Neural network controller based on PID using an extended Kalman filter algorithm for multi-variable non-linear control system

The Proportional Integral Derivative (PID) controller is widely used in the industrial control application, which is only suitable for the single input/single output (SISO) with known-parameters of the linear system. However, many researchers have been proposed the neural network controller based on PID (NNPID) to apply for both of the single and multi-variable control system but the NNPID controller that uses the conventional gradient descent-learning algorithm has many disadvantages such as a low speed of the convergent stability, difficult to set initial values, especially, restriction of the degree of system complexity. Therefore, this paper presents an improvement of recurrent neural network controller based on PID, including a controller structure improvement and a modified extended Kalman filter (EKF) learning algorithm for weight update rule, called ENNPID controller. We apply the proposed controller to the dynamic system including inverted pendulum, and DC motor system by the MATLAB simulation. From our experimental results, it shows that the performance of the proposed controller is higher than the other PID-like controllers in terms of fast convergence and fault tolerance that are highly required.


Can recursive bisection alone produce routable placements?

This work focuses on congestion-driven placement of standard cells into rows in the fixed-die context. We summarize the state-of-the-art after two decades of research in recursive bisection placement and implement a new placer, called Capo, to empirically study the achievable limits of the approach. From among recently proposed improvements to recursive bisection, Capo incorporates a leading-edge multilevel min-cut partitioner [7], techniques for partitioning with small tolerance [8], optimal min-cut partitioners and end-case min-wirelength placers [5], previously unpublished partitioning tolerance computations, and block splitting heuristics. On the other hand, our “good enough” implementation does not use “overlapping” [17], multi-way partitioners [17, 20], analytical placement, or congestion estimation [24, 35]. In order to run on recent industrial placement instances, Capo must take into account fixed macros, power stripes and rows with different allowed cell orientations. Capo reads industry-standard LEF/DEF, as well as formats of the GSRC bookshelf for VLSI CAD algorithms [6], to enable comparisons on available placement instances in the fixed-die regime.
Capo clearly demonstrates that despite a potential mismatch of objectives, improved mincut bisection can still lead to improved placement wirelength and congestion. Our experiments on recent industrial benchmarks fail to give a clear answer to the question in the title of this paper. However, they validate a series of improvements to recursive bisection and point out a need for transparent congestion management techniques that do not worsen the wirelength of already routable placements. Our experimental flow, which validates fixed-die placement results by violation-free detailed auto-routability, provides a new norm for comparison of VLSI placement implementations.


Factors Affecting Repurchase Intention of Smartphone : A Case Study of Huawei Smartphone in Yangon , Myanmar

This study investigates the relationship between functional value, price consciousness, word of mouth, brand image, attitude towards product and repurchases intention of a smartphone brand. To do so, a survey was conducted by distributing 420 questionnaires in 7 different shopping malls in Yangon, Myanmar. The data collected was analyzed using SPSS and hypotheses were examined by employing the Pearson correlation and multiple linear regression. The results show that there is a positive and significant relationship among functional value, word of mouth, price consciousness towards attitude towards product and brand image, attitude towards product, word of mouth influences repurchase intention. Based on these results, it seems that the smart phone company needs to develop marketing strategy to increase repurchase intention.


Finding the face in the crowd: an anger superiority effect.

Facial gestures have been given an increasingly critical role in models of emotion. The biological significance of interindividual transmission of emotional signals is a pivotal assumption for placing the face in a central position in these models. This assumption invited a logical corollary, examined in this article: Face-processing should be highly efficient. Three experiments documented an asymmetry in the processing of emotionally discrepant faces embedded in crowds. The results suggested that threatening faces pop out of crowds, perhaps as a result of a preattentive, parallel search for signals of direct threat.


Combination of Texture and Geometric Features for Age Estimation in Face Images

Automatic age estimation from facial images has recently received an increasing interest due to a variety of applications, such as surveillance, human-computer interaction, forensics, and recommendation systems. Despite such advances, age estimation remains an open problem due to several challenges associated with the aging process. In this work, we develop and analyze an automatic age estimation method from face images based on a combination of textural and geometric features. Experiments are conducted on the Adience dataset (Adience Benchmark, 2017; Eidinger et al., 2014), a large known benchmark used to evaluate both age and gender classification approaches.


BIGT control optimisation for overall loss reduction

In this paper we present the latest results of utilizing MOS-control (MOSctrl) to optimize the performance of the Bi-mode Insulated Gate Transistor (BIGT) chip. The adaption of the BIGT technology enables higher output power per footprint. However, to enable the full performance benefit of the BIGT, the optimisation of the known standard MOS gate control is necessary. This optimisation is being demonstrated over the whole current and temperature range for the BIGT diode turn-off and BIGT turn-on operation. It is shown that the optimum control can offer a performance increase up to 20% for high voltage devices.


Evaluation of information technology investment: a data envelopment analysis approach

The increasing use of information technology (IT) has resulted in a need for evaluating the productivity impacts of IT. The contemporary IT evaluation approach has focused on return on investment and return on management. IT investment has impacts on different stages of business operations. For example, in the banking industry, IT plays a key role in effectively generating (i) funds from the customer in the forms of deposits and then (ii) profits by using deposits as investment funds. Existing approaches based upon data envelopment analysis (DEA) only measure the IT efficiency or impact on one specific stage when a multi-stage business process is present. A detailed model is needed to characterize the impact of IT on each stage of the business operation. The current paper develops a DEA non-linear programming model to evaluate the impact of IT on multiple stages along with information on how to distribute the IT-related resources so that the efficiency is maximized. It is shown that this non-linear program can be treated as a parametric linear program. It is also shown that if there is only one intermediate measure, then the non-linear DEA model becomes a linear program. Our approach is illustrated with an example taken from previous studies. 2004 Elsevier Ltd. All rights reserved.


Comparing memory systems for chip multiprocessors

There are two basic models for the on-chip memory in CMP systems:hardware-managed coherent caches and software-managed streaming memory. This paper performs a direct comparison of the two modelsunder the same set of assumptions about technology, area, and computational capabilities. The goal is to quantify how and when they differ in terms of performance, energy consumption, bandwidth requirements, and latency tolerance for general-purpose CMPs. We demonstrate that for data-parallel applications, the cache-based and streaming models perform and scale equally well. For certain applications with little data reuse, streaming scales better due to better bandwidth use and macroscopic software prefetching. However, the introduction of techniques such as hardware prefetching and non-allocating stores to the cache-based model eliminates the streaming advantage. Overall, our results indicate that there is not sufficient advantage in building streaming memory systems where all on-chip memory structures are explicitly managed. On the other hand, we show that streaming at the programming model level is particularly beneficial, even with the cache-based model, as it enhances locality and creates opportunities for bandwidth optimizations. Moreover, we observe that stream programming is actually easier with the cache-based model because the hardware guarantees correct, best-effort execution even when the programmer cannot fully regularize an application's code.


Positive and negative emotional verbal stimuli elicit activity in the left amygdala.

The human amygdala's involvement in negative emotion is well established, but relatively little is known regarding its role in positive emotion. Here we examined the neural response to emotionally positive, negative, and neutral words using fMRI. Relative to neutral words, positive and negative emotional words elicited greater activity in the left amygdala. Positive but not negative words elicited activity in dorsal and ventral striatal regions which have been linked in previous neuroimaging studies to reward and positive affect, including caudate, putamen, globus pallidus, and accumbens. These findings provide the first direct evidence that the amygdala is involved in emotional reactions elicited by both positive and negative emotional words, and further indicate that positive words additionally activate brain regions related to reward.


Broadband Radial Waveguide Spatial Combiner

A broadband eight-way spatial combiner using coaxial probes and radial waveguides has been proposed and designed. The simple electromagnetic modeling for the radial waveguide power divider/combiner has been developed using equivalent-circuit method. The measured 10-dB return loss and 1-dB insertion loss bandwidth of this waveguide spatial combiner are all demonstrated to be about 8 GHz.


Privacy Aspects of Recommender Systems

The popularity of online recommender systems has soared; they are deployed in numerous websites and gather tremendous amounts of user data that are necessary for the recommendation purposes. This data, however, may pose a severe threat to user privacy, if accessed by untrusted parties or used inappropriately. Hence, it is of paramount importance for recommender system designers and service providers to find a sweet spot, which allows them to generate accurate recommendations and guarantee the privacy of their users. In this chapter we overview the state of the art in privacy enhanced recommendations. We analyze the risks to user privacy imposed by recommender systems, survey the existing solutions, and discuss the privacy implications for the users of the recommenders. We conclude that a considerable effort is still required to develop practical recommendation solutions that provide adequate privacy guarantees, while at the same time facilitating the delivery of high-quality recommendations to their users. Arik Friedman NICTA, Australia, e-mail: arik.friedman@nicta.com.au Bart Knijnenburg UC Irvine, USA, e-mail: bart.k@uci.edu Kris Vanhecke iMinds Ghent University, Belgium, e-mail: kris.vanhecke@intec.ugent.be Luc Martens iMinds Ghent University, Belgium, e-mail: luc.martens@intec.ugent.be Shlomo Berkovsky CSIRO, Australia, e-mail: shlomo.berkovsky@csiro.au 1 Accepted to be included in the 2nd edition of the Recommender Systems handbook. Please do not share publicly, and consult the authors before citing this chapter.


ArgumenText: Searching for Arguments in Heterogeneous Sources

Argument mining is a core technology for enabling argument search in large corpora. However, most current approaches fall short when applied to heterogeneous texts. In this paper, we present an argument retrieval system capable of retrieving sentential arguments for any given controversial topic. By analyzing the highest-ranked results extracted from Web sources, we found that our system covers 89% of arguments found in expert-curated lists of arguments from an online debate portal, and also identifies additional valid arguments.


A qualitative study of Ragnarök Online private servers: in-game sociological issues

In the last decade, online games have garnered much attention as more and more players gather on game servers. In parallel, communities of illegal private server players and administrators have spread and might host hundreds of thousands of players. To study the Korean online game Ragnarök Online, we conducted interviews to collect qualitative data on two private servers as well as on the official French server for the game. This paper discusses some of the reasons why Ragnarök Online private servers might attract players and how examining private servers' characteristics could help improve official game servers.


A 28GHz SiGe BiCMOS phase invariant VGA

The paper describes a technique to design a phase invariant variable gain amplifier (VGA). Variable gain is achieved by varying the bias current in a BJT, while the phase variation is minimized by designing a local feedback network such that the applied base to emitter voltage has a bias-dependent phase variation which compensates the inherent phase variation of the transconductance. Two differential 28GHz VGA variants based on these principles achieve <;5° phase variation over 8dB and 18dB of gain control range, respectively, with phase invariance maintained over PVT. Implemented in GF 8HP BiCMOS technology, the VGAs achieve 18dB nominal gain, 4GHz bandwidth, and IP1dB > -13dBm while consuming 35mW.


Bacteriocins: developing innate immunity for food.

Bacteriocins are bacterially produced antimicrobial peptides with narrow or broad host ranges. Many bacteriocins are produced by food-grade lactic acid bacteria, a phenomenon which offers food scientists the possibility of directing or preventing the development of specific bacterial species in food. This can be particularly useful in preservation or food safety applications, but also has implications for the development of desirable flora in fermented food. In this sense, bacteriocins can be used to confer a rudimentary form of innate immunity to foodstuffs, helping processors extend their control over the food flora long after manufacture.


DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging

Brain imaging efforts are being increasingly devoted to decode the functioning of the human brain. Among neuroimaging techniques, resting-state fMRI (R-fMRI) is currently expanding exponentially. Beyond the general neuroimaging analysis packages (e.g., SPM, AFNI and FSL), REST and DPARSF were developed to meet the increasing need of user-friendly toolboxes for R-fMRI data processing. To address recently identified methodological challenges of R-fMRI, we introduce the newly developed toolbox, DPABI, which was evolved from REST and DPARSF. DPABI incorporates recent research advances on head motion control and measurement standardization, thus allowing users to evaluate results using stringent control strategies. DPABI also emphasizes test-retest reliability and quality control of data processing. Furthermore, DPABI provides a user-friendly pipeline analysis toolkit for rat/monkey R-fMRI data analysis to reflect the rapid advances in animal imaging. In addition, DPABI includes preprocessing modules for task-based fMRI, voxel-based morphometry analysis, statistical analysis and results viewing. DPABI is designed to make data analysis require fewer manual operations, be less time-consuming, have a lower skill requirement, a smaller risk of inadvertent mistakes, and be more comparable across studies. We anticipate this open-source toolbox will assist novices and expert users alike and continue to support advancing R-fMRI methodology and its application to clinical translational studies.


On the Traveling Salesman Problem with Simple Temporal Constraints

Many real-world applications require the successful combination of spatial and temporal reasoning. In this paper, we study the general framework of the Traveling Salesman Problem with Simple Temporal Constraints. Representationally, this framework subsumes the Traveling Salesman Problem, Simple Temporal Problems, as well as many of the frameworks described in the literature. We analyze the theoretical properties of the combined problem providing strong inapproximability results for the general problem, and positive results for


A Survey on Detection of Reasons Behind Infant Cry Using Speech Processing

Infant cry analysis is necessary to detect the reasons behind the cry. Infants can communicate through their cry only. They express their physical and emotional needs through their cry. The crying of an infant can be stopped if the reason behind their cry is known. Therefore, identifying the reason behind infant cry is very essential for medical follow up. In this paper, a literature survey is done to study, analyze, compare the existing approaches and their limitations. This survey helped us to propose a new approach for detecting the reasons behind Infant's cry. The proposed model will use input as the cry signal and from these signal patterns the unique features are extracted using MFCCs, LPCCs and Pitch, etc. These features will help discriminating the patterns from one another. These extracted features will be used to train Neural Network Multilayer classifier. This classifier will be used to identify multiple classes of reasons behind infant cry such as hunger, pain, sleep, discomfort, etc. Efforts will be made for achieving good accuracy results through this approach.


Convolutional neural networks: an illustration in TensorFlow

C onvolutional neural networks (CNNs) have interesting and widely varied architectures catering to the requirement of learning features from three dimensional and structured data volumes (with a particular example being images, whether single or multi-channel). However, there are certain design considerations common to all CNN architectures that enable us to craft correct implementations of the architectures suited to our objectives. It is more than just mathematical calculations—it's an art in itself! Thus, in the present tutorial, we'll put our knowledge of CNNs and their distinct layers in practice by making use of Google's open-source machine learning (ML) platform, TensorFlow. The tutorial is fairly simple and follows the steps detailed online [1] in light of added insights, which can be assimilated from the basics of CNN architectural concepts. The TensorFlow platform was released in November 2015, in a climate of stiff competition from well-established deep learning and ML packages such as Torch and Theano. (The performance comparisons between the packages goes beyond the scope of this piece). The platform has been undergoing constant improvements since its release, thanks in part to a nascent community of contributors. TensorFlow packs quite a punch owing to its significant positive characteristics. Firstly, the package's open source nature makes it transparent, customizable, and extensible by the end user. Secondly, the documentation support is rich and elaborate, which is a critical factor in accelerating user adoption. Lastly, the fact that the package has been backed by Google, with some of the best ML and AI researchers being among its active contributors, is the cherry on top. Additionally it's compatibility with newer versions of Python (version 3.3 and above), optional GPU utilization, parallelizable execution, and visualization support via TensorBoard further enrich its impressive repertoire. Its Achilles heel—which has prevented widespread adoption—is its unavailability on the Windows OS (TensorFlow is only available on Mac OSX and Linux platforms), and its non-operability in distributed environments, as of now (it's bound to change soon). The question as to why the MNIST dataset [2] was chosen for this tutorial is a very pertinent one. The answer is the handwritten digits dataset has been ubiquitous since its release in 1998 by Yann LeCunn [3]. Having since become the textbook dataset for neural network classification tasks, it is a good choice in our context of getting acquainted with CNNs. The dataset consists of 60,000 training examples and 10,000 test examples, each being a grayscale …


TextCatcher: a method to detect curved and challenging text in natural scenes

In this paper, we propose a text detection algorithm which is hybrid and multi-scale. First, it relies on a connected component-based approach: After the segmentation of the image, a classification step using a new wavelet descriptor spots the letters. A new graph modeling and its traversal procedure allow to form candidate text areas. Second, a texture-based approach discards the false positives. Finally, the detected text areas are precisely cut out and a new binarization step is introduced. The main advantage of our method is that few assumptions are put forward. Thus, “challenging texts” like multi-sized, multi-colored, multi-oriented or curved text can be localized. The efficiency of TextCatcher has been validated on three different datasets: Two come from the ICDAR competition, and the third one contains photographs we have taken with various daily life texts. We present both qualitative and quantitative results.


Put your money where your mouth is! Explaining collective action tendencies through group-based anger and group efficacy.

Insights from appraisal theories of emotion are used to integrate elements of theories on collective action. Three experiments with disadvantaged groups systematically manipulated procedural fairness (Study 1), emotional social support (Study 2), and instrumental social support (Study 3) to examine their effects on collective action tendencies through group-based anger and group efficacy. Results of structural equation modeling showed that procedural fairness and emotional social support affected the group-based anger pathway (reflecting emotion-focused coping), whereas instrumental social support affected the group efficacy pathway (reflecting problem-focused coping), constituting 2 distinct pathways to collective action tendencies. Analyses of the means suggest that collective action tendencies become stronger the more fellow group members "put their money where their mouth is." The authors discuss how their dual pathway model integrates and extends elements of current approaches to collective action.


Taxonomy of intrusion risk assessment and response system

In recent years, we have seen notable changes in the way attackers infiltrate computer systems compromising their functionality. Research in intrusion detection systems aims to reduce the impact of these attacks. In this paper, we present a taxonomy of intrusion response systems (IRS) and Intrusion Risk Assessment (IRA), two important components of an intrusion detection solution. We achieve this by classifying a number of studies published during the last two decades . We discuss the key features of existing IRS and IRA. We show how characterizing security risks and choosing the right countermeasures are an important and challenging part of designing an IRS and an IRA. Poorly designed IRS and IRA may reduce network performance and wrongly disconnect users from a network. We propose techniques on how to address these challenges and highlight the need for a comprehensive defense mechanism approach. We believe that this taxonomy will open up interesting areas for future research in the growing field of intrusion risk assessment and response systems.


Segmentation of Medical Ultrasound Images Using Convolutional Neural Networks with Noisy Activating Functions

The attempts to segment medical ultrasound images have had limited success than the attempts to segment images from other medical imaging modalities. In this project, we attempt to segment medical ultrasound images using convolutional neural networks (CNNs) with a group of noisy activation functions which have recently been demonstrated to improve the performance of neural networks. We report on the segmentation results using a U-Net-like CNN with noisy rectified linear unit (NReLU) functions, noisy hard sigmoid (NHSigmoid) functions, and noisy hard tanh (NHTanh) function on a small data set.


GSA: A Gravitational Search Algorithm

In recent years, various heuristic optimization methods have been developed. Many of these methods are inspired by swarm behaviors in nature. In this paper, a new optimization algorithm based on the law of gravity and mass interactions is introduced. In the proposed algorithm, the searcher agents are a collection of masses which interact with each other based on the Newtonian gravity and the laws of motion. The proposed method has been compared with some well-known heuristic search methods. The obtained results confirm the high performance of the proposed method in solving various nonlinear functions. 2009 Elsevier Inc. All rights reserved.


Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion

Here, we demonstrate that subject motion produces substantial changes in the timecourses of resting state functional connectivity MRI (rs-fcMRI) data despite compensatory spatial registration and regression of motion estimates from the data. These changes cause systematic but spurious correlation structures throughout the brain. Specifically, many long-distance correlations are decreased by subject motion, whereas many short-distance correlations are increased. These changes in rs-fcMRI correlations do not arise from, nor are they adequately countered by, some common functional connectivity processing steps. Two indices of data quality are proposed, and a simple method to reduce motion-related effects in rs-fcMRI analyses is demonstrated that should be flexibly implementable across a variety of software platforms. We demonstrate how application of this technique impacts our own data, modifying previous conclusions about brain development. These results suggest the need for greater care in dealing with subject motion, and the need to critically revisit previous rs-fcMRI work that may not have adequately controlled for effects of transient subject movements.


Modeling regional and psychophysiologic interactions in fMRI: the importance of hemodynamic deconvolution

The analysis of functional magnetic resonance imaging (fMRI) time-series data can provide information not only about task-related activity, but also about the connectivity (functional or effective) among regions and the influences of behavioral or physiologic states on that connectivity. Similar analyses have been performed in other imaging modalities, such as positron emission tomography. However, fMRI is unique because the information about the underlying neuronal activity is filtered or convolved with a hemodynamic response function. Previous studies of regional connectivity in fMRI have overlooked this convolution and have assumed that the observed hemodynamic response approximates the neuronal response. In this article, this assumption is revisited using estimates of underlying neuronal activity. These estimates use a parametric empirical Bayes formulation for hemodynamic deconvolution.


Common regions of the human frontal lobe recruited by diverse cognitive demands

Though many neuroscientific methods have been brought to bear in the search for functional specializations within prefrontal cortex, little consensus has emerged. To assess the contribution of functional neuroimaging, this article reviews patterns of frontal-lobe activation associated with a broad range of different cognitive demands, including aspects of perception, response selection, executive control, working memory, episodic memory and problem solving. The results show a striking regularity: for many demands, there is a similar recruitment of mid-dorsolateral, mid-ventrolateral and dorsal anterior cingulate cortex. Much of the remainder of frontal cortex, including most of the medial and orbital surfaces, is largely insensitive to these demands. Undoubtedly, these results provide strong evidence for regional specialization of function within prefrontal cortex. This specialization, however, takes an unexpected form: a specific frontal-lobe network that is consistently recruited for solution of diverse cognitive problems.


Prefrontal cortical function and anxiety: controlling attention to threat-related stimuli

Threat-related stimuli are strong competitors for attention, particularly in anxious individuals. We used functional magnetic resonance imaging (fMRI) with healthy human volunteers to study how the processing of threat-related distractors is controlled and whether this alters as anxiety levels increase. Our work builds upon prior analyses of the cognitive control functions of lateral prefrontal cortex (lateral PFC) and anterior cingulate cortex (ACC). We found that rostral ACC was strongly activated by infrequent threat-related distractors, consistent with a role for this area in responding to unexpected processing conflict caused by salient emotional stimuli. Participants with higher anxiety levels showed both less rostral ACC activity overall and reduced recruitment of lateral PFC as expectancy of threat-related distractors was established. This supports the proposal that anxiety is associated with reduced top-down control over threat-related distractors. Our results suggest distinct roles for rostral ACC and lateral PFC in governing the processing of task-irrelevant, threat-related stimuli, and indicate reduced recruitment of this circuitry in anxiety.


Other minds in the brain: a functional imaging study of “theory of mind” in story comprehension

The ability of normal children and adults to attribute independent mental states to self and others in order to explain and predict behaviour ("theory of mind") has been a focus of much recent research. Autism is a biologically based disorder which appears to be characterised by a specific impairment in this "mentalising" process. The present paper reports a functional neuroimaging study with positron emission tomography in which we studied brain activity in normal volunteers while they performed story comprehension tasks necessitating the attribution of mental states. The resultant brain activity was compared with that measured in two control tasks: "physical" stories which did not require this mental attribution, and passages of unlinked sentences. Both story conditions, when compared to the unlinked sentences, showed significantly increased regional cerebral blood flow in the following regions: the temporal poles bilaterally, the left superior temporal gyrus and the posterior cingulate cortex. Comparison of the "theory of mind" stories with "physical" stores revealed a specific pattern of activation associated with mental state attribution: it was only this task which produced activation in the medial frontal gyrus on the left (Brodmann's area 8). This comparison also showed significant activation in the posterior cingulate cortex. These surprisingly clear-cut findings are discussed in relation to previous studies of brain activation during story comprehension. The localisation of brain regions involved in normal attribution of mental states and contextual problem solving is feasible and may have implication for the neural basis of autism.


Daemo: A Self-Governed Crowdsourcing Marketplace

Crowdsourcing marketplaces provide opportunities for autonomous and collaborative professional work as well as social engagement. However, in these marketplaces, workers feel disrespected due to unreasonable rejections and low payments, whereas requesters do not trust the results they receive. The lack of trust and uneven distribution of power among workers and requesters have raised serious concerns about sustainability of these marketplaces. To address the challenges of trust and power, this paper introduces Daemo, a self-governed crowdsourcing marketplace. We propose a prototype task to improve the work quality and open-governance model to achieve equitable representation. We envisage Daemo will enable workers to build sustainable careers and provide requesters with timely, quality labor for their businesses.


Discriminative shape from shading in uncalibrated illumination

Estimating surface normals from just a single image is challenging. To simplify the problem, previous work focused on special cases, including directional lighting, known reflectance maps, etc., making shape from shading impractical outside the lab. To cope with more realistic settings, shading cues need to be combined and generalized to natural illumination. This significantly increases the complexity of the approach, as well as the number of parameters that require tuning. Enabled by a new large-scale dataset for training and analysis, we address this with a discriminative learning approach to shape from shading, which uses regression forests for efficient pixel-independent prediction and fast learning. Von Mises-Fisher distributions in the leaves of each tree enable the estimation of surface normals. To account for their expected spatial regularity, we introduce spatial features, including texton and silhouette features. The proposed silhouette features are computed from the occluding contours of the surface and provide scale-invariant context. Aside from computational efficiency, they enable good generalization to unseen data and importantly allow for a robust estimation of the reflectance map, extending our approach to the uncalibrated setting. Experiments show that our discriminative approach outperforms state-of-the-art methods on synthetic and real-world datasets.


System Integration and Power-Flow Management for a Series Hybrid Electric Vehicle Using Supercapacitors and Batteries

In this paper, system integration and power-flow management algorithms for a four-wheel-driven series hybrid electric vehicle (HEV) having multiple power sources composed of a diesel-engine-based generator, lead acid battery bank, and supercapacitor bank are presented. The super-capacitor is utilized as a short-term energy storage device to meet the dynamic performance of the vehicle, while the battery is utilized as a mid-term energy storage for the electric vehicle (EV) mode operation due to its higher energy density. The generator based on an interior permanent magnet machine (IPMM), run by a diesel engine, provides the average power for the normal operation of the vehicle. Thanks to the proposed power-flow management algorithm, each of the energy sources is controlled appropriately and also the dynamic performance of the vehicle has been improved. The proposed power-flow management algorithm has been experimentally verified with a full-scale prototype vehicle.


Linear AC LED driver with the multi-level structure and variable current regulator

This paper proposes a linear AC LED driver for LED lighting applications. The proposed circuit is small in size because the circuit structure consists of only semiconductors and resistors without any reactors and electrolytic capacitors. The current bypass circuit which is connected in parallel to the LED string consists of single MOSFET, single zener diode and two resistors. The MOSFET is operated in an active state by a self-bias circuit. Thus, an external controller and high voltage gate drivers are not required. The proposed circuit is experimentally validated by using a 7 W prototype. From the experimental results, the THD of input current is 2.1% and the power factor is 0.999. In addition, the simulation loss analysis demonstrates an efficiency of 87% for a 7 W prototype.


Space-Vector PWM Control Synthesis for an H-Bridge Drive in Electric Vehicles

This paper deals with a synthesis of space-vector pulsewidth modulation (SVPWM) control methods applied for an H-bridge inverter feeding a three-phase permanent-magnet synchronous machine (PMSM) in electric-vehicle (EV) applications. First, a short survey of existing architectures of power converters, particularly those adapted to degraded operating modes, is presented. Standard SVPWM control methods are compared with three innovative methods using EV drive specifications in the normal operating mode. Then, a rigorous analysis of the margins left in the control strategy is presented for a semiconductor switch failure to fulfill degraded operating modes. Finally, both classic and innovative strategies are implemented in numerical simulation; their results are analyzed and discussed.


WALNUT: Waging Doubt on the Integrity of MEMS Accelerometers with Acoustic Injection Attacks

Cyber-physical systems depend on sensors to make automated decisions. Resonant acoustic injection attacks are already known to cause malfunctions by disabling MEMS-based gyroscopes. However, an open question remains on how to move beyond denial of service attacks to achieve full adversarial control of sensor outputs. Our work investigates how analog acoustic injection attacks can damage the digital integrity of a popular type of sensor: the capacitive MEMS accelerometer. Spoofing such sensors with intentional acoustic interference enables an out-of-spec pathway for attackers to deliver chosen digital values to microprocessors and embedded systems that blindly trust the unvalidated integrity of sensor outputs. Our contributions include (1) modeling the physics of malicious acoustic interference on MEMS accelerometers, (2) discovering the circuit-level security flaws that cause the vulnerabilities by measuring acoustic injection attacks on MEMS accelerometers as well as systems that employ on these sensors, and (3) two software-only defenses that mitigate many of the risks to the integrity of MEMS accelerometer outputs. We characterize two classes of acoustic injection attacks with increasing levels of adversarial control: output biasing and output control. We test these attacks against 20 models of capacitive MEMS accelerometers from 5 different manufacturers. Our experiments find that 75% are vulnerable to output biasing, and 65% are vulnerable to output control. To illustrate end-to-end implications, we show how to inject fake steps into a Fitbit with a $5 speaker. In our self-stimulating attack, we play a malicious music file from a smartphone's speaker to control the on-board MEMS accelerometer trusted by a local app to pilot a toy RC car. In addition to offering hardware design suggestions to eliminate the root causes of insecure amplification and filtering, we introduce two low-cost software defenses that mitigate output biasing attacks: randomized sampling and 180 degree out-of-phase sampling. These software-only approaches mitigate attacks by exploiting the periodic and predictable nature of the malicious acoustic interference signal. Our results call into question the wisdom of allowing microprocessors and embedded systems to blindly trust that hardware abstractions alone will ensure the integrity of sensor outputs.


Ant-Q: A Reinforcement Learning Approach to the Traveling Salesman Problem

In this paper we introduce Ant-Q, a family of algorithms which present many similarities with Q-learning (Watkins, 1989), and which we apply to the solution of symmetric and asymmetric instances of the traveling salesman problem (TSP). Ant-Q algorithms were inspired by work on the ant system (AS), a distributed algorithm for combinatorial optimization based on the metaphor of ant colonies which was recently proposed in (Dorigo, 1992; Dorigo, Maniezzo and Colorni, 1996). We show that AS is a particular instance of the Ant-Q family, and that there are instances of this family which perform better than AS. We experimentally investigate the functioning of Ant-Q and we show that the results obtained by Ant-Q on symmetric TSP's are competitive with those obtained by other heuristic approaches based on neural networks or local search. Finally, we apply Ant-Q to some difficult asymmetric TSP's obtaining very good results: Ant-Q was able to find solutions of a quality which usually can be found only by very specialized algorithms.


All-at-once Optimization for Coupled Matrix and Tensor Factorizations

Joint analysis of data from multiple sources has the potential to improve our understanding of the underlying structures in complex data sets. For instance, in restaurant recommendation systems, recommendations can be based on rating histories of customers. In addition to rating histories, customers’ social networks (e.g., Facebook friendships) and restaurant categories information (e.g., Thai or Italian) can also be used to make better recommendations. The task of fusing data, however, is challenging since data sets can be incomplete and heterogeneous, i.e., data consist of both matrices, e.g., the person by person social network matrix or the restaurant by category matrix, and higher-order tensors, e.g., the “ratings” tensor of the form restaurant by meal by person. In this paper, we are particularly interested in fusing data sets with the goal of capturing their underlying latent structures. We formulate this problem as a coupled matrix and tensor factorization (CMTF) problem where heterogeneous data sets are modeled by fitting outer-product models to higher-order tensors and matrices in a coupled manner. Unlike traditional approaches solving this problem using alternating algorithms, we propose an all-at-once optimization approach called CMTF-OPT (CMTF-OPTimization), which is a gradient-based optimization approach for joint analysis of matrices and higher-order tensors. We also extend the algorithm to handle coupled incomplete data sets. Using numerical experiments, we demonstrate that the proposed all-at-once approach is more accurate than the alternating least squares approach.


A Tagged Corpus and a Tagger for Urdu

In this paper, we describe a release of a sizeable monolingual Urdu corpus automatically tagged with part-of-speech tags. We extend the work of Jawaid and Bojar (2012) who use three different taggers and then apply a voting scheme to disambiguate among the different choices suggested by each tagger. We run this complex ensemble on a large monolingual corpus and release the tagged corpus. Additionally, we use this data to train a single standalone tagger which will hopefully significantly simplify Urdu processing. The standalone tagger obtains the accuracy of 88.74% on test data.


Learning as search optimization: approximate large margin methods for structured prediction

Mappings to structured output spaces (strings, trees, partitions, etc.) are typically learned using extensions of classification algorithms to simple graphical structures (eg., linear chains) in which search and parameter estimation can be performed exactly. Unfortunately, in many complex problems, it is rare that exact search or parameter estimation is tractable. Instead of learning exact models and searching via heuristic means, we embrace this difficulty and treat the structured output problem in terms of approximate search. We present a framework for learning as search optimization, and two parameter updates with convergence the-orems and bounds. Empirical evidence shows that our integrated approach to learning and decoding can outperform exact models at smaller computational cost.


Bricolage versus breakthrough : distributed and embedded agency in technology entrepreneurship

We develop a perspective on technology entrepreneurship as involving agency that is distributed across different kinds of actors. Each actor becomes involved with a technology, and, in the process, generates inputs that result in the transformation of an emerging technological path. The steady accumulation of inputs to a technological path generates a momentum that enables and constrains the activities of distributed actors. In other words, agency is not only distributed, but it is embedded as well. We explicate this perspective through a comparative study of processes underlying the emergence of wind turbines in Denmark and in United States. Through our comparative study, we flesh out “bricolage” and “breakthrough” as contrasting approaches to the engagement of actors in shaping technological paths. © 2002 Elsevier Science B.V. All rights reserved.


Threshold selection using Renyi's entropy


UTHealth at SemEval-2016 Task 12: an End-to-End System for Temporal Information Extraction from Clinical Notes

The 2016 Clinical TempEval challenge addresses temporal information extraction from clinical notes. The challenge is composed of six sub-tasks, each of which is to identify: (1) event mention spans, (2) time expression spans, (3) event attributes, (4) time attributes, (5) events’ temporal relations to the document creation times (DocTimeRel), and (6) narrative container relations among events and times. In this article, we present an end-to-end system that addresses all six sub-tasks. Our system achieved the best performance for all six sub-tasks when plain texts were given as input. It also performed best for narrative container relation identification when gold standard event/time annotations were given.


Discovering Molecular Functional Groups Using Graph Convolutional Neural Networks

Functional groups (FGs) serve as a foundation for analyzing chemical properties of organic molecules. Automatic discovery of FGs will impact various fields of research, including medicinal chemistry, by reducing the amount of lab experiments required for discovery or synthesis of new molecules. Here, we investigate methods based on graph convolutional neural networks (GCNNs) for localizing FGs that contribute to specific chemical properties. Molecules are modeled as undirected graphs with atoms as nodes and bonds as edges. Using this graph structure, we trained GCNNs in a supervised way on experimentally-validated molecular training sets to predict specific chemical properties, e.g., toxicity. Upon learning a GCNN, we analyzed its activation patterns to automatically identify FGs using four different methods: gradient-based saliency maps, Class Activation Mapping (CAM), gradient-weighted CAM (Grad-CAM), and Excitation Back-Propagation. We evaluated the contrastive power of these methods with respect to the specificity of the identified molecular substructures and their relevance for chemical functions. GradCAM had the highest contrastive power and generated qualitatively the best FGs. This work paves the way for automatic analysis and design of new molecules.


How to make large self-organizing maps for nonvectorial data

The self-organizing map (SOM) represents an open set of input samples by a topologically organized, finite set of models. In this paper, a new version of the SOM is used for the clustering, organization, and visualization of a large database of symbol sequences (viz. protein sequences). This method combines two principles: the batch computing version of the SOM, and computation of the generalized median of symbol strings.


Structurally-Sensitive Multi-Scale Deep Neural Network for Low-Dose CT Denoising

Computed tomography (CT) is a popular medical imaging modality and enjoys wide clinical applications. At the same time, the X-ray radiation dose associated with CT scannings raises a public concern due to its potential risks to the patients. Over the past years, major efforts have been dedicated to the development of low-dose CT (LDCT) methods. However, the radiation dose reduction compromises the signal-to-noise ratio, leading to strong noise and artifacts that down-grade the CT image quality. In this paper, we propose a novel 3-D noise reduction method, called structurally sensitive multi-scale generative adversarial net, to improve the LDCT image quality. Specifically, we incorporate 3-D volumetric information to improve the image quality. Also, different loss functions for training denoising models are investigated. Experiments show that the proposed method can effectively preserve the structural and textural information in reference to the normal-dose CT images and significantly suppress noise and artifacts. Qualitative visual assessments by three experienced radiologists demonstrate that the proposed method retrieves more information and outperforms competing methods.


Massive parallelization of approximate nearest neighbor search on KD-tree for high-dimensional image descriptor matching


REGULARIZATION TOOLS: A Matlab package for analysis and solution of discrete ill-posed problems

The package REGULARIZATION TOOLS consists of 54 Matlab routines for analysis and solution of discrete ill-posed problems, i.e., systems of linear equations whose coefficient matrix has the properties that its condition number is very large, and its singular values decay gradually to zero. Such problems typically arise in connection with discretization of Fredholm integral equations of the first kind, and similar ill-posed problems. Some form of regularization is always required in order to compute a stabilized solution to discrete ill-posed problems. The purpose of REGULARIZATION TOOLS is to provide the user with easy-to-use routines, based on numerical robust and efficient algorithms, for doing experiments with regularization of discrete ill-posed problems. By means of this package, the user can experiment with different regularization strategies, compare them, and draw conclusions from these experiments that would otherwise require a major programming effert. For discrete ill-posed problems, which are indeed difficult to treat numerically, such an approach is certainly superior to a single black-box routine. This paper describes the underlying theory gives an overview of the package; a complete manual is also available.


Transparent and Efficient CFI Enforcement with Intel Processor Trace

Current control flow integrity (CFI) enforcement approaches either require instrumenting application executables and even shared libraries, or are unable to defend against sophisticated attacks due to relaxed security policies, or both, many of them also incur high runtime overhead. This paper observes that the main obstacle of providing transparent and strong defense against sophisticated adversaries is the lack of sufficient runtime control flow information. To this end, this paper describes FlowGuard, a lightweight, transparent CFI enforcement approach by a novel reuse of Intel Processor Trace (IPT), a recent hardware feature that efficiently captures the entire runtime control flow. The main challenge is that IPT is designed for offline performance analysis and software debugging such that decoding collected control flow traces is prohibitively slow on the fly. FlowGuard addresses this challenge by reconstructing applications' conservative control flow graphs (CFG) to be compatible with the compressed encoding format of IPT, and labeling the CFG edges with credits in the help of fuzzing-like dynamic training. At runtime, FlowGuard separates fast and slow paths such that the fast path compares the labeled CFGs with the IPT traces for fast filtering, while the slow path decodes necessary IPT traces for strong security. We have implemented and evaluated FlowGuard on a commodity Intel Skylake machine with IPT support. Evaluation results show that FlowGuard is effective in enforcing CFI for several applications, while introducing only small performance overhead. We also show that, with minor hardware extensions, the performance overhead can be further reduced.


Predictive coding as a model of cognition

Previous work has shown that predictive coding can provide a detailed explanation of a very wide range of low-level perceptual processes. It is also widely believed that predictive coding can account for high-level, cognitive, abilities. This article provides support for this view by showing that predictive coding can simulate phenomena such as categorisation, the influence of abstract knowledge on perception, recall and reasoning about conceptual knowledge, context-dependent behavioural control, and naive physics. The particular implementation of predictive coding used here (PC/BC-DIM) has previously been used to simulate low-level perceptual behaviour and the neural mechanisms that underlie them. This algorithm thus provides a single framework for modelling both perceptual and cognitive brain function.


Fine-tuning Deep Convolutional Networks for Plant Recognition

This paper describes the participation of the ECOUAN team in the LifeCLEF 2015 challenge. We used a deep learning approach in which the complete system was learned without hand-engineered components. We pre-trained a convolutional neural network using 1.8 million images and used a fine-tuning strategy to transfer learned recognition capabilities from general domains to the specific challenge of Plant Identification task. The classification accuracy obtained by our method outperformed the best result obtained in 2014. Our group obtained the 4th position among all teams and the 10th position among 18 runs.


A Hybrid PCA-CART-MARS-Based Prognostic Approach of the Remaining Useful Life for Aircraft Engines

Prognostics is an engineering discipline that predicts the future health of a system. In this research work, a data-driven approach for prognostics is proposed. Indeed, the present paper describes a data-driven hybrid model for the successful prediction of the remaining useful life of aircraft engines. The approach combines the multivariate adaptive regression splines (MARS) technique with the principal component analysis (PCA), dendrograms and classification and regression trees (CARTs). Elements extracted from sensor signals are used to train this hybrid model, representing different levels of health for aircraft engines. In this way, this hybrid algorithm is used to predict the trends of these elements. Based on this fitting, one can determine the future health state of a system and estimate its remaining useful life (RUL) with accuracy. To evaluate the proposed approach, a test was carried out using aircraft engine signals collected from physical sensors (temperature, pressure, speed, fuel flow, etc.). Simulation results show that the PCA-CART-MARS-based approach can forecast faults long before they occur and can predict the RUL. The proposed hybrid model presents as its main advantage the fact that it does not require information about the previous operation states of the input variables of the engine. The performance of this model was compared with those obtained by other benchmark models (multivariate linear regression and artificial neural networks) also applied in recent years for the modeling of remaining useful life. Therefore, the PCA-CART-MARS-based approach is very promising in the field of prognostics of the RUL for aircraft engines.


Interpreting random forest models using a feature contribution method

Model interpretation is one of the key aspects of the model evaluation process. The explanation of the relationship between model variables and outputs is easy for statistical models, such as linear regressions, thanks to the availability of model parameters and their statistical significance. For “black box” models, such as random forest, this information is hidden inside the model structure. This work presents an approach for computing feature contributions for random forest classification models. It allows for the determination of the influence of each variable on the model prediction for an individual instance. Interpretation of feature contributions for two UCI benchmark datasets shows the potential of the proposed methodology. The robustness of results is demonstrated through an extensive analysis of feature contributions calculated for a large number of generated random forest models.


Methodology of band rejection / addition for microstrip antennas design using slot line theory and current distribution analysis

Radio or wireless communication means to transfer information over long distance without using any wires. Millions of people exchange information every day using pager, cellular, telephones, laptops, various types of personal digital assistance (PDAs) and other wireless communication products. The worldwide interoperability for microwave access (Wi-Max) aims to provide wireless data over a long distance in variety of ways. It was based on IEEE 802.16 standard. It is an effective metropolitan area access technique with many favorable features like flexibility, cost, efficiency and fast networking, which not only provides wireless access, but also serves to expand the access to wired network. The coverage area of Wi-Max is around 30-50 km. it can provide high 100 Mbps data rates in 20 MHz bandwidth on fixed and nomadic applications in the 2-11 GHz frequencies. In this paper, a methodology of band rejection/addition for microstrip antennas design using slot line theory and current distribution analysis has been introduced and analyzed. The analysis and design are done by a commercial software. The radiation characteristics, such as; return loss, VSWR, input impedance, and the surface current densities have been introduced and discussed. Finally, the proposed optimum antenna design structure has been fabricated and the measured S-parameters of the proposed structure can be analyzed with network analyzer and compared with simulation results to demonstrate the excellent performance and meet the requirements for wireless communication applications.


Distance Metric Learning with Application to Clustering with Side-Information

Many algorithms rely critically on being given a good metric over their inputs. For instance, data can often be clustered in many “plausible” ways, and if a clustering algorithm such as K-means initially fails to find one that is meaningful to a user, the only recourse may be for the user to manually tweak the metric until sufficiently good clusters are found. For these and other applications requiring good metrics, it is desirable that we provide a more systematic way for users to indicate what they consider “similar.” For instance, we may ask them to provide examples. In this paper, we present an algorithm that, given examples of similar (and, if desired, dissimilar) pairs of points in , learns a distance metric over that respects these relationships. Our method is based on posing metric learning as a convex optimization problem, which allows us to give efficient, local-optima-free algorithms. We also demonstrate empirically that the learned metrics can be used to significantly improve clustering performance.


Comparing Clusterings - An Overview

As the amount of data we nowadays have to deal with becomes larger and larger, the methods that help us to detect structures in the data and to identify interesting subsets in the data become more and more important. One of these methods is clustering, i.e. segmenting a set of elements into subsets such that the elements in each subset are somehow ”similiar” to each other and elements of different subsets are ”unsimilar”. In the literature we can find a large variety of clustering algorithms, each having certain advantages but also certain drawbacks. Typical questions that arise in this context comprise:


Discriminant Adaptive Nearest Neighbor Classification

Nearest neighbor classification expects the class conditional probabilities to be locally constant, and suffers from bias in high dimensions We propose a locally adaptive form of nearest neighbor classification to try to finesse this curse of dimensionality. We use a local linear discriminant analysis to estimate an effective metric for computing neighborhoods. We determine the local decision boundaries from centroid information, and then shrink neighborhoods in directions orthogonal to these local decision boundaries, and elongate them parallel to the boundaries. Thereafter, any neighborhood-based classifier can be employed, using the modified neighborhoods. The posterior probabilities tend to be more homogeneous in the modified neighborhoods. We also propose a method for global dimension reduction, that combines local dimension information. In a number of examples, the methods demonstrate the potential for substantial improvements over nearest neighbour classification. Introduction We consider a discrimination problem with d classes and N training observations. The training observations consist of predictor measurements x = (zl,z2,...zp) on p predictors and the known class memberships. Our goal is to predict the class membership of an observation with predictor vector x0 Nearest neighbor classification is a simple and appealing approach to this problem. We find the set of K nearest neighbors in the training set to x0 and then classify x0 as the most frequent class among the K neighbors. Nearest neighbors is an extremely flexible classification scheme, and does not involve any pre-processing (fitting) of the training data. This can offer both space and speed advantages in very large problems: see Cover (1968), Duda & Hart (1973), McLachlan (1992) for background material on nearest neighborhood classification. Cover & Hart (1967) show that the one nearest neighbour rule has asymptotic error rate at most twice the Bayes rate. However in finite samples the curse of dimensionality can severely hurt the nearest neighbor rule. The relative radius of the nearest-neighbor sphere grows like r 1/p where p is the dimension and r the radius for p = 1, resulting in severe bias at the target point x. Figure 1 illustrates the situation for a simple example. Figure 1: The vertical strip denotes the NN region using only the X coordinate to find the nearest neighbor for the target point (solid dot). The sphere shows the NN region using both coordinates, and we see in this case it has extended into the class 1 region (and found the wrong class in this instance). Our illustration here is based on a 1-NN rule, but the same phenomenon ccurs for k-NN rules as well. Nearest neighbor techniques are based on the assumption that locally the class posterior probabilities are constant. While that is clearly true in the vertical strip using only coordinate X, using X and Y this is no longer true. The techniques outlined in the abstract are designed to overcome these problems. Figure 2 shows an example. There are two classes in two dimensions, one of which almost completely surrounds the other. The left panel shows a nearest neighborhood of size 25 at the target point (shown as origin), which is chosen to near the class boundary. The right panel shows the same size neighborhood using our discriminant adap142 KDD--95 From: KDD-95 Proceedings. Copyright © 1995, AAAI (www.aaai.org). All rights reserved.


Constrained K-means Clustering with Background Knowledge

Clustering is traditionally viewed as an unsupervised method for data analysis. However, in some cases information about the problem domain is available in addition to the data instances themselves. In this paper, we demonstrate how the popular k-means clustering algorithm can be profitably modified to make use of this information. In experiments with artificial constraints on six data sets, we observe improvements in clustering accuracy. We also apply this method to the real-world problem of automatically detecting road lanes from GPS data and observe dramatic increases in performance.


Promoting Student Metacognition

Imagine yourself as the instructor of an introductory undergraduate biology course. Two students from your course independently visit your office the week after the first exam. Both students are biology majors. Both regularly attend class and submit their assignments on time. Both appear to be eager, dedicated, and genuine students who want to learn biology. During each of their office hours visits, you ask them to share how they prepared for the first exam. Their stories are strikingly different (inspired by Ertmer and Newby, 1996). During office hours, Josephina expresses that she was happy the exam was on a Monday, because she had a lot of time to prepare the previous weekend. She shares that she started studying after work on Saturday evening and did not go out with friends that night. When queried, she also shares that she reread all of the assigned textbook material and made flashcards of the bold words in the text. She feels that she should have done well on the test, because she studied all Saturday night and all day on Sunday. She feels that she did everything she could do to prepare. That said, she is worried about what her grade will be, and she wants you to know that she studied really hard, so she should get a good grade on the exam. Later in the week, Maya visits your office. When asked how she prepared for the first exam, she explains that she has regularly reviewed the PowerPoint slides each evening after class since the beginning of the term 4 weeks ago. She also read the assigned textbook pages weekly, but expresses that she spent most of her time comparing the ideas in the PowerPoint slides with the information in the textbook to see how they were similar and different. She found several places in which things seemed not to agree, which confused her. She kept a running list of these confusions each week. When you ask what she did with these confusions, she shares that she


A Survey of Text Summarization Extractive Techniques

Text Summarization is condensing the source text into a shorter version preserving its information content and overall meaning. It is very difficult for human beings to manually summarize large documents of text. Text Summarization methods can be classified into extractive and abstractive summarization. An extractive summarization method consists of selecting important sentences, paragraphs etc. from the original document and concatenating them into shorter form. The importance of sentences is decided based on statistical and linguistic features of sentences. An abstractive summarization method consists of understanding the original text and re-telling it in fewer words. It uses linguistic methods to examine and interpret the text and then to find the new concepts and expressions to best describe it by generating a new shorter text that conveys the most important information from the original text document. In this paper, a Survey of Text Summarization Extractive techniques has been presented.


Multiple SVM-RFE for gene selection in cancer classification with expression data

This paper proposes a new feature selection method that uses a backward elimination procedure similar to that implemented in support vector machine recursive feature elimination (SVM-RFE). Unlike the SVM-RFE method, at each step, the proposed approach computes the feature ranking score from a statistical analysis of weight vectors of multiple linear SVMs trained on subsamples of the original training data. We tested the proposed method on four gene expression datasets for cancer classification. The results show that the proposed feature selection method selects better gene subsets than the original SVM-RFE and improves the classification accuracy. A Gene Ontology-based similarity assessment indicates that the selected subsets are functionally diverse, further validating our gene selection method. This investigation also suggests that, for gene expression-based cancer classification, average test error from multiple partitions of training and test sets can be recommended as a reference of performance quality.


Photogeometric Scene Flow for High-Detail Dynamic 3D Reconstruction

Photometric stereo (PS) is an established technique for high-detail reconstruction of 3D geometry and appearance. To correct for surface integration errors, PS is often combined with multiview stereo (MVS). With dynamic objects, PS reconstruction also faces the problem of computing optical flow (OF) for image alignment under rapid changes in illumination. Current PS methods typically compute optical flow and MVS as independent stages, each one with its own limitations and errors introduced by early regularization. In contrast, scene flow methods estimate geometry and motion, but lack the fine detail from PS. This paper proposes photogeometric scene flow (PGSF) for high-quality dynamic 3D reconstruction. PGSF performs PS, OF, and MVS simultaneously. It is based on two key observations: (i) while image alignment improves PS, PS allows for surfaces to be relit to improve alignment, (ii) PS provides surface gradients that render the smoothness term in MVS unnecessary, leading to truly data-driven, continuous depth estimates. This synergy is demonstrated in the quality of the resulting RGB appearance, 3D geometry, and 3D motion.


Data Driven Ontology Evaluation

The evaluation of ontologies is vital for the growth of the Semantic Web. We consider a number of problems in evaluating a knowledge artifact like an ontology. We propose in this paper that one approach to ontology evaluation should be corpus or data driven. A corpus is the most accessible form of knowledge and its use allows a measure to be derived of the ‘fit’ between an ontology and a domain of knowledge. We consider a number of methods for measuring this ‘fit’ and propose a measure to evaluate structural fit, and a probabilistic approach to identifying the best ontology.


Learning Across Scales - Multiscale Methods for Convolution Neural Networks

In this work, we establish the relation between optimal control and training deep Convolution Neural Networks (CNNs). We show that the forward propagation in CNNs can be interpreted as a time-dependent nonlinear differential equation and learning can be seen as controlling the parameters of the differential equation such that the network approximates the data-label relation for given training data. Using this continuous interpretation, we derive two new methods to scale CNNs with respect to two different dimensions. The first class of multiscale methods connects low-resolution and high-resolution data using prolongation and restriction of CNN parameters inspired by algebraic multigrid techniques. We demonstrate that our method enables classifying highresolution images using CNNs trained with low-resolution images and vice versa and warm-starting the learning process. The second class of multiscale methods connects shallow and deep networks and leads to new training strategies that gradually increase the depths of the CNN while re-using parameters for initializations.


Toolpath Planning for Continuous Extrusion Additive Manufacturing

Recent work in additive manufacturing has introduced a new class of 3D printers that operate by extruding slurries and viscous mixtures such as silicone, glass, epoxy, and concrete, but because of the fluid flow properties of these materials it is difficult to stop extrusion once the print has begun. Conventional toolpath generation for 3D printing is based on the assumption that the flow of material can be controlled precisely and the resulting path includes instructions to disable extrusion and move the print head to another portion of the model. A continuous extrusion printer cannot disable material flow, and so these toolpaths produce low quality prints with wasted material. This paper outlines a greedy algorithm for post-processing toolpath instructions that employs a Traveling Salesperson Problem (TSP) solver to reduce the distance traveled between subsequent space-filling curves and layers, which reduces unnecessary extrusion by at least 20% for simple object models on an open-source 3D printer.


Model Predictive Control System Design of a passenger car for a Valet Parking Scenario


Artificial Intelligence MArkup Language: A Brief Tutorial

The purpose of this paper is to serve as a reference guide for the development of chatterbots implemented with the AIML language. In order to achieve this, the main concepts in Pattern Recognition area are described because the AIML uses such theoretical framework in their syntactic and semantic structures. After that, AIML language is described and each AIML command/tag is followed by an application example. Also, the usage of AIML embedded tags for the handling of sequence dialogue limitations between humans and machines is shown. Finally, computer systems that assist in the design of chatterbots with the AIML language are classified and described.


An Analysis of the Elastic Net Approach to the Traveling Salesman Problem

This paper analyzes the elastic net approach (Durbin and Willshaw 1987) to the traveling salesman problem of finding the shortest path through a set of cities. The elastic net approach jointly minimizes the length of an arbitrary path in the plane and the distance between the path points and the cities. The tradeoff between these two requirements is controlled by a scale parameter K. A global minimum is found for large K, and is then tracked to a small value. In this paper, we show that (1) in the small K limit the elastic path passes arbitrarily close to all the cities, but that only one path point is attracted to each city, (2) in the large K limit the net lies at the center of the set of cities, and (3) at a critical value of K the energy function bifurcates. We also show that this method can be interpreted in terms of extremizing a probability distribution controlled by K. The minimum at a given K corresponds to the maximum a posteriori (MAP) Bayesian estimate of the tour under a natural statistical interpretation. The analysis presented in this paper gives us a better understanding of the behavior of the elastic net, allows us to better choose the parameters for the optimization, and suggests how to extend the underlying ideas to other domains.


A Comparative Analysis and Study of Multiview CNN Models for Joint Object Categorization and Pose Estimation

In the Object Recognition task, there exists a dichotomy between the categorization of objects and estimating object pose, where the former necessitates a view-invariant representation, while the latter requires a representation capable of capturing pose information over different categories of objects. With the rise of deep architectures, the prime focus has been on object category recognition. Deep learning methods have achieved wide success in this task. In contrast, object pose estimation using these approaches has received relatively less attention. In this work, we study how Convolutional Neural Networks (CNN) architectures can be adapted to the task of simultaneous object recognition and pose estimation. We investigate and analyze the layers of various CNN models and extensively compare between them with the goal of discovering how the layers of distributed representations within CNNs represent object pose information and how this contradicts with object category representations. We extensively experiment on two recent large and challenging multi-view datasets and we achieve better than the state-of-the-art.


Optimizing esthetics for implant restorations in the anterior maxilla: anatomic and surgical considerations.

The placement of dental implants in the anterior maxilla is a challenge for clinicians because of patients' exacting esthetic demands and difficult pre-existing anatomy. This article presents anatomic and surgical considerations for these demanding indications for implant therapy. First, potential causes of esthetic implant failures are reviewed, discussing anatomic factors such as horizontal or vertical bone deficiencies and iatrogenic factors such as improper implant selection or the malpositioning of dental implants for an esthetic implant restoration. Furthermore, aspects of preoperative analysis are described in various clinical situations, followed by recommendations for the surgical procedures in single-tooth gaps and in extended edentulous spaces with multiple missing teeth. An ideal implant position in all 3 dimensions is required. These mesiodistal, apicocoronal, and orofacial dimensions are well described, defining "comfort" and "danger" zones for proper implant position in the anterior maxilla. During surgery, the emphasis is on proper implant selection to avoid oversized implants, careful and low-trauma soft tissue handling, and implant placement in a proper position using either a periodontal probe or a prefabricated surgical guide. If missing, the facial bone wall is augmented using a proper surgical technique, such as guided bone regeneration with barrier membranes and appropriate bone grafts and/or bone substitutes. Finally, precise wound closure using a submerged or a semi-submerged healing modality is recommended. Following a healing period of between 6 and 12 weeks, a reopening procedure is recommended with a punch technique to initiate the restorative phase of therapy.


EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks.

INTRODUCTION
The ability to continuously and unobtrusively monitor levels of task engagement and mental workload in an operational environment could be useful in identifying more accurate and efficient methods for humans to interact with technology. This information could also be used to optimize the design of safer, more efficient work environments that increase motivation and productivity.

METHODS
The present study explored the feasibility of monitoring electroencephalo-graphic (EEG) indices of engagement and workload acquired unobtrusively and quantified during performance of cognitive tests. EEG was acquired from 80 healthy participants with a wireless sensor headset (F3-F4,C3-C4,Cz-POz,F3-Cz,Fz-C3,Fz-POz) during tasks including: multi-level forward/backward-digit-span, grid-recall, trails, mental-addition, 20-min 3-Choice Vigilance, and image-learning and memory tests. EEG metrics for engagement and workload were calculated for each 1 -s of EEG.

RESULTS
Across participants, engagement but not workload decreased over the 20-min vigilance test. Engagement and workload were significantly increased during the encoding period of verbal and image-learning and memory tests when compared with the recognition/ recall period. Workload but not engagement increased linearly as level of difficulty increased in forward and backward-digit-span, grid-recall, and mental-addition tests. EEG measures correlated with both subjective and objective performance metrics.

DISCUSSION
These data in combination with previous studies suggest that EEG engagement reflects information-gathering, visual processing, and allocation of attention. EEG workload increases with increasing working memory load and during problem solving, integration of information, analytical reasoning, and may be more reflective of executive functions. Inspection of EEG on a second-by-second timescale revealed associations between workload and engagement levels when aligned with specific task events providing preliminary evidence that second-by-second classifications reflect parameters of task performance.


MuDeL: a language and a system for describing and generating mutants

Mutation Testing is an approach for assessing the quality of a test case suite by analyzing its ability in distinguishing the product under test from a set of alternative products, the so-called mutants. The mutants are generated from the product under test by applying a set of mutant operators, which produce products with slight syntactical differences. The mutant operators are usually based on typical errors that occurs during the software development and can be related to a fault model. In this paper, we propose a language — named MuDeL — for describing mutant operators aiming not only at automating the mutant generation, but also at providing precision and formality to the operator descriptions. The language was designed using concepts that come from transformational and logical programming paradigms, as well as from context-free grammar theory. The language is illustrated with some simple examples. We also describe the mudelgen system, developed to support this language.


Direct-on-line-start permanent-magnet-assisted synchronous reluctance machine with ferrite magnets

The possibility of applying low-cost ferrite magnets in a direct-on-line-start permanent-magnet-assisted synchronous reluctance machine (DOL PMaSynRM) for industrial applications, such as pump or ventilation systems was studied. The main target of this machine topology was to achieve a higher efficiency class compared to a counterpart induction machine (IM). Because of the weak properties of the ferrite magnets the motor had to be optimized for a low magnetic stress starting from the permanent magnets' point of view. The optimization procedure includes finding of a suitable rotor construction with a proper number of barriers, thickness of the barriers and thickness of the ferrite magnets. More detailed attention was dedicated to the analysis and elimination of irreversible demagnetization risk of ferrite magnets during start up period.


Internet use and the logics of personal empowerment in health.

OBJECTIVES
The development of personal involvement and responsibility has become a strategic issue in health policy. The main goal of this study is to confirm the coexistence of three logics of personal empowerment through health information found on the Internet.

METHODS
A theoretical framework was applied to analyze personal empowerment from the user's perspective. A well-established Canadian Web site that offers information on personal health was used as a case study. A close-ended questionnaire was completed online by 2275 visitors and members of the Web site.

RESULTS
The findings confirm that the development of feelings of competence and control through Internet use is structured around three different logics. This implies three types of aptitudes that are fostered when the Internet is used to seek health information: doing what is prescribed (the professional logic), making choices based on personal judgment (the consumer logic), and mutual assistance (the community logic).

CONCLUSIONS
A recurring issue in three logics is the balance of roles and responsibilities required between the individual and the health provider.


Serendipity in the Research Literature: A Phenomenology of Serendipity Reporting

The role of information sciences is to connect people with the information they need to accomplish the tasks that contribute to the greater societal good. While evidence of the wonderful contributions arising from serendipitous events abound, the framework describing the information behaviors exhibited during a serendipitous experience is just emerging and additional detail regarding the factors influencing those behaviors is needed in order to support these experiences effectively. Furthermore, it is important to understand the whole process of serendipity to fully appreciate the impact of research policies, disciplinary traditions and academic reporting practices on this unique type of information behavior. This study addresses those need by examining the phenomenon of serendipity as it is reported by biomedical and radiography researchers. A mixed method content analysis of existing research reports will be incorporated with semi-structured interviews of serendipity reporters to gain a robust understanding of the phenomenon of serendipity, and provide detail that may inform the design of information environments.


Zero-forcing beamforming with block diagonalization scheme for Coordinated Multi-Point transmission

Coordinated Multi-Point (CoMP) transmission is a technology targeted for Long Term Evolution Advanced (LTE-A). The Joint Processing (JP) technique for CoMP can maximize system performance, which is achieved mainly with channel information-based beamforming algorithms. Precoding methods perform differently in various CoMP scenarios. In this paper, we propose a joint processing scheme in downlink CoMP transmission system. We apply block diagonal beamforming to downlink transmission, and assume perfect knowledge of downlink channels and transmit messages at each transmit point.


Social Computing for Mobile Big Data

Mobile big data contains vast statistical features in various dimensions, including spatial, temporal, and the underlying social domain. Understanding and exploiting the features of mobile data from a social network perspective will be extremely beneficial to wireless networks, from planning, operation, and maintenance to optimization and marketing.


Tobler ' s First Law of Geography , Self Similarity , and Perlin Noise : A Large Scale Analysis of Gradient Distribution in Southern Utah with Application to Procedural Terrain Generation

A statistical analysis finds that in a 160,000 square kilometer region of southern Utah gradients appear to be exponentially distributed at resolutions from 5m up to 1km. A simple modification to the Perlin noise generator changing the gradient distribution in each octave to an exponential distribution results in realistic and interesting procedurally generated terrain. The inverse transform sampling method is used in the amortized noise algorithm to achieve an exponential distribution in each octave, resulting in the generation of infinite non-repeating terrain with the same characteristics.


"My Data Just Goes Everywhere: " User Mental Models of the Internet and Implications for Privacy and Security

Many people use the Internet every day yet know little about how it really works. Prior literature diverges on how people’s Internet knowledge affects their privacy and security decisions. We undertook a qualitative study to understand what people do and do not know about the Internet and how that knowledge affects their responses to privacy and security risks. Lay people, as compared to those with computer science or related backgrounds, had simpler mental models that omitted Internet levels, organizations, and entities. People with more articulated technical models perceived more privacy threats, possibly driven by their more accurate understanding of where specific risks could occur in the network. Despite these differences, we did not find a direct relationship between people’s technical background and the actions they took to control their privacy or increase their security online. Consistent with other work on user knowledge and experience, our study suggests a greater emphasis on policies and systems that protect privacy and security without relying too much on users’ security practices.


Thirty-Five Years of Research on Neuro-Linguistic Programming . NLP Research Data Base . State of the Art or Pseudoscientific Decoration ?

The huge popularity of Neuro-Linguistic Programming (NLP) therapies and training has not been accompanied by knowledge of the empirical underpinnings of the concept. The article presents the concept of NLP in the light of empirical research in the Neuro-Linguistic Programming Research Data Base. From among 315 articles the author selected 63 studies published in journals from the Master Journal List of ISI. Out of 33 studies, 18.2% show results supporting the tenets of NLP, 54.5% results non-supportive of the NLP tenets and 27.3% brings uncertain results. The qualitative analysis indicates the greater weight of the non-supportive studies and their greater methodological worth against the ones supporting the tenets. Results contradict the claim of an empirical basis of NLP.


Cloud Computing: A Review

Cloud computing is a development of parallel, distributed and grid computing which provides computing potential as a service to clients rather than a product. Clients can access software resources, valuable information and hardware devices as a subscribed and monitored service over a network through cloud computing.Due to large number of requests for access to resources and service level agreements between cloud service providers and clients, few burning issues in cloud environment like QoS, Power, Privacy and Security, VM Migration, Resource Allocation and Scheduling need attention of research community.Resource allocation among multiple clients has to be ensured as per service level agreements. Several techniques have been invented and tested by research community for generation of optimal schedules in cloud computing. A few promising approaches like Metaheuristics, Greedy, Heuristic technique and Genetic are applied for task scheduling in several parallel and distributed systems. This paper presents a review on scheduling proposals in cloud environment.


Patient satisfaction and medication adherence assessment amongst patients at the diabetes medication therapy adherence clinic.

AIMS
To determine the satisfaction and current adherence status of patients with diabetes mellitus at the diabetes Medication Therapy Adherence Clinic and the relationship between patient satisfaction and adherence.

METHODS
This cross-sectional descriptive study was carried out at three government hospitals in the state of Johor, Malaysia. Patient's satisfaction was measured using the Patient Satisfaction with Pharmaceutical Care Questionnaire; medication adherence was measured using the eight-item Morisky Medication Adherence Scale.

RESULTS
Of n=165 patients, 87.0% of patients were satisfied with DMTAC service (score 60-100) with mean scores of 76.8. On the basis of MMAS, 29.1% had a medium rate and 26.1% had a high rate of adherence. Females are 3.02 times more satisfied with the pharmaceutical service compared to males (OR 3.03, 95% CI 1.12-8.24, p<0.05) and non-Malays are less satisfied with pharmaceutical care provided during DMTAC compared to Malays (OR 0.32, 95% CI 0.12-0.85, p<0.05). Older patients age group ≥60 years were 3.29 times more likely to adhere to their medications (OR 3.29, 95% CI 1.10-9.86, p<0.05). Females were the most adherent compared to males (OR 2.33, 95%CI 1.10-4.93, p<0.05) and patients with secondary level of education were 2.72 times more adherent to their medications compared to those in primary school and no formal education (OR 2.72, 95%CI 1.13-6.55, p<0.05). There is a significant (p<0.01), positive fair correlation (r=0.377) between satisfaction and adherence.

CONCLUSION
Patients were highly satisfied with DMTAC service, while their adherence levels were low. There is an association between patient satisfaction and adherence.


Wind Turbine Structural Health Monitoring : A Short Investigation Based on SCADA Data

The use of offshore wind farms has been growing in recent years, as steadier and higher wind speeds can be generally found over water compared to land. Moreover, as human activities tend to complicate the construction of land wind farms, offshore locations, which can be found more easily near densely populated areas, can be seen as an attractive choice. However, the cost of an offshore wind farm is relatively high, and therefore their reliability is crucial if they ever need to be fully integrated into the energy arena. As wind turbines have become more complex, efficient, and expensive structures, they require more sophisticated monitoring systems, especially in offshore sites where the financial losses due to failure could be substantial. This paper presents the preliminary analysis of supervisor control and data acquisition (SCADA) extracts from the Lillgrund wind farm for the purposes of structural health monitoring. A machine learning approach is applied in order to produce individual power curves, and then predict measurements of the power produced of each wind turbine from the measurements of the other wind turbines in the farm. A comparison between neural network and Gaussian process regression is also made.


Deep Residual Learning for Image Recognition

Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously. We explicitly reformulate the layers as learning residual functions with reference to the layer inputs, instead of learning unreferenced functions. We provide comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth. On the ImageNet dataset we evaluate residual nets with a depth of up to 152 layers - 8× deeper than VGG nets [40] but still having lower complexity. An ensemble of these residual nets achieves 3.57% error on the ImageNet test set. This result won the 1st place on the ILSVRC 2015 classification task. We also present analysis on CIFAR-10 with 100 and 1000 layers. The depth of representations is of central importance for many visual recognition tasks. Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset. Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions1, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.


Learning a Deep Convolutional Network for Image Super-Resolution

We propose a deep learning method for single image superresolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep convolutional neural network (CNN) [15] that takes the lowresolution image as the input and outputs the high-resolution one. We further show that traditional sparse-coding-based SR methods can also be viewed as a deep convolutional network. But unlike traditional methods that handle each component separately, our method jointly optimizes all layers. Our deep CNN has a lightweight structure, yet demonstrates state-of-the-art restoration quality, and achieves fast speed for practical on-line usage.


A Public Domain Dataset for Human Activity Recognition using Smartphones

Human-centered computing is an emerging research field that aims to understand human behavior and integrate users and their social context with computer systems. One of the most recent, challenging and appealing applications in this framework consists in sensing human body motion using smartphones to gather context information about people actions. In this context, we describe in this work an Activity Recognition database, built from the recordings of 30 subjects doing Activities of Daily Living (ADL) while carrying a waist-mounted smartphone with embedded inertial sensors, which is released to public domain on a well-known on-line repository. Results, obtained on the dataset by exploiting a multiclass Support Vector Machine (SVM), are also acknowledged.


On deep learning-based channel decoding

We revisit the idea of using deep neural networks for one-shot decoding of random and structured codes, such as polar codes. Although it is possible to achieve maximum a posteriori (MAP) bit error rate (BER) performance for both code families and for short codeword lengths, we observe that (i) structured codes are easier to learn and (ii) the neural network is able to generalize to codewords that it has never seen during training for structured, but not for random codes. These results provide some evidence that neural networks can learn a form of decoding algorithm, rather than only a simple classifier. We introduce the metric normalized validation error (NVE) in order to further investigate the potential and limitations of deep learning-based decoding with respect to performance and complexity.


Automated plant Watering system

In daily operations related to farming or gardening Watering is the most important cultural practice and the most labor-intensive task. No matter whichever weather it is, either too hot and dry or too cloudy and wet, you want to be able to control the amount of water that reaches your plants. Modern watering systems could be effectively used to water plants when they need it. But this manual process of watering requires two important aspects to be considered: when and how much to water. In order to replace manual activities and making gardener's work easier, we have create automatic plant watering system. By adding automated plant watering system to your garden or agricultural field, you will help all of your plants reach their fullest potential as well as conserving water. Using sprinklers drip emitters, or a combination of both, we can design a system that is ideal for every plant in our yard. For implementation of automatic plant watering system, we have used combination of sprinkler systems, pipes, and nozzles. This project uses the ATmega328 microcontroller. It is programmed to sense moisture level of plants at particular instance of time, if the moisture content is less than specified threshold which is predefined according to particular plant's water need then desired amount of water is supplied till it reaches threshold. Generally, plants need to be watered twice a day, morning and evening. Thus, the microcontroller is programmed to water plants two times per day. System is designed in such a way that it reports its current state as well as remind the user to add water to the tank. All this notifications are made through mobile application. We hope that through this prototype we all can enjoy having plants, without being worried about absent or forgetfulness.


Energy-Efficient I/O Thread Schedulers for NVMe SSDs on NUMA

Non-volatile memory express (NVMe) based SSDs and the NUMA platform are widely adopted in servers to achieve faster storage speed and more powerful processing capability. As of now, very little research has been conducted to investigate the performance and energy efficiency of the state-of-the-art NUMA architecture integrated with NVMe SSDs, an emerging technology used to host parallel I/O threads. As this technology continues to be widely developed and adopted, we need to understand the runtime behaviors of such systems in order to design software runtime systems that deliver optimal performance while consuming only the necessary amount of energy. This paper characterizes the runtime behaviors of a Linux-based NUMA system employing multiple NVMe SSDs. Our comprehensive performance and energy-efficiency study using massive numbers of parallel I/O threads shows that the penalty due to CPU contention is much smaller than that due to remote access of NVMe SSDs. Based on this insight, we develop a dynamic "lesser evil" algorithm called ESN, to minimize the impact of these two types of penalties. ESN is an energy-efficient profiling-based I/O thread scheduler for managing I/O threads accessing NVMe SSDs on NUMA systems. Our empirical evaluation shows that ESN can achieve optimal I/O throughput and latency while consuming up to 50% less energy and using fewer CPUs.