Neural Relation Extraction via Inner-Sentence Noise Reduction and Transfer Learning

Extracting relations is critical for knowledge base completion and construction in which distant supervised methods are widely used to extract relational facts automatically with the existing knowledge bases. However, the automatically constructed datasets comprise amounts of low-quality sentences containing noisy words, which is neglected by current distant supervised methods resulting in unacceptable precisions. To mitigate this problem, we propose a novel word-level distant supervised approach for relation extraction. We first build Sub-Tree Parse (STP) to remove noisy words that are irrelevant to relations. Then we construct a neural network inputting the subtree while applying the entity-wise attention to identify the important semantic features of relational words in each instance. To make our model more robust against noisy words, we initialize our network with a priori knowledge learned from the relevant task of entity classification by transfer learning. We conduct extensive experiments using the corpora of New York Times (NYT) and Freebase. Experiments show that our approach is effective and improves the area of Precision/Recall (PR) from 0.35 to 0.39 over the state-of-the-art work.


An Evaluation of Aggregation Techniques in Crowdsourcing

As the volumes of AI problems involving human knowledge are likely to soar, crowdsourcing has become essential in a wide range of world-wide-web applications. One of the biggest challenges of crowdsourcing is aggregating the answers collected from the crowd since the workers might have wide-ranging levels of expertise. In order to tackle this challenge, many aggregation techniques have been proposed. These techniques, however, have never been compared and analyzed under the same setting, rendering a ‘right’ choice for a particular application very difficult. Addressing this problem, this paper presents a benchmark that offers a comprehensive empirical study on the performance comparison of the aggregation techniques. Specifically, we integrated several stateof-the-art methods in a comparable manner, and measured various performance metrics with our benchmark, including computation time, accuracy, robustness to spammers, and adaptivity to multi-labeling. We then provide in-depth analysis of benchmarking results, obtained by simulating the crowdsourcing process with different types of workers. We believe that the findings from the benchmark will be able to serve as a practical guideline for crowdsourcing applications.


ONTOLOGY ALIGNMENT USING MACHINE LEARNING TECHNIQUES

In semantic web, ontology plays an important role to provide formal definitions of concepts and relationships. Therefore, communicating similar ontologies becomes essential to provide ontologies interpretability and extendibility. Thus, it is inevitable to have similar but not the same ontologies in a particular domain, since there might be several definitions for a given concept. This paper presents a method to combine similarity measures of different categories without having ontology instances or any user feedbacks towards aligning two given ontologies. To align different ontologies efficiently, K Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT) and AdaBoost classifiers are investigated. Each classifier is optimized based on the lower cost and better classification rate. Experimental results demonstrate that the F-measure criterion improves to 99% using feature selection and combination of AdaBoost and DT classifiers, which is highly comparable, and outperforms the previous reported F-measures. Computer Engineering Department, Shahid Chamran University, Ahvaz, Iran.


Pictogrammar: an AAC device based on a semantic grammar

As many as two-thirds of individuals with an Autism Spectrum Disorder (ASD) also have language impairments, which can range from mild limitations to complete non-verbal behavior. For such cases, there are several Augmentative and Alternative Communication (AAC) devices available. These are computer-designed tools in order to help people with ASD to palliate or overcome such limitations, at least partially. Some of the most popular AAC devices are based on pictograms, so that a pictogram is the graphical representation of a simple concept and sentences are composed by concatenating a number of such pictograms. Usually, these tools have to manage a vocabulary made up of hundreds of pictograms/concepts, with no or very poor knowledge of the language at semantic and pragmatic level. In this paper we present Pictogrammar, an AAC system which takes advantage of SUpO and PictOntology. SUpO (Simple Upper Ontology) is a formal semantic ontology which is made up of detailed knowledge of facts of everyday life such as simple words, with special interest in linguistic issues, allowing automated grammatical supervision. PictOntology is an ontology developed to manage sets of pictograms, linked to SUpO. Both ontologies make possible the development of tools which are able to take advantage of a formal semantics.


Multi-modal Mean-Fields via Cardinality-Based Clamping

Mean Field inference is central to statistical physics. It has attracted much interest in the Computer Vision community to efficiently solve problems expressible in terms of large Conditional Random Fields. However, since it models the posterior probability distribution as a product of marginal probabilities, it may fail to properly account for important dependencies between variables. We therefore replace the fully factorized distribution of Mean Field by a weighted mixture of such distributions, that similarly minimizes the KL-Divergence to the true posterior. By introducing two new ideas, namely, conditioning on groups of variables instead of single ones and using a parameter of the conditional random field potentials, that we identify to the temperature in the sense of statistical physics to select such groups, we can perform this minimization efficiently. Our extension of the clamping method proposed in previous works allows us to both produce a more descriptive approximation of the true posterior and, inspired by the diverse MAP paradigms, fit a mixture of Mean Field approximations. We demonstrate that this positively impacts real-world algorithms that initially relied on mean fields.


Design and Implementation of a Reliable Wireless Real-Time Home Automation System Based on Arduino Uno Single-Board Microcontroller

—This paper presents design and implementation concepts for a wireless real-time home automation system based on Arduino Uno microcontroller as central controllers. The proposed system has two operational modes. The first one is denoted as a manually–automated mode in which the user can monitor and control the home appliances from anywhere over the world using the cellular phone through Wi-Fi communication technology. The second one is referred to a self-automated mode that makes the controllers to be capable of monitoring and controlling different appliances in the home automatically in response to the signals comes from the related sensors. To support the usefulness of the proposed technique, a hardware implementation with Matlab-GUI platform for the proposed system is carried out and the reliability of the system is introduced. The proposed system is shown to be a simple, cost effective and flexible that making it a suitable and a good candidate for the smart home future. I. INTRODUCTION ecently, man's work and life are increasingly tight with the rapid growth in communications and information technology. The informationized society has changed human being's way of life as well as challenged the traditional residence. Followed by the rapid economic expansion, living standard keeps raising up day by day that people have a higher requirement for dwelling functions. The intellectualized society brings diversified information where safe, economic, comfortable and convenient life has become the ideal for every modern family [1].


Security Keys: Practical Cryptographic Second Factors for the Modern Web

“Security Keys” are second-factor devices that protect users against phishing and man-in-the-middle attacks. Users carry a single device and can self-register it with any online service that supports the protocol. The devices are simple to implement and deploy, simple to use, privacy preserving, and secure against strong attackers. We have shipped support for Security Keys in the Chrome web browser and in Google’s online services. We show that Security Keys lead to both an increased level of security and user satisfaction by analyzing a two year deployment which began within Google and has extended to our consumer-facing web applications. The Security Key design has been standardized by the FIDO Alliance, an organization with more than 250 member companies spanning the industry. Currently, Security Keys have been deployed by Google, Dropbox, and GitHub. An updated and extended tech report is available at https://github.com/google/u2fref-code/docs/SecurityKeys_TechReport.pdf.


A web-based bayesian intelligent tutoring system for computer programming

In this paper, we present a Web-based intelligent tutoring system, called BITS. The decision making process conducted in our intelligent system is guided by a Bayesian network approach to support students in learning computer programming. Our system takes full advantage of Bayesian networks, which are a formal framework for uncertainty management in Artificial Intelligence based on probability theory. We discuss how to employ Bayesian networks as an inference engine to guide the students’ learning processes . In addition, we describe the architecture of BITS and the role of each module in the system. Whereas many tutoring systems are static HTML Web pages of a class textbook or lecture notes, our intelligent system can help a student nav igate through the online course materials, recommend learning goals, and generate appropriate reading sequences.


Adjustable Autonomy : From Theory to Implementation

Recent exciting, ambitious applications in agent technology involve agents acting individually or in teams in support of critical activities of individual humans or entire human organizations. Applications range from intelligent homes [13], to "routine" organizational coordination[16], to electronic commerce[4] to long-term space missions[12, 6]. These new applications have brought forth an increasing interest in agents’ adjustable autonomy (AA), i.e., in agents’ dynamically adjusting their own level of autonomy based on the situation[8]. In fact, many of these applications will not be deployed, unless reliable AA reasoning is a central component. At the heart of AA is the question of whether and when agents should make autonomous decisions and when they should transfer decision-making control to other entities (e.g., human users). Unfortunately, previous work in adjustable autonomy has focused on individual agent-human interactions and tile techniques developed fail to scale-up to complex heterogeneous organizations. Indeed, as a first step, we focused on a smallscale, but real-world agent-human organization called Electric Elves, where an individual agent and human worked together within a larger multiagent context. Although’the application limits the interactions among entities, key weaknesses of previous approaches to adjustable autonomy are readily apparent. In particular, previous approaches to transferof-control are seen to be too rigid, employing one-shot transfersof-control that can result in unacceptable coordination failures. Furthermore, the previous approaches ignore potential costs (e.g., from delays) to an agent’s team due to such transfers of control. To remedy such problems, we propose a novel approach to AA, based on the notion of a transfer-of-control strategy. A transfer-of-control strategy consists of a conditional sequence of two types of actions: (i) actions to transfer decision-making control (e.g., from the agent to the user or vice versa) and (ii) actions to change an agent’s pre-specified coordination constraints with team members, aimed at minimizing miscoordination costs. The goal is for high quality individual decisions to be made with minimal disruption to the coordination of the team. We operationalize such strategies via Markov decision processes (MDPs) which select the optimal strategy given an uncertain environment and costs to individuals and teams. We have developed a general reward function and state representation for such an MDP, to facilitate application of the approach to different domains. We present results from a careful evaluation of this approach, including via its use in our real-world, deployed Electric Elves system.


The Granular Tabu Search and Its Application to the Vehicle-Routing Problem


An Item-based Collaborative Filtering Recommendation Algorithm Using Slope One Scheme Smoothing

Collaborative filtering is one of the most important technologies in electronic commerce. With the development of recommender systems, the magnitudes of users and items grow rapidly, resulted in the extreme sparsity of user rating data set. Traditional similarity measure methods work poor in this situation, make the quality of recommendation system decreased dramatically. Poor quality is one major challenge in collaborative filtering recommender systems. Sparsity of users’ ratings is the major reason causing the poor quality. To address this issue, an item-based collaborative filtering recommendation algorithm using slope one scheme smoothing is presented. This approach predicts item ratings that users have not rated by the employ of slope one scheme, and then uses Pearson correlation similarity measurement to find the target items’ neighbors, lastly produces the recommendations. The experiments are made on a common data set using different recommender algorithms. The results show that the proposed approach can improve the accuracy of the collaborative filtering recommender system.


Design methodology using inversion coefficient for low-voltage low-power CMOS voltage reference

This paper presents an analog design methodology, using the selection of inversion coefficient of MOS devices, to design low voltage and low-power (LVLP) CMOS voltage references. These circuits often work under subthreshold operation. Hence, there is a demand for analog design methods that optimize the sizing process of transistors working in weak and moderate inversion. The advantage of the presented method -- compared with the traditional approach to design circuits -- is the reduction of design cycle time and minimization of trial-and-error simulations, if the proposed equations are used. As a case study, a LVLP voltage reference based on subthreshold MOSFETs with supply voltage of 0.7 V was designed for 0.18-¼m CMOS technology.


Exploring commonalities across participants in the neural representation of objects.

The question of whether the neural encodings of objects are similar across different people is one of the key questions in cognitive neuroscience. This article examines the commonalities in the internal representation of objects, as measured with fMRI, across individuals in two complementary ways. First, we examine the commonalities in the internal representation of objects across people at the level of interobject distances, derived from whole brain fMRI data, and second, at the level of spatially localized anatomical brain regions that contain sufficient information for identification of object categories, without making the assumption that their voxel patterns are spatially matched in a common space. We examine the commonalities in internal representation of objects on 3T fMRI data collected while participants viewed line drawings depicting various tools and dwellings. This exploratory study revealed the extent to which the representation of individual concepts, and their mutual similarity, is shared across participants.


Hierarchy of fibrillar organization levels in the polytene interphase chromosomes of Chironomus.


Video Paragraph Captioning Using Hierarchical Recurrent Neural Networks

We present an approach that exploits hierarchical Recurrent Neural Networks (RNNs) to tackle the video captioning problem, i.e., generating one or multiple sentences to describe a realistic video. Our hierarchical framework contains a sentence generator and a paragraph generator. The sentence generator produces one simple short sentence that describes a specific short video interval. It exploits both temporal-and spatial-attention mechanisms to selectively focus on visual elements during generation. The paragraph generator captures the inter-sentence dependency by taking as input the sentential embedding produced by the sentence generator, combining it with the paragraph history, and outputting the new initial state for the sentence generator. We evaluate our approach on two large-scale benchmark datasets: YouTubeClips and TACoS-MultiLevel. The experiments demonstrate that our approach significantly outperforms the current state-of-the-art methods with BLEU@4 scores 0.499 and 0.305 respectively.


Autonomous Self-Assembly in Swarm-Bots

In this paper, we discuss the self-assembling capabilities of the swarm-bot, a distributed robotics concept that lies at the intersection between collective and self-reconfigurable robotics. A swarm-bot is comprised of autonomous mobile robots called s-bots. S-bots can either act independently or self-assemble into a swarm-bot by using their grippers. We report on experiments in which we study the process that leads a group of s-bots to self-assemble. In particular, we present results of experiments in which we vary the number of s-bots (up to 16 physical robots), their starting configurations, and the properties of the terrain on which self-assembly takes place. In view of the very successful experimental results, swarm-bot qualifies as the current state of the art in autonomous self-assembly


Facial Expression Recognition Using Enhanced Deep 3D Convolutional Neural Networks

Deep Neural Networks (DNNs) have shown to outperform traditional methods in various visual recognition tasks including Facial Expression Recognition (FER). In spite of efforts made to improve the accuracy of FER systems using DNN, existing methods still are not generalizable enough in practical applications. This paper proposes a 3D Convolutional Neural Network method for FER in videos. This new network architecture consists of 3D Inception-ResNet layers followed by an LSTM unit that together extracts the spatial relations within facial images as well as the temporal relations between different frames in the video. Facial landmark points are also used as inputs to our network which emphasize on the importance of facial components rather than the facial regions that may not contribute significantly to generating facial expressions. Our proposed method is evaluated using four publicly available databases in subject-independent and cross-database tasks and outperforms state-of-the-art methods.


A Java API and Web Service Gateway for wireless M-Bus

M-Bus Systems are used to support the remote data exchange with meter units through a network. They provide an easy extend able and cost effective method to connect many meter devices to one coherent network. Master applications operate as concentrated data collectors and are used to process measured values further. As modern approaches facilitate the possibility of a Smart Grid, M-Bus can be seen as the foundation of this technology. With the current focus on a more effective power grid, Smart Meters and Smart Grids are an important research topic. This bachelor thesis first gives an overview of the M-Bus standard and then presents a Java library and API to access M-Bus devices remotely in a standardized way through Web Services. Integration into common IT applications requires interoperable interfaces which also facilitate automated machine-to-machine communication. The oBIX (Open Building Information Exchange) standard provides such standardized objects and thus is used to create a Web Service gateway for the Java API.


AWEsome: An open-source test platform for airborne wind energy systems

In this paper we present AWEsome (Airborne Wind Energy Standardized Open-source Model Environment), a test platform for airborne wind energy systems that consists of low-cost hardware and is entirely based on open-source software. It can hence be used without the need of large financial investments, in particular by research groups and startups to acquire first experiences in their flight operations, to test novel control strategies or technical designs, or for usage in public relations. Our system consists of a modified off-the-shelf model aircraft that is controlled by the pixhawk autopilot hardware and the ardupilot software for fixed wing aircraft. The aircraft is attached to the ground by a tether. We have implemented new flight modes for the autonomous tethered flight of the aircraft along periodic patterns. We present the principal functionality of our algorithms. We report on first successful tests of these modes in real flights. ∗Author names are in alphabetical order. a Universität Bonn, bechtle@physik.uni-bonn.de b Universität Bonn, thomasgehrmann@gmx.net c Humboldt-Universität zu Berlin, csieg@physik.hu-berlin.de d Daidalos Capital, zillmann@daidalos-capital.com awesome.physik.uni-bonn.de 1 ar X iv :1 70 4. 08 69 5v 1 [ cs .S Y ] 2 7 A pr 2 01 7 Section


Optimization of Belt-Type Electrostatic Separation of Triboaerodynamically Charged Granular Plastic Mixtures

Electrostatic separation is frequently employed for the selective sorting of conducting and insulating materials for waste electric and electronic equipment. In a series of recent papers, the authors have presented several novel triboelectrostatic separation installations for the recycling of the main categories of plastics contained in information technology wastes. The aim of the present work is to optimize the design and the operation of such an installation, composed of a belt-type electrostatic separator associated to a propeller-type tribocharging device. The experimental design method is employed for modeling and optimizing the separation of a mixture of high-impact polystyrene and acrylonitrile butadiene styrene granules originating from shredded out-of-use computer cases. A distinct experiment is carried out on three synthetic mixtures of virgin plastic granules: 75% polyamide (PA)+ 25% polycarbonate (PC), 50% PA+ 50% PC, and 25% PA + 75% PC. The best results are obtained for the mixtures containing equal quantities of the two constituents.


Transformerless micro-inverter for grid-connected photovoltaic systems

The leakage currents caused by high-frequency common-mode (CM) voltage have become a major concern in transformerless photovoltaic (PV) inverters. This paper addresses to a review on dc-ac converters applied to PV systems that can avoid the circulation of leakage currents. Looking for a lower cost and higher reliability solution, a 250 W PV transformerless micro-inverter prototype based on the bipolar full-bridge topology was built and tested. As it is confirmed by experimental results, this topology is able to maintain the CM voltage constant and prevent the circulation of CM currents through the circuit.


GQ ( λ ) : A general gradient algorithm for temporal-difference prediction learning with eligibility traces

A new family of gradient temporal-difference learning algorithms have recently been introduced by Sutton, Maei and others in which function approximation is much more straightforward. In this paper, we introduce the GQ(λ) algorithm which can be seen as extension of that work to a more general setting including eligibility traces and off-policy learning of temporally abstract predictions. These extensions bring us closer to the ultimate goal of this work—the development of a universal prediction learning algorithm suitable for learning experientially grounded knowledge of the world. Eligibility traces are essential to this goal because they bridge the temporal gaps in cause and effect when experience is processed at a temporally fine resolution. Temporally abstract predictions are also essential as the means for representing abstract, higher-level knowledge about courses of action, or options. GQ(λ) can be thought of as an extension of Q-learning. We extend existing convergence results for policy evaluation to this setting and carry out a forward-view/backwardview analysis to derive and prove the validity of the new algorithm. Introduction One of the main challenges in artificial intelligence (AI) is to connect the low-level experience to high-level representations (grounded world knowledge). Low-level experience refers to rich signals received back and forth between the agent and the world. Recent theoretical developments in temporal-difference learning combined with mathematical ideas developed for temporally abstract options, known as intra-option learning, can be used to address this challenge (Sutton, 2009). Intra-option learning (Sutton, Precup, and Singh, 1998) is seen as a potential method for temporalabstraction in reinforcement learning. Intra-option learning is a type of off-policy learning. Off-policy learning refers to learning about a target policy while following another policy, known as behavior policy. Offpolicy learning arises in Q-learning where the target policy is a greedy optimal policy while the behavior policy is exploratory. It is also needed for intra-option learning. Intra-option methods look inside options and allow AI agent to learn about multiple different options simultaneously from a single stream of received data. Option refers to a temporally course of actions with a termination condition. Options are ubiquitous in our everyday life. For example, to go for hiking, we need to consider and evaluate multiple options such as transportation options to the hiking trail. Each option includes a course of primitive actions and only is excited in particular states. The main feature of intra-option learning is its ability to predict the consequences of each option policy without executing it while data is received from a different policy. Temporal difference (TD) methods in reinforcement learning are considered as powerful techniques for prediction problems. In this paper, we consider predictions always in the form of answers to the questions. Questions are like “If of follow this trail, would I see a creek?” The answers to such questions are in the form of a single scalar (value function) that tells us about the expected future consequences given the current state. In general, due to the large number of states, it is not feasible to compute the exact value of each state entry. One of the key features of TD methods is their ability to generalize predictions to states that may not have visited; this is known as function approximation. Recently, Sutton et al. (2009b) and Maei et al. (2009) introduced a new family of gradient TD methods in which function approximation is much more straightforward than conventional methods. Prior to their work, the existing classical TD algorithms (e.g.; TD(λ) and Q-learning) with function approximation could become unstable and diverge (Baird, 1995; Tsitsiklis and Van Roy, 1997). In this paper, we extend their work to a more general setting that includes off-policy learning of temporally abstract predictions and eligibility traces. Temporally abstract predictions are essential for representing higher-level knowledge about the course of actions, or options (Sutton et al., 1998). Eligibility traces bridge between the temporal gaps when experience is processes at a temporally fine resolution. In this paper, we introduce the GQ(λ) algorithm that can be thought of as an extension to Q-learning (Watkins and Dayan, 1989); one of the most popular off-policy learning algorithms in reinforcement learning. Our algorithm incorporates gradient-descent ideas originally developed by Sutton et al. (2009a,b), for option conditional predictions with varying eligibility traces. We extend existing convergence results for policy evaluation to this setting and carry forward-view/backwardview analysis and prove the validity of the new algorithm. The organization of the paper is as follows: First, we describe the problem setting and define our notations. Then we introduce the GQ(λ) algorithm and describe how to use it. In the next sections we provide derivation of the algorithm and carry out analytical analysis on the equivalence of TD forward-view/backward-view. We finish the paper with convergence proof and conclusion section. Notation and background We consider the problem of policy evaluation in finite state-action Markov Decision Process (MDP). Under standard conditions, however, our results can be extended to MDPs with infinite state–action pairs. We use a standard reinforcement learning (RL) framework. In this setting, data is obtained from a continually evolving MDP with states st ∈ S, actions at ∈ A, and rewards rt ∈ <, for t = 1, 2, . . ., with each state and reward as a function of the preceding state and action. Actions are chosen according to the behavior policy b, which is assumed fixed and exciting, b(s, a) > 0,∀s, a. We consider the transition probabilities between state– action pairs, and for simplicity we assume there is a finite number N of state–action pairs. Suppose the agent find itself at time t in a state– action pair st, at. The agent likes its answer at that time to tell something about the future sequence st+1, at+1, . . . , st+k if actions from t + 1 on were taken according to the option until it terminated at time t+k. The option policy is denoted π : S × A → [0, 1] and whose termination condition is denoted β : S → [0, 1]. The answer is always in the form of a single number, and of course we have to be more specific about what we are trying to predict. There are two common cases: 1) we are trying to predict the outcome of the option; we want to know about the expected value of some function of the state at the time the option terminates. We call this function the outcome target function, and denote it z : S → <, 2) we are trying to predict the transient; that is, what happens during the option rather than its end. The most common thing to predict about the transient is the total or discounted reward during the option. We denote the reward function r : S ×A → <. Finally, the answer could conceivably be a mixture of both a transient and an outcome. Here we will present the algorithm for answering questions with both an outcome part z and a transient part r, with the two added together. In the common place where one wants only one of the two, the other is set to zero. Now we can start to state the goal of learning more precisely. In particular, we would like our answer to be equal to the expected value of the outcome target function at termination plus the cumulative sum of the transient reward function along the way: Q(st, at) (1) ≡ E [ rt+1 + γrt+2 + · · ·+ γrt+k + zt+k | π, β ] , where γ ∈ (0, 1] is discount factor and Q(s, a) denotes action value function that evaluates policy π given state-action pair s, a. To simplify the notation, from now on, we drop the superscript π on action values. In many problems the number of state-action pairs is large and therefore it is not feasible to compute the action values for each state-action entry. Therefore, we need to approximate the action values through generalization techniques. Here, we use linear function approximation; that is, the answer to a question is always formed linearly as Qθ(s, a) = θ>φ(s, a) ≈ Q(s, a) for all s ∈ S and a ∈ A, where θ ∈ < is a learned weight vector and φ(s, a) ∈ < indicates a state–action feature vector. The goal is to learn parameter vector θ through a learning method such as TD learning. The above (1) describes the target in a Monte Carlo sense, but of course we want to include the possibility of temporal-difference learning; one of the widely used techniques in reinforcement learning. To do this, we provide an eligibility-trace function λ : S → [0, 1] as described in Sutton and Barto (1998). We let eligibilitytrace function, λ, to vary over different states. In the next section, first we introduce GQ(λ); a general temporal-difference learning algorithm that is stable under off-policy training, and show how to use it. Then in later sections we provide the derivation of the algorithm and convergence proof. The GQ(λ) algorithm In this section we introduce the GQ(λ) algorithm for off-policy learning about the outcomes and transients of options, in other words, intra-option GQ(λ) for learning the answer to a question chosen from a wide (possibly universal) class of option-conditional predictive questions. To specify the question one provides four functions: π and β, for the option, and z and r, for the target functions. To specify how the answers will be formed one provides their functional form (here in linear form), the feature vectors φ(s, a) for all state–action pairs, and the eligibility-trace function λ. The discount factor γ can be taken to be 1, and thus ignored; the same effect as discounting can be achieved through the choice of β. Now, we specify the GQ(λ) algorithm as follows: The weight vector θ ∈ < is initialized arbitrarily. The secondary weight vector w ∈ < is init


Efficient Program Analyses Using Deductive and Semantic Methodologies

Program analysis is the process of gathering deeper insights about a source code and analysing them to resolve software problems of arbitrary complexity. The key challenge in program analysis is to keep it fast, precise and straightforward. This research focuses on three key objectives to achieve an efficient program analysis: (i) expressive data representation, (ii) optimised data structure and (iii) fast data processing mechanisms. State of the art technologies such as Resource Description Framework (RDF) as data representation format, triplestores as the storage & processing layer, and datalog to represent program analysis rules are considered in our research. diagram(BDD) to be embedded in the triplestore. Additionally, an ontology is being designed to standardise the definitions of concepts and representation of the knowledge in the program analysis domain.


Neuroimaging: Decoding mental states from brain activity in humans

Recent advances in human neuroimaging have shown that it is possible to accurately decode a person's conscious experience based only on non-invasive measurements of their brain activity. Such 'brain reading' has mostly been studied in the domain of visual perception, where it helps reveal the way in which individual experiences are encoded in the human brain. The same approach can also be extended to other types of mental state, such as covert attitudes and lie detection. Such applications raise important ethical issues concerning the privacy of personal thought.


Content-based multimedia information retrieval: State of the art and challenges

Extending beyond the boundaries of science, art, and culture, content-based multimedia information retrieval provides new paradigms and methods for searching through the myriad variety of media all over the world. This survey reviews 100+ recent articles on content-based multimedia information retrieval and discusses their role in current research directions which include browsing and search paradigms, user studies, affective computing, learning, semantic queries, new features and media types, high performance indexing, and evaluation techniques. Based on the current state of the art, we discuss the major challenges for the future.


Machine learning classifiers and fMRI: A tutorial overview

Interpreting brain image experiments requires analysis of complex, multivariate data. In recent years, one analysis approach that has grown in popularity is the use of machine learning algorithms to train classifiers to decode stimuli, mental states, behaviours and other variables of interest from fMRI data and thereby show the data contain information about them. In this tutorial overview we review some of the key choices faced in using this approach as well as how to derive statistically significant results, illustrating each point from a case study. Furthermore, we show how, in addition to answering the question of 'is there information about a variable of interest' (pattern discrimination), classifiers can be used to tackle other classes of question, namely 'where is the information' (pattern localization) and 'how is that information encoded' (pattern characterization).


Evidence in Management and Organizational Science : Assembling the Field ’ s Full Weight of Scientific Knowledge Through Syntheses

This chapter advocates the good scientific practice of systematic research syntheses in Management and Organizational Science (MOS). A research synthesis is the systematic accumulation, analysis and reflective interpretation of the full body of relevant empirical evidence related to a question. It is the * Corresponding author. Email: rousseau@andrew.cmu.edu 476 • The Academy of Management Annals critical first step in effective use of scientific evidence. Synthesis is not a conventional literature review. Literature reviews are often position papers, cherrypicking studies to advocate a point of view. Instead, syntheses systematically identify where research findings are clear (and where they aren’t), a key first step to establishing the conclusions science supports. Syntheses are also important for identifying contested findings and productive lines for future research. Uses of MOS evidence, that is, the motives for undertaking a research synthesis include scientific discovery and explanation, improved management practice guidelines, and formulating public policy. We identify six criteria for establishing the evidentiary value of a body of primary studies in MOS. We then pinpoint the stumbling blocks currently keeping the field from making effective use of its ever-expanding base of empirical studies. Finally, this chapter outlines (a) an approach to research synthesis suitable to the domain of MOS; and (b) supporting practices to make synthesis a collective MOS project. Evidence in Management and Organizational Science It is the nature of the object that determines the form of its possible science. (Bhaskar, 1998, p. 3) Uncertain knowledge is better than ignorance. (Mitchell, 2000, p. 9) This chapter is motivated by the failure of Management and Organizational Science (MOS) to date to make full effective use of its available research evidence. Failure to make effective use of scientific evidence is a problem both management scholars and practitioners face. Effective use of evidence, as we employ the term here, means to assemble and interpret a body of primary studies relevant to a question of fact, and then take appropriate action based upon the conclusions drawn. For science, appropriate action might be to direct subsequent research efforts elsewhere if the science is clear, or to recommend a new tact if findings are inconclusive. For practice, appropriate action might begin with a summary of key findings to share with educators, thought leaders, consultants, and the broader practice community. Unfortunately, bodies of evidence in MOS are seldom assembled or interpreted in the systematic fashion needed to permit their confident use. A systematic review of the full body of evidence is the key first step in formulating a science-based conclusion. As a consequence at present, neither the MOS scholar nor the practitioner can readily claim to be well-informed. This lapse has many causes. Two are central to our failure to use MOS evidence well: (1) overvaluing novelty to the detriment of accumulating convergent findings; and (2) the general absence of systematic research syntheses. These two causes are intertwined in that, as we shall show, use of research syntheses ties closely with how a field gauges the value of its research. This chapter’s subject, the systematic research synthesis, is not to be confused Evidence in Management and Organizational Science • 477 with a conventional literature review, its less systematic, non-representative counterpart. Systematic research syntheses assemble, analyze and interpret a comprehensive body of evidence in a highly reflective fashion according to six evidentiary criteria we describe. The why, what, and how of research synthesis in MOS is this chapter’s focus. The explosion of management research since World War II has created knowledge products at a rate far outpacing our current capacity for recall, sense-making, and use. In all likelihood, MOS’s knowledge base will continue to expand. We estimate over 200 peer-reviewed journals currently publish MOS research. These diverse outlets reflect the fact that MOS is not a discipline; it is an area of inter-related research activities cutting across numerous disciplines and subfields. The area’s expansion translates into a body of knowledge that is increasingly fragmented (Rousseau, 1997), transdisciplinary (Whitley, 2000), and interdependent with advancements in other social sciences (Tranfield, Denyer & Smart, 2003). The complicated state of MOS research makes it tough to know what we know, especially as specialization spawns research communities that often don’t and sometimes can’t talk with each other.


A Bridging Framework for Model Optimization and Deep Propagation

Optimizing task-related mathematical model is one of the most fundamental methodologies in statistic and learning areas. However, generally designed schematic iterations may hard to investigate complex data distributions in real-world applications. Recently, training deep propagations (i.e., networks) has gained promising performance in some particular tasks. Unfortunately, existing networks are often built in heuristic manners, thus lack of principled interpretations and solid theoretical supports. In this work, we provide a new paradigm, named Propagation and Optimization based Deep Model (PODM), to bridge the gaps between these different mechanisms (i.e., model optimization and deep propagation). On the one hand, we utilize PODM as a deeply trained solver for model optimization. Different from these existing network based iterations, which often lack theoretical investigations, we provide strict convergence analysis for PODM in the challenging nonconvex and nonsmooth scenarios. On the other hand, by relaxing the model constraints and performing end-to-end training, we also develop a PODM based strategy to integrate domain knowledge (formulated as models) and real data distributions (learned by networks), resulting in a generic ensemble framework for challenging real-world applications. Extensive experiments verify our theoretical results and demonstrate the superiority of PODM against these state-of-the-art approaches.


Fast Alternating LS Algorithms for High Order CANDECOMP/PARAFAC Tensor Factorizations

CANDECOMP/PARAFAC (CP) has found numerous applications in wide variety of areas such as in chemometrics, telecommunication, data mining, neuroscience, separated representations. For an order- N tensor, most CP algorithms can be computationally demanding due to computation of gradients which are related to products between tensor unfoldings and Khatri-Rao products of all factor matrices except one. These products have the largest workload in most CP algorithms. In this paper, we propose a fast method to deal with this issue. The method also reduces the extra memory requirements of CP algorithms. As a result, we can accelerate the standard alternating CP algorithms 20-30 times for order-5 and order-6 tensors, and even higher ratios can be obtained for higher order tensors (e.g., N ≥ 10). The proposed method is more efficient than the state-of-the-art ALS algorithm which operates two modes at a time (ALSo2) in the Eigenvector PLS toolbox, especially for tensors with order N ≥ 5 and high rank.


Planning With Agents : An Efficient Approach Using Hierarchical Dynamic Decision Networks

To be useful in solving real world problems, agents need to be able to act in environments in which it may not be possible to be completely aware of the current state and where actions do not always work as planned. Additional complexity is added to the problem when one considers groups of agents working together. By casting the agent planning problem as a partially observable Markov decision problem (POMDP), optimal policies can be generated for partially observable and stochastic environments. Exact solutions, however, are notoriously difficult to find for problems of a realistic nature. We introduce a hierarchical decision network-based planning algorithm that can generate high quality plans during execution while demonstrating significant time savings. We also discuss how this approach is particularly applicable to planning in a multiagent environment as compared to other POMDP-based planning algorithms. We present experimental results comparing our algorithm with results obtained by current POMDP and hierarchical POMDP (HPOMDP) methods.


Multi-label learning: a review of the state of the art and ongoing research

Multi-label learning is quite a recent supervised learning paradigm. Owing to its capabilities to improve performance in problems where a pattern may have more than one associated class, it has attracted the attention of researchers, producing an increasing number of publications. The paper presents an up-todate overview about multi-label learning with the aim of sorting and describing the main approaches developed till now. The formal definition of the paradigm, the analysis of its impact on the literature, its main applications, works developed and ongoing research are presented.


Occlusion-capable multiview volumetric three-dimensional display.

Volumetric 3D displays are frequently purported to lack the ability to reconstruct scenes with viewer-position-dependent effects such as occlusion. To counter these claims, a swept-screen 198-view horizontal-parallax-only 3D display is reported here that is capable of viewer-position-dependent effects. A digital projector illuminates a rotating vertical diffuser with a series of multiperspective 768 x 768 pixel renderings of a 3D scene. Evidence of near-far object occlusion is reported. The aggregate virtual screen surface for a stationary observer is described, as are guidelines to construct a full-parallax system and the theoretical ability of the present system to project imagery outside of the volume swept by the screen.


Ranking Attacks Based on Vulnerability Analysis

Now that multiple-known attacks can affect one software product at the same time, it is necessary to rank and prioritize those attacks in order to establish a better defense. The purpose of this paper is to provide a set of security metrics to rank attacks based on vulnerability analysis. The vulnerability information is retrieved from a vulnerability management ontology, which integrates commonly used standards like CVE, CWE, CVSS, and CAPEC. Among the benefits of ranking attacks through the method proposed here are: a more effective mitigation or prevention of attack patterns against systems, a better foundation to test software products, and a better understanding of vulnerabilities and attacks.


Stability analysis of negative impedance converter

Negative Impedance Converter (NIC) have received much attention to improve the restriction of antenna gain-bandwidth tradeoff relationship. However, a significant problem with NIC is the potential instability of unwanted oscillation due to the presence of positive feedback. For solving this problem, we propose a NIC circuit technique which is stable and has wideband characteristics of negative impedance.


Medical robotics—Regulatory, ethical, and legal considerations for increasing levels of autonomy

The regulatory, ethical, and legal barriers imposed on medical robots necessitate careful consideration of different levels of autonomy, as well as the context for use.


Activity recognition from acceleration data based on discrete consine transform and SVM

This paper developed a high-accuracy human activity recognition system based on single tri-axis accelerometer for use in a naturalistic environment. This system exploits the discrete cosine transform (DCT), the Principal Component Analysis (PCA) and Support Vector Machine (SVM) for classification human different activity. First, the effective features are extracted from accelerometer data using DCT. Next, feature dimension is reduced by PCA in DCT domain. After implementing the PCA, the most invariant and discriminating information for recognition is maintained. As a consequence, Multi-class Support Vector Machines is adopted to distinguish different human activities. Experiment results show that the proposed system achieves the best accuracy is 97.51%, which is better than other approaches.


Language Problems and ADHD Symptoms: How Specific Are the Links?

Symptoms of inattention and hyperactivity frequently co-occur with language difficulties in both clinical and community samples. We explore the specificity and strength of these associations in a heterogeneous sample of 254 children aged 5 to 15 years identified by education and health professionals as having problems with attention, learning and/or memory. Parents/carers rated pragmatic and structural communication skills and behaviour, and children completed standardised assessments of reading, spelling, vocabulary, and phonological awareness. A single dimension of behavioural difficulties including both hyperactivity and inattention captured behaviour problems. This was strongly and negatively associated with pragmatic communication skills. There was less evidence for a relationship between behaviour and language structure: behaviour ratings were more weakly associated with the use of structural language in communication, and there were no links with direct measures of literacy. These behaviour problems and pragmatic communication difficulties co-occur in this sample, but impairments in the more formal use of language that impact on literacy and structural communication skills are tied less strongly to behavioural difficulties. One interpretation is that impairments in executive function give rise to both behavioural and social communication problems, and additional or alternative deficits in other cognitive abilities impact on the development of structural language skills.


Soft tissue cephalometric analysis: diagnosis and treatment planning of dentofacial deformity.

This article will present a new soft tissue cephalometric analysis tool. This analysis may be used by the orthodontist and surgeon as an aid in diagnosis and treatment planning. The analysis is a radiographic instrument that was developed directly from the philosophy expressed in Arnett and Bergman "Facial keys to orthodontic diagnosis and treatment planning, Parts I and II" (Am J Orthop Dentofacial Orthod 1993; 103:299-312 and 395-411). The novelty of this approach, as with the "Facial Keys" articles, is an emphasis on soft tissue facial measurement.


A DTN-Based Sensor Data Gathering for Agricultural Applications

This paper presents our field experience in data collection from remote sensors. By letting tractors, farmers, and sensors have short-range radio communication devices with delay-disruption tolerant networking (DTN), we can collect data from those sensors to our central database. Although, several implementations have been made with cellular phones or mesh networks in the past, DTN-based systems for such applications are still under explored. The main objective of this paper is to present our practical implementation and experiences in DTN-based data collection from remote sensors. The software, which we have developed for this research, has about 50 kbyte footprint, which is much smaller than any other DTN implementation. We carried out an experiment with 39 DTN nodes at the University of Tokyo assuming an agricultural scenario. They achieved 99.8% success rate for data gathering with moderate latency, showing sufficient usefulness in data granularity.


Detection of Unhealthy Region of plant leaves using Neural Network

A leaf is an organ of vascular plant and is the principal lateral appendage of the stem. Each leaf has a set of features that differentiate it from the other leaves, such as margin and shape. This work proposes a comparison of supervised plant leaves classification using different approaches, based on different representations of these leaves, and the chosen algorithm. Beginning with the representation of leaves, we presented leaves by a fine-scale margin feature histogram, by a Centroid Contour Distance Curve shape signature, or by an interior texture feature histogram in 64 element vector for each one, after we tried different combination among these features to optimize results. We classified the obtained vectors. Then we evaluate the classification using cross validation. The obtained results are very interesting and show the importance of each feature. The classification of plant leaf images with biometric features. Traditionally, the trained taxonomic perform this process by following various tasks. The taxonomic usually classify the plants based on flowering and associative phenomenon. It was found that this process was time consuming and difficult. The biometric features of plants leaf like venation make this classification easy. Leaf biometric feature are analyzed using computer based method like morphological feature analysis and artificial neural network and Naive bayes based classifier. KNN classification model take input as the leaf venation morphological feature and classify them into four different species. The result of this classification based on leaf venation is achieved 96.53% accuracy in the training of the model for classification of leaves provide the 91% accuracy in testing to classify the leaf images. Keyword: Artificial neural network, K-NN Classification, Leaf venation pattern, Morphological Features


Earlier Identification of Children with Autism Spectrum Disorder: An Automatic Vocalisation-Based Approach

Autism spectrum disorder (ASD) is a neurodevelopmental disorder usually diagnosed in or beyond toddlerhood. ASD is defined by repetitive and restricted behaviours, and deficits in social communication. The early speech-language development of individuals with ASD has been characterised as delayed. However, little is known about ASD-related characteristics of pre-linguistic vocalisations at the feature level. In this study, we examined pre-linguistic vocalisations of 10-month-old individuals later diagnosed with ASD and a matched control group of typically developing individuals (N = 20). We segmented 684 vocalisations from parent-child interaction recordings. All vocalisations were annotated and signal-analytically decomposed. We analysed ASD-related vocalisation specificities on the basis of a standardised set (eGeMAPS) of 88 acoustic features selected for clinical speech analysis applications. 54 features showed evidence for a differentiation between vocalisations of individuals later diagnosed with ASD and controls. In addition, we evaluated the feasibility of automated, vocalisation-based identification of individuals later diagnosed with ASD. We compared linear kernel support vector machines and a 1-layer bidirectional long short-term memory neural network. Both classification approaches achieved an accuracy of 75% for subject-wise identification in a subject-independent 3-fold cross-validation scheme. Our promising results may be an important contribution en-route to facilitate earlier identification of ASD.


High sigma measurement of random threshold voltage variation in 14nm Logic FinFET technology

Random variation of threshold voltage (Vt) in MOSFETs plays a central role in determining the minimum operating voltage of products in a given process technology. Properly characterizing Vt variation requires a large volume of measurements of minimum size devices to understand the high sigma behavior. At the same time, a rapid measurement approach is required to keep the total measurement time practical. Here we describe a new test structure and measurement approach that enables practical characterization of Vt distributions to high sigma and its application to 14nm Logic FinFET technology. We show that both NMOS and PMOS single fin devices have very low random Vt variation of 19mV and 24mV respectively, normally distributed out to +/-5σ.


Medicinal and pharmaceutical uses of seaweed natural products: A review

In the last three decades the discovery of metabolites with biological activities from macroalgae has increased significantly. However, despite the intense research effort by academic and corporate institutions, very few products with real potential have been identified or developed. Based on Silverplatter MEDLINE and Aquatic Biology, Aquaculture & Fisheries Resources databases, the literature was searched for natural products from marine macroalgae in the Rhodophyta, Phaeophyta and Chlorophyta with biological and pharmacological activity. Substances that currently receive most attention from pharmaceutical companies for use in drug development, or from researchers in the field of medicine-related research include: sulphated polysaccharides as antiviral substances, halogenated furanones from Delisea pulchra as antifouling compounds, and kahalalide F from a species of Bryopsis as a possible treatment of lung cancer, tumours and AIDS. Other substances such as macroalgal lectins, fucoidans, kainoids and aplysiatoxins are routinely used in biomedical research and a multitude of other substances have known biological activities. The potential pharmaceutical, medicinal and research applications of these compounds are discussed.


Black cattle body shape and temperature measurement using thermography and KINECT sensor

Black cattle body shape and temperature measurement system is introduced. It is important to evaluate the quality of Japanese black cattle periodically during their growth process. Not only the weight and size of cattle, but also the posture, shape, and temperature need to be tracked as primary evaluation criteria. In the present study, KINECT sensor and thermal camera obtains the body shape and its temperature. The whole system is calibrated to operate in a common coordinate system. Point cloud data are obtained from different angles and reconstructed in a computer. The thermal data are captured too. Both point cloud data and thermal information are combined by considering the orientation of the cow. The collected information is used to evaluate and estimate cattle conditions.


Adaptive Mobile Robot Navigation and Mapping

The task of building a map of an unknown environment and concurrently using that map to navigate is a central problem in mobile robotics research. This paper addresses the problem of how to perform concurrent mapping and localization (CML) adaptively using sonar. Stochastic mapping is a feature-based approach to CML that generalizes the extended Kalman lter to incorporate vehicle localization and environmental mapping. We describe an implementation of stochastic mapping that uses a delayed nearest neighbor data association strategy to initialize new features into the map, match measurements to map features, and delete out-of-date features. We introduce a metric for adaptive sensing which is de ned in terms of Fisher information and represents the sum of the areas of the error ellipses of the vehicle and feature estimates in the map. Predicted sensor readings and expected dead-reckoning errors are used to estimate the metric for each potential action of the robot, and the action which yields the lowest cost (i.e., the maximum information) is selected. This technique is demonstrated via simulations, in-air sonar experiments, and underwater sonar experiments. Results are shown for 1) adaptive control of motion and 2) adaptive control of motion and scanning. The vehicle tends to explore selectively di erent objects in the environment. The performance of this adaptive algorithm is shown to be superior to straight-line motion and random motion.


A comparison of track-to-track fusion algorithms for automotive sensor fusion

In exteroceptive automotive sensor fusion, sensor data are usually only available as processed, tracked object data and not as raw sensor data. Applying a Kalman filter to such data leads to additional delays and generally underestimates the fused objectspsila covariance due to temporal correlations of individual sensor data as well as inter-sensor correlations. We compare the performance of a standard asynchronous Kalman filter applied to tracked sensor data to several algorithms for the track-to-track fusion of sensor objects of unknown correlation, namely covariance union, covariance intersection, and use of cross-covariance. For the simulation setup used in this paper, covariance intersection and use of cross-covariance turn out to yield significantly lower errors than a Kalman filter at a comparable computational load.


A Reverse Approach to Named Entity Extraction and Linking in Microposts

In this paper, we present a pipeline for named entity extraction and linking that is designed specifically for noisy, grammatically inconsistent domains where traditional named entity techniques perform poorly. Our approach leverages a large knowledge base to improve entity recognition, while maintaining the use of traditional NER to identify mentions that are not co-referent with any entities in the knowledge base.


The Study of a Dual-Mode Ring Oscillator

An analytical investigation of a dual-mode ring oscillator is presented. The ring oscillator is designed using a CMOS 0.18-μm technology with differential eight-stage delay cells employing auxiliary input devices. With a proper startup control, the oscillator operates in two different modes covering two different frequency bands. A nonlinear model, along with the linearization method, is used to obtain the transient and steady-state behaviors of the dual-mode ring oscillator. The analytical derivations are verified through HSPICE simulation. The oscillator operates at the frequency bands from 2-5 GHz and from 0.1-2 GHz, respectively.


Deep Learning for Wind Speed Forecasting in Northeastern Region of Brazil

Deep Learning is one of the latest approaches in the field of artificial neural networks. Since they were first proposed in mid-2006, Deep Learning models have obtained state-of-art results in some problems with classification and pattern recognition. However, such models have been little used in time series forecasting. This work aims to investigate the use of some of these architectures in this kind of problem, specifically in predicting the hourly average speed of winds in the Northeastern region of Brazil. The results showed that Deep Learning offers a good alternative for performing this task, overcoming some results of previous works.


Characterizing large-scale use of a direct manipulation application in the wild

Examining large-scale, long-term application use is critical to understanding how an application meets the needs of its user community. However, there have been few published analyses of long-term use of desktop applications, and none that have examined applications that support creating and modifying content using direct manipulation. In this paper, we present an analysis of 2 years of usage data from an instrumented version of the GNU Image Manipulation Program, including data from over 200 users. In the course of our analysis, we show that previous findings concerning the sparseness of command use and idiosyncrasy of users’ command vocabularies extend to a new domain and interaction style. These findings motivate continued research in adaptive and mixed-initiative interfaces. We also describe the novel application of a clustering technique to characterize a user community’s higher-level tasks from low-level logging data.


On traveling path and related problems for a mobile station in a rechargeable sensor network

Wireless power transfer is a promising technology to fundamentally address energy problems in a wireless sensor network. To make such a technology work effectively, a vehicle is needed to carry a charger to travel inside the network. On the other hand, it has been well recognized that a mobile base station offers significant advantages over a fixed one. In this paper, we investigate an interesting problem of co-locating the mobile base station on the wireless charging vehicle. We study an optimization problem that jointly optimizes traveling path, stopping points, charging schedule, and flow routing. Our study is carried out in two steps. First, we study an idealized problem that assumes zero traveling time, and develop a provably near-optimal solution to this idealized problem. In the second step, we show how to develop a practical solution with non-zero traveling time and quantify the performance gap between this solution and the unknown optimal solution to the original problem.


An LLCL Power Filter for Single-Phase Grid-Tied Inverter

This paper presents a new topology of higher order power filter for grid-tied voltage-source inverters, named the LLCL filter, which inserts a small inductor in the branch loop of the capacitor in the traditional LCL filter to compose a series resonant circuit at the switching frequency. Particularly, it can attenuate the switching-frequency current ripple components much better than an LCL filter, leading to a decrease in the total inductance and volume. Furthermore, by decreasing the inductance of a grid-side inductor, it raises the characteristic resonance frequency, which is beneficial to the inverter system control. The parameter design criteria of the proposed LLCL filter is also introduced. The comparative analysis and discussions regarding the traditional LCL filter and the proposed LLCL filter have been presented and evaluated through experiment on a 1.8-kW-single-phase grid-tied inverter prototype.


Development of a SiC JFET-Based Six-Pack Power Module for a Fully Integrated Inverter

In this paper, a fully integrated silicon carbide (SiC)-based six-pack power module is designed and developed. With 1200-V, 100-A module rating, each switching element is composed of four paralleled SiC junction gate field-effect transistors (JFETs) with two antiparallel SiC Schottky barrier diodes. The stability of the module assembly processes is confirmed with 1000 cycles of -40°C to +200°C thermal shock tests with 1.3°C/s temperature change. The static characteristics of the module are evaluated and the results show 55 mΩ on-state resistance of the phase leg at 200°C junction temperature. For switching performances, the experiments demonstrate that while utilizing a 650-V voltage and 60-A current, the module switching loss decreases as the junction temperature increases up to 150°C. The test setup over a large temperature range is also described. Meanwhile, the shoot-through influenced by the SiC JFET internal capacitance as well as package parasitic inductances are discussed. Additionally, a liquid cooled three-phase inverter with 22.9 cm × 22.4 cm × 7.1 cm volume and 3.53-kg weight, based on this power module, is designed and developed for electric vehicle and hybrid electric vehicle applications. A conversion efficiency of 98.5% is achieved at 10 kHz switching frequency at 5 kW output power. The inverter is evaluated with coolant temperature up to 95°C successfully.


Stability of photovoltaic and wind turbine grid-connected inverters for a large set of grid impedance values

The aim of this paper is to analyze the stability problems of grid connected inverters used in distributed generation. Complex controllers (e.g., multiple rotating dq-frames or resonant-based) are often required to compensate low frequency grid voltage background distortion and an LCL-filter is usually adopted for the high frequency one. The possible wide range of grid impedance values (distributed generation is suited for remote areas with radial distribution plants) challenge the stability and the effectiveness of the LCL-filter-based current controlled system. It has been found out and it will be demonstrated in this paper that the use of active damping helps to stabilise the system in respect to many different kinds of resonances. The use of active damping results in an easy plug-in feature of the generation system in a vast range of grid conditions and in a more flexible operation of the overall system able to manage sudden grid changes. In the paper, a vast measurement campaign made on a single-phase system and on a three-phase system used as scale prototypes for photovoltaic and wind turbines, respectively, validate the analysis.


10 kV/120 A SiC DMOSFET half H-bridge power modules for 1 MVA solid state power substation

In this paper, the extension of SiC power technology to higher voltage 10 kV/10 A SiC DMOSFETs and SiC JBS diodes is discussed. A new 10 kV/120 A SiC power module using these 10 kV SiC devices is also described which enables a compact 13.8 kV to 465/√3 solid state power substation (SSPS) rated at 1 MVA.


Overview of Control and Grid Synchronization for Distributed Power Generation Systems

Renewable energy sources like wind, sun, and hydro are seen as a reliable alternative to the traditional energy sources such as oil, natural gas, or coal. Distributed power generation systems (DPGSs) based on renewable energy sources experience a large development worldwide, with Germany, Denmark, Japan, and USA as leaders in the development in this field. Due to the increasing number of DPGSs connected to the utility network, new and stricter standards in respect to power quality, safe running, and islanding protection are issued. As a consequence, the control of distributed generation systems should be improved to meet the requirements for grid interconnection. This paper gives an overview of the structures for the DPGS based on fuel cell, photovoltaic, and wind turbines. In addition, control structures of the grid-side converter are presented, and the possibility of compensation for low-order harmonics is also discussed. Moreover, control strategies when running on grid faults are treated. This paper ends up with an overview of synchronization methods and a discussion about their importance in the control


Facial recognition using histogram of gradients and support vector machines

Face recognition is widely used in computer vision and in many other biometric applications where security is a major concern. The most common problem in recognizing a face arises due to pose variations, different illumination conditions and so on. The main focus of this paper is to recognize whether a given face input corresponds to a registered person in the database. Face recognition is done using Histogram of Oriented Gradients (HOG) technique in AT & T database with an inclusion of a real time subject to evaluate the performance of the algorithm. The feature vectors generated by HOG descriptor are used to train Support Vector Machines (SVM) and results are verified against a given test input. The proposed method checks whether a test image in different pose and lighting conditions is matched correctly with trained images of the facial database. The results of the proposed approach show minimal false positives and improved detection accuracy.


Straight to the Tree: Constituency Parsing with Neural Syntactic Distance

In this work, we propose a novel constituency parsing scheme. The model predicts a vector of real-valued scalars, named syntactic distances, for each split position in the input sentence. The syntactic distances specify the order in which the split points will be selected, recursively partitioning the input, in a top-down fashion. Compared to traditional shiftreduce parsing schemes, our approach is free from the potential problem of compounding errors, while being faster and easier to parallelize. Our model achieves competitive performance amongst single model, discriminative parsers in the PTB dataset and outperforms previous models


Performance Analysis of Software-Defined Networking (SDN)

Software-Defined Networking (SDN) approaches were introduced as early as the mid-1990s, but just recently became a well-established industry standard. Many network architectures and systems adopted SDN, and vendors are choosing SDN as an alternative to the fixed, predefined, and inflexible protocol stack. SDN offers flexible, dynamic, and programmable functionality of network systems, as well as many other advantages such as centralized control, reduced complexity, better user experience, and a dramatic decrease in network systems and equipment costs. However, SDN characterization and capabilities, as well as workload of the network traffic that the SDN-based systems handle, determine the level of these advantages. Moreover, the enabled flexibility of SDN-based systems comes with a performance penalty. The design and capabilities of the underlying SDN infrastructure influence the performance of common network tasks, compared to a dedicated solution. In this paper we analyze two issues: a) the impact of SDN on raw performance (in terms of throughput and latency) under various workloads, and b) whether there is an inherent performance penalty for a complex, more functional, SDN infrastructure. Our results indicate that SDN does have a performance penalty, however, it is not necessarily related to the complexity level of the underlying SDN infrastructure.


Applications of Lucid Dreaming in Sports

The following article has above all a practical orientation. The various possibilities for the application of lucid dreaming in sports training are presented and briefly illustrated. These theses are based on findings from experiments with experienced lucid dreamers who were instructed to carry out various routine and sport-related actions while lucid-dreaming, with the object of observing the effects on both dreaming and waking states (Tholey, 1981a). Additionally, I report here on numerous spontaneous accounts of athletes who have mastered lucid dreaming, as well as my own years of experience as a lucid dreamer and as an active competitor in different types of sports.


A Review of Heuristic Global Optimization Based Artificial Neural Network Training Approahes

Received Nov 5, 2016 Revised Jan 9, 2017 Accepted Feb 18, 2017 Artificial Neural Networks have earned popularity in recent years because of their ability to approximate nonlinear functions. Training a neural network involves minimizing the mean square error between the target and network output. The error surface is nonconvex and highly multimodal. Finding the minimum of a multimodal function is a NP complete problem and cannot be solved completely. Thus application of heuristic global optimization algorithms that computes a good global minimum to neural network training is of interest. This paper reviews the various heuristic global optimization algorithms used for training feedforward neural networks and recurrent neural networks. The training algorithms are compared in terms of the learning rate, convergence speed and accuracy of the output produced by the neural network. The paper concludes by suggesting directions for novel ANN training algorithms based on recent advances in global optimization. Keyword:


Nursing bedside clinical handover - an integrated review of issues and tools.

AIMS AND OBJECTIVES
This article reviews the available literature that supports implementing bedside clinical handover in nursing clinical practice and then seeks to identify key issues if any.

BACKGROUND
Clinical handover practices are recognised as being an essential component in the effective transfer of clinical care between health practitioners. It is recognised that the point where a patient is 'handed over' from one clinician to another is significant in maintaining continuity of care and that doing this poorly can have significant safety issues for the patient.

DESIGN
An integrated literature review.

METHOD
A literature review of 45 articles was undertaken to understand bedside clinical handover and the issues related to the implementation of this process.

RESULTS
It was identified that there are a number of clinical handover mnemonics available that provide structure to the process and that areas such as confidentiality, inclusion of the patient/carer and involving the multidisciplinary team remain topical issues for practitioners in implementing good clinical handover practices.

CONCLUSIONS
This literature review identified a lack of literature available about the transfer of responsibility and accountability during clinical handover and auditing practices of the clinical handover process. The nurses were more concerned about confidentiality issues than were patients. The use of a structured tool was strongly supported; however, no one singular tool was considered suitable for all clinical areas.

RELEVANCE TO CLINICAL PRACTICE
Nursing clinicians seeking to implement best practice within their professional speciality should consider some of the issues raised within this article and seek to address these issues by developing strategies to overcome them.


Forward Error Correction for DNA Data Storage

We report on a strong capacity boost in storing digital data in synthetic DNA. In principle, synthetic DNA is an ideal media to archive digital data for very long times because the achievable data density and longevity outperforms today’s digital data storage media by far. On the other hand, neither the synthesis, nor the amplification and the sequencing of DNA strands can be performed error-free today and in the foreseeable future. In order to make synthetic DNA available as digital data storage media, specifically tailored forward error correction schemes have to be applied. For the purpose of realizing a DNA data storage, we have developed an efficient and robust forwarderror-correcting scheme adapted to the DNA channel. We based the design of the needed DNA channel model on data from a proof-of-concept conducted 2012 by a team from the Harvard Medical School [1]. Our forward error correction scheme is able to cope with all error types of today’s DNA synthesis, amplification and sequencing processes, e.g. insertion, deletion, and swap errors. In a successful experiment, we were able to store and retrieve error-free 22 MByte of digital data in synthetic DNA recently. The found residual error probability is already in the same order as it is in hard disk drives and can be easily improved further. This proves the feasibility to use synthetic DNA as longterm digital data storage media.


CLIP: Continuous Location Integrity and Provenance for Mobile Phones

Many location-based services require a mobile user to continuously prove his location. In absence of a secure mechanism, malicious users may lie about their locations to get these services. Mobility trace, a sequence of past mobility points, provides evidence for the user's locations. In this paper, we propose a Continuous Location Integrity and Provenance (CLIP) Scheme to provide authentication for mobility trace, and protect users' privacy. CLIP uses low-power inertial accelerometer sensor with a light-weight entropy-based commitment mechanism and is able to authenticate the user's mobility trace without any cost of trusted hardware. CLIP maintains the user's privacy, allowing the user to submit a portion of his mobility trace with which the commitment can be also verified. Wireless Access Points (APs) or colocated mobile devices are used to generate the location proofs. We also propose a light-weight spatial-temporal trust model to detect fake location proofs from collusion attacks. The prototype implementation on Android demonstrates that CLIP requires low computational and storage resources. Our extensive simulations show that the spatial-temporal trust model can achieve high (> 0.9) detection accuracy against collusion attacks.


Security and privacy in mobile crowdsourcing networks: challenges and opportunities

The mobile crowdsourcing network (MCN) is a promising network architecture that applies the principles of crowdsourcing to perform tasks with human involvement and powerful mobile devices. However, it also raises some critical security and privacy issues that impede the application of MCNs. In this article, in order to better understand these critical security and privacy challenges, we first propose a general architecture for a mobile crowdsourcing network comprising both crowdsourcing sensing and crowdsourcing computing. After that, we set forth several critical security and privacy challenges that essentially capture the characteristics of MCNs. We also formulate some research problems leading to possible research directions. We expect this work will bring more attention to further investigation on security and privacy solutions for mobile crowdsourcing networks.


DAC-MACS: Effective Data Access Control for Multiauthority Cloud Storage Systems

Data access control is an effective way to ensure data security in the cloud. However, due to data outsourcing and untrusted cloud servers, the data access control becomes a challenging issue in cloud storage systems. Existing access control schemes are no longer applicable to cloud storage systems, because they either produce multiple encrypted copies of the same data or require a fully trusted cloud server. Ciphertext-policy attribute-based encryption (CP-ABE) is a promising technique for access control of encrypted data. However, due to the inefficiency of decryption and revocation, existing CP-ABE schemes cannot be directly applied to construct a data access control scheme for multiauthority cloud storage systems, where users may hold attributes from multiple authorities. In this paper, we propose data access control for multiauthority cloud storage (DAC-MACS), an effective and secure data access control scheme with efficient decryption and revocation. Specifically, we construct a new multiauthority CP-ABE scheme with efficient decryption, and also design an efficient attribute revocation method that can achieve both forward security and backward security. We further propose an extensive data access control scheme (EDAC-MACS), which is secure under weaker security assumptions.


Learning Long Term Dependencies via Fourier Recurrent Units

It is a known fact that training recurrent neural networks for tasks that have long term dependencies is challenging. One of the main reasons is the vanishing or exploding gradient problem, which prevents gradient information from propagating to early layers. In this paper we propose a simple recurrent architecture, the Fourier Recurrent Unit (FRU), that stabilizes the gradients that arise in its training while giving us stronger expressive power. Specifically, FRU summarizes the hidden states h along the temporal dimension with Fourier basis functions. This allows gradients to easily reach any layer due to FRU’s residual learning structure and the global support of trigonometric functions. We show that FRU has gradient lower and upper bounds independent of temporal dimension. We also show the strong expressivity of sparse Fourier basis, from which FRU obtains its strong expressive power. Our experimental study also demonstrates that with fewer parameters the proposed architecture outperforms other recurrent architectures on many tasks.


The Role of Classification in Knowledge Representation and Discovery

The link between classification and knowledge is explored. Classification schemes have properties that enable the representation of entities and relationships in structures that reflect knowledge of the domain being classified. The strengths and limitations of four classificatory approaches are described in terms of their ability to reflect, discover, and create new knowledge. These approaches are hierarchies, trees, paradigms, and faceted analysis. Examples are provided of the way in which knowledge and the classification process affect each other. INTRODUCTION Developments in our ability to store and retrieve large amounts of' information have stimulated an interest in new ways to exploit this information for advancing human knowledge. This article describes the relationship between knowledge representation (as manifested in classifications) and the processes of knowledge discovery and creation. How does the classification process enable or constrain knowing something or discovering new knowledge about something? In what ways might we develop classifications that will enhance our ability to discover meaningful information in our data stores? The first part of the article describes several representative classificatory structures-hierarchies, trees, paradigms, and faceted analysis with the aim of identifying how these structures serve as knowledge representations and in what ways they can be used for knowledge discovery and creation. The second part of the discussion includes examples from existing classification schemes and discusses how the schemes reflect or fail to reflect knowledge. KNOWLEDGE, THEORY, AND CLASSIFICATION Scholars in many fields, from philosophy to cybernetics, have long discussed the concept of knowledge and the problems of representing knowledge in information systems. The distinction is drawn between merely observing, perceiving, or even describing things and truly knowing them. To know implies a process of integration of facts about objects and the context in which the objects and processes exist. Even in colloquial usage, knowledge about someone or something is always expressed in terms of deep relationships and meanings as well as its place in time and space. To know cars means not only understanding car mechanics but also knowledge of the interplay of the mechanical processes and perhaps even factors such as aesthetics, economics, and psychology. The process of knowledge discovery and creation in science has traditionally followed the path of systematic exploration, observation, description, analysis, and synthesis and testing of phenomena and facts, all conducted within the communication framework of a particular research community with its accepted methodology and set of techniques. We know the process is not entirely rational but often is sparked and then fueled by insight, hunches, and leaps of faith (Bronowski, 1978). Moreover, research is always conducted within a particular political and cultural reality (Olson, 1998). Each researcher and, on a larger scale, each research community at various points must gather up the disparate pieces and in some way communicate what is known, expressing it in such a way as to be useful for further discovery and understanding. A variety of formats exist for the expression of knowledge e.g., theories, models, formulas, descriptive reportage of many sorts, and polemical essays. Of these formats, science particularly values theories and models because they are a “symbolic dimension of experience as opposed to the apprehension of brute fact” (Kaplan, 1963, p. 294) and can therefore be symbolically extended to cover new experiences. A theory thus explains a particular fact by abstracting the relationship of that fact to other facts. Grand, or covering, theories explain facts in an especially eloquent way and in a very wide (some would say, universal) set of situations. Thus, Darwinian, Marxist, or Freudian theories, for example, attempt to explain processes and behaviors in many contexts, but they do so at a high level of abstraction. There are relatively few grand theories, however, and we rely on the explanatory and descriptive usefulness of more “local” theoriestheories that explain a more limited domain but with greater specificity. CLASSIFICATION AS KNOWLEDGE REPRESENTATION How are theories built? How does knowledge accumulate and then get shaped into a powerful representation? There are, of course, many processes involved, but often one of them is the process of classification. Classification is the meaningful clustering of experience. The process of classification can be used in a formative way and is thus useful during the preliminary stages of inquiry as a heuristic tool in discovery, analysis, and theorizing (Davies, 1989). Once concepts gel and the relationships among concepts become understood, a classification can be used as a rich representation of what is known and is thus useful in communication and in generating a fresh cycle of exploration, comparison, and theorizing. Kaplan (1963) states that “theory is not the aggregate of the new laws but their connectedness, as a bridge consists of girders only in that the girders are joined together in a particular way” (p. 297). A good classification functions in much the same way that a theory does, connecting concepts in a useful structure. If successful, it is, like a theory, descriptive, explanatory, heuristic, fruitful, and perhaps also elegant, parsimonious, and robust (Kwasnik, l992b). There are many approaches to the process of classification and to the construction of the foundation of classification schemes. Each kind of classification process has different goals, and each type of classification scheme has different structural properties as well as different strengths and weaknesses in terms of knowledge representation and knowledge discovery. The following is a representative sample of some common approaches and structures. HIERARCHIES We have inherited our understanding of hierarchical classifications from Aristotle (Ackrill, 1963), who posited that all nature comprised a unified whole. The whole could be subdivided, like a chicken leg at the joint, into “natural” classes, and each class further into subclasses, and so on-this process following an orderly and systematic set of rules of association and distinction. How do we know what a natural dividing place is, and how do we arrive at the rules for division and subdivision? According to Aristotle, only exhaustive observation can reveal each entity’s true (essential) attributes, and only philosophy can guide us in determining the necessary and sufficient attributes for membership in any given class. In fact, according to Aristotle’s philosophy, it is only when an entity is properly classed, and its essential properties identified, that we can say we truly know it. This is the aim of science, he claims-i.e., to unambiguously classify all phenomena by their essential (true) qualities. While Aristotle’s legacy is alive in spirit in modern applications of classification, most practitioners recognize that a pure and complete hierarchy is essentially possible only in the ideal. Nevertheless, in knowledge domains that have theoretical foundations (such as germ theory in medicine and the theory of evolution in biology), hierarchies are the preferred structures for knowledge representation (see, for example, the excerpt from the Medical Subject Headings [MeSH] in Figure 1. Based on the MeSH excerpt in Figure 1, note that hierarchies have strict structural requirements: Inclusiveness: The top class (in this case, EYEDISEASES) is the most inclusive class and describes the domain of the classification. The top class includes all its subclasses and sub-subclasses. Put another way, all the classes in the example are included in the top class: EYEDISEASES. Species/differentia: A true hierarchy has only one type of relationship between its superand subclasses and this is the generic relationship, also known as species/differentia, or more colloquially as the is-a relationship. In a generic relationship, ALLERGIC CONJUNCTIVITIS is a kind of CONJUNCTIVITIS, which in turn is a kind of CONJUNCTIVAL DISEASE, which in turn is a kind of EYEDISEASE. Inheritance: This requirement of strict class inclusion ensures that everything that is true for entities in any given class is also true for entities in its subclasses and sub-subclasses. Thus whatever is true of EYE DISEASES (as a whole) is also true of CONJUNCTIVAL Whatever is true of CONJUNCTIVAL DISEASES (as a whole) is also true of CONJUNCTIVITIS, and so on. This property is called inheritance, that is, attributes are inherited by a subclass from its superclass. Transitivity: Since attributes are inherited, all sub-subclasses are members of not only their immediate superclass but of every superclass above that one. Thus if BACTERIAL CONJUNCTIVITIS is a kind Of CONJUNCTIVITIS, and CONJUNCTIVITIS is a kind Of CONJUNCTIVAL DISEASE, then, by the rules of transitivity, BACTERIAL CONJUNCTIVITIS is also a kind of CONJUNCTIVAL DISEASE, and so on. This property is called transitivity. Systematic and predictable rules for association and distinction: The rules for grouping entities in a class (i.e., creating a species) are determined beforehand, as are the rules for creating distinct subclasses (differentia). Thus all entities in a given class are like each other in some predictable (and predetermined) way, and these entities differ from entities in sibling classes in some predictable (and predetermined) way. In the example above, CONJUNCTIVAL and CORNEAL DISEASES are alike in that they are both kinds of EYEDISEASES. They are differentiated from each other along some predictable and systematic criterion of distinction (in this case “part of the eye affected”) Mutual exclusivity: A given entity can belong to only one class. This property is called mutual exclusivity. Necessary and sufficient criteria: In a pure


A soft, bistable valve for autonomous control of soft actuators

Almost all pneumatic and hydraulic actuators useful for mesoscale functions rely on hard valves for control. This article describes a soft, elastomeric valve that contains a bistable membrane, which acts as a mechanical “switch” to control air flow. A structural instability—often called “snap-through”—enables rapid transition between two stable states of the membrane. The snap-upward pressure, ΔP1 (kilopascals), of the membrane differs from the snap-downward pressure, ΔP2 (kilopascals). The values ΔP1 and ΔP2 can be designed by changing the geometry and the material of the membrane. The valve does not require power to remain in either “open” or “closed” states (although switching does require energy), can be designed to be bistable, and can remain in either state without further applied pressure. When integrated in a feedback pneumatic circuit, the valve functions as a pneumatic oscillator (between the pressures ΔP1 and ΔP2), generating periodic motion using air from a single source of constant pressure. The valve, as a component of pneumatic circuits, enables (i) a gripper to grasp a ball autonomously and (ii) autonomous earthworm-like locomotion using an air source of constant pressure. These valves are fabricated using straightforward molding and offer a way of integrating simple control and logic functions directly into soft actuators and robots.


Malware detection by text and data mining

Cyber frauds are a major security threat to the banking industry worldwide. Malware is one of the manifestations of cyber frauds. Malware authors use Application Programming Interface (API) calls to perpetrate these crimes. In this paper, we propose a static analysis method to detect Malware based on API call sequences using text and data mining in tandem. We analyzed the dataset available at CSMINING group. First, we employed text mining to extract features from the dataset consisting a series of API calls. Further, mutual information is invoked for feature selection. Then, we resorted to over-sampling to balance the data set. Finally, we employed various data mining techniques such as Decision Tree (DT), Multi Layer Perceptron (MLP), Support Vector Machine (SVM), Probabilistic Neural Network (PNN) and Group Method for Data Handling (GMDH). We also applied One Class SVM (OCSVM). Throughout the paper, we used 10-fold cross validation technique for testing the techniques. We observed that SVM and OCSVM achieved 100% sensitivity after balancing the dataset.


The changing paradigm of air pollution monitoring.

The air pollution monitoring paradigm is rapidly changing due to recent advances in (1) the development of portable, lower-cost air pollution sensors reporting data in near-real time at a high-time resolution, (2) increased computational and visualization capabilities, and (3) wireless communication/infrastructure. It is possible that these advances can support traditional air quality monitoring by supplementing ambient air monitoring and enhancing compliance monitoring. Sensors are beginning to provide individuals and communities the tools needed to understand their environmental exposures with these data individual and community-based strategies can be developed to reduce pollution exposure as well as understand linkages to health indicators. Each of these areas as well as corresponding challenges (e.g., quality of data) and potential opportunities associated with development and implementation of air pollution sensors are discussed.


Quantitative analysis of small-plastic debris on beaches in the Hawaiian Archipelago.

Small-plastic beach debris from nine coastal locations throughout the Hawaiian Archipelago was analyzed. At each beach, replicate 20 l samples of sediment were collected, sieved for debris between 1 and 15 mm in size, sorted by type, counted and weighed. Small-plastic debris occurred on all of the beaches, but the greatest quantity was found at three of the most remote beaches on Midway Atoll and Moloka'i. Of the debris analyzed, 72% by weight was plastic. A total of 19100 pieces of plastic were collected from the nine beaches, 11% of which was pre-production plastic pellets. This study documents for the first time the presence of small-plastic debris on Hawaiian beaches and corroborates estimates of the abundance of plastics in the marine environment in the North Pacific.