INTERACTIVE SAND-COVERED TERRAIN SURFACE MODEL WITH HAPTIC FEEDBACK

We describe a method for an intuitive haptic rendering and modeling of GIS terrain models represented as triangular irregular networks (TIN). The TIN terrain model does not allow representation of concavities and overhangs but it is sufficient for most GIS applications. Model editing is achieved by a set of virtual tools with various shapes which are controlled by a haptic device with force feedback. The haptic feedback makes manipulation with the terrain model much more intuitive than by the mouse, because it operates in 3D and it also provides better control with the force. The haptic device is point-based, so the forces provided by different tools must be mapped to a point. This, in effect, helps eliminating certain vibrations that could be caused by numerical errors and insufficient scene sampling. Our approach allows an adaptive adjustment of the level of detail of the model according to the terrain properties and user requirements. Haptic visualization in GIS models has been already used but such solutions are not common. Unlike existing approaches, our solution uses a general shape of the tool, it allows interactive editing of the TIN model, and it utilizes parallelism and coherence between consecutive states of the model that make haptic visualization faster and smoother.


That's So Annoying!!!: A Lexical and Frame-Semantic Embedding Based Data Augmentation Approach to Automatic Categorization of Annoying Behaviors using #petpeeve Tweets

We propose a novel data augmentation approach to enhance computational behavioral analysis using social media text. In particular, we collect a Twitter corpus of the descriptions of annoying behaviors using the #petpeeve hashtags. In the qualitative analysis, we study the language use in these tweets, with a special focus on the fine-grained categories and the geographic variation of the language. In quantitative analysis, we show that lexical and syntactic features are useful for automatic categorization of annoying behaviors, and frame-semantic features further boost the performance; that leveraging large lexical embeddings to create additional training instances significantly improves the lexical model; and incorporating frame-semantic embedding achieves the best overall performance.


The wave kernel signature: A quantum mechanical approach to shape analysis

We introduce the Wave Kernel Signature (WKS) for characterizing points on non-rigid three-dimensional shapes. The WKS represents the average probability of measuring a quantum mechanical particle at a specific location. By letting vary the energy of the particle, the WKS encodes and separates information from various different Laplace eigenfrequencies. This clear scale separation makes the WKS well suited for a large variety of applications. Both theoretically and in quantitative experiments we demonstrate that the WKS is substantially more discriminative and therefore allows for better feature matching than the commonly used Heat Kernel Signature (HKS). As an application of the WKS in shape analysis we show results on shape matching.


Orexins and Orexin Receptors: A Family of Hypothalamic Neuropeptides and G Protein-Coupled Receptors that Regulate Feeding Behavior

The hypothalamus plays a central role in the integrated control of feeding and energy homeostasis. We have identified two novel neuropeptides, both derived from the same precursor by proteolytic processing, that bind and activate two closely related (previously) orphan G protein-coupled receptors. These peptides, termed orexin-A and -B, have no significant structural similarities to known families of regulatory peptides. prepro-orexin mRNA and immunoreactive orexin-A are localized in neurons within and around the lateral and posterior hypothalamus in the adult rat brain. When administered centrally to rats, these peptides stimulate food consumption. prepro-orexin mRNA level is up-regulated upon fasting, suggesting a physiological role for the peptides as mediators in the central feedback mechanism that regulates feeding behavior.


BETTER GUN ENFORCEMENT , LESS CRIME *

Research Summary: Project Safe Neighborhoods (PSN), which for the past several years has been the major federal initiative to combat gun violence, includes several elements (such as gun locks and other efforts to reduce gun availability) that research suggests are likely to have at best modest effects on gun crime. In general, enforcement activities targeted at the “demand side” of the underground gun market currently enjoy stronger empirical support. However much of PSN’s budget has been devoted to increasing the severity of punishment, such as by federalizing gun cases, which seems to be less effective than targeted street-level enforcement designed to increase the probability of punishment for gun carrying or use in crime.


Modeling Document Novelty with Neural Tensor Network for Search Result Diversification

Search result diversification has attracted considerable attention as a means to tackle the ambiguous or multi-faceted information needs of users. One of the key problems in search result diversification is novelty, that is, how to measure the novelty of a candidate document with respect to other documents. In the heuristic approaches, the predefined document similarity functions are directly utilized for defining the novelty. In the learning approaches, the novelty is characterized based on a set of handcrafted features. Both the similarity functions and the features are difficult to manually design in real world due to the complexity of modeling the document novelty. In this paper, we propose to model the novelty of a document with a neural tensor network. Instead of manually defining the similarity functions or features, the new method automatically learns a nonlinear novelty function based on the preliminary representation of the candidate document and other documents. New diverse learning to rank models can be derived under the relational learning to rank framework. To determine the model parameters, loss functions are constructed and optimized with stochastic gradient descent. Extensive experiments on three public TREC datasets show that the new derived algorithms can significantly outperform the baselines, including the state-of-the-art relational learning to rank models.


An Experimental Comparison of RGB, YIQ, LAB, HSV, and Opponent Color Models

The increasing availability of affordable color raster graphics displays has made it important to develop a better understanding of how color can be used effectively in an interactive environment. Most contemporary graphics displays offer a choice of some 16 million colors; the user's problem is to find the right color.
Folklore has it that the RGB color space arising naturally from color display hardware is user-hostile and that other color models such as the HSV scheme are preferable. Until now there has been virtually no experimental evidence addressing this point.
We describe a color matching experiment in which subjects used one of two tablet-based input techniques, interfaced through one of five color models, to interactively match target colors displayed on a CRT.
The data collected show small but significant differences between models in the ability of subjects to match the five target colors used in this experiment. Subjects using the RGB color model matched quickly but inaccurately compared with those using the other models. The largest speed difference occurred during the early convergence phase of matching. Users of the HSV color model were the slowest in this experiment, both during the convergence phase and in total time to match, but were relatively accurate. There was less variation in performance during the second refinement phase of a match than during the convergence phase.
Two-dimensional use of the tablet resulted in faster but less accurate performance than did strictly one-dimensional usage.
Significant learning occurred for users of the Opponent, YIQ, LAB, and HSV color models, and not for users of the RGB color model.


Modeling the Diffuse Paintings of 'Sumie'


A globally stable adaptive congestion control scheme for Internet-style networks with delay

In this paper, we develop, analyze and implement a congestion control scheme in a noncooperative game framework, where each user's cost function is composed of a pricing function proportional to the queueing delay experienced by the user, and a fairly general utility function which captures the user demand for bandwidth. Using a network model based on fluid approximations and through a realistic modeling of queues, we establish the existence of a unique equilibrium as well as its global asymptotic stability for a general network topology, where boundary effects are also taken into account. We also provide sufficient conditions for system stability when there is a bottleneck link shared by multiple users experiencing nonnegligible communication delays. In addition, we study an adaptive pricing scheme using hybrid systems concepts. Based on these theoretical foundations, we implement a window-based, end-to-end congestion control scheme, and simulate it in ns-2 network simulator on various network topologies with sizable propagation delays.


Language Oriented Programming: The Next Programming Paradigm

Ideally, being a programmer means I can do anything on a computer. I have complete freedom, complete control. But in reality, programmers today have very It is time to begin the next technology revolution in software development, and the shape of this revolution is becoming more and more clear. The next programming paradigm is nearly upon us. It is not yet fully formed—different parts have different names: Intentional programming, MDA, generative programming, etc. I suggest uniting all of these new approaches under one name, ‘language-oriented programming’, and this article explains the main principles of this new programming paradigm. Today’s mainstream approach to programming has some crucial built-in assumptions which hold us back like chains around our necks, though most programmers don’t realize this. With all the progress made so far in programming, we are still in the Stone Age. We’ve got our trusty stone axe (object-oriented programming), which serves us well, but tends to chip and crack when used against the hardest problems. To advance beyond stone, we must tame fire. Only then can we forge new tools and spark a new age of invention and an explosion of new technologies. I’m talking about the limitations of programming which force the programmer to think like the computer rather than having the computer think more like the programmer. These are serious, deeply-ingrained limitations which will take a lot of effort to overcome. I’m not being pretentious when I say that this will be the next big paradigm shift in programming. We will need to completely redefine the way we write programs. In this article, I present my view and my current work toward Language Oriented Programming (LOP). First I will show what is wrong with mainstream programming today, then I’ll explain the concept of LOP by using the example of my existing implementation, the Meta Programming System (MPS). This article is intended to give you a bird’s-eye-view of LOP, to spark interest in the idea, and hopefully to generate feedback and discussion.


Estimating Phoneme Class Conditional Probabilities from Raw Speech Signal using Convolutional Neural Networks

In hybrid hidden Markov model/artificial neural networks (HMM/ANN) automatic speech recognition (ASR) system, the phoneme class conditional probabilities are estimated by first extracting acoustic features from the speech signal based on prior knowledge such as, speech perception or/and speech production knowledge, and, then modeling the acoustic features with an ANN. Recent advances in machine learning techniques, more specifically in the field of image processing and text processing, have shown that such divide and conquer strategy (i.e., separating feature extraction and modeling steps) may not be necessary. Motivated from these studies, in the framework of convolutional neural networks (CNNs), this paper investigates a novel approach, where the input to the ANN is raw speech signal and the output is phoneme class conditional probability estimates. On TIMIT phoneme recognition task, we study different ANN architectures to show the benefit of CNNs and compare the proposed approach against conventional approach where, spectral-based feature MFCC is extracted and modeled by a multilayer perceptron. Our studies show that the proposed approach can yield comparable or better phoneme recognition performance when compared to the conventional approach. It indicates that CNNs can learn features relevant for phoneme classification automatically from the raw speech signal.


A Self-Organizing Spatial Vocabulary

Language is a shared set of conventions for mapping meanings to utterances. This paper explores self-organization as the primary mechanism for the formation of a vocabulary. It reports on a computational experiment in which a group of distributed agents develop ways to identify each other using names or spatial descriptions. It is also shown that the proposed mechanism copes with the acquisition of an existing vocabulary by new agents entering the community and with an expansion of the set of meanings.


Entity Matching: How Similar Is Similar

Entity matching that finds records referring to the same entity is an important operation in data cleaning and integration. Existing studies usually use a given similarity function to quantify the similarity of records, and focus on devising index structures and algorithms for efficient entity matching. However it is a big challenge to define “how similar is similar” for real applications, since it is rather hard to automatically select appropriate similarity functions. In this paper we attempt to address this problem. As there are a large number of similarity functions, and even worse thresholds may have infinite values, it is rather expensive to find appropriate similarity functions and thresholds. Fortunately, we have an observation that different similarity functions and thresholds have redundancy, and we have an opportunity to prune inappropriate similarity functions. To this end, we propose effective optimization techniques to eliminate such redundancy, and devise efficient algorithms to find the best similarity functions. The experimental results on both real and synthetic datasets show that our method achieves high accuracy and outperforms the baseline algorithms.


On the Representation and Estimation of

This paper describes a general method for estimating the nominal relationship and expected error (covariance) between coordinate frames representing the relative locations of objects. The frames may be known only indirectly through a series of spatial relationships, each with its associated error, arisingfrom diverse causes, including positioning errors, measurement errors, or tolerances in part dimensions. This estimation method can be used to answer such questions as whether a camera attached, to a robot is likely to have a particular reference object in its field of view. The calculated estimates agree well with those/rom an independent Monte Carlo simulation. The method makes it possible to decide in advance whether an uncertain relationship is known accurately enough for some task and, i f not, how much of an improvement in locational knowledge a proposed sensor will provide. The method presented can be generalized to six degrees of freedom and provides a practical means of estimating the relationships (position and orientation) among objects, as well as estimating the uncertainty associated with the relationships.


Effect of Video Game Practice on Spatial Skills in Girls and Boys

A study of the effect of video game practice on spatial abilities in girls ond boys wos carried out. Spatial performance, measured using two subtests of a computerized spatial skills battery, was significantly better in boys than in girls during pretest assessment. Subjects then practiced on an action video game, Marble Madness, or a computerized word game, Conjecture. Video game practice wos significantly more effective than the word game in improving spatiol performance on the posttest assessment; there was no significant interaction of gender with experimental treotment. However, video game practice was more effective for children who started out with relatively poor spatial skills. The pattern of results suggests that video games may be useful in equalizing individual differences in spatial skill performance, including those associated with gender.


DDoS: Survey of Traceback Methods

The problem of identifying Distributed Denial of Service (DDoS) is one of the hardest threats in the internet security. It is important to protect the resource and trace from the Denial of Service (DoS) attack, but it is difficult to distinguish normal traffic and DoS attack traffic because the DoS generally hide their identities/origins. Especially the attackers often use incorrect or spoofed source IP address, so tracing the source of the denial of service is hardest in internet. Lot of techniques and methodologies are used to trace the DDoS attacks. This paper presents some of the mostly used predicting traceback techniques to solve the problem. The main goal of this paper is appraise the different traceback techniques of the DDoS. This paper evaluates the different traceback methods of the


A theory of multi-layer flat refractive geometry

Flat refractive geometry corresponds to a perspective camera looking through single/multiple parallel flat refractive mediums. We show that the underlying geometry of rays corresponds to an axial camera. This realization, while missing from previous works, leads us to develop a general theory of calibrating such systems using 2D-3D correspondences. The pose of 3D points is assumed to be unknown and is also recovered. Calibration can be done even using a single image of a plane. We show that the unknown orientation of the refracting layers corresponds to the underlying axis, and can be obtained independently of the number of layers, their distances from the camera and their refractive indices. Interestingly, the axis estimation can be mapped to the classical essential matrix computation and 5-point algorithm [15] can be used. After computing the axis, the thicknesses of layers can be obtained linearly when refractive indices are known, and we derive analytical solutions when they are unknown. We also derive the analytical forward projection (AFP) equations to compute the projection of a 3D point via multiple flat refractions, which allows non-linear refinement by minimizing the reprojection error. For two refractions, AFP is either 4th or 12th degree equation depending on the refractive indices. We analyze ambiguities due to small field of view, stability under noise, and show how a two layer system can be well approximated as a single layer system. Real experiments using a water tank validate our theory.


SPIKING NEURAL NETWORKS SPIKING NEURAL NETWORKS

Background: Infectious complications are the major cause of death in acute pancreatitis. Small bowel bacterial overgrowth and subsequent bacterial translocation are held responsible for the vast majority of these infections. Goal of this study is to determine whether selected probiotics are capable of preventing infectious complications without the disadvantages of antibiotic prophylaxis; antibiotic resistance and fungal overgrowth. Methods/design: PROPATRIA is a double-blind, placebo-controlled randomised multicenter trial in which 200 patients will be randomly allocated to a multispecies probiotic preparation (Ecologic 641) or placebo. The study is performed in all 8 Dutch University Hospitals and 7 non-University hospitals. The study-product is administered twice daily through a nasojejunal tube for 28 days or until discharge. Patients eligible for randomisation are adult patients with a first onset of predicted severe acute pancreatitis: Imrie criteria 3 or more, CRP 150 mg/L or more, APACHE II score 8 or more. Exclusion criteria are post-ERCP pancreatitis, malignancy, infection/sepsis caused by a second disease, intra-operative diagnosis of pancreatitis and use of probiotics during the study. Administration of the study product is started within 72 hours after onset of abdominal pain. The primary endpoint is the total number of infectious complications. Secondary endpoints are mortality, necrosectomy, antibiotic resistance, hospital stay and adverse events. To demonstrate that probiotic prophylaxis reduces the proportion of patients with infectious complications from 50% to 30%, with alpha 0,05 and power 80%, a total sample size of 200 patients was calculated. Conclusion: The PROPATRIA study is aimed to show a reduction in infectious complications due to early enteral use of multispecies probiotics in severe acute pancreatitis. Published: 29 September 2004 BMC Surgery 2004, 4:12 doi:10.1186/1471-2482-4-12 Received: 21 July 2004 Accepted: 29 September 2004 This article is available from: http://www.biomedcentral.com/1471-2482/4/12 © 2004 Besselink et al; licensee BioMed Central Ltd. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Randoop: feedback-directed random testing for Java

R<scp>ANDOOP</scp> for Java generates unit tests for Java code using feedback-directed random test generation. Below we describe R<scp>ANDOOP</scp>'s input, output, and test generation algorithm. We also give an overview of RANDOOP's annotation-based interface for specifying configuration parameters that affect R<scp>ANDOOP</scp>'s behavior and output.


An orchestrated survey of methodologies for automated software test case generation

Test case generation is among the most labour-intensive tasks in software testing. It also has a strong impact on the effectiveness and efficiency of software testing. For these reasons, it has been one of the most active research topics in software testing for several decades, resulting in many different approaches and tools. This paper presents an orchestrated survey of the most prominent techniques for automatic generation of software test cases, reviewed in self-standing sections. The techniques presented include: (a) structural testing using symbolic execution, (b) model-based testing, (c) combinatorial testing, (d) random testing and its variant of adaptive random testing, and (e) search-based testing. Each section is odel-based testing contributed by world-renowned active researchers on the technique, and briefly covers the basic ideas underlying the method, the current state of the art, a discussion of the open research problems, and a rchestrated survey earch-based software testing


Pex-White Box Test Generation for .NET

Pex automatically produces a small test suite with high code coverage for a .NET program. To this end, Pex performs a systematic program analysis (using dynamic symbolic execution, similar to pathbounded model-checking) to determine test inputs for Parameterized Unit Tests. Pex learns the program behavior by monitoring execution traces. Pex uses a constraint solver to produce new test inputs which exercise different program behavior. The result is an automatically generated small test suite which often achieves high code coverage. In one case study, we applied Pex to a core component of the .NET runtime which had already been extensively tested over several years. Pex found errors, including a serious issue.


Software Testing Research: Achievements, Challenges, Dreams

Software engineering comprehends several disciplines devoted to prevent and remedy malfunctions and to warrant adequate behaviour. Testing, the subject of this paper, is a widespread validation approach in industry, but it is still largely ad hoc, expensive, and unpredictably effective. Indeed, software testing is a broad term encompassing a variety of activities along the development cycle and beyond, aimed at different goals. Hence, software testing research faces a collection of challenges. A consistent roadmap of the most relevant challenges to be addressed is here proposed. In it, the starting point is constituted by some important past achievements, while the destination consists of four identified goals to which research ultimately tends, but which remain as unreachable as dreams. The routes from the achievements to the dreams are paved by the outstanding research challenges, which are discussed in the paper along with interesting ongoing work.


SEGAN: Speech Enhancement Generative Adversarial Network

Current speech enhancement techniques operate on the spectral domain and/or exploit some higher-level feature. The majority of them tackle a limited number of noise conditions and rely on first-order statistics. To circumvent these issues, deep networks are being increasingly used, thanks to their ability to learn complex functions from large example sets. In this work, we propose the use of generative adversarial networks for speech enhancement. In contrast to current techniques, we operate at the waveform level, training the model end-to-end, and incorporate 28 speakers and 40 different noise conditions into the same model, such that model parameters are shared across them. We evaluate the proposed model using an independent, unseen test set with two speakers and 20 alternative noise conditions. The enhanced samples confirm the viability of the proposed model, and both objective and subjective evaluations confirm the effectiveness of it. With that, we open the exploration of generative architectures for speech enhancement, which may progressively incorporate further speech-centric design choices to improve their performance.


The power of naive query segmentation

We address the problem of query segmentation: given a keyword query submitted to a search engine, the task is to group the keywords into phrases, if possible. Previous approaches to the problem achieve good segmentation performance on a gold standard but are fairly intricate. Our method is easy to implement and comes with a comparable accuracy.


Opposite Effects of Δ-9-Tetrahydrocannabinol and Cannabidiol on Human Brain Function and Psychopathology

Δ-9-tetrahydrocannabinol (Δ-9-THC) and Cannabidiol (CBD), the two main ingredients of the Cannabis sativa plant have distinct symptomatic and behavioral effects. We used functional magnetic resonance imaging (fMRI) in healthy volunteers to examine whether Δ-9-THC and CBD had opposite effects on regional brain function. We then assessed whether pretreatment with CBD can prevent the acute psychotic symptoms induced by Δ-9-THC. Fifteen healthy men with minimal earlier exposure to cannabis were scanned while performing a verbal memory task, a response inhibition task, a sensory processing task, and when viewing fearful faces. Subjects were scanned on three occasions, each preceded by oral administration of Δ-9-THC, CBD, or placebo. BOLD responses were measured using fMRI. In a second experiment, six healthy volunteers were administered Δ-9-THC intravenously on two occasions, after placebo or CBD pretreatment to examine whether CBD could block the psychotic symptoms induced by Δ-9-THC. Δ-9-THC and CBD had opposite effects on activation relative to placebo in the striatum during verbal recall, in the hippocampus during the response inhibition task, in the amygdala when subjects viewed fearful faces, in the superior temporal cortex when subjects listened to speech, and in the occipital cortex during visual processing. In the second experiment, pretreatment with CBD prevented the acute induction of psychotic symptoms by Δ-9-tetrahydrocannabinol. Δ-9-THC and CBD can have opposite effects on regional brain function, which may underlie their different symptomatic and behavioral effects, and CBD's ability to block the psychotogenic effects of Δ-9-THC.


Graph Based Relational Features for Collective Classification

Statistical Relational Learning (SRL) methods have shown that classification accuracy can be improved by integrating relations between samples. Techniques such as iterative classification or relaxation labeling achieve this by propagating information between related samples during the inference process. When only a few samples are labeled and connections between samples are sparse, collective inference methods have shown large improvements over standard feature-based ML methods. However, in contrast to feature based ML, collective inference methods require complex inference procedures and often depend on the strong assumption of label consistency among related samples. In this paper, we introduce new relational features for standard ML methods by extracting information from direct and indirect relations. We show empirically on three standard benchmark datasets that our relational features yield results comparable to collective inference methods. Finally we show that our proposal outperforms these methods when additional information is available.


Key organizational factors in data warehouse architecture selection

a r t i c l e i n f o Keywords: Data warehousing Data warehouse architecture Archictecture selection Independent data marts Bus architecture Enterprise data warehouse Hub and spoke Federated Even though data warehousing has been in existence for over a decade, companies are still uncertain about a critical decision — which data warehouse architecture to implement? Based on the existing literature, theory, and interviews with experts, a research model was created that identifies the various contextual factors that affect the selection decision. The results from the field survey and multinomial logistic regression suggest that various combinations of organizational factors influence data warehouse architecture selection. The strategic view of the data warehouse prior to implementation emerged as a key determinant. The research suggests an overall model for predicting the data warehouse architecture selection decision. Over the past decade, many companies have made data warehouses the foundation of their decision support infrastructures. These data repositories provide a solution to a recurring problem that has plagued companies' decision support initiatives — the lack of clean, accurate, timely, and integrated data. Whether the data is used for queries and reporting, decision support systems (DSS), executive information systems (EIS), online analytical processing (OLAP), or data mining, data warehouses provide the data that " fuels " these applications [36]. Data warehouses are also critical enablers of current strategic initiatives such as customer relationship management (CRM), business performance management (BPM), and supply chain management [18,23,35,85]. Despite the importance and growing experience with data ware-housing, there is still a considerable discussion and disagreement over which architecture to use. Bill Inmon, commonly referred to as " the father of data warehousing, " advocates what is variously called the hub and spoke architecture (i.e., a centralized data warehouse with dependent data marts), Corporate Information Factory, DW 2.0 (i.e., Inmon's current term), or enterprise data warehouse architecture [23]. Another architecture alternative is the data mart bus architecture with linked dimensional data marts (i.e., bus architecture), advocated by Ralph Kimball, the other preeminent figure in data warehousing [52]. 1 These and other architectures (e.g., independent data marts and federated) have fundamental differences and strong advocates. The selection of an appropriate architecture is an important decision. A study by the Meta Group found that architecture selection is one of the key factors influencing data warehousing success [54]. A Gartner report identified the architecture selection decision as one of the five problem …


Experienced Grey Wolf Optimizer through Reinforcement Learning and Neural Networks

In this paper, a variant of Grey Wolf Optimizer (GWO) that uses reinforcement learning principles combined with neural networks to enhance the performance is proposed. The aim is to overcome, by reinforced learning, the common challenges of setting the right parameters for the algorithm. In GWO, a single parameter is used to control the exploration/exploitation rate which influences the performance of the algorithm. Rather than using a global way to change this parameter for all the agents, we use reinforcement learning to set it on an individual basis. The adaptation of the exploration rate for each agent depends on the agent’s own experience and the current terrain of the search space. In order to achieve this, an experience repository is built based on the neural network to map a set of agents’ states to a set of corresponding actions that specifically influence the exploration rate. The experience repository is updated by all the search agents to reflect experience and to enhance the future actions continuously. The resulted algorithm is called Experienced Grey Wolf Optimizer (EGWO) and its performance is assessed on solving feature selection problems and on finding optimal weights for neural networks algorithm. We use a set of performance indicators to evaluate the efficiency of the method. Results over various datasets demonstrate an advance of the EGWO over the original GWO and other meta-heuristics such as genetic algorithms and particle swarm optimization.


Second-order complex random vectors and normal distributions

We formulate as a deconvolution problem the causalhoncausal non-Gaussian multichannel autoregressive (AR) parameter estimation problem. The super exponential aljporithm presented in a recent paper by Shalvi and Weinstein is generalized to the vector case. We present an adaptive implementation that is very attractive since it is higher order statistics (HOS) based b u t does not present the high comlputational complexity of methods proposed up to now.


One selector-one resistor (1S1R) crossbar array for high-density flexible memory applications

Lack of a suitable selection device to suppress sneak current has impeded the development of 4F2 crossbar memory array utilizing stable and scalable bipolar resistive-switching. We report a high-performance nonlinear bipolar selector realized by a simple Ni/TiO2/Ni MIM structure with a high current density of 105 A/cm2, and a Ni/TiO2/Ni/HfO2/Pt vertically stacked 1S1R cell capable of gigabit memory implementation. Furthermore, the demonstration of 1S1R array fabricated completely at room temperature on a plastic substrate highlights the promise of future extremely low-cost flexible nonvolatile memory.


A Low Cost Eeg Based Bci Prosthetic Using Motor Imagery

Brain Computer Interfaces (BCI) provide the opportunity to control external devices using the brain ElectroEncephaloGram (EEG) signals. In this paper we propose two software framework in order to control a 5 degree of freedom robotic and prosthetic hand. Results are presented where an Emotiv Cognitive Suite (i.e. the 1 st framework) combined with an embedded software system (i.e. an open source Arduino board) is able to control the hand through character input associated with the taught actions of the suite. This system provides evidence of the feasibility of brain signals being a viable approach to controlling the chosen prosthetic. Results are then presented in the second framework. This latter one allowed for the training and classification of EEG signals for motor imagery tasks. When analysing the system, clear visual representations of the performance and accuracy are presented in the results using a confusion matrix, accuracy measurement and a feedback bar signifying signal strength. Experiments with various acquisition datasets were carried out and with a critical evaluation of the results given. Finally depending on the classification of the brain signal a Python script outputs the driving command to the Arduino to control the prosthetic. The proposed architecture performs overall good results for the design and implementation of economically convenient BCI and prosthesis.


Methods for ordinal peer grading

Massive Online Open Courses have the potential to revolutionize higher education with their wide outreach and accessibility, but they require instructors to come up with scalable alternates to traditional student evaluation. Peer grading -- having students assess each other -- is a promising approach to tackling the problem of evaluation at scale, since the number of "graders" naturally scales with the number of students. However, students are not trained in grading, which means that one cannot expect the same level of grading skills as in traditional settings. Drawing on broad evidence that ordinal feedback is easier to provide and more reliable than cardinal feedback [5, 38, 29, 9], it is therefore desirable to allow peer graders to make ordinal statements (e.g. "project X is better than project Y") and not require them to make cardinal statements (e.g. "project X is a B-"). Thus, in this paper we study the problem of automatically inferring student grades from ordinal peer feedback, as opposed to existing methods that require cardinal peer feedback. We formulate the ordinal peer grading problem as a type of rank aggregation problem, and explore several probabilistic models under which to estimate student grades and grader reliability. We study the applicability of these methods using peer grading data collected from a real class --- with instructor and TA grades as a baseline --- and demonstrate the efficacy of ordinal feedback techniques in comparison to existing cardinal peer grading methods. Finally, we compare these peer-grading techniques to traditional evaluation techniques.


Watch Me Code: Programming Mentorship Communities on Twitch.tv

Live streaming-an emerging practice of broadcasting video of oneself in real time to an online audience-is often used by people to portray themselves engaged in a craft such as programming. Viewers of these 'creative streams' gather to watch the streamer at work and to interact with the streamer and other audience members. However, little is known about how creative streamers engage with their audience, how their viewership communities form and operate, and how creative streams may support learning. In this study, we used a participant-observer method to study game development streams on the live streaming site Twitch.tv. We found that live streams support the growth of learning-focused communities that mentor both the streamer and each other during and after streams. We show the influence of streamers in creating a space for learning and motivating learners. Finally, we discuss implications for online education and communities of practice.


A multi-dimensional meter-adaptive method for automatic segmentation of music

Music structure appears on a wide variety of temporal levels (notes, bars, phrases, etc). Its highest-level expression is therefore dependent on music's lower-level organization, especially beats and bars. We propose a method for automatic structure segmentation that uses musically meaningful information and is content-adaptive. It relies on a meter-adaptive signal representation that prevents from the use of empirical parameters. Moreover, our method is designed to combine multiple signal features to account for various musical dimensions. Finally, it also combines multiple structural principles that yield complementary results. The resulting algorithm proves to already outperform state-of-the-art methods, especially within small tolerance windows, and yet offers several encouraging improvement directions.


X-shaped inferior extensor retinaculum and its doubtful use in the Bröstrom–Gould procedure

The inferior extensor retinaculum (IER) is an aponeurotic structure located in the anterior aspect of the ankle. According to the literature, it can be used to reinforce a repair of the anterior talofibular ligament in ankle instability. Despite its usual description as an Y-shaped structure, it is still unclear which part of the retinaculum is used for this purpose, or if it is instead the crural fascia that is being used. The purpose of this study is to define the anatomical characteristics of the IER to better understand its role in the Broström–Gould procedure. Twenty-one ankles were dissected. The morphology of the IER and its relationship with neighbouring structures were recorded. Seventeen (81%) of the IER in this study had an X-shaped morphology, with the presence of an additional oblique superolateral band. This band, by far the thinnest of the retinaculum, is supposed to be used to reinforce the repair of the anterior talofibular ligament. The intermediate dorsal cutaneous nerve (lateral branch of the superficial peroneal nerve) was found to cross the retinaculum in all cases. The IER is most commonly seen as an X-shaped structure, but the fact that the oblique superolateral band is a thin band of tissue probably indicates that it may not add significant strength to ankle stability. Furthermore, the close relationship of the retinaculum with the superficial peroneal nerve is another factor to consider before deciding to perform a Broström–Gould procedure. These anatomical findings advise against the use of the Gould augmentation.


Construction projects selection and risk assessment by fuzzy AHP and fuzzy TOPSIS methodologies

Construction projects are initiated in dynamic environment which result in circumstances of high uncertainty and risks due to accumulation of many interrelated parameters. The purpose of this study is to use novel analytic tools to evaluate the construction projects and their overall risks under incomplete and uncertain situations. It was also aimed to place the risk in a proper category and predict the level of it in advance to develop strategies and counteract the high-risk factors. The study covers identifying the key risk criteria of construction projects at King Abdulaziz University (KAU), and assessing the criteria by the integrated hybrid methodologies. The proposed hybrid methodologies were initiated with a survey for data collection. The relative importance index (RII) method was applied to prioritize the project risks based on the data obtained. The construction projects were then categorized by fuzzy AHP and fuzzy TOPSIS methodologies. Fuzzy AHP (FAHP) was used to create favorable weights for fuzzy linguistic variable of construction projects overall risk. The fuzzy TOPSIS method is very suitable for solving group decision making problems under the fuzzy environment. It attempted to incorporate vital qualitative attributes in performance analysis of construction projects and transformed the qualitative data into equivalent quantitative measures. Thirty construction projects were studied with respect to five main criteria that are the time, cost, quality, safety and environment sustainability. The results showed that these novel methodologies are able to assess the overall risks of construction projects, select the project that has the lowest risk with the contribution of relative importance index. This approach will have potential applications in the future. Crown Copyright © 2014 Published by Elsevier B.V. All rights reserved.


Noise Modeling for RF CMOS Circuit Simulation

The RF noise in 0.18m CMOS technology has been measured and modeled. In contrast to some other groups, we find only a moderate enhancement of the drain current noise for shortchannel MOSFETs. The gate current noise on the other hand is more significantly enhanced, which is explained by the effects of the gate resistance. The experimental results are modeled with a nonquasi-static RF model, based on channel segmentation, which is capable of predicting both drain and gate current noise accurately. Experimental evidence is shown for two additional noise mechanisms: 1) avalanche noise associated with the avalanche current from drain to bulk and 2) shot noise in the direct-tunneling gate leakage current. Additionally, we show low-frequency noise measurements, which strongly point toward an explanation of the1 noise based on carrier trapping, not only in n-channel MOSFETs, but also in p-channel MOSFETs.


Topology Aware Fully Convolutional Networks for Histology Gland Segmentation

[1] Sirinukunwattana et al. Gland Segmentation in colon histology images: the GlaS contest, arXiv 2016 [2] Long et al. Fully convolutional networks for semantic segmentation, CVPR 2015 [3] Chend et al. Semantic image segmentation with deep convolutional nets and fully connected CRFs, arXiv 2014 [4] Zheng et al. Conditional random fields as recurrent neural networks, ICCV 2015 Alexnet-FCN (6 layers) FCN-8s (16 layers) UNet (23 layers) DN (26 layers) 0.93 0.79 0.86 0.83


Learning to predict eye fixations for semantic contents using multi-layer sparse network

In this paper, we present a novel model for saliency prediction under a unified framework of feature integration. The model distinguishes itself by directly learning from natural images and automatically incorporating higher-level semantic information in a scalable manner for gaze prediction. Unlike most existing saliency models that rely on specific features or object detectors, our model learns multiple stages of features that mimic the hierarchical organization of the ventral stream in the visual cortex and integrate them by adapting their weights based on the ground-truth fixation data. To accomplish this, we utilize a multi-layer sparse network to learn low-, midand high-level features from natural images and train a linear support vector machine (SVM) for weight adaption and feature integration. Experimental results show that our model could learn high-level semantic features like faces and texts and can perform competitively among existing approaches in predicting eye fixations. & 2014 Elsevier B.V. All rights reserved.


E2E NLG Challenge Submission: Towards Controllable Generation of Diverse Natural Language

In natural language generation (NLG), the task is to generate utterances from a more abstract input, such as structured data. An added challenge is to generate utterances that contain an accurate representation of the input, while reflecting the fluency and variety of human-generated text. In this paper, we report experiments with NLG models that can be used in task oriented dialogue systems. We explore the use of additional input to the model to encourage diversity and control of outputs. While our submission does not rank highly using automated metrics, qualitative investigation of generated utterances suggests the use of additional information in neural network NLG systems to be a promising research direction.


A Novel Earth Mover's Distance Methodology for Image Matching with Gaussian Mixture Models

The similarity or distance measure between Gaussian mixture models (GMMs) plays a crucial role in content-based image matching. Though the Earth Mover's Distance (EMD) has shown its advantages in matching histogram features, its potentials in matching GMMs remain unclear and are not fully explored. To address this problem, we propose a novel EMD methodology for GMM matching. We first present a sparse representation based EMD called SR-EMD by exploiting the sparse property of the underlying problem. SR-EMD is more efficient and robust than the conventional EMD. Second, we present two novel ground distances between component Gaussians based on the information geometry. The perspective from the Riemannian geometry distinguishes the proposed ground distances from the classical entropy-or divergence-based ones. Furthermore, motivated by the success of distance metric learning of vector data, we make the first attempt to learn the EMD distance metrics between GMMs by using a simple yet effective supervised pair-wise based method. It can adapt the distance metrics between GMMs to specific classification tasks. The proposed method is evaluated on both simulated data and benchmark real databases and achieves very promising performance.


Collaborative color calibration for multi-camera systems

This paper proposes a collaborative color calibration meth od for multi-camera systems. The multi-camera color calibr t on problem is formulated as an overdetermined linear system, i n which a dynamic range shaping is incorporated to ensure the high contrasts for captured images. The cameras are calibrated w ith the parameters obtained by solving the linear system. Fo r non-planar multi-camera systems, we design a novel omnidirectional co lor checker and present a method for establishing global cor respondences to facilitate automatic color calibration without m anual adjustment. According to experimental results on bot h synthetic and real-system datasets, the proposed method shows high perfo rmance in achieving inter-camera color consistency and hig h dynamic range. Thanks to the generality of the linear system formula tion and the flexibility of the designed color checker, the pr oposed method is applicable to various multi-camera systems.


Dynamic Reserve Prices for Repeated Auctions: Learning from Bids - Working Paper

A large fraction of online advertisements are sold via repeated second price auctions. In these auctions, the reserve price is the main tool for the auctioneer to boost revenues. In this work, we investigate the following question: Can changing the reserve prices based on the previous bids improve the revenue of the auction, taking into account the long-term incentives and strategic behavior of the bidders? We show that if the distribution of the valuations is known and satisfies the standard regularity assumptions, then the optimal mechanism has a constant reserve. However, when there is uncertainty in the distribution of the valuations and competition among the bidders, previous bids can be used to learn the distribution of the valuations and to update the reserve prices. We present a simple approximately incentive-compatible and optimal dynamic reserve mechanism that can significantly improve the revenue over the best static reserve in such settings.


DeepAnomaly: Combining Background Subtraction and Deep Learning for Detecting Obstacles and Anomalies in an Agricultural Field

Convolutional neural network (CNN)-based systems are increasingly used in autonomous vehicles for detecting obstacles. CNN-based object detection and per-pixel classification (semantic segmentation) algorithms are trained for detecting and classifying a predefined set of object types. These algorithms have difficulties in detecting distant and heavily occluded objects and are, by definition, not capable of detecting unknown object types or unusual scenarios. The visual characteristics of an agriculture field is homogeneous, and obstacles, like people, animals and other obstacles, occur rarely and are of distinct appearance compared to the field. This paper introduces DeepAnomaly, an algorithm combining deep learning and anomaly detection to exploit the homogenous characteristics of a field to perform anomaly detection. We demonstrate DeepAnomaly as a fast state-of-the-art detector for obstacles that are distant, heavily occluded and unknown. DeepAnomaly is compared to state-of-the-art obstacle detectors including "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" (RCNN). In a human detector test case, we demonstrate that DeepAnomaly detects humans at longer ranges (45-90 m) than RCNN. RCNN has a similar performance at a short range (0-30 m). However, DeepAnomaly has much fewer model parameters and (182 ms/25 ms =) a 7.28-times faster processing time per image. Unlike most CNN-based methods, the high accuracy, the low computation time and the low memory footprint make it suitable for a real-time system running on a embedded GPU (Graphics Processing Unit).


CDnet 2014: An Expanded Change Detection Benchmark Dataset

Change detection is one of the most important lowlevel tasks in video analytics. In 2012, we introduced the changedetection.net (CDnet) benchmark, a video dataset devoted to the evalaution of change and motion detection approaches. Here, we present the latest release of the CDnet dataset, which includes 22 additional videos (70; 000 pixel-wise annotated frames) spanning 5 new categories that incorporate challenges encountered in many surveillance settings. We describe these categories in detail and provide an overview of the results of more than a dozen methods submitted to the IEEE Change DetectionWorkshop 2014. We highlight strengths and weaknesses of these methods and identify remaining issues in change detection.


Does Online Technology Make Us More or Less Sociable? A Preliminary Review and Call for Research.

How does online technology affect sociability? Emerging evidence-much of it inconclusive-suggests a nuanced relationship between use of online technology (the Internet, social media, and virtual reality) and sociability (emotion recognition, empathy, perspective taking, and emotional intelligence). Although online technology can facilitate purely positive behavior (e.g., charitable giving) or purely negative behavior (e.g., cyberbullying), it appears to affect sociability in three ways, depending on whether it allows a deeper understanding of people's thoughts and feelings: (a) It benefits sociability when it complements already-deep offline engagement with others, (b) it impairs sociability when it supplants deeper offline engagement for superficial online engagement, and (c) it enhances sociability when deep offline engagement is otherwise difficult to attain. We suggest potential implications and moderators of technology's effects on sociability and call for additional causal research.


A Rule-Based Approach For Effective Sentiment Analysis

The success of Web 2.0 applications has made online social media websites tremendous assets for supporting critical business intelligence applications. The knowledge gained from social media can potentially lead to the development of novel services that are better tailored to users’ needs and at the same time meet the objectives of businesses offering them. Online consumer reviews are one of the critical social media contents. Proper analysis of consumer reviews not only provides valuable information to facilitate the purchase decisions of customers but also helps merchants or product manufacturers better understand general responses of customers on their products for marketing campaign improvement. This study aims at designing an approach for supporting the effective analysis of the huge volume of online consumer reviews and, at the same time, settling the major limitations of existing approaches. Specifically, the proposed rule-based sentiment analysis (R-SA) technique employs the class association rule mining algorithm to automatically discover interesting and effective rules capable of extracting product features or opinion sentences for a specific product feature interested. According to our preliminary evaluation results, the R-SA technique performs well in comparison with its benchmark technique.


Making Sense of Entities and Quantities in Web Tables

HTML tables and spreadsheets on the Internet or in enterprise intranets often contain valuable information, but are created ad-hoc. As a result, they usually lack systematic names for column headers and clear vocabulary for cell values. This limits the re-use of such tables and creates a huge heterogeneity problem when comparing or aggregating multiple tables.
 This paper aims to overcome this problem by automatically canonicalizing header names and cell values onto concepts, classes, entities and uniquely represented quantities registered in a knowledge base.
 To this end, we devise a probabilistic graphical model that captures coherence dependencies between cells in tables and candidate items in the space of concepts, entities and quantities. We give specific consideration to quantities which are mapped into a "measure, value, unit" triple over a taxonomy of physical (e.g. power consumption), monetary (e.g. revenue), temporal (e.g. date) and dimensionless (e.g. counts) measures.
 Our experiments with Web tables from diverse domains demonstrate the viability of our method and its benefits over baselines.


A Laplacian Framework for Option Discovery in Reinforcement Learning

Representation learning and option discovery are two of the biggest challenges in reinforcement learning (RL). Proto-RL is a well known approach for representation learning in MDPs. The representations learned with this framework are called proto-value functions (PVFs). In this paper we address the option discovery problem by showing how PVFs implicitly define options. We do it by introducing eigenpurposes, intrinsic reward functions derived from the learned representations. The options discovered from eigenpurposes traverse the principal directions of the state space. They are useful for multiple tasks because they are independent of the agents’ intentions. Moreover, by capturing the diffusion process of a random walk, different options act at different time scales, making them helpful for exploration strategies. We demonstrate features of eigenpurposes in traditional tabular domains as well as in Atari 2600 games.


Accessible Crowdwork?: Understanding the Value in and Challenge of Microtask Employment for People with Disabilities

We present the first formal study of crowdworkers who have disabilities via in-depth open-ended interviews of 17 people (disabled crowdworkers and job coaches for people with disabilities) and a survey of 631 adults with disabilities. Our findings establish that people with a variety of disabilities currently participate in the crowd labor marketplace, despite challenges such as crowdsourcing workflow designs that inadvertently prohibit participation by, and may negatively affect the worker reputations of, people with disabilities. Despite such challenges, we find that crowdwork potentially offers different opportunities for people with disabilities relative to the normative office environment, such as job flexibility and lack of a need to rely on public transit. We close by identifying several ways in which crowd labor platform operators and/or individual task requestors could improve the accessibility of this increasingly important form of employment.


Breakfast reduces declines in attention and memory over the morning in schoolchildren

Twenty-nine schoolchildren were tested throughout the morning on 4 successive days, having a different breakfast each day (either of the cereals Cheerios or Shreddies, glucose drink or No breakfast). A series of computerised tests of attention, working memory and episodic secondary memory was conducted prior to breakfast and again 30, 90, 150 and 210 min later. The glucose drink and No breakfast conditions were followed by declines in attention and memory, but the declines were significantly reduced in the two cereal conditions. This study provides objective evidence that a typical breakfast of cereal rich in complex carbohydrates can help maintain mental performance over the morning.


Customer relationship management capabilities Measurement , antecedents and consequences

Purpose – This study seeks to extend the resource-based view to the context of customer relationship management. It is intended to develop a measurement model of customer relationship management (CRM) capabilities, and to explore the key antecedents and performance consequences of CRM capabilities. Design/methodology/approach – Questionnaire survey was used to collect data. In order to develop a reliable and valid measurement model of CRM capabilities, several rounds of questionnaire survey were conducted, and hypotheses were tested by utilizing the technique of structural equation modeling. Findings – A three-factor (customer interaction management capability, customer relationship upgrading capability and customer win-back capability) measurement model of CRM capabilities is developed and tested. Furthermore, results support the hypothesized influences of customer orientation, customer-centric organizational system and CRM technology on CRM capabilities, as well as the influence of CRM capabilities on organizational performance. Practical implications – This study provides a useful measurement mode of CRM capabilities that managers can use to evaluate the status in quo of CRM capabilities of their firms. Managers may also improve their CRM programs more effectively and efficiently by deploying such strategic resources of firms as customer orientation, customer-centric organizational system and CRM technology to build and strengthen their CRM capabilities. Originality/value – The paper addresses significant gaps in the current literature by taking a capability view of CRM, developing a valid measurement model of CRM capabilities, and examining how possession of important CRM resources influences the effective deployment of CRM capabilities.


Reduced interference in working memory following mindfulness training is associated with increases in hippocampal volume

Proactive interference occurs when previously relevant information interferes with retaining newer material. Overcoming proactive interference has been linked to the hippocampus and deemed critical for cognitive functioning. However, little is known about whether and how this ability can be improved or about the neural correlates of such improvement. Mindfulness training emphasizes focusing on the present moment and minimizing distraction from competing thoughts and memories. It improves working memory and increases hippocampal density. The current study examined whether mindfulness training reduces proactive interference in working memory and whether such improvements are associated with changes in hippocampal volume. 79 participants were randomized to a 4-week web-based mindfulness training program or a similarly structured creative writing active control program. The mindfulness group exhibited lower proactive interference error rates compared to the active control group following training. No group differences were found in hippocampal volume, yet proactive interference improvements following mindfulness training were significantly associated with volume increases in the left hippocampus. These results provide the first evidence to suggest that (1) mindfulness training can protect against proactive interference, and (2) that these benefits are related to hippocampal volumetric increases. Clinical implications regarding the application of mindfulness training in conditions characterized by impairments to working memory and reduced hippocampal volume such as aging, depression, PTSD, and childhood adversity are discussed.


Real-Time Top-View People Counting Based on a Kinect and NVIDIA Jetson TK1 Integrated Platform

In this paper, we describe how to establish an embedded framework for real-time top-view people counting. The development of our system consists of two parts, i.e. establishing an embedded signal processing platform and designing a people counting algorithm for the embedded system. For the hardware platform construction, we use Kinect as the camera and exploit NVIDIA Jetson TK1 board as the embedded processing platform. We describe how to build a channel to make Kinect for windows version 2.0 communicate with Jetson TK1. Based on the embedded system, we adapt a water filling based scheme for top-view people counting, which integrates head detection based on water drop, people tracking and counting. Gaussian Mixture Model is used to construct and update the background model. The moving people in each video frame are extracted using background subtraction method. Additionally, the water filling algorithm is used to segment head area as Region Of Interest(ROI). Tracking and counting people are performed by calculating the distance of ROI center point before and after the frame. The whole framework is flexible and practical for real-time application.


Mobile technology and the digitization of healthcare.

The convergence of science and technology in our dynamic digital era has resulted in the development of innovative digital health devices that allow easy and accurate characterization in health and disease. Technological advancements and the miniaturization of diagnostic instruments to modern smartphone-connected and mobile health (mHealth) devices such as the iECG, handheld ultrasound, and lab-on-a-chip technologies have led to increasing enthusiasm for patient care with promises to decrease healthcare costs and to improve outcomes. This 'hype' for mHealth has recently intersected with the 'real world' and is providing important insights into how patients and practitioners are utilizing digital health technologies. It is also raising important questions regarding the evidence supporting widespread device use. In this state-of-the-art review, we assess the current literature of mHealth and aim to provide a framework for the advances in mHealth by understanding the various device, patient, and clinical factors as they relate to digital health from device designs and patient engagement, to clinical workflow and device regulation. We also outline new strategies for generation and analysis of mHealth data at the individual and population-based levels.


Experimental implementation of underactuated potential energy shaping on a powered ankle-foot orthosis

Traditional control methodologies of rehabilitation orthoses/exoskeletons aim at replicating normal kinematics and thus fall into the category of kinematic control. This control paradigm depends on pre-defined reference trajectories, which can be difficult to adjust between different locomotor tasks and human subjects. An alternative control category, kinetic control, enforces kinetic goals (e.g., torques or energy) instead of kinematic trajectories, which could provide a flexible learning environment for the user while freeing up therapists to make corrections. We propose that the theory of underactuated potential energy shaping, which falls into the category of kinetic control, could be used to generate virtual body-weight support for stroke gait rehabilitation. After deriving the nonlinear control law and simulating it on a human-like biped model, we implemented this controller on a powered ankle-foot orthosis that was designed specifically for testing torque control strategies. Experimental results with an able-bodied human subject demonstrate the feasibility of the control approach for both positive and negative virtual body-weight augmentation.


Distance estimation with 2 . 5 D anchors and its application to robot navigation

Estimating the distance of a target object from a single image is a challenging task since a large variation in the object appearance makes the regression of the distance difficult. In this paper, to tackle such the challenge, we propose 2.5D anchors which provide the candidate of distances based on a perspective camera model. This candidate is expected to relax the difficulty of the regression model since only the residual from the candidate distance needs to be taken into account. We show the effectiveness of the regression with our proposed anchors, by comparing with ordinary regression methods and state-of-the-art 3D object detection methods, through Pascal 3D+ TV monitor and KITTI car experiments. In addition, we also show an example of practical uses of our proposed method in a real-time system, robot navigation, by integrating with ROS-based simultaneous localization and mapping.


Machine morality: bottom-up and top-down approaches for modelling human moral faculties

The implementation of moral decision making abilities in artificial intelligence (AI) is a natural and necessary extension to the social mechanisms of autonomous software agents and robots. Engineers exploring design strategies for systems sensitive to moral considerations in their choices and actions will need to determine what role ethical theory should play in defining control architectures for such systems. The architectures for morally intelligent agents fall within two broad approaches: the top-down imposition of ethical theories, and the bottom-up building of systems that aim at goals or standards which may or may not be specified in explicitly theoretical terms. In this paper we wish to provide some direction for continued research by outlining the value and limitations inherent in each of these approaches.


A systematic review of implementation strategies for assessment, prevention, and management of ICU delirium and their effect on clinical outcomes

INTRODUCTION
Despite recommendations from professional societies and patient safety organizations, the majority of ICU patients worldwide are not routinely monitored for delirium, thus preventing timely prevention and management. The purpose of this systematic review is to summarize what types of implementation strategies have been tested to improve ICU clinicians' ability to effectively assess, prevent and treat delirium and to evaluate the effect of these strategies on clinical outcomes.

METHOD
We searched PubMed, Embase, PsychINFO, Cochrane and CINAHL (January 2000 and April 2014) for studies on implementation strategies that included delirium-oriented interventions in adult ICU patients. Studies were suitable for inclusion if implementation strategies' efficacy, in terms of a clinical outcome, or process outcome was described.

RESULTS
We included 21 studies, all including process measures, while 9 reported both process measures and clinical outcomes. Some individual strategies such as "audit and feedback" and "tailored interventions" may be important to establish clinical outcome improvements, but otherwise robust data on effectiveness of specific implementation strategies were scarce. Successful implementation interventions were frequently reported to change process measures, such as improvements in adherence to delirium screening with up to 92%, but relating process measures to outcome changes was generally not possible. In meta-analyses, reduced mortality and ICU length of stay reduction were statistically more likely with implementation programs that employed more (six or more) rather than less implementation strategies and when a framework was used that either integrated current evidence on pain, agitation and delirium management (PAD) or when a strategy of early awakening, breathing, delirium screening and early exercise (ABCDE bundle) was employed. Using implementation strategies aimed at organizational change, next to behavioral change, was also associated with reduced mortality.

CONCLUSION
Our findings may indicate that multi-component implementation programs with a higher number of strategies targeting ICU delirium assessment, prevention and treatment and integrated within PAD or ABCDE bundle have the potential to improve clinical outcomes. However, prospective confirmation of these findings is needed to inform the most effective implementation practice with regard to integrated delirium management and such research should clearly delineate effective practice change from improvements in clinical outcomes.


Ontology and Information Systems

Ontology as a branch of philosophy is the science of what is, of the kinds and structures of objects, properties, events, processes and relations in every area of reality. ‘Ontology’ is often used by philosophers as a synonym for ‘metaphysics’ (literally: ‘what comes after the Physics’), a term which was used by early students of Aristotle to refer to what Aristotle himself called ‘first philosophy’. The term ‘ontology’ (or ontologia) was itself coined in 1613, independently, by two philosophers, Rudolf Göckel (Goclenius), in his Lexicon philosophicum and Jacob Lorhard (Lorhardus), in his Theatrum philosophicum. The first occurrence in English recorded by the OED appears in Bailey’s dictionary of 1721, which defines ontology as ‘an Account of being in the Abstract’.


Deep convolutional acoustic word embeddings using word-pair side information

Recent studies have been revisiting whole words as the basic modelling unit in speech recognition and query applications, instead of phonetic units. Such whole-word segmental systems rely on a function that maps a variable-length speech segment to a vector in a fixed-dimensional space; the resulting acoustic word embeddings need to allow for accurate discrimination between different word types, directly in the embedding space. We compare several old and new approaches in a word discrimination task. Our best approach uses side information in the form of known word pairs to train a Siamese convolutional neural network (CNN): a pair of tied networks that take two speech segments as input and produce their embeddings, trained with a hinge loss that separates same-word pairs and different-word pairs by some margin. A word classifier CNN performs similarly, but requires much stronger supervision. Both types of CNNs yield large improvements over the best previously published results on the word discrimination task.


Water-walking devices

We report recent efforts in the design and construction of water-walking machines inspired by insects and spiders. The fundamental physical constraints on the size, proportion and dynamics of natural water-walkers are enumerated and used as design criteria for analogous mechanical devices. We report devices capable of rowing along the surface, leaping off the surface and climbing menisci by deforming the free surface. The most critical design constraint is that the devices be lightweight and non-wetting. Microscale manufacturing techniques and new man-made materials such as hydrophobic coatings and thermally actuated wires are implemented. Using highspeed cinematography and flow visualization, we compare the functionality and dynamics of our devices with those of their natural counterparts.


Genome engineering in Saccharomyces cerevisiae using CRISPR-Cas systems

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and CRISPR-associated (Cas) systems in bacteria and archaea use RNA-guided nuclease activity to provide adaptive immunity against invading foreign nucleic acids. Here, we report the use of type II bacterial CRISPR-Cas system in Saccharomyces cerevisiae for genome engineering. The CRISPR-Cas components, Cas9 gene and a designer genome targeting CRISPR guide RNA (gRNA), show robust and specific RNA-guided endonuclease activity at targeted endogenous genomic loci in yeast. Using constitutive Cas9 expression and a transient gRNA cassette, we show that targeted double-strand breaks can increase homologous recombination rates of single- and double-stranded oligonucleotide donors by 5-fold and 130-fold, respectively. In addition, co-transformation of a gRNA plasmid and a donor DNA in cells constitutively expressing Cas9 resulted in near 100% donor DNA recombination frequency. Our approach provides foundations for a simple and powerful genome engineering tool for site-specific mutagenesis and allelic replacement in yeast.


Cognitive programs: software for attention's executive

What are the computational tasks that an executive controller for visual attention must solve? This question is posed in the context of the Selective Tuning model of attention. The range of required computations go beyond top-down bias signals or region-of-interest determinations, and must deal with overt and covert fixations, process timing and synchronization, information routing, memory, matching control to task, spatial localization, priming, and coordination of bottom-up with top-down information. During task execution, results must be monitored to ensure the expected results. This description includes the kinds of elements that are common in the control of any kind of complex machine or system. We seek a mechanistic integration of the above, in other words, algorithms that accomplish control. Such algorithms operate on representations, transforming a representation of one kind into another, which then forms the input to yet another algorithm. Cognitive Programs (CPs) are hypothesized to capture exactly such representational transformations via stepwise sequences of operations. CPs, an updated and modernized offspring of Ullman's Visual Routines, impose an algorithmic structure to the set of attentional functions and play a role in the overall shaping of attentional modulation of the visual system so that it provides its best performance. This requires that we consider the visual system as a dynamic, yet general-purpose processor tuned to the task and input of the moment. This differs dramatically from the almost universal cognitive and computational views, which regard vision as a passively observing module to which simple questions about percepts can be posed, regardless of task. Differing from Visual Routines, CPs explicitly involve the critical elements of Visual Task Executive (vTE), Visual Attention Executive (vAE), and Visual Working Memory (vWM). Cognitive Programs provide the software that directs the actions of the Selective Tuning model of visual attention.


A line feature based SLAM with low grade range sensors using geometric constraints and active exploration for mobile robot

This paper describes a geometrically constrained Extended Kalman Filter (EKF) framework for a line feature based SLAM, which is applicable to a rectangular indoor environment. Its focus is on how to handle sparse and noisy sensor data, such as PSD infrared sensors with limited range and limited number, in order to develop a low-cost navigation system. It has been applied to a vacuum cleaning robot in our research. In order to meet the real-time objective with low computing power, we develop an efficient line feature extraction algorithm based upon an iterative end point fit (IEPF) technique assisted by our constrained version of the Hough transform. It uses a geometric constraint that every line is orthogonal or parallel to each other because in a general indoor setting, most furniture and walls satisfy this constraint. By adding this constraint to the measurement model of EKF, we build a geometrically constrained EKF framework which can estimate line feature positions more accurately as well as allow their covariance matrices to converge more rapidly when compared to the case of an unconstrained EKF. The experimental results demonstrate the accuracy and robustness to the presence of sensor noise and errors in an actual indoor environment.


Ontology based Semantic Search Engine

We live in a connected age when every entity is linked to each other through numerous relationships thus forming complex networks, information networks etc., In this meshy scenario, storing analyzing, managing extracting information is going to be highly challenging task. Information retrieval happened to be an activity that only a few people engaged in. due to the advent of search engines and communication facilities, millions of people are engaged in information retrieval and extraction process. The information retrieval deals with finding a set of documents relevant to the user query. Commercial search engines like Google deals with key word search which is based on Boolean logical queries. The major disadvantage of this kind of keyword search is that it returns a lot of irrelevant information to the users which results in low precision. Nowadays the field of information retrieval is moving towards semantic level from syntactic level. These semantic search engines are based on the concept of ontology which gives a Meta data representation of concepts. In this paper we focus on the retrieval mechanisms related to it. This includes various phases in which the ontology is designed first and then the indexing and retrieval phases work. Key wordsOntology, semantic web, Information retrieval, Query builder I.INTRODUCTION In this digital age each and every concept is linked to each other through various relationships forming the complex information web. By passing through the web we face some sort of challenges in storing and extracting the information from them. We therefore rely on search engines to overcome those difficulties. Any way these search engines facilitate only keyword search which does not returns accurate results to the external user. Aiming to solve the limitations of keyword based models, the idea of semantic search, understood as searching by meanings rather than by literals has been the focus of a wide body of research in the information retrieval and the semantic web communities Semantic search has been present in the information retrieval since the early eighties. Some of these approaches are based on the statistical methods that study the co-occurrence of terms that capture and exploit tough and fuzzy conceptualizations. Other Information retrieval approaches apply linguistic algorithms modeled on human language processing structures and taxonomies, where the level of conceptualization is often shallow and sparse, especially the level of relations, which are commonly at the core of expressing user needs and finding the answers. Ontologies are used to represent knowledge in a conceptual manner that can be distributed among various applications. The ontology has its application in information retrieval where it exploits the knowledge bases enhancing the semantic search, steering in one hand the use of fully fledged ontologies in the semantic based perspective, and on the other the consideration of unstructured content as target search space. In other words, this work explores the use of semantic information to support more expensive queries and more accurate results, while the retrieval problem is formulated in a way that is consistent with the Information retrieval field, thus drawing benefit from the state of art in this area and enabling more realistic and applicable approaches. II. RELEATED WORK A. Semantic web Current World Wide Web (WWW) is a huge library of interlinked documents that are transferred by computers and presented to people. It has grown from hypertext systems, but the difference is that anyone can contribute to it. This also means that the quality of information or even the persistence of documents cannot be generally guaranteed. Current WWW contains a lot of information and knowledge, but machines usually serve only to deliver and present the content of documents describing the knowledge. People have to connect all the sources of relevant information and interpret them themselves Semantic web is an effort to enhance current web so that computers can process the information presented on WWW, interpret and connect it, to help humans to find required knowledge. In the same way as WWW is a huge distributed hypertext system, semantic web is intended to form a huge distributed knowledge based system. The focus of semantic web is to share data instead of documents. In other words, it is a project that should provide a common framework that allows data to be shared and reused across application, enterprise, and community boundaries. It is a collaborative effort led by World Wide Web Consortium (W3C). The semantic web has got its peak in recent years by the use of explicit metadata representation of information in the web. The metadata representation is embedded in web page using RDF to enhance the visualization of results. Semantic Web can be seen as a huge engineering solution... but it is more than that. We will find that as it becomes easier to publish data in a repurposable form, so more people will want to publish data, and there will be a knock-on or domino effect. N.Vanjulavalli et al IJCSET |August 2012 | Vol 2, Issue 8, 1349-1353 ISSN:2231-0711 Available online at www.ijcset.net 1349 We may find that a large number of Semantic Web applications can be used for a variety of different tasks, increasing the modularity of applications on the Web. But enough subjective reasoning. onto how this will be accomplished. The Semantic Web is generally built on syntaxes which use URIs to represent data, usually in triples based structures: i.e. many triples of URI data that can be held in databases, or interchanged on the world Wide Web using a set of particular syntaxes developed especially for the task. These syntaxes are called "Resource Description Framework" syntaxes. Once information is in RDF form, it becomes easy to process it, since RDF is a generic format, which already has many parsers. XML RDF is quite a verbose specification, and it can take some getting used to (for example, to learn XML RDF properly, you need to understand a little about XML and namespaces beforehand...), but let's take a quick look at an example of XML RDF right now:<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdfsyntax-ns#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:foaf="http://xmlns.com/0.1/foaf/" > <rdf:Description rdf:about=""> <dc:creator rdf:parseType="Resource"> <foaf:name>Sean B. Palmer</foaf:name> </dc:creator> <dc:title>The Semantic Web: An Introduction</dc:title>


Automatic crop irrigation system

India is agriculture based nation. It is necessary to improve the productivity and quality of agro based products. The proposed design is an automatic system that aids the famers in irrigation process. It keeps notifying the farmer through an on-board LCD display and messages that is sent on the farmer's cellular number. This proposed design is also helpful for the farmers who are facing power failure issues to maintain a uniform water supply due to power failure or inadequate and non-uniform water supply. The automatic irrigation system also keeps the farmer updated with all the background activities through a SIM900 Module that sends messages on the registered number. This device can be a turning point for our society. The device is easily affordable by the farmers of the country. This proposed design is helpful for reducing the human labour. This is a low budget system with an essential social application.


Automatic drip irrigation system using fuzzy logic and mobile technology

An intelligent drip irrigation system optimizes water and fertilizer use for agricultural crops using wireless sensors and fuzzy logic. The wireless sensor networks consists of many sensor nodes, hub and control unit. The sensor collects real-time data such as temperature, soil humidity. This data is sent to the hub using the wireless technology. The hub processes the data using fuzzy logic and decides the time duration for keeping the valves open. Accordingly, the drip irrigation system is implemented for a particular amount of time. The whole system is powered by photovoltaic cells and has a communication link which allows the system to be monitored, controlled, and scheduled through cellular text messages. The system can quickly and accurately calculate water demand amount of crops, which can provide a scientific basis for water-saving irrigation, as well as a method to optimize the amount of fertilizer used.


Remote Sensing and Control of an Irrigation System Using a Distributed Wireless Sensor Network

Efficient water management is a major concern in many cropping systems in semiarid and arid areas. Distributed in-field sensor-based irrigation systemsoffer a potential solution to support site-specific irrigation management that allows producers to maximize their productivity while saving water. This paper describes details of the design and instrumentation of variable rate irrigation, a wireless sensor network, and software for real-time in-field sensing and control of a site-specific precision linear-move irrigation system. Field conditions were site-specifically monitored by six in-field sensor stations distributed across the field based on a soil property map, and periodically sampled and wirelessly transmitted to a base station. An irrigation machine was converted to be electronically controlled by a programming logic controller that updates georeferenced location of sprinklers from a differential Global Positioning System (GPS) and wirelessly communicates with a computer at the base station. Communication signals from the sensor network and irrigation controller to the base station were successfully interfaced using low-cost Bluetooth wireless radio communication. Graphic user interface-based software developed in this paper offered stable remote access to field conditions and real-time control and monitoring of the variable-rate irrigation controller.


AUTOMATED IRRIGATION SYSTEM USING THERMOELECTRIC GENERATOR AS SOIL MOISTURE DETECTOR

This paper shows the design and fabrication of a Thermo-electric generator (TEG) and the implementation of an automated irrigation system using this TEG as a soil moisture detector. The TEG inserted in two heat exchangers is capable of finding the thermal difference between the air and the soil that establishes a relationship with the soil’s moisture condition. Being able to obtain the soil moisture level from the TEG’s output, a microcontroller is used to automate the irrigation system. The irrigation system adapts to the soil area’s condition it irrigates based from the moisture it detects via the TEG. The water consumption of the soil is controlled by the automated irrigation system based on the soil’s condition and therefore, promotes water conservation compared to that of the water consumption of manual irrigation system. It also optimizes plant growth in that it waters it to the correct moisture level at the right time.


A Wireless Design of Low-Cost Irrigation System Using ZigBee Technology

At present, labor-saving and water-saving technology is a key issue in irrigation. A wireless solution for intelligent field irrigation system dedicated to Jew's-ear planting in Lishui, Zhejiang, China, based on ZigBee technology was proposed in this paper. Instead of conventional wired connection, the wireless design made the system easy installation and maintenance. The hardware architecture and software algorithm of wireless sensor/actuator node and portable controller, acting as the end device and coordinator in ZigBee wireless sensor network respectively, were elaborated in detail. The performance of the whole system was evaluated in the end. The long-time smooth and proper running of the system in the field proved its high reliability and practicability. As an explorative application of wireless sensor network in irrigation management, this paper offered a methodology to establish large-scale remote intelligent irrigation system.


Axial Force and Vibroacoustic Analysis of External-Rotor Axial-Flux Motors

This paper presents a detailed analysis of the axial electromagnetic force and vibroacoustic behavior of the external-rotor axial-flux motors. First, the spatial and temporal characteristics of axial force acting on the surface of magnets are derived analytically and validated through the 2-D Fourier decomposition. Subsequently, a multiphysics model is established to calculate the vibration and noise of an external-rotor axial-flux in-wheel motor, which integrates the control model, the electromagnetic model, the structural model, and the acoustic radiation model and takes the uneven distribution of axial force into account. The accuracy of the multiphysics model is verified by the noise test. The vibroacoustic mechanism of axial-flux motors is revealed based on the multiphysics model, the influence of current harmonics and dead time effect on the vibration and noise are also analyzed. Finally, the main orders of tested noise are explained by the theoretical analysis. It is found that the zeroth spatial order axial force is dominant for the generation of the vibration and noise in axial-flux motors. The harmonic current may deteriorate the vibroacoustic behavior, but depending on its amplitude and phase. This study provides guidance for the design of low-noise axial-flux motors.


Model-based policy gradients with parameter-based exploration by least-squares conditional density estimation

The goal of reinforcement learning (RL) is to let an agent learn an optimal control policy in an unknown environment so that future expected rewards are maximized. The model-free RL approach directly learns the policy based on data samples. Although using many samples tends to improve the accuracy of policy learning, collecting a large number of samples is often expensive in practice. On the other hand, the model-based RL approach first estimates the transition model of the environment and then learns the policy based on the estimated transition model. Thus, if the transition model is accurately learned from a small amount of data, the model-based approach is a promising alternative to the model-free approach. In this paper, we propose a novel model-based RL method by combining a recently proposed model-free policy search method called policy gradients with parameter-based exploration and the state-of-the-art transition model estimator called least-squares conditional density estimation. Through experiments, we demonstrate the practical usefulness of the proposed method.


A Smart Home Appliances Power Management System for Handicapped, Elder and Blind People

Smart home has become a visible concept that attracts the collaboration of various areas of science and engineering. Increasing the power efficiency and power management has become an important research topic since a decade. In this research article, the focus is to ease the life of elder and handicapped people. In point of fact as compare to the healthy people, elderly and disabled people experiences difficulties to perform their everyday activities. Elderly and disabled people can be supported by using smart homes by providing them secure, safe, and controlled environments. The developed system allows the users to be able to control the appliances with least physical efforts. More particularly, the designed system allow users to switch the home appliances ON and OFF just by sending message command by using Android application or SMS with the help of a cell phone. Not only the far remote monitoring, but the local management of appliances is made possible by using the Bluetooth technology. The experimental results demonstrate that the user can control the home appliances with the designed Android application by using the voice, Bluetooth, and SMS inputs.


Detecting Singleton Review Spammers Using Semantic Similarity

Online reviews have increasingly become a very important resource for consumers when making purchases. Though it is becoming more and more difficult for people to make well-informed buying decisions without being deceived by fake reviews. Prior works on the opinion spam problem mostly considered classifying fake reviews using behavioral user patterns. They focused on prolific users who write more than a couple of reviews, discarding one-time reviewers. The number of singleton reviewers however is expected to be high for many review websites. While behavioral patterns are effective when dealing with elite users, for one-time reviewers, the review text needs to be exploited. In this paper we tackle the problem of detecting fake reviews written by the same person using multiple names, posting each review under a different name. We propose two methods to detect similar reviews and show the results generally outperform the vectorial similarity measures used in prior works. The first method extends the semantic similarity between words to the reviews level. The second method is based on topic modeling and exploits the similarity of the reviews topic distributions using two models: bag-of-words and bag-of-opinion-phrases. The experiments were conducted on reviews from three different datasets: Yelp (57K reviews), Trustpilot (9K reviews) and Ott dataset (800 reviews).


Fuzzy sets, advanced fuzzy sets and hybrids

Fuzzy Sets were proposed several years ago with various extensions in later years. Each extension has advantages over the fuzzy sets. Rough sets are used to handle incomplete information. Interval-valued fuzzy sets deal with uncertainty and vagueness. Intuitionistic fuzzy sets contain a sub-interval hesitation degree that lies between membership and non-membership degrees. Soft sets overcome the problem of insufficiency of parameterization. Advanced fuzzy sets have myriad number of advantages due to their applications in realistic examples. This paper briefly explains the abstract of fuzzy sets, advanced fuzzy sets and their hybrids. Advanced fuzzy sets include rough sets, intuitionistic fuzzy sets, interval-valued fuzzy sets and soft sets.


The habitual consumer

Consumers sometimes act like creatures of habit, automatically repeating past behavior with little regard to current goals and valued outcomes. To explain this phenomenon, we show that habits are a specific form of automaticity in which responses are directly cued by the contexts (e.g., locations, preceding actions) that consistently covaried with past performance. Habits are prepotent responses that are quick to activate in memory over alternatives and that have a slow-to-modify memory trace. In daily life, the tendency to act on habits is compounded by everyday demands, including time pressures, distraction, and self-control depletion. However, habits are not immune to deliberative processes. Habits are learned largely as people pursue goals in daily life, and habits are broken through the strategic deployment of effortful self-control. Also, habits influence the post hoc inferences that people make about their behavior. © 2009 Society for Consumer Psychology. Published by Elsevier Inc. All rights reserved.


Reputation and e-commerce : eBay auctions and the asymmetrical impact of positive and negative ratings

This analysis explores the impact and nature of reputation as related to e-commerce by looking at the importance of a seller’s reputational rating on the final bid price associated with eBay auctions. Positive reputational ratings emerged as mildly influential in determining final bid price. However, negative reputational ratings emerged as highly influential and detrimental. Thus, we find strong evidence for the importance of reputation when engaging in e-commerce and equally strong evidence concerning the exaggerated influence of negative reputation. © 2001 Elsevier Science Inc. All rights reserved.