench made up from 6 V, 4.5 Ah battery and 7.5 V, 120 F supercapacitor together with a fuel cell of 50 W, 10 A, experiment was conducted. Results show that constructing a control system to restrict fuel cell's and batteries' current slope and maintaining dc bus voltage in accordance with the reference values using MPC was feasible and effectively done.


title: Robustly representing inferential uncertainty in deep neural networks through sampling

abstract: As deep neural networks (DNNs) are applied to increasingly challenging problems, they will need to be able to represent their own uncertainty. Modeling uncertainty is one of the key features of Bayesian methods. Using Bernoulli dropout with sampling at prediction time has recently been proposed as an efficient and well performing variational inference method for DNNs. However, sampling from other multiplicative noise based variational distributions has not been investigated in depth. We evaluated Bayesian DNNs trained with Bernoulli or Gaussian multiplicative masking of either the units (dropout) or the weights (dropconnect). We tested the calibration of the probabilistic predictions of Bayesian convolutional neural networks (CNNs) on MNIST and CIFAR-10. Sampling at prediction time increased the calibration of the DNNs' probabalistic predictions. Sampling weights, whether Gaussian or Bernoulli, led to more robust representation of uncertainty compared to sampling of units. However, using either Gaussian or Bernoulli dropout led to increased test set classification accuracy. Based on these findings we used both Bernoulli dropout and Gaussian dropconnect concurrently, which we show approximates the use of a spike-and-slab variational distribution without increasing the number of learned parameters. We found that spike-and-slab sampling had higher test set performance than Gaussian dropconnect and more robustly represented its uncertainty compared to Bernoulli dropout.


title: Trichilemmomal Horn Arising from a Nevus Sebaceus and Pilar Cyst: A Case Report and Review of the Literature

abstract: Nevus sebaceus can give rise to multiple neoplasms, some of which have the potential to become malignant. We describe the occurrence of a previously unreported combination of a cutaneous horn proliferating from a trichilemmoma overlying a multilocular pilar cyst and nevus sebaceus. An elderly woman with a several years’ history of multiple scalp subcutaneous nodules and an enlarging cutaneous horn presented to our clinic. The nodule underlying the large exophytic horn was tender and occasionally bled. The lesion was removed by excisional biopsy due to concern for malignancy. Histologically, a verrucous trichilemmoma was identified overlying irritated pilar cysts and a nevus sebaceus. Phosphatase and tensin homolog (PTEN) mutations are frequently found in trichilemmomas and HRAS mutations in nevus sebaceus. Mammalian target of rapamycin (mTOR) is a part of the final common pathway for HRAS and PTEN raising the therapeutic question if rapamycin can be used to treat these neoplasms.


title: AUTOMATIC TIMBRE CLASSIFICATION OF ETHNOMUSICOLOGICAL AUDIO RECORDINGS

abstract: Automatic timbre characterization of audio signals can help to measure similarities between sounds and is of in-terest for automatic or semi-automatic databases indexing. The most effective methods use machine learning approaches which require qualitative and diversified training databases to obtain accurate results. In this paper, we introduce a diversified database composed of worldwide non-western instruments audio recordings on which is evaluated a com-putationally effective timbre classification method. A com-parative evaluation based on the well studied Iowa musical instruments database shows results comparable with those of state-of-the-art methods. Thus, the proposed method offers a practical solution for automatic ethnomusicologi-cal indexing of a database composed of diversified sounds with various quality. The relevance of audio features for the timbre characterization is also discussed in the context of non-western instruments analysis.


title: System architecture of Wireless Body Sensor Networks

abstract: Wireless Body Sensor Networks (WBSNs) emerged as a design solution of choice for a number of wearable and ubiquitous monitoring applications. Although useful as stand alone monitoring systems, full potential of WBSNs might be explored by integrating individual on-body systems into hierarchical real-time systems to support unobtrusive ubiquitous applications with intermittent communication. However, resource constrains of the lowest levels of the system architecture demand careful system design and specific trade offs in the WBSN design space. Typical issues include proper design of the wireless communication system, buffering, and real-time latency. These issues are application dependent and critically influence system performance and user acceptance. This paper presents system architecture of the hierarchical wireless body sensor networks and design issues in the WBSN design space.


title: Design of a New Electromagnetic Brake for Actuator Locking Mechanism in Aerospace Vehcile

abstract: Electromagnetic (EM) brakes are gaining importance in aerospace vehicles for various critical applications. In guided missiles, they are primarily used for locking the actuator when the missile is in carriage either by road or by air such that the position of the actuator is not changed when servo is in the off condition. In this paper, a new EM brake is designed which is more energy efficient and reliable compared to a conventional solenoid operated brake. The nonlinear magnetic analysis is carried out on the brake, and the hardware is realized and tested. The test results are promising and this topology can be adapted for aerospace and other critical applications.


title: Neuroergonomics: a review of applications to physical and cognitive work

abstract: Neuroergonomics is an emerging science that is defined as the study of the human brain in relation to performance at work and in everyday settings. This paper provides a critical review of the neuroergonomic approach to evaluating physical and cognitive work, particularly in mobile settings. Neuroergonomics research employing mobile and immobile brain imaging techniques are discussed in the following areas of physical and cognitive work: 1) physical work parameters; 2) physical fatigue; 3) vigilance and mental fatigue; 4) training and neuroadaptive systems; and 5) assessment of concurrent physical and cognitive work. Finally, the integration of brain and body measurements in investigating workload and fatigue, in the context of mobile brain/body imaging ('MoBI'), is discussed.


title: Differentially Private Online Learning for Cloud-Based Video Recommendation With Multimedia Big Data in Social Networks

abstract: With the rapid growth in multimedia services and the enormous offers of video content in online social networks, users have difficulty in obtaining their interests. Therefore, various personalized recommendation systems have been proposed. However, they ignore that the accelerated proliferation of social media data has led to the big data era, which has greatly impeded the process of video recommendation. In addition, none of them has considered both the privacy of users’ contexts (e.g., social status, ages, and hobbies) and video service vendors’ repositories, which are extremely sensitive and of significant commercial value. To handle these problems, we propose a cloud-assisted differentially private video recommendation system based on distributed online learning. In our framework, service vendors are modeled as distributed cooperative learners, recommending videos according to user's context, while simultaneously adapting the video-selection strategy based on user-click feedback to maximize total user clicks (reward). Considering the sparsity and heterogeneity of big social media data, we also propose a novel geometric differentially private model, which can greatly reduce the performance loss. Our simulation shows the proposed algorithms outperform other existing methods and keep a delicate balance between the total reward and privacy preserving level.


title: A system for understanding imaged infographics and its applications

abstract: Information graphics, or infographics, are visual representations of information, data or knowledge. Understanding of infographics in documents is a relatively new research problem, which becomes more challenging when infographics appear as raster images. This paper describes technical details and practical applications of the system we built for recognizing and understanding imaged infographics located in document pages. To recognize infographics in raster form, both graphical symbol extraction and text recognition need to be performed. The two kinds of information are then auto-associated to capture and store the semantic information carried by the infographics. Two practical applications of the system are introduced in this paper, including supplement to traditional optical character recognition (OCR) system and providing enriched information for question answering (QA). To test the performance of our system, we conducted experiments using a collection of downloaded and scanned infographic images. Another set of scanned document pages from the University of Washington document image database were used to demonstrate how the system output can be used by other applications. The results obtained confirm the practical value of the system.


title: Commitments to Regulate Social Web Services Operation

abstract: This paper discusses how social Web services are held responsible for the actions they take at run time. Compared to (regular) Web services, social Web services perform different actions, for instance establishing and maintaining networks of contacts and forming with some privileged contacts strong and long lasting collaborative groups. Assessing these actions' outcomes, to avoid any violation, occurs through commitments that the social Web services are required to bind to. Two types of commitments are identified: social commitments that guarantee the proper use of the social networks in which the social Web services sign up, and business commitments that guarantee the proper development of composite Web services in response to users' requests. Detecting commitment violation and action prohibition using monitoring results in imposing sanctions on the “guilty” social Web services and taking corrective actions. A system for commitment management in terms of definition, binding, monitoring, and violation detection is also discussed in this paper.


title: A review of the harmonic and unbalance effects in electrical distribution networks due to EV charging

abstract: For wide use of electric vehicles (EVs), there are different aspects of the electric power system to consider for making it ready for the increased load by battery charging. The topics include power production, peak load management, distribution transmission capacity but also distribution network power quality and many more. This paper presents an overview on the likely power quality impacts in the distribution networks associated with EV charging. Based on a literature review, focus is especially put on harmonics and load unbalance in the network. Most relevant papers observing these topics are presented summarizing their contribution. The power quality aspects in distribution networks discussed here are not often presented within analysis of permissible EV penetration levels. Harmonics or voltage unbalance and effects associated with these could introduce additional limits to the EV charging capacity for the distribution networks. Therefore the analysis on the EV charging influence on these power quality topics requires also high-priority discussions before drawing conclusions on the distribution networks capabilities.


title: Representation Properties of Networks: Kolmogorov's Theorem Is Irrelevant

abstract: Many neural networks can be regarded as attempting to approximate a multivariate function in terms of one-input one-output units. This note considers the problem of an exact representation of nonlinear mappings in terms of simpler functions of fewer variables. We review Kolmogorov's theorem on the representation of functions of several variables in terms of functions of one variable and show that it is irrelevant in the context of networks for learning.


title: Enabling PROFINET devices to work in IoT: Characterization and requirements

abstract: The rise of Internet of Things (IoT) has impacted the industrial automation world. Traditional fleldbus and new fieldbus based on Real Time Ethernet (as PROFINET) have been challenged to include IoT solutions, whose real possibilities are still under evaluation. On the other hand, there are properties of industrial automation devices (e.g. robustness, “proven in use”, explosion proof) that may be useful also in typical IoT scenarios (e.g. building automation). Therefore, the paper proposes a reversed approach, where a new architecture enables the use of existing, legacy, industry-grade devices (e.g. PROFINET Sensors, actuators, and Programmable Logic controllers — PLC) to be used across the Internet. In the proposed system, the PROFINET network can be put into work by means of transparent proxies that use IoT paradigms. The paper presents and discusses a set of basic requirements and a preliminary performance evaluation of a sample application. The results show that the 99-percentile of the transfer time (from proxy to proxy) in the proposed system is less than 100ms, and it is stable even varying the PROFINET cycle time.


title: FPGA based speed control of Brushless DC Motors using IOPT Petri Net models

abstract: This paper describes how to implement a functional Brushless DC Motor open-loop speed controller from simple IOPT Petri Net models, using the integrated development environment offered by IOPT-Tools, without the need to manually write software or hardware descriptions. IOPT nets are a Petri net class specifically designed to support the implementation of embedded system controllers. The IOPT-Tools Web service (http://gres.uninova.pt) includes an interactive graphical editor to design IOPT models, a model-checking framework consisting of a state-space generator and a query system, and automatic code generation tools to produce software (C) or hardware (VHDL) controller implementations. The Brushless DC Motor speed controller was decomposed into several subsystems, including a noise-filter, quadrature-decoder, PWM generator and an BLDC commutation-manager. These sub-systems were modeled using simple IOPT models, analyzed using the model-checking tools, resulting in the automatic creation of VHDL modules for each sub-system. To finish the entire project, a top VHDL module is used to instantiate copies of the automatically generated components and specify signals to connect the components to the external world. With the addition of an inverter board, a working prototype was implemented and successfully tested.


title: Exploring Interactions and Perceptions of Kinetic Wearables

abstract: Jewelry and accessories have long been objects for decorating the human body; however they remain static and non-interactive. This work explores opportunities for accessory-like kinetic wearables and their association with individual style. We developed Kino, a kinetic accessory system which enables both aesthetic and functional clothing-specific design possibilities. We engaged both fashion designers and every-day users to unpack envisioned use cases and perceptions of the system. Participants viewed the devices not as gadgets but as companions due to their close proximity to the body. They envisioned a wide range of usage scenarios, highlighting the complexity of mobility in relation to personal style. We observe how mobility offers opportunities for fluid representations of self, which is unachievable though static clothing and accessories. We also outline how personalized aesthetics is important for the meaning making of novel on-body devices.


title: Biogeography-based optimization and the solution of the power flow problem

abstract: Biogeography-based optimization (BBO) is a novel evolutionary algorithm that is based on the mathematics of biogeography. Biogeography is the study of the geographical distribution of biological organisms. In the BBO model, problem solutions are represented as islands, and the sharing of features between solutions is represented as immigration and emigration between the islands. This paper presents an application of the BBO algorithm to the power flow problem for an IEEE 30-bus Test Case system. The BBO solution is compared with the solution of the same problem using a genetic algorithm (GA). The results of Monte Carlo simulations indicate that the BBO algorithm consistently performs better than the GA in determining an optimal solution to the power flow problem.


A multimodal approach for automatic assessment of school principals' oral presentation during pre-service training program.


title: Database development supporting offline update using CRDT: (Conflict-free replicated data types)

abstract: Database is nowadays a crucial data storage for application needs. Currently, there are several existing popular databases such as relational database like SQL and non-relational database (i.e. NoSQL). However, both types have their own drawbacks which could not be utilized in both online and offline modes concurrently. Therefore, the authors develop a database that supports both modes especially availability in offline mode without sacrificing consistency. The database is integrated with CRDT (conflict-free replicated data types) algorithm to handle and solve conflicts in synchronization process. Some tools and technologies are used in the database development including Apache Thrift for middleware, LevelDB for server database, and IndexedDB for temporary client database. The purpose of this development is to build a CRDT database with usable API for developer users. Finally, it is tested with scenarios to satisfy essential operations: add, remove, and update.


title: Nonparametric statistical testing of EEG- and MEG-data

abstract: In this paper, we show how ElectroEncephaloGraphic (EEG) and MagnetoEncephaloGraphic (MEG) data can be analyzed statistically using nonparametric techniques. Nonparametric statistical tests offer complete freedom to the user with respect to the test statistic by means of which the experimental conditions are compared. This freedom provides a straightforward way to solve the multiple comparisons problem (MCP) and it allows to incorporate biophysically motivated constraints in the test statistic, which may drastically increase the sensitivity of the statistical test. The paper is written for two audiences: (1) empirical neuroscientists looking for the most appropriate data analysis method, and (2) methodologists interested in the theoretical concepts behind nonparametric statistical tests. For the empirical neuroscientist, a large part of the paper is written in a tutorial-like fashion, enabling neuroscientists to construct their own statistical test, maximizing the sensitivity to the expected effect. And for the methodologist, it is explained why the nonparametric test is formally correct. This means that we formulate a null hypothesis (identical probability distribution in the different experimental conditions) and show that the nonparametric test controls the false alarm rate under this null hypothesis.


title: Use of primary-side information to perform online estimation of the secondary-side information and mutual inductance in wireless inductive link

abstract: It is well-known that the power transfer efficiency of wireless inductive link depends on factors including the mutual inductance between the primary and secondary coils, quality factors of the coils, and resonant tank components. It is thus advantageous to have the real-time information about the aforesaid factors, in order to determine the optimal operating frequency and monitor the load condition. This paper will present a technique that utilizes the primary-side electrical quantities to perform real-time estimation of the mutual inductance, value of the secondary-side resonant capacitor, and electrical quantities on the secondary side. The technique can be applied to three scenarios: 1) with unknown value of the secondary-side resonant capacitor and known secondary-side voltage, 2) with known value of the secondary-side resonant capacitor and unknown secondary-side voltage, and 3) unknown value of the secondary-side resonant capacitor and unknown secondary-side voltage. Detailed mathematical treatment will be given. An experimental prototype using planar transformer has been built and evaluated on a test bed. This technique is also implemented as a control scheme of a cascaded converter to regulate the output power. Experimental results are found to be in close agreement with the theoretical predictions.


Survey on Image Resizing Techniques


title: Preparing for the Unknown: Learning a Universal Policy with Online System Identification

abstract: We present a new method of learning control policies that successfully operate under unknown dynamic models. We create such policies by leveraging a large number of training examples that are generated using a physical simulator. Our system is made of two components: a Universal Policy (UP) and a function for Online System Identification (OSI). We describe our control policy as universal because it is trained over a wide array of dynamic models. These variations in the dynamic model may include differences in mass and inertia of the robots' components, variable friction coefficients, or unknown mass of an object to be manipulated. By training the Universal Policy with this variation, the control policy is prepared for a wider array of possible conditions when executed in an unknown environment. The second part of our system uses the recent state and action history of the system to predict the dynamics model parameters mu. The value of mu from the Online System Identification is then provided as input to the control policy (along with the system state). Together, UP-OSI is a robust control policy that can be used across a wide range of dynamic models, and that is also responsive to sudden changes in the environment. We have evaluated the performance of this system on a variety of tasks, including the problem of cart-pole swing-up, the double inverted pendulum, locomotion of a hopper, and block-throwing of a manipulator. UP-OSI is effective at these tasks across a wide range of dynamic models. Moreover, when tested with dynamic models outside of the training range, UP-OSI outperforms the Universal Policy alone, even when UP is given the actual value of the model dynamics. In addition to the benefits of creating more robust controllers, UP-OSI also holds out promise of narrowing the Reality Gap between simulated and real physical systems.


title: Design And Modeling Of An Axial Flux Permanent Magnet Generator With Double Layer Tooth-Concentrated Fractional Windings.

abstract: Axial Flux Permanent Magnet Machines benefit from manufacturing advantages and reduction in end winding length through the use of tooth-concentrated fractional windings but display increased space harmonic content, leading to higher eddy current losses in the rotor iron and permanent magnets. The choice of a double layer tooth-concentrated fractional winding for the modeling and design of a 24 Slots/20 poles Axial Flux Permanent Magnet Generator is justified by a comparison between the single and double layer winding.


title: "Change Takes Time:" A Look at the Growth and Development of the Coalition of Essential Schools.

abstract: There is not a specific blueprint here for a top‐to‐bottom restructuring of the system. That's been tried and is being tried, with the result being a high degree of failure. In any case, such a blueprint or model would violate the first of the imperatives: giving room to teachers and students to work and learn in their own way (Sizer 1985a).


title: Floating search methods in feature selection

abstract: Sequential search methods characterized by a dynamically changing number of features included or eliminated at each step, henceforth "floating" methods, are presented. They are shown to give very good results and to be computationally more effective than the branch and bound method.


title: Impacts of bulk soil microbial community structure on rhizosphere microbiomes of Zea mays

abstract: Background and aims ::: It has frequently been shown that plants interact with soils to shape rhizosphere microbiomes. However, previous work has not distinguished between effects of soil properties per se, and effects attributable to the resident microbial communities of those soils. We aimed to test whether differences in the structure of bulk soil microbial communities, within a given soil type, would carry over to impact the structure of the rhizosphere microbial community.


title: A 6–18-GHz GaAs Multifunction Chip With 8-bit True Time Delay and 7-bit Amplitude Control

abstract: This paper presents a monolithic microwave integrated circuit multifunction chip implemented using a 0.25- $\mu \text{m}$ gallium arsenide pseudomorphic high-electron-mobility transistor process for use in a phased array jammer transmitter. This chip, which operates in the frequency range of 6–18 GHz, provides several functions including 8-bit true time delay (TTD), 7-bit attenuation, wideband amplification, and digital serial-to-parallel conversion for 15-bit control. The TTD and attenuation coverage are 255 ps with a step of 1 ps and 31.75 dB with a step of 0.25 dB, respectively. The TTD employing an artificial transmission line configuration demonstrates a root-mean-square delay error of less than 1.7 ps in the operating frequency range. The chip has a compact size of $4\,\,\hbox {mm} \times 5$ mm and exhibits a typical gain of 12 dB and an output P1dB of 16.5 dBm over a frequency range of 6–18 GHz.


title: Cyber physical surveillance system for Internet of Vehicles

abstract: Internet of Vehicle (IoV) is an essential part of the Intelligent Transportation system (ITS) which is growing exponentially in the automotive industry domain. The term IoV is used in this paper for Internet of Vehicles. IoV is conceptualized for sharing traffic, safety and several other vehicle-related information between vehicles and end user. In recent years, the number of connected vehicles has increased allover the world. Having information sharing and connectivity as its advantage, IoV also faces the challenging task in the cybersecurity-related matters. The future consists of crowded places in an interconnected world through wearable's, sensors, smart phones etc. We are converging towards IoV technology and interactions with crowded space of connected peoples. However, this convergence demands high-security mechanism from the connected crowd as-well-as other connected vehicles to safeguard of proposed IoV system. In this paper, we coin the term of smart people crowd (SPC) and the smart vehicular crowd (SVC) for the Internet of Vehicles (IoV). These specific crowds of SPC and SVC are the potential cyber attackers of the smart IoV. People connected to the internet in the crowded place are known as a smart crowd. They have interfacing devices with sensors and the environment. A smart crowd would also consist of the random number of smart vehicles. With the future converging in to the smart connected framework for crowds, vehicles and connected vehicles, we present a novel cyber-physical surveillance system (CPSS) framework to tackle the security threats in the crowded environment for the smart automotive industry and provide the cyber security mechanism in the crowded places. We also describe an overview of use cases and their security challenges on the Internet of Vehicles.


Forensics of Thermal Side-Channel in Additive Manufacturing Systems


title: Efficient transaction processing in SAP HANA database: the end of a column store myth

abstract: The SAP HANA database is the core of SAP's new data management platform. The overall goal of the SAP HANA database is to provide a generic but powerful system for different query scenarios, both transactional and analytical, on the same data representation within a highly scalable execution environment. Within this paper, we highlight the main features that differentiate the SAP HANA database from classical relational database engines. Therefore, we outline the general architecture and design criteria of the SAP HANA in a first step. In a second step, we challenge the common belief that column store data structures are only superior in analytical workloads and not well suited for transactional workloads. We outline the concept of record life cycle management to use different storage formats for the different stages of a record. We not only discuss the general concept but also dive into some of the details of how to efficiently propagate records through their life cycle and moving database entries from write-optimized to read-optimized storage formats. In summary, the paper aims at illustrating how the SAP HANA database is able to efficiently work in analytical as well as transactional workload environments.


title: Robust material discrimination by a soft anthropomorphic finger with tactile and thermal sense

abstract: Thermal sense plays an important role in object recognition. The idea to identify materials by heat flow from the skin to the touched materials have been introduced to artificial thermal sensors and proved effective. Moreover, the addition of tactile sensors is found to improve the performance of material identification by obtaining further properties of objects such as roughness and stiffness. However, the application of these sensors to autonomous recognition by a robot is not a well investigated topic. Contact with the objects will vary in such cases, but no previous sensor has shown the ability of material recognition robust to change in contact conditions with the required size and softness. The limitation is partly due to the design of current thermal sensors where the skin and the sensing devices are separated. This paper introduces a soft anthropomorphic finger with both thermal and tactile sensing elements embedded in soft polyurethane material. Experimental results show the ability to discriminate materials robust to variety in contact condition by virtue of the deformable tissue and compensation by the tactile sensors. Recognition robust to change of temperature is also realized by an additional thermal sensor.


title: Bogie failure mode analysis for railway freight car based on FMECA

abstract: The method of failure modes, effect and criticality analysis (FMECA) is one of absolutely necessarily elements in reliability systems engineering. The effective application of FMECA can improve the freight car quality and reliability. The basic principle of FMECA is introduced. The method to apply FMECA into bogie failure of railway freight car is proposed. The quantitative and qualitative evaluation for failure effects are realized by means of computer aided technology. We can obtain every failure circumstances and their criticality degree, find out potential failure bottleneck problems and put forward corresponding modified measures. The proposed method provides a valuable reference for the modification of structural design, maintenance decision and life management of the bogie.


title: Rules of action in Dutch Work Organizations

abstract: This paper explores the historical roots of rules of action in Dutch work organizations by linking their codes to the long-term development of sector specialization, collective economic and institutional actors, corporate governance and work coordination. The focus is on behaviour and coordination at profit-making firms as the outcome of the historical interplay between industries and institutions. The rules of action in contemporary Dutch work organizations are epitomized by the catchwords consultation, consensus and compromise. The persistent practice of these three Cs, such is the central argument of the paper, is essential to the perceived success of the 'Polder Model'


title: Bistability, Causality, and Complexity in Cortical Networks: An In Vitro Perturbational Study

abstract: Measuring the spatiotemporal complexity of cortical responses to direct perturbations provides a reliable index of the brain's capacity for consciousness in humans under both physiological and pathological conditions. Upon loss of consciousness, the complex pattern of causal interactions observed during wakefulness collapses into a stereotypical slow wave, suggesting that cortical bistability may play a role. Bistability is mainly expressed in the form of slow oscillations, a default pattern of activity that emerges from cortical networks in conditions of functional or anatomical disconnection. Here, we employ an in vitro model to understand the relationship between bistability and complexity in cortical circuits. We adapted the perturbational complexity index applied in humans to electrically stimulated cortical slices under different neuromodulatory conditions. At this microscale level, we demonstrate that perturbational complexity can be effectively modulated by pharmacological reduction of bistability and, albeit to a lesser extent, by enhancement of excitability, providing mechanistic insights into the macroscale measurements performed in humans.


title: Video-Based Person Re-Identification With Accumulative Motion Context

abstract: Video-based person re-identification plays a central role in realistic security and video surveillance. In this paper, we propose a novel accumulative motion context (AMOC) network for addressing this important problem, which effectively exploits the long-range motion context for robustly identifying the same person under challenging conditions. Given a video sequence of the same or different persons, the proposed AMOC network jointly learns appearance representation and motion context from a collection of adjacent frames using a two-stream convolutional architecture. Then, AMOC accumulates clues from motion context by recurrent aggregation, allowing effective information flow among adjacent frames and capturing dynamic gist of the persons. The architecture of AMOC is end-to-end trainable, and thus, motion context can be adapted to complement appearance clues under unfavorable conditions ( e.g. , occlusions). Extensive experiments are conduced on three public benchmark data sets, i.e. , the iLIDS-VID, PRID-2011, and MARS data sets, to investigate the performance of AMOC. The experimental results demonstrate that the proposed AMOC network outperforms state-of-the-arts for video-based re-identification significantly and confirm the advantage of exploiting long-range motion context for video-based person re-identification, validating our motivation evidently.


title: Deep Learning for Logo Recognition

abstract: In this paper we propose a method for logo recognition using deep learning. Our recognition pipeline is composed of a logo region proposal followed by a Convolutional Neural Network (CNN) specifically trained for logo classification, even if they are not precisely localized. Experiments are carried out on the FlickrLogos-32 database, and we evaluate the effect on recognition performance of synthetic versus real data augmentation, and image pre-processing. Moreover, we systematically investigate the benefits of different training choices such as class-balancing, sample-weighting and explicit modeling the background class (i.e. no-logo regions). Experimental results confirm the feasibility of the proposed method, that outperforms the methods in the state of the art.


title: Multi-functional 3D printed and embedded sensors for satellite qualification structures

abstract: Three dimensional (3D) printing has recently gained attention in a variety of industries ranging from aerospace to biomedical. However, in order to create truly functional 3D printed structures, electronic functionality must be integrated into building sequence. This work explores the integration of both printed sensors (copper capacitive touch sensors) and embedded COTS sensors (surface mount accelerometers) in order to fabricate a space-flight qualification test coupon in the shape of a 1U CubeSat (10 × 10 × 10 cm) as required for stress testing. The 3D printed electronics were fabricated by an enhanced Multi3D 3D Printing system, allowing the direct integration of 3D printed dielectric structures with electronics components fabricated together using a single non-assembly build sequence. Both sensors and structures successfully demonstrated electronic functionality after full encapsulation, and show promise for integration in space based cube satellites.


title: Region segmentation in histopathological breast cancer images using deep convolutional neural network

abstract: Computer aided diagnosis of breast cancers often relies on automatic image analysis of histopathology images. The automatic region segmentation in breast cancer is challenging due to: i) large regional variations, and ii) high computational costs of pixel-wise segmentation. Deep convolutional neural network (CNN) is proven to be an effective method for image recognition and classification. However, it is often computationally expensive. In this paper, we propose to apply a fast scanning deep convolutional neural network (fCNN) to pixel-wise region segmentation. The fCNN removes the redundant computations in the original CNN without sacrificing its performance. In our experiment it takes only 2.3 seconds to segment an image with size 1000 × 1000. The comparison experiments show that the proposed system outperforms both the LBP feature-based and texton-based pixel-wise methods.


title: A cost-sensitive adaptation engine for server consolidation of multitier applications

abstract: Virtualization-based server consolidation requires runtime resource reconfiguration to ensure adequate application isolation and performance, especially for multitier services that have dynamic, rapidly changing workloads and responsiveness requirements. While virtualization makes reconfiguration easy, indiscriminate use of adaptations such as VM replication, VM migration, and capacity controls has performance implications. This paper demonstrates that ignoring these costs can have significant impacts on the ability to satisfy response-time-based SLAs, and proposes a solution in the form of a cost-sensitive adaptation engine that weighs the potential benefits of runtime reconfiguration decisions against their costs. Extensive experimental results based on live workload traces show that the technique is able to maximize SLA fulfillment under typical time-of-day workload variations as well as flash crowds, and that it exhibits significantly improved transient behavior compared to approaches that do not account for adaptation costs.


title: DOI: 10.1007/s11665-011-9858-7 1059-9495/$19.00 Applicability of Shape Memory Alloy Wire for an Active, Soft Orthotic

abstract: Current treatments for gait pathologies associated with neuromuscular disorders may employ a passive, rigid brace. While these provide certain benefits, they can also cause muscle atrophy. In this study, we examined NiTi shape memory alloy (SMA) wires that were annealed into springs to develop an active, soft orthotic (ASO) for the knee. Actively controlled SMA springs may provide variable assistances depending on factors such as when, during the gait cycle, the springs are activated; ongoing muscle activity level; and needs of the wearer. Unlike a passive brace, an active orthotic may provide individualized control, assisting the muscles so that they may be used more appropriately, and possibly leading to a re-education of the neuro-motor system and eventual independence from the orthotic system. A prototype was tested on a suspended, robotic leg to simulate the swing phase of a typical gait. The total deflection generated by the orthotic depended on the knee angle and the total number of actuators triggered, with a max deflection of 35°. While SMA wires have a high energy density, they require a significant amount of power. Furthermore, the loaded SMA spring response times were much longer than the natural frequency of an average gait for the power conditions tested. While the SMA wires are not appropriate for correction of gait pathologies as currently implemented, the ability to have a soft, actuated material could be appropriate for slower timescale applications.


Techniques for Smart Traffic Control: An In-depth Review


title: SparkR: Scaling R Programs with Spark

abstract: R is a popular statistical programming language with a number of extensions that support data processing and machine learning tasks. However, interactive data analysis in R is usually limited as the R runtime is single threaded and can only process data sets that fit in a single machine's memory. We present SparkR, an R package that provides a frontend to Apache Spark and uses Spark's distributed computation engine to enable large scale data analysis from the R shell. We describe the main design goals of SparkR, discuss how the high-level DataFrame API enables scalable computation and present some of the key details of our implementation.


Thickness and Competition in Ride-Sharing Markets


title: An Overview of the EPIC Architecture for Cognition and Performance with Application to Human-Computer Interaction

abstract: EPIC (Executive Process-Interactive Control) is a cognitive architecture especially suited for modeling human multimodal and multiple-task performance. The EPIC architecture includes peripheral sensory-motor processors surrounding a production-rule cognitive processor and is being used to construct precise computational models for a variety of human-computer interaction situations. We briefly describe some of these models to demonstrate how EPIC clarifies basic properties of human performance and provides usefully precise accounts of performance speed.


title: A Robust and Efficient Approach to License Plate Detection

abstract: This paper presents a robust and efficient method for license plate detection with the purpose of accurately localizing vehicle license plates from complex scenes in real time. A simple yet effective image downscaling method is first proposed to substantially accelerate license plate localization without sacrificing detection performance compared with that achieved using the original image. Furthermore, a novel line density filter approach is proposed to extract candidate regions, thereby significantly reducing the area to be analyzed for license plate localization. Moreover, a cascaded license plate classifier based on linear support vector machines using color saliency features is introduced to identify the true license plate from among the candidate regions. For performance evaluation, a data set consisting of 3977 images captured from diverse scenes under different conditions is also presented. Extensive experiments on the widely used Caltech license plate data set and our newly introduced data set demonstrate that the proposed approach substantially outperforms state-of-the-art methods in terms of both detection accuracy and run-time efficiency, increasing the detection ratio from 91.09% to 96.62% while decreasing the run time from 672 to 42 ms for processing an image with a resolution of 1082×728 . The executable code and our collected data set are publicly available.


title: Recurrent neural network based language model

abstract: A new recurrent neural network based language model (RNN LM) with applications to speech recognition is presented. Results indicate that it is possible to obtain around 50% reduction of perplexity by using mixture of several RNN LMs, compared to a state of the art backoff language model. Speech recognition experiments show around 18% reduction of word error rate on the Wall Street Journal task when comparing models trained on the same amount of data, and around 5% on the much harder NIST RT05 task, even when the backoff model is trained on much more data than the RNN LM. We provide ample empirical evidence to suggest that connectionist language models are superior to standard n-gram techniques, except their high computational (training) complexity. Index Terms: language modeling, recurrent neural networks, speech recognition


title: Visual Robot Task Planning

abstract: Prospection is key to solving challenging problems in new environments, but it has not been deeply explored as applied to task planning for perception-driven robotics. We propose visual robot task planning, where we take in an input image and must generate a sequence of high-level actions and associated observations that achieve some task. In this paper, we describe a neural network architecture and associated planning algorithm that (1) learns a representation of the world that can generate prospective futures, (2) uses this generative model to simulate the result of sequences of high-level actions in a variety of environments, and (3) evaluates these actions via a variant of Monte Carlo Tree Search to find a viable solution to a particular problem. Our approach allows us to visualize intermediate motion goals and learn to plan complex activity from visual information, and used this to generate and visualize task plans on held-out examples of a block-stacking simulation.


title: External Prior Guided Internal Prior Learning for Real Noisy Image Denoising

abstract: Most of existing image denoising methods learn image priors from either external data or the noisy image itself to remove noise. However, priors learned from external data may not be adaptive to the image to be denoised, while priors learned from the given noisy image may not be accurate due to the interference of corrupted noise. Meanwhile, the noise in real-world noisy images is very complex, which is hard to be described by simple distributions such as Gaussian distribution, making real noisy image denoising a very challenging problem. We propose to exploit the information in both external data and the given noisy image, and develop an external prior guided internal prior learning method for real noisy image denoising. We first learn external priors from an independent set of clean natural images. With the aid of learned external priors, we then learn internal priors from the given noisy image to refine the prior model. The external and internal priors are formulated as a set of orthogonal dictionaries to efficiently reconstruct the desired image. Extensive experiments are performed on several real noisy image datasets. The proposed method demonstrates highly competitive denoising performance, outperforming state-of-the-art denoising methods including those designed for real noisy images.


HARDWARE-FRIENDLY CONVOLUTIONAL NEURAL NETWORK WITH EVEN-NUMBER FILTER SIZE


title: A Novel Electricity Transaction Mode of Microgrids Based on Blockchain and Continuous Double Auction

abstract: The installed capacity of distributed generation (DG) based on renewable energy sources has increased continuously in power systems, and its market-oriented transaction is imperative. However, traditional transaction management based on centralized organizations has many disadvantages, such as high operation cost, low transparency, and potential risk of transaction data modification. Therefore, a decentralized electricity transaction mode for microgrids is proposed in this study based on blockchain and continuous double auction (CDA) mechanism. A buyer and seller initially complete the transaction matching in the CDA market. In view of the frequent price fluctuation in the CDA market, an adaptive aggressiveness strategy is used to adjust the quotation timely according to market changes. DG and consumer exchange digital certificate of power and expenditure on the blockchain system and the interests of consumers are then guaranteed by multi-signature when DG cannot generate power due to failure or other reasons. The digital certification of electricity assets is replaced by the sequence number with specific tags in the transaction script, and the size of digital certification can be adjusted according to transaction energy quantity. Finally, the feasibility of market mechanism through specific microgrid case and settlement process is also provided.


title: The meaning and use of the area under a receiver operating characteristic (ROC) curve.

abstract: A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly chosen non-diseased subject. Moreover, this probability of a correct ranking is the same quantity that is estimated by the already well-studied nonparametric Wilcoxon statistic. These two relationships are exploited to (a) provide rapid closed-form expressions for the approximate magnitude of the sampling variability, i.e., standard error that one uses to accompany the area under a smoothed ROC curve, (b) guide in determining the size of the sample required to provide a sufficiently reliable estimate of this area, and (c) determine how large sample sizes should be to ensure that one can statistically detect difference...


title: Modeling, Simulation of Multi Standard Wireless Receivers in MATLAB/SIMULINK

abstract: We present modeling of different block sets of direct conversion receiver in SIMULINK tool-kit environment for wireless local area network (WLAN) applications. Main block set of RF receiver circuit viz. low noise amplifiers (LNA), mixers, oscillators, filters and programmable gain amplifiers are implemented. While modeling different block sets, we have incorporated both ideal and non-ideal characteristics. Main incorporated non-idealities are due to thermal noise, characterized by the noise figure and the signal-to-noise ratio (SNR) non-linearity due to second order input-intercept point (IIP2) and third-order input intercept points (IIP3). Complete RF receiver for 4G standard is implemented. Our results show that SNR of RF receiver is improved compared to similar other implementations. Gain and noise parameters have been discussed at different frequencies to show usefulness of our work.


title: A Multisecret Value Access Control Framework for Airliner in Multinational Air Traffic Management

abstract: When been threatened by hijacking or suicide-by-pilots, the airliner may either crash itself or be shot down due to the potential of the suicide attack. There exist some solutions that allow air traffic controllers or federal agents to take over pilots’ authority in the emergency. Though rarely, an air traffic controller may abuse this privilege to mishandle airliners that leads to catastrophic events. In this paper, to mitigate such risks, we propose a multisecret value access control framework based on new designed and existing cryptographic techniques such as xor-based secret sharing schemes (SSSs). It not only satisfies the efficiency requirement but also assures that each nation owns a unique secret value. We further develop and implement the xor-based SSS on Linux system. Both experimental results and performance evaluation demonstrate that our solution is not only efficient and bust also secure by design for the multinational air traffic management.


title: TensorFlow: Biology’s Gateway to Deep Learning?

abstract: TensorFlow is Google's recently released open-source software for deep learning. What are its applications for computational biology?


title: The Application of Spline Functions and Bezier Curves to AGV Path Planning

abstract: The paper describes a path planning method for an automated guided vehicle (AGV). The method consists of two steps, the calculation of a path by using Bezier curves and the calculation of a path trajectory by using a Ho and Cook spline-interpolation algorithm. The generation of parallel path curves for left-hand and right-hand AGV wheels is elaborated. The solution for the generation of sharp curves is derived. The proposed path planning method has been tested on tricycle-type AGVs with a front stabilization wheel and two rear powered wheels.


title: SamzaSQL: Scalable Fast Data Management with Streaming SQL

abstract: As the data-driven economy evolves, enterprises have come to realize a competitive advantage in being able to act on high volume, high velocity streams of data. Technologies such as distributed message queues and streaming processing platforms that can scale to thousands of data stream partitions on commodity hardware are a response. However, the programming API provided by these systems is often low-level, requiring substantial custom code that adds to the programmer learning curve and maintenance overhead. Additionally, these systems often lack SQL querying capabilities that have proven popular on Big Data systems like Hive, Impala or Presto. We define a minimal set of extensions to standard SQL for data stream querying and manipulation. These extensions are prototyped in SamzaSQL, a new tool for streaming SQL that compiles streaming SQL into physical plans that are executed on Samza, an open-source distributed stream processing framework. We compare the performance of streaming SQL queries against native Samza applications and discuss usability improvements. SamzaSQL is a part of the open source Apache Samza project and will be available for general use.


OPTIMAL PUMP OPERATION OF WATER DISTRIBUTION SYSTEMS USING GENETIC ALGORITHMS ABSTRACT


title: What drives memory-driven attentional capture? The effects of memory type, display type, and search type.

abstract: An important question is whether visual attention (the ability to select relevant visual information) and visual working memory (the ability to retain relevant visual information) share the same content representations. Some past research has indicated that they do: Singleton distractors interfered more strongly with a visual search task when they were identical or related to the object held in memory. However, other research has failed to find such effects despite using very similar procedures. The present study, using the same combined working memory and attentional capture paradigm, demonstrates which factors do (varied mapping, low stimulus energy) and which factors do not (exact type of visual memory method used, difficult nature of search, heterogeneity of displays, and instruction) contribute to this discrepancy.


title: Image Manifolds

abstract: A collection of related N by M images, such as a set of faces, may be modeled by a manifold embedded in an NM- dimensional Euclidean space called an image manifold. With the modeling of image spaces as manifolds, geometrical properties of image manifolds can be studied either theoretically or experimentally. A practical result of the investigation of image manifolds provides an insight into image source entropy (i.e., image compressibility), a subject about which, oddly, little is known. The investigation begins with the most basic properties of a manifold, its dimension and its curvature. The study of dimensionality reveals a high embedding ratio, which gives promise of very high compression rates. The curvature of image manifolds is shown to be large indicating that application of traditional linear transform techniques may not fulfill this promise.


title: A multilayer perceptron-based medical decision support system for heart disease diagnosis

abstract: The medical diagnosis by nature is a complex and fuzzy cognitive process, and soft computing methods, such as neural networks, have shown great potential to be applied in the development of medical decision support systems (MDSS). In this paper, a multiplayer perceptron-based decision support system is developed to support the diagnosis of heart diseases. The input layer of the system includes 40 input variables, categorized into four groups and then encoded using the proposed coding schemes. The number of nodes in the hidden layer is determined through a cascade learning process. Each of the 5 nodes in the output layer corresponds to one heart disease of interest. In the system, the missing data of a patient are handled using the substituting mean method. Furthermore, an improved back propagation algorithm is used to train the system. A total of 352 medical records collected from the patients suffering from five heart diseases have been used to train and test the system. In particular, three assessment methods, cross validation, holdout and bootstrapping, are applied to assess the generalization of the system. The results show that the proposed MLP-based decision support system can achieve very high diagnosis accuracy (>90%) and comparably small intervals (<5%), proving its usefulness in support of clinic decision process of heart diseases.


title: Emotion Analysis of Songs Based on Lyrical and Audio Features

abstract: In this paper, a method is proposed to detect the emotion of a song based on its lyrical and audio features. Lyrical features are generated by segmentation of lyrics during the process of data extraction. ANEW and WordNet knowledge is then incorporated to compute Valence and Arousal values. In addition to this, linguistic association rules are applied to ensure that the issue of ambiguity is properly addressed. Audio features are used to supplement the lyrical ones and include attributes like energy, tempo, and danceability. These features are extracted from The Echo Nest, a widely used music intelligence platform. Construction of training and test sets is done on the basis of social tags extracted from the last.fm website. The classification is done by applying feature weighting and stepwise threshold reduction on the k-Nearest Neighbors algorithm to provide fuzziness in the classification.


title: Re-evaluation of the 2,2-Diphenyl-1-picrylhydrazyl Free Radical (DPPH) Assay for Antioxidant Activity

abstract: Kinetics and stoichiometry of reactions between the 2,2-diphenyl-1-picrylhydrazyl (DPPH) stable radical and 25 antioxidant compounds with different structure, molecular weight, number of −OH groups, and redox potential were investigated by recording the loss of DPPH• absorbance at 515 nm continuously for 10 min. A series of antioxidant concentrations was tested to determine linear response ranges and reaction saturation points. The primary feature distinguishing antioxidant activity—rate of initial reaction (<30 s)—was controlled by whether the dominant antioxidant mechanism was electron (very fast) or hydrogen atom (slow) transfer and by impairment of steric accessibility to the DPPH radical site by bulky ring adducts and multiple phenolic rings. Results raise serious questions regarding application of the DPPH assay for ranking antioxidants and natural extracts and suggest possible redirection of this assay to distinguish active reaction mechanisms by comparing reactions rates and patterns in different ...


The Quadratic Assignment Problem: A Survey and Recent Developments


title: doi: 10.1111/j.1368-423X.2007.00218.x The Tobit model with a non-zero threshold

abstract: The standard Tobit maximum likelihood estimator under zero censoring threshold produces inconsistent parameter estimates, when the constant censoring threshold γ is non-zero and unknown. Unfortunately, the recording of a zero rather than the actual censoring threshold value is typical of economic data. Non-trivial minimum purchase prices for most goods, fixed cost for doing business or trading, social customs such as those involving charitable donations, and informal administrative recording practices represent common examples of non-zero constant censoring threshold where the constant threshold is not readily available to the econometrician. Monte Carlo results show that this bias can be extremely large in practice. A new estimator is proposed to estimate the unknown censoring threshold. It is shown that the estimator is superconsistent and follows an exponential distribution in large samples. Due to the superconsistency, the asymptotic distribution of the maximum likelihood estimator of other parameters is not affected by the estimation uncertainty of the censoring threshold. Copyright Royal Economic Society 2007


title: In Vitro and In Vivo Activity of First Generation Cephalosporins Against Leptospira

abstract: Third generation cephalosporins are commonly used in the treatment of leptospirosis. The efficacy of first generation cephalosporins has been less well-studied. Susceptibility testing of 13 Leptospira strains (11 serovars) to cefazolin and cephalexin was conducted using broth microdilution. Median minimal inhibitory concentration (MIC) for cefazolin and cephalexin ranged from < 0.016 to 2 μg/mL (MIC 90 = 0.5 μg/mL) and from 1 to 8 μg/mL (MIC 90 = 8 μg/mL), respectively. Efficacy of cefazolin and cephalexin in an acute lethal hamster model of leptospirosis was studied. Survival rates for cefazolin were 80%, 100%, and 100%, and survival rates for cephalexin were 50%, 80%, and 100% (treated with 5, 25, and 50 mg/kg per day for 5 days, respectively). Each treatment group showed improved survival compared with no treatment ( P < 0.01), and none of the therapies, regardless of dose, was statistically significantly different than doxycycline. These results support a potential role for first generation cephalosporins as alternative therapies for leptospirosis.


title: Sieve: Actionable Insights from Monitored Metrics in Microservices

abstract: Major cloud computing operators provide powerful monitoring tools to understand the current (and prior) state of the distributed systems deployed in their infrastructure. While such tools provide a detailed monitoring mechanism at scale, they also pose a significant challenge for the application developers/operators to transform the huge space of monitored metrics into useful insights. These insights are essential to build effective management tools for improving the efficiency, resiliency, and dependability of distributed systems. ::: This paper reports on our experience with building and deploying Sieve - a platform to derive actionable insights from monitored metrics in distributed systems. Sieve builds on two core components: a metrics reduction framework, and a metrics dependency extractor. More specifically, Sieve first reduces the dimensionality of metrics by automatically filtering out unimportant metrics by observing their signal over time. Afterwards, Sieve infers metrics dependencies between distributed components of the system using a predictive-causality model by testing for Granger Causality. We implemented Sieve as a generic platform and deployed it for two microservices-based distributed systems: OpenStack and ShareLatex. Our experience shows that (1) Sieve can reduce the number of metrics by at least an order of magnitude (10 - 100$\times$), while preserving the statistical equivalence to the total number of monitored metrics; (2) Sieve can dramatically improve existing monitoring infrastructures by reducing the associated overheads over the entire system stack (CPU - 80%, storage - 90%, and network - 50%); (3) Lastly, Sieve can be effective to support a wide-range of workflows in distributed systems - we showcase two such workflows: orchestration of autoscaling, and Root Cause Analysis (RCA).