Simultaneous Mosaicing and Tracking with an Event Camera

An event camera is a silicon retina which outputs not a sequence of video frames like a standard camera, but a stream of asynchronous spikes, each with pixel location, sign and precise timing, indicating when individual pixels record a threshold log intensity change. By encoding only image change, it offers the potential to transmit the information in a standard video but at vastly reduced bitrate, and with huge added advantages of very high dynamic range and temporal resolution. However, event data calls for new algorithms, and in particular we believe that algorithms which incrementally estimate global scene models are best placed to take full advantages of its properties. Here, we show for the first time that an event stream, with no additional sensing, can be used to track accurate camera rotation while building a persistent and high quality mosaic of a scene which is super-resolution accurate and has high dynamic range. Our method involves parallel camera rotation tracking and template reconstruction from estimated gradients, both operating on an event-by-event basis and based on probabilistic filtering.


Depth Transfer: Depth Extraction from Video Using Non-Parametric Sampling

We describe a technique that automatically generates plausible depth maps from videos using non-parametric depth sampling. We demonstrate our technique in cases where past methods fail (non-translating cameras and dynamic scenes). Our technique is applicable to single images as well as videos. For videos, we use local motion cues to improve the inferred depth maps, while optical flow is used to ensure temporal depth consistency. For training and evaluation, we use a Kinect-based system to collect a large data set containing stereoscopic videos with known depths. We show that our depth estimation technique outperforms the state-of-the-art on benchmark databases. Our technique can be used to automatically convert a monoscopic video into stereo for 3D visualization, and we demonstrate this through a variety of visually pleasing results for indoor and outdoor scenes, including results from the feature film Charade.


Aerial Lidar Data Classification using AdaBoost

We use the AdaBoost algorithm to classify 3D aerial lidar scattered height data into four categories: road, grass, buildings, and trees. To do so we use five features: height, height variation, normal variation, lidar return intensity, and image intensity. We also use only lidar-derived features to organize the data into three classes (the road and grass classes are merged). We apply and test our results using ten regions taken from lidar data collected over an area of approximately eight square miles, obtaining higher than 92% accuracy. We also apply our classifier to our entire dataset, and present visual classification results both with and without uncertainty. We implement and experiment with several variations within the AdaBoost family of algorithms. We observe that our results are robust and stable over all the various tests and algorithmic variations. We also investigate features and values that are most critical in distinguishing between the classes. This insight is important in extending the results from one geographic region to another.


Bio-inspired design and dynamic maneuverability of a minimally actuated six-legged robot

Rapidly running arthropods like cockroaches make use of passive dynamics to achieve remarkable locomotion performance with regard to stability, speed, and maneuverability. In this work, we take inspiration from these organisms to design, fabricate, and control a 10cm, 24 gram underactuated hexapedal robot capable of running at 14 body lengths per second and performing dynamic turning maneuvers. Our design relies on parallel kinematic mechanisms fabricated using the scaled smart composite microstructures (SCM) process and viscoelastic polymer legs with tunable stiffness. In addition to the novel robot design, we present experimental validation of the lateral leg spring (LLS) locomotion model's prediction that dynamic turning can be achieved by modulating leg stiffness. Finally, we present and validate a leg design for active stiffness control using shape memory alloy and demonstrate the ability of the robot to execute near-gymnastic 90° turns in the span of five strides.


C
1-Almost Periodic Solutions of BAM Neural Networks with Time-Varying Delays on Time Scales

On a new type of almost periodic time scales, a class of BAM neural networks is considered. By employing a fixed point theorem and differential inequality techniques, some sufficient conditions ensuring the existence and global exponential stability of C1-almost periodic solutions for this class of networks with time-varying delays are established. Two examples are given to show the effectiveness of the proposed method and results.


Understanding IEEE 1451-Networked smart transducer interface standard - What is a smart transducer?

This article introduces the IEEE 1451 standard for networked smart transducers. It discusses the concepts of smart transducers, IEEE 1451 smart transducers, the architecture of the IEEE 1451 family of standards, application of IEEE 1451, and example implementations of the IEEE 1451 standards. In conclusion, the IEEE 1451 suite of standards provides a set of standard interfaces for networked smart transducers, helping to achieve sensor plug and play and interoperability for industry and government.


The Past, Present, and Future of Silicon Photonics

The pace of the development of silicon photonics has quickened since 2004 due to investment by industry and government. Commercial state-of-the-art CMOS silicon-on-insulator (SOI) foundries are now being utilized in a crucial test of 1.55-mum monolithic optoelectronic (OE) integration, a test sponsored by the Defense Advanced Research Projects Agency (DARPA). The preliminary results indicate that the silicon photonics are truly CMOS compatible. R&D groups have now developed 10-100-Gb/s electro-optic modulators, ultrafast Ge-on-Si photodetectors, efficient fiber-to-waveguide couplers, and Si Raman lasers. Electrically pumped silicon lasers are under intense investigation, with several approaches being tried; however, lasing has not yet been attained. The new paradigm for the Si-based photonic and optoelectric integrated circuits is that these chip-scale networks, when suitably designed, will operate at a wavelength anywhere within the broad spectral range of 1.2-100 mum, with cryocooling needed in some cases


From Facial Expression Recognition to Interpersonal Relation Prediction

Interpersonal relation defines the association, e.g., warm, friendliness, and dominance, between two or more people. We investigate if such fine-grained and high-level relation traits can be characterized and quantified from face images in the wild. We address this challenging problem by first studying a deep network architecture for robust recognition of facial expressions. Unlike existing models that typically learn from facial expression labels alone, we devise an effective multitask network that is capable of learning from rich auxiliary attributes such as gender, age, and head pose, beyond just facial expression data. While conventional supervised training requires datasets with complete labels (e.g., all samples must be labeled with gender, age, and expression), we show that this requirement can be relaxed via a novel attribute propagation method. The approach further allows us to leverage the inherent correspondences between heterogeneous attribute sources despite the disparate distributions of different datasets. With the network we demonstrate state-of-the-art results on existing facial expression recognition benchmarks. To predict inter-personal relation, we use the expression recognition network as branches for a Siamese model. Extensive experiments show that our model is capable of mining mutual context of faces for accurate fine-grained interpersonal prediction.


The subepithelial connective tissue graft palatal donor site: anatomic considerations for surgeons.

Surgeons must become completely familiar with the anatomy of the palatal donor site to feel confident in providing the subepithelial connective tissue graft procedure. Variations in the size and shape of the hard palate affect the dimensions of donor tissue harvested, as well as the location of the greater palatine neurovascular bundle. This article classifies palatal vaults according to height as high, average, and shallow. Illustrations and cadaver dissection are utilized to demonstrate that surgeons can gain substantial donor tissue specimens without encountering the neurovascular bundle. Actions to be followed in the unlikely event that the neurovasculature is encountered are reviewed.


The biology of facial fillers.

The biologic behavior of a facial filler determines its advantages and disadvantages. The purpose of this article is to look at the relevant biology as part of a logical basis for making treatment decisions. Historical perspectives and biologic characteristics such as local tissue reaction (including phagocytosis and granulomatous inflammation) cross-linking, particle concentration, immunogenicity, biofilm formation, gel hardness, and collagen neogenesis are considered. Bovine collagen is the most immunogenic facial filler. Porcine and bioengineered human collagen implants have very low immunogenicity, but allergic reactions and elevations of IgG are possible. Cross-linking and concentration affect the longevity of collagen and hyaluronic acid fillers. Gel hardness affects how a hyaluronic acid filler flows through the syringe and needle. Calcium hydroxylapatite, poly-L-lactic acid, and polymethylmethacrylate fillers have been shown to stimulate collagen neogenesis. It appears that any facial filler can form a granuloma. Bacterial biofilms may play a role in the activation of quiescent granulomas. Various authors interpret the definition and significance of a granuloma differently.


Robust entropy-based endpoint detection for speech recognition in noisy environments

This paper presents an entropy-based algorithm for accurate and robust endpoint detection for speech recognition under noisy environments. Instead of using the conventional energy-based features, the spectral entropy is developed to identify the speech segments accurately. Experimental results show that this algorithm outperforms the energy-based algorithms in both detection accuracy and recognition performance under noisy environments, with an average error rate reduction of more than 16%.


Customer-Centric Strategic Planning: Integrating CRM in Online Business Systems

Customer Relationship Management (CRM) is increasingly found at the top of corporate agendas. Online companies in particular are embracing CRM as a major element of corporate strategy, because online technological applications permit a precise segmentation, profiling and targeting of customers, and the competitive pressures of the digital markets require a customer-centric corporate culture. The implementation of CRM systems in online organisation determines a complex restructuring of all organisational elements and processes. The strategic planning process will have to adapt to new customer-centric procedures. The present paper analyses the implementation process of a CRM system in online retail businesses and develops a model of the strategic planning function in a customer-centric context.


A Generic Coordinate Descent Framework for Learning from Implicit Feedback

In recent years, interest in recommender research has shifted from explicit feedback towards implicit feedback data. A diversity of complex models has been proposed for a wide variety of applications. Despite this, learning from implicit feedback is still computationally challenging. So far, most work relies on stochastic gradient descent (SGD) solvers which are easy to derive, but in practice challenging to apply, especially for tasks with many items. For the simple matrix factorization model, an efficient coordinate descent (CD) solver has been previously proposed. However, efficient CD approaches have not been derived for more complex models. In this paper, we provide a new framework for deriving efficient CD algorithms for complex recommender models. We identify and introduce the property of k-separable models. We show that k-separability is a sufficient property to allow efficient optimization of implicit recommender problems with CD. We illustrate this framework on a variety of state-of-the-art models including factorization machines and Tucker decomposition. To summarize, our work provides the theory and building blocks to derive efficient implicit CD algorithms for complex recommender models.


Object-Extraction and Question-Parsing using CCG

Accurate dependency recovery has recently been reported for a number of wide-coverage statistical parsers using Combinatory Categorial Grammar (CCG). However, overall figures give no indication of a parser’s performance on specific constructions, nor how suitable a parser is for specific applications. In this paper we give a detailed evaluation of a CCG parser on object extraction dependencies found in WSJ text. We also show how the parser can be used to parse questions for Question Answering. The accuracy of the original parser on questions is very poor, and we propose a novel technique for porting the parser to a new domain, by creating new labelled data at the lexical category level only. Using a supertagger to assign categories to words, trained on the new data, leads to a dramatic increase in question parsing accuracy.


Mining Sequential Patterns: Generalizations and Performance Improvements

The problem of mining sequential patterns was recently introduced in [AS95]. We are given a database of sequences, where each sequence is a list of transactions ordered by transaction-time, and each transaction is a set of items. The problem is to discover all sequential patterns with a user-speci ed minimum support, where the support of a pattern is the number of data-sequences that contain the pattern. An example of a sequential pattern is \5% of customers bought `Foundation' and `Ringworld' in one transaction, followed by `Second Foundation' in a later transaction". We generalize the problem as follows. First, we add time constraints that specify a minimum and/or maximum time period between adjacent elements in a pattern. Second, we relax the restriction that the items in an element of a sequential pattern must come from the same transaction, instead allowing the items to be present in a set of transactions whose transaction-times are within a user-speci ed time window. Third, given a user-de ned taxonomy (is-a hierarchy) on items, we allow sequential patterns to include items across all levels of the taxonomy. We present GSP, a new algorithm that discovers these generalized sequential patterns. Empirical evaluation using synthetic and real-life data indicates that GSP is much faster than the AprioriAll algorithm presented in [AS95]. GSP scales linearly with the number of data-sequences, and has very good scale-up properties with respect to the average datasequence size. Also, Department of Computer Science, University of Wisconsin, Madison.


Continuous control with deep reinforcement learning

We adapt the ideas underlying the success of Deep Q-Learning to the continuous action domain. We present an actor-critic, model-free algorithm based on the deterministic policy gradient that can operate over continuous action spaces. Using the same learning algorithm, network architecture and hyper-parameters, our algorithm robustly solves more than 20 simulated physics tasks, including classic problems such as cartpole swing-up, dexterous manipulation, legged locomotion and car driving. Our algorithm is able to find policies whose performance is competitive with those found by a planning algorithm with full access to the dynamics of the domain and its derivatives. We further demonstrate that for many of the tasks the algorithm can learn policies “end-to-end”: directly from raw pixel inputs.


MCMCTS PCG 4 SMB : Monte Carlo Tree Search to Guide Platformer Level Generation

Markov chains are an enticing option for machine learned generation of platformer levels, but offer poor control for designers and are likely to produce unplayable levels. In this paper we present a method for guiding Markov chain generation using Monte Carlo Tree Search that we call Markov Chain Monte Carlo Tree Search (MCMCTS). We demonstrate an example use for this technique by creating levels trained on a corpus of levels from Super Mario Bros. We then present a player modeling study that was run with the hopes of using the data to better inform the generation of levels in future work.


Peak-End Effects on Player Experience in Casual Games

The peak-end rule is a psychological heuristic observing that people's retrospective assessment of an experience is strongly influenced by the intensity of the peak and final moments of that experience. We examine how aspects of game player experience are influenced by peak-end manipulations to the sequence of events in games that are otherwise objectively identical. A first experiment examines players' retrospective assessments of two games (a pattern matching game based on Bejeweled and a point-and-click reaction game) when the sequence of difficulty is manipulated to induce positive, negative and neutral peak-end effects. A second experiment examines assessments of a shootout game in which the balance between challenge and skill is similarly manipulated. Results across the games show that recollection of challenge was strongly influenced by peak-end effects; however, results for fun, enjoyment, and preference to repeat were varied -- sometimes significantly in favour of the hypothesized effects, sometimes insignificant, but never against the hypothesis.


Efficient Spectral Feature Selection with Minimum Redundancy

Spectral feature selection identifies relevant features by measuring their capability of preserving sample similarity. It provides a powerful framework for both supervised and unsupervised feature selection, and has been proven to be effective in many real-world applications. One common drawback associated with most existing spectral feature selection algorithms is that they evaluate features individually and cannot identify redundant features. Since redundant features can have significant adverse effect on learning performance, it is necessary to address this limitation for spectral feature selection. To this end, we propose a novel spectral feature selection algorithm to handle feature redundancy, adopting an embedded model. The algorithm is derived from a formulation based on a sparse multi-output regression with a L2;1-norm constraint. We conduct theoretical analysis on the properties of its optimal solutions, paving the way for designing an efficient path-following solver. Extensive experiments show that the proposed algorithm can do well in both selecting relevant features and removing redundancy.


Reduction of Fuzzy Rules and Membership Functions and Its Application to Fuzzy PI and PD Type Controllers

Fuzzy controller’s design depends mainly on the rule base and membership functions over the controller’s input and output ranges. This paper presents two different approaches to deal with these design issues. A simple and efficient approach; namely, Fuzzy Subtractive Clustering is used to identify the rule base needed to realize Fuzzy PI and PD type controllers. This technique provides a mechanism to obtain the reduced rule set covering the whole input/output space as well as membership functions for each input variable. But it is found that some membership functions projected from different clusters have high degree of similarity. The number of membership functions of each input variable is then reduced using a similarity measure. In this paper, the fuzzy subtractive clustering approach is shown to reduce 49 rules to 8 rules and number of membership functions to 4 and 6 for input variables (error and change in error) maintaining almost the same level of performance. Simulation on a wide range of linear and nonlinear processes is carried out and results are compared with fuzzy PI and PD type controllers without clustering in terms of several performance measures such as peak overshoot, settling time, rise time, integral absolute error (IAE) and integral-of-time multiplied absolute error (ITAE) and in each case the proposed schemes shows an identical performance.


Removal of fixed impulse noise from digital images using Bezier curve based interpolation

In this paper, we propose a Bezier curve based interpolation technique to eliminate the fixed impulse noise from digital images as well as maintaining the edges of the image. To eliminate the noise, we make the noisy image to pass through two steps, where in the first step we found out the pixels affected by the impulse noise, and in second step, edge protecting process is done using Bezier interpolation technique. Promising results were obtained for images having more than 80 percent of the image pixels are affected. Our proposed algorithm is producing better results in comparison to existing algorithms.


12 Artificial Intelligence for Space Applications

The ambitious short-term and long-term goals set down by the various national space agencies call for radical advances in several of the main space engineering areas, the design of intelligent space agents certainly being one of them. In recent years, this has led to an increasing interest in artificial intelligence by the entire aerospace community. However, in the current state of the art, several open issues and showstoppers can be identified. In this chapter, we review applications of artificial intelligence in the field of space engineering and space technology and identify open research questions and challenges. In particular, the following topics are identified and discussed: distributed artificial intelligence, enhanced situation self-awareness, and decision support for spacecraft system design.


Knowledge , Strategy , and the Theory of the Firm Author ( s ) :

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. Wiley-Blackwell and John Wiley & Sons are collaborating with JSTOR to digitize, preserve and extend access to Strategic Management Journal. This paper argues that firms have particular institutional capabilities that allow them to protect knowledge from expropriation and imitation more effectively than market contracting. I argue that it is these generalized institutional capabilities that allow firms to generate and protect the unique resources and capabilities that are central to the strategic theory of the firm.


The social structure of free and open source software development

Metaphors, such as the Cathedral and Bazaar, used to describe the organization of FLOSS projects typically place them in sharp contrast to proprietary development by emphasizing FLOSS’s distinctive social and communications structures. But what do we really know about the communication patterns of FLOSS projects? How generalizable are the projects that have been studied? Is there consistency across FLOSS projects? Questioning the assumption of distinctiveness is important because practitioner-advocates from within the FLOSS community rely on features of social structure to describe and account for some of the advantages of FLOSS production. To address this question, we examined 120 project teams from SourceForge, representing a wide range of FLOSS project types, for their communications centralization as revealed in the interactions in the bug tracking system. We found that FLOSS development teams vary widely in their communications centralization, from projects completely centered on one developer to projects that are highly decentralized and exhibit a distributed pattern of conversation between developers and active users. We suggest, therefore, that it is wrong to assume that FLOSS projects are distinguished by a particular social structure merely because they are FLOSS. Our findings suggest that FLOSS projects might have to work hard to achieve the expected development advantages which have been assumed to flow from “going open.” In addition, the variation in communications structure across projects means that communications centralization is useful for comparisons between FLOSS teams. We


The Cathedral and the Bazaar

Permission is granted to copy, distribute and/or modify this document under the terms of the Open Publication License, version 2.0. $Date: 2002/08/02 09:02:14 $ Revision History Revision 1.57 11 September 2000 esr New major section “How Many Eyeballs Tame Complexity”. Revision 1.52 28 August 2000 esr MATLAB is a reinforcing parallel to Emacs. Corbatoó & Vyssotsky got it in 1965. Revision 1.51 24 August 2000 esr First DocBook version. Minor updates to Fall 2000 on the time-sensitive material. Revision 1.49 5 May 2000 esr Added the HBS note on deadlines and scheduling. Revision 1.51 31 August 1999 esr This the version that O’Reilly printed in the first edition of the book. Revision 1.45 8 August 1999 esr Added the endnotes on the Snafu Principle, (pre)historical examples of bazaar development, and originality in the bazaar. Revision 1.44 29 July 1999 esr Added the “On Management and the Maginot Line” section, some insights about the usefulness of bazaars for exploring design space, and substantially improved the Epilog. Revision 1.40 20 Nov 1998 esr Added a correction of Brooks based on the Halloween Documents. Revision 1.39 28 July 1998 esr I removed Paul Eggert’s ’graph on GPL vs. bazaar in response to cogent aguments from RMS on Revision 1.31 February 1


Social translucence: an approach to designing systems that support social processes

We are interested in desiging systems that support communication and collaboration among large groups of people over computing networks. We begin by asking what properties of the physical world support graceful human-human communication in face-to-face situations, and argue that it is possible to design digital systems that support coherent behavior by making participants and their activites visible to one another. We call such systems “socially translucent systems” and suggest that they have three characteristics—visbility, awareness, and accountability—which enable people to draw upon their experience and expertise to structure their interactions with one another. To motivate and focus our ideas we develop a vision of knowledge communities, conversationally based systems that support the creation, management and reuse of knowledge in a social context. We describe our experience in designing and deploying one layer of functionality for knowledge communities, embodied in a working system called “Barbie” and discuss research issues raised by a socially translucent approach to design.


Cave or Community? An Empirical Examination of 100 Mature Open Source Projects

Starting with Eric Raymond’s groundbreaking work, The Cathedral and the Bazaar, open-source software (OSS) has commonly been regarded as work produced by a community of developers. Yet, given the nature of software programs, one also hears of developers with no lives that work very hard to achieve great product results. In this paper, I sought empirical evidence that would help us understand which is more commonthe cave (i.e., lone producer) or the community. Based on a study of the top 100 mature products on Sourceforge, I find a few surprising things. First, most OSS programs are developed by individuals, rather than communities. The median number of developers in the 100 projects I looked at was 4 and the mode was 1numbers much lower than previous ones reported for highly successful projects! Second, most OSS programs do not generate a lot of discussion. Third, products with more developers tend to be viewed and downloaded more often. Fourth, the number of developers associated with a project is unrelated to the age of the project. Fifth, the larger the project, the smaller the percent of project administrators.


Case Reports of Adipose-derived Stem Cell Therapy for Nasal Skin Necrosis after Filler Injection

With the gradual increase of cases using fillers, cases of patients treated by non-medical professionals or inexperienced physicians resulting in complications are also increasing. We herein report 2 patients who experienced acute complications after receiving filler injections and were successfully treated with adipose-derived stem cell (ADSCs) therapy. Case 1 was a 23-year-old female patient who received a filler (Restylane) injection in her forehead, glabella, and nose by a non-medical professional. The day after her injection, inflammation was observed with a 3×3 cm skin necrosis. Case 2 was a 30-year-old woman who received a filler injection of hyaluronic acid gel (Juvederm) on her nasal dorsum and tip at a private clinic. She developed erythema and swelling in the filler-injected area A solution containing ADSCs harvested from each patient's abdominal subcutaneous tissue was injected into the lesion at the subcutaneous and dermis levels. The wounds healed without additional treatment. With continuous follow-up, both patients experienced only fine linear scars 6 months postoperatively. By using adipose-derived stem cells, we successfully treated the acute complications of skin necrosis after the filler injection, resulting in much less scarring, and more satisfactory results were achieved not only in wound healing, but also in esthetics.


Peer support services for individuals with serious mental illnesses: assessing the evidence.

OBJECTIVE
This review assessed the level of evidence and effectiveness of peer support services delivered by individuals in recovery to those with serious mental illnesses or co-occurring mental and substance use disorders.

METHODS
Authors searched PubMed, PsycINFO, Applied Social Sciences Index and Abstracts, Sociological Abstracts, Social Services Abstracts, Published International Literature on Traumatic Stress, the Educational Resources Information Center, and the Cumulative Index to Nursing and Allied Health Literature for outcome studies of peer support services from 1995 through 2012. They found 20 studies across three service types: peers added to traditional services, peers in existing clinical roles, and peers delivering structured curricula. Authors judged the methodological quality of the studies using three levels of evidence (high, moderate, and low). They also described the evidence of service effectiveness.

RESULTS
The level of evidence for each type of peer support service was moderate. Many studies had methodological shortcomings, and outcome measures varied. The effectiveness varied by service type. Across the range of methodological rigor, a majority of studies of two service types--peers added and peers delivering curricula--showed some improvement favoring peers. Compared with professional staff, peers were better able to reduce inpatient use and improve a range of recovery outcomes, although one study found a negative impact. Effectiveness of peers in existing clinical roles was mixed.

CONCLUSIONS
Peer support services have demonstrated many notable outcomes. However, studies that better differentiate the contributions of the peer role and are conducted with greater specificity, consistency, and rigor would strengthen the evidence.


Formal Theory of Creativity, Fun, and Intrinsic Motivation (1990–2010)

The simple, but general formal theory of fun and intrinsic motivation and creativity (1990-2010) is based on the concept of maximizing intrinsic reward for the active creation or discovery of novel, surprising patterns allowing for improved prediction or data compression. It generalizes the traditional field of active learning, and is related to old, but less formal ideas in aesthetics theory and developmental psychology. It has been argued that the theory explains many essential aspects of intelligence including autonomous development, science, art, music, and humor. This overview first describes theoretically optimal (but not necessarily practical) ways of implementing the basic computational principles on exploratory, intrinsically motivated agents or robots, encouraging them to provoke event sequences exhibiting previously unknown, but learnable algorithmic regularities. Emphasis is put on the importance of limited computational resources for online prediction and compression. Discrete and continuous time formulations are given. Previous practical, but nonoptimal implementations (1991, 1995, and 1997-2002) are reviewed, as well as several recent variants by others (2005-2010). A simplified typology addresses current confusion concerning the precise nature of intrinsic motivation.


Deep neural network for manufacturing quality prediction

Expected product quality is affected by multi-parameter in complex manufacturing processes. Product quality prediction can offer the possibility of designing better system parameters at the early production stage. Many existing approaches fail at providing favorable results duo to shallow architecture in prediction model that can not learn multi-parameter's features insufficiently. To address this issue, a deep neural network (DNN), consisting of a deep belief network (DBN) in the bottom and a regression layer on the top, is proposed in this paper. The DBN uses a greedy algorithm for unsupervised feature learning. It could learn effective features for manufacturing quality prediction in an unsupervised pattern which has been proven to be effective for many fields. Then the learned features are inputted into the regression tool, and the quality predictions are obtained. One type of manufacturing system with multi-parameter is investigated by the proposed DNN model. The experiments show that the DNN has good performance of the deep architecture, and overwhelms the peer shallow models. It is recommended from this study that the deep learning technique is more promising in manufacturing quality prediction.


A linguistic ontology of space for natural language processing

a r t i c l e i n f o a b s t r a c t We present a detailed semantics for linguistic spatial expressions supportive of computational processing that draws substantially on the principles and tools of ontological engineering and formal ontology. We cover language concerned with space, actions in space and spatial relationships and develop an ontological organization that relates such expressions to general classes of fixed semantic import. The result is given as an extension of a linguistic ontology, the Generalized Upper Model, an organization which has been used for over a decade in natural language processing applications. We describe the general nature and features of this ontology and show how we have extended it for working particularly with space. Treaitng the semantics of natural language expressions concerning space in this way offers a substantial simplification of the general problem of relating natural spatial language to its contextualized interpretation. Example specifications based on natural language examples are presented, as well as an evaluation of the ontology's coverage, consistency, predictive power, and applicability.


A Survey of Intrusion Detection Systems for Mobile Ad Hoc Networks

Tactical Mobile Ad-hoc Networks (MANETs) are widely deployed in military organizations that have critical needs of communication even with the absence of fixed infrastructure. The lack of communication infrastructure and dynamic topology makes MANETs vulnerable to a wide range attacks. Tactical military operations are among the prime users of ad-hoc networks. The military holds higher standards of security requirements and thus require special intrusion detection applications. Conventional Intrusion Detection System (IDS) require central control and monitoring entities and thus cannot be applied to MANETs. Solutions to secure these networks are based on distributed and cooperative security. This paper presents a survey of IDS specifically for MANETs and also highlights the strengths and weakness of each model. We present a unique evaluation matrix for measuring the effectiveness of IDS for MANETs in an emergency response scenario.


Enabling agricultural automation to optimize utilization of water, fertilizer and insecticides by implementing Internet of Things (IoT)

With the proliferation of smart devices, Internet can be extended into the physical realm of Internet-of-Things (IoT) by deploying them into a communicating-actuating network. In Ion, sensors and actuators blend seamlessly with the environment; collaborate globally with each other through internet to accomplish a specific task. Wireless Sensor Network (WSN) can be integrated into Ion to meet the challenges of seamless communication between any things (e.g., humans or objects). The potentialities of IoT can be brought to the benefit of society by developing novel applications in transportation and logistics, healthcare, agriculture, smart environment (home, office or plant). This research gives a framework of optimizing resources (water, fertilizers, insecticides and manual labour) in agriculture through the use of IoT. The issues involved in the implementation of applications are also investigated in the paper. This frame work is named as AgriTech.


A review on stress inducement stimuli for assessing human stress using physiological signals

Assessing human stress in real-time is more difficult and challenging today. The present review deals about the measurement of stress in laboratory environment using different stress inducement stimuli by the help of physiological signals. Previous researchers have been used different stress inducement stimuli such as stroop colour word test (CWT), mental arithmetic test, public speaking task, cold pressor test, computer games and works used to induce the stress. Most of the researchers have been analyzed stress using questionnaire based approach and physiological signals. The several physiological signals like Electrocardiogram (ECG), Electromyogram (EMG), Galvanic Skin Response (GSR), Blood Pressure (BP), Skin Temperature (ST), Blood Volume Pulse (BVP), respiration rate (RIP) and Electroencephalogram (EEG) were briefly investigated to identify the stress. Different statistical methods like Analysis of variance (ANOVA), two-way ANOVA, Multivariate analysis of variance (MANOVA), t-test, paired t-tests and student t-tests have used to describe the correlation between stress inducement stimuli, subjective parameters (age, gender and etc.,) and physiological signals. This present works aims to find the most appropriate stress inducement stimuli, physiological signals and statistical method to efficiently asses the human stress.


Towards Principled Methods for Training Generative Adversarial Networks

The goal of this paper is not to introduce a single algorithm or method, but to make theoretical steps towards fully understanding the training dynamics of generative adversarial networks. In order to substantiate our theoretical analysis, we perform targeted experiments to verify our assumptions, illustrate our claims, and quantify the phenomena. This paper is divided into three sections. The first section introduces the problem at hand. The second section is dedicated to studying and proving rigorously the problems including instability and saturation that arize when training generative adversarial networks. The third section examines a practical and theoretically grounded direction towards solving these problems, while introducing new tools to study them.


Design Exploration of Hybrid CMOS and Memristor Circuit by New Modified Nodal Analysis

Design of hybrid circuits and systems based on CMOS and nano-device requires rethinking of fundamental circuit analysis to aid design exploration. Conventional circuit analysis with modified nodal analysis (MNA) cannot consider new nano-devices such as memristor together with the traditional CMOS devices. This paper has introduced a new MNA method with magnetic flux (Φ) as new state variable. New SPICE-like circuit simulator is thereby developed for the design of hybrid CMOS and memristor circuits. A number of CMOS and memristor-based designs are explored, such as oscillator, chaotic circuit, programmable logic, analog-learning circuit, and crossbar memory, where their functionality, performance, reliability and power can be efficiently verified by the newly developed simulator. Specifically, one new 3-D-crossbar architecture with diode-added memristor is also proposed to improve integration density and to avoid sneak path during read-write operation.


Facial Expression Recognition with Convolutional Neural Networks

Facial expression recognition systems have attracted much research interest within the field of artificial intelligence. Many established facial expression recognition (FER) systems apply standard machine learning to extracted image features, and these methods generalize poorly to previously unseen data. This project builds upon recent research to classify images of human faces into discrete emotion categories using convolutional neural networks (CNNs). We experimented with different architectures and methods such as fractional max-pooling and finetuning, ultimately achieving an accuracy of 0.48 in a sevenclass classification task.


Object Constraint Language (OCL): A Definitive Guide

The Object Constraint Language (OCL) started as a complement of the UML notation with the goal to overcome the limitations of UML (and in general, any graphical notation) in terms of precisely specifying detailed aspects of a system design. Since then, OCL has become a key component of any model-driven engineering (MDE) technique as the default language for expressing all kinds of (meta)model query, manipulation and specification requirements. Among many other applications, OCL is frequently used to express model transformations (as part of the source and target patterns of transformation rules), well-formedness rules (as part of the definition of new domain-specific languages), or code-generation templates (as a way to express the generation patterns and rules). This chapter pretends to provide a comprehensive view of this language, its many applications and available tool support as well as the latest research developments and open challenges around it.


Topic models in information retrieval


Generating Descriptions with Grounded and Co-referenced People

Learning how to generate descriptions of images or videos received major interest both in the Computer Vision and Natural Language Processing communities. While a few works have proposed to learn a grounding during the generation process in an unsupervised way (via an attention mechanism), it remains unclear how good the quality of the grounding is and whether it benefits the description quality. In this work we propose a movie description model which learns to generate description and jointly ground (localize) the mentioned characters as well as do visual co-reference resolution between pairs of consecutive sentences/clips. We also propose to use weak localization supervision through character mentions provided in movie descriptions to learn the character grounding. At training time, we first learn how to localize characters by relating their visual appearance to mentions in the descriptions via a semi-supervised approach. We then provide this (noisy) supervision into our description model which greatly improves its performance. Our proposed description model improves over prior work w.r.t. generated description quality and additionally provides grounding and local co-reference resolution. We evaluate it on the MPII Movie Description dataset using automatic and human evaluation measures and using our newly collected grounding and co-reference data for characters.


Water content of latent fingerprints - Dispelling the myth.

Changing procedures in the handling of rare and precious documents in museums and elsewhere, based on assumptions about constituents of latent fingerprints, have led the author to an examination of available data. These changes appear to have been triggered by one paper using general biological data regarding eccrine sweat production to infer that deposited fingerprints are mostly water. Searching the fingerprint literature has revealed a number of reference works similarly quoting figures for average water content of deposited fingerprints of 98% or more. Whilst accurate estimation is difficult there is no evidence that the residue on fingers could be anything like 98% water, even if there were no contamination from sebaceous glands. Consideration of published analytical data of real fingerprints, and several theoretical considerations regarding evaporation and replenishment rates, indicates a probable initial average water content of a fingerprint, soon after deposition, of 20% or less.


A multiscale measure for mixing

We present a multiscale measure for mixing that is based on the concept of weak convergence and averages the “mixedness” of an advected scalar field at various scales. This new measure, referred to as the Mix-Norm, resolves the inability of the L2 variance of the scalar density field to capture small-scale variations when advected by chaotic maps or flows. In addition, the Mix-Norm succeeds in capturing the efficiency of a mixing protocol in the context of a particular initial scalar field, wherein Lyapunov-exponent based measures fail to do so. We relate the Mix-Norm to the classical ergodic theoretic notion of mixing and present its formulation in terms of the power spectrum of the scalar field. We demonstrate the utility of the Mix-Norm by showing how it measures the efficiency of mixing due to various discrete dynamical systems and to diffusion. In particular, we show that the Mix-Norm can capture known exponential and algebraic mixing properties of certain maps. We also analyze numerically the behaviour of scalar fields evolved by the Standard Map using the Mix-Norm. © 2005 Elsevier B.V. All rights reserved.


Artificial cooperative search algorithm for numerical optimization problems

In this paper, a new two-population based global search algorithm, the Artificial Cooperative Search Algorithm (ACS), is introduced. ACS algorithm has been developed to be used in solving real-valued numerical optimization problems. For purposes of examining the success of ACS algorithm in solving numerical optimization problems, 91 benchmark problems that have different specifications were used in the detailed tests. The success of ACS algorithm in solving the related benchmark problems was compared to the successes obtained by PSO, SADE, CLPSO, BBO, CMA-ES, CK and DSA algorithms in solving the related benchmark problems by using Wilcoxon Signed-Rank Statistical Test with Bonferroni-Holm correction. The results obtained in the statistical analysis demonstrate that the success achieved by ACS algorithm in solving numerical optimization problems is better in comparison to the other computational intelligence algorithms used in this paper. 2012 Elsevier Inc. All rights reserved.


Abstraction Refinement for Probabilistic Software

ion Refinement for Probabilistic Software Mark Kattenbelt, Marta Kwiatkowska, Gethin Norman, and David Parker Oxford University Computing Laboratory, Parks Road, Oxford, OX1 3QD Abstract. We present a methodology and implementation for verifying ANSI-C programs that exhibit probabilistic behaviour, such as failures or randomisation. We use abstraction-refinement techniques that represent We present a methodology and implementation for verifying ANSI-C programs that exhibit probabilistic behaviour, such as failures or randomisation. We use abstraction-refinement techniques that represent probabilistic programs as Markov decision processes and their abstractions as stochastic two-player games. Our techniques target quantitative properties of software such as “the maximum probability of file-transfer failure” or “the minimum expected number of loop iterations” and the abstractions we construct yield lower and upper bounds on these properties, which then guide the refinement process. We build upon stateof-the-art techniques and tools, using SAT-based predicate abstraction, symbolic implementations of probabilistic model checking and components from GOTO-CC, SATABS and PRISM. Experimental results show that our approach performs very well in practice, successfully verifying actual networking software whose complexity is significantly beyond the scope of existing probabilistic verification tools.


A study of SSL Proxy attacks on Android and iOS mobile applications

According to recent articles in popular technology websites, some mobile applications function in an insecure manner when presented with untrusted SSL certificates. These non-browser based applications seem to, in the absence of a standard way of alerting a user of an SSL error, accept any certificate presented to it. This paper intends to research these claims and show whether or not an invisible proxy based SSL attack can indeed steal user's credentials from mobile applications, and which types applications are most likely to be vulnerable to this attack vector. To ensure coverage of the most popular platforms, applications on both Android 4.2 and iOS 6 are tested. The results of our study showed that stealing credentials is indeed possible using invisible proxy man in the middle attacks.


Transient effects in application of PWM inverters to induction motors

Standard squirrel cage induction motors are subjected to nonsinusoidal wave shapes, when supplied from adjustable frequency inverters. In addition to causing increased heating, these wave patterns can be destructive to the insulation. Pulse width modulated (PWM) inverter output amplitudes and risetimes are investigated, and motor insulation capabilities are discussed. Voltage reflections are simulated for various cable lengths and risetimes and are presented graphically. Simulations confirm potential problems with long cables and short risetimes. Application precautions are also discussed.<<ETX>>


Achieving Fairness through Adversarial Learning: an Application to Recidivism Prediction

Recidivism prediction scores are used across the USA to determine sentencing and supervision for hundreds of thousands of inmates. One such generator of recidivism prediction scores is Northpointe's Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) score, used in states like California and Florida, which past research has shown to be biased against black inmates according to certain measures of fairness. To counteract this racial bias, we present an adversarially-trained neural network that predicts recidivism and is trained to remove racial bias. When comparing the results of our model to COMPAS, we gain predictive accuracy and get closer to achieving two out of three measures of fairness: parity and equality of odds. Our model can be generalized to any prediction and demographic. This piece of research contributes an example of scientific replication and simplification in a high-stakes real-world application like recidivism prediction.


Perpetual development: A model of the Linux kernel life cycle

Software evolution is widely recognized as an important and common phenomenon, whereby the system follows an ever-extending development trajectory w ith intermittent releases. Nevertheless there have been only few lifecycle models that attempt to por tray such evolution. We use the evolution of the Linux kernel as the basis for the formulation of such a model, integrating the progress in time with growth of the codebase, and differentiating bet ween development of new functionality and maintenance of production versions. A unique elem nt of the model is the sequence of activities involved in releasing new production versions, and how this has changed with the growth of Linux. In particular, the release follow-up phase before th forking of a new development version, which was prominent in early releases of production ve rsions, has been eliminated in favor of a concurrent merge window in the release of 2.6.x versions . We also show that a piecewise linear model with increasing slopes provides the best descr iption of the growth of Linux. The perpetual development model is used as a framework in which c ommonly recognized benefits of incremental and evolutionary development may be demonstra ted, nd to comment on issues such as architecture, conservation of familiarity, and failed p rojects. We suggest that this model and variants thereof may apply to many other projects in additio n to Linux.


Robust Monte Carlo localization for mobile robots

Mobile robot localization is the problem of determining a robot's pose from sensor data. Monte Carlo Localization is a family of algorithms for localization based on particle filters, which are approximate Bayes filters that use random samples for posterior estimation. Recently, they have been applied with great success for robot localization. Unfortunately, regular particle filters perform poorly in certain situations. Mixture-MCL, the algorithm described here, overcomes these problems by using a "dual" sampler, integrating two complimentary ways of generating samples in the estimation. To apply this algorithm for mobile robot localization, a kd-tree is learned from data that permits fast dual sampling. Systematic empirical results obtained using data collected in crowded public places illustrate superior performance, robustness, and efficiency, when compared to other state-of-the-art localization algorithms.


Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories

This paper presents a method for recognizing scene categories based on approximate global geometric correspondence. This technique works by partitioning the image into increasingly fine sub-regions and computing histograms of local features found inside each sub-region. The resulting "spatial pyramid" is a simple and computationally efficient extension of an orderless bag-of-features image representation, and it shows significantly improved performance on challenging scene categorization tasks. Specifically, our proposed method exceeds the state of the art on the Caltech-101 database and achieves high accuracy on a large database of fifteen natural scene categories. The spatial pyramid framework also offers insights into the success of several recently proposed image descriptions, including Torralba’s "gist" and Lowe’s SIFT descriptors.


What, where and who? Classifying events by scene and object recognition

We propose a first attempt to classify events in static images by integrating scene and object categorizations. We define an event in a static image as a human activity taking place in a specific environment. In this paper, we use a number of sport games such as snow boarding, rock climbing or badminton to demonstrate event classification. Our goal is to classify the event in the image as well as to provide a number of semantic labels to the objects and scene environment within the image. For example, given a rowing scene, our algorithm recognizes the event as rowing by classifying the environment as a lake and recognizing the critical objects in the image as athletes, rowing boat, water, etc. We achieve this integrative and holistic recognition through a generative graphical model. We have assembled a highly challenging database of 8 widely varied sport events. We show that our system is capable of classifying these event classes at 73.4% accuracy. While each component of the model contributes to the final recognition, using scene or objects alone cannot achieve this performance.


Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns

ÐThis paper presents a theoretically very simple, yet efficient, multiresolution approach to gray-scale and rotation invariant texture classification based on local binary patterns and nonparametric discrimination of sample and prototype distributions. The method is based on recognizing that certain local binary patterns, termed auniform,o are fundamental properties of local image texture and their occurrence histogram is proven to be a very powerful texture feature. We derive a generalized gray-scale and rotation invariant operator presentation that allows for detecting the auniformo patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis. The proposed approach is very robust in terms of gray-scale variations since the operator is, by definition, invariant against any monotonic transformation of the gray scale. Another advantage is computational simplicity as the operator can be realized with a few operations in a small neighborhood and a lookup table. Excellent experimental results obtained in true problems of rotation invariance, where the classifier is trained at one particular rotation angle and tested with samples from other rotation angles, demonstrate that good discrimination can be achieved with the occurrence statistics of simple rotation invariant local binary patterns. These operators characterize the spatial configuration of local image texture and the performance can be further improved by combining them with rotation invariant variance measures that characterize the contrast of local image texture. The joint distributions of these orthogonal measures are shown to be very powerful tools for rotation invariant texture analysis. Index TermsÐNonparametric, texture analysis, Outex, Brodatz, distribution, histogram, contrast.


Robust Real-Time Face Detection


Emotion recognition system using brain and peripheral signals: Using correlation dimension to improve the results of EEG

This paper proposed a multimodal fusion between brain and peripheral signals for emotion detection. The input signals were electroencephalogram, galvanic skin resistance, temperature, blood pressure and respiration, which can reflect the influence of emotion on the central nervous system and autonomic nervous system respectively. The acquisition protocol is based on a subset of pictures which correspond to three specific areas of valance-arousal emotional space (positively excited, negatively excited, and calm). The features extracted from input signals, and to improve the results, correlation dimension as a strong nonlinear feature is used for brain signals. The performance of the Quadratic Discriminant Classifier has been evaluated on different feature sets: peripheral signals, EEG's, and both. In comparison among the results of different feature sets, EEG signals seem to perform better than other physiological signals, and the results confirm the interest of using brain signals as peripherals in emotion assessment. According to the improvement in EEG results compare in each raw of the table, it seems that nonlinear features would lead to better understanding of how emotional activities work.


A Geospatial Decision Support System for Drought Risk Management

We are developing an advanced Geospatial Decision Support System (GDSS) to improve the quality and accessibility of drought related data for drought risk management. This is part of a Digital Government project aimed at developing and integrating new information technologies for improved government services in the USDA Risk Management Agency (RMA) and the Natural Resources Conservation Service (NRCS). Our overall goal is to substantially improve RMA's delivery of risk management services in the near-term and provide a foundation and directions for the future. We integrate spatio-temporal knowledge discovery techniques into our GDSS using a combination of data mining techniques applied to rich, geospatial, time-series data. Our data mining objectives are to: 1) find relationships between user-specified target episodes and other climatic events and 2) predict the target episodes. Understanding relationships between changes in soil moisture regimes and global climatic events such as El Niño could provide a reasonable drought mitigation strategy for farmers to adjust planting dates, hybrid selection, plant populations, tillage practices or crop rotations. This work highlights the innovative data mining approaches integral to our project's success and provides preliminary results that indicate our system’s potential to substantially improve RMA's delivery of drought risk management services.


A Secure Microservice Framework for IoT

The Internet of Things (IoT) has connected an incredible diversity of devices in novel ways, which has enabled exciting new services and opportunities. Unfortunately, IoT systems also present several important challenges to developers. This paper proposes a vision for how we may build IoT systems in the future by reconceiving IoT's fundamental unit of construction not as a "thing", but rather as a widely and finely distributed "microservice" already familiar to web service engineering circles. Since IoT systems are quite different from more established uses of microservice architectures, success of the approach depends on adaptations that enable them to met the key challenges that IoT systems present. We argue that a microservice approach to building IoT systems can combine in a mutually enforcing way with patterns for microservices, API gateways, distribution of services, uniform service discovery, containers, and access control. The approach is illustrated using two case studies of IoT systems in personal health management and connected autonomous vehicles. Our hope is that the vision of a microservices approach will help focus research that can fill in current gaps preventing more effective, interoperable, and secure IoT services and solutions in a wide variety of contexts.


Doubly Fed Induction Generator Systems For Variable Speed Wind Turbine

This paper presents results of a study concerning the dynamic behaviour of a wind energy system powered by a doubly fed induction generator with the rotor connected to the electric network through an AC-AC converter. A tendency to put up more and more wind turbines can be observed all over the world. Also, there is awareness in taking into account the requirements of a clean environment, due to the need to preserve our habitat. Renewable energy sources not contributing to the enhanced greenhouse effect, especially wind power, are becoming an important component of the total generation. Hence, research concerning the dynamic behaviour of wind energy systems is important to achieve a better knowledge.


Local Monotonic Attention Mechanism for End-to-End Speech And Language Processing

Recently, encoder-decoder neural networks have shown impressive performance on many sequence-related tasks. The architecture commonly uses an attentional mechanism which allows the model to learn alignments between the source and the target sequence. Most attentional mechanisms used today is based on a global attention property which requires a computation of a weighted summarization of the whole input sequence generated by encoder states. However, it is computationally expensive and often produces misalignment on the longer input sequence. Furthermore, it does not fit with monotonous or left-to-right nature in several tasks, such as automatic speech recognition (ASR), grapheme-to-phoneme (G2P), etc. In this paper, we propose a novel attention mechanism that has local and monotonic properties. Various ways to control those properties are also explored. Experimental results on ASR, G2P and machine translation between two languages with similar sentence structures, demonstrate that the proposed encoderdecoder model with local monotonic attention could achieve significant performance improvements and reduce the computational complexity in comparison with the one that used the standard global attention architecture.


Local Deep Neural Networks for Age and Gender Classification

Local deep neural networks have been recently introduced for gender recognition. Although, they achieve very good performance they are very computationally expensive to train. In this work, we introduce a simplified version of local deep neural networks which significantly reduces the training time. Instead of using hundreds of patches per image, as suggested by the original method, we propose to use 9 overlapping patches per image which cover the entire face region. This results in a much reduced training time, since just 9 patches are extracted per image instead of hundreds, at the expense of a slightly reduced performance. We tested the proposed modified local deep neural networks approach on the LFW and Adience databases for the task of gender and age classification. For both tasks and both databases the performance is up to 1% lower compared to the original version of the algorithm. We have also investigated which patches are more discriminative for age and gender classification. It turns out that the mouth and eyes regions are useful for age classification, whereas just the eye region is useful for gender classification.


Neuroprotective effect of Bacopa monnieri on beta-amyloid-induced cell death in primary cortical culture.

AIM OF THE STUDY
Bacopa monnieri (Brahmi) is extensively used in traditional Indian medicine as a nerve tonic and thought to improve memory. To examine the neuroprotective effects of Brahmi extract, we tested its protection against the beta-amyloid protein (25-35) and glutamate-induced neurotoxicity in primary cortical cultured neurons.

MATERIALS AND METHODS
Neuroprotective effects were determined by measuring neuronal cell viability following beta-amyloid and glutamate treatment with and without Brahmi extract. Mechanisms of neuroprotection were evaluated by monitoring cellular oxidative stress and acetylcholinesterase activity.

RESULTS
Our result demonstrated that Brahmi extract protected neurons from beta-amyloid-induced cell death, but not glutamate-induced excitotoxicity. This neuroprotection was possibly due to its ability to suppress cellular acetylcholinesterase activity but not the inhibition of glutamate-mediated toxicity. In addition, culture medium containing Brahmi extract appeared to promote cell survival compared to neuronal cells growing in regular culture medium. Further study showed that Brahmi-treated neurons expressed lower level of reactive oxygen species suggesting that Brahmi restrained intracellular oxidative stress which in turn prolonged the lifespan of the culture neurons. Brahmi extract also exhibited both reducing and lipid peroxidation inhibitory activities.

CONCLUSIONS
From this study, the mode of action of neuroprotective effects of Brahmi appeared to be the results of its antioxidant to suppress neuronal oxidative stress and the acetylcholinesterase inhibitory activities. Therefore, treating patients with Brahmi extract may be an alternative direction for ameliorating neurodegenerative disorders associated with the overwhelming oxidative stress as well as Alzheimer's disease.


On the control of planar cable-driven parallel robot via classic controllers and tuning with intelligent algorithms

This paper presents different classical control approaches for planar cable-driven parallel robots. For the proposed robot, PD and PID controllers are designed based on the concept of pole placement method. In order to optimize and tune the controller parameters of planar cable-driven parallel robot, Differential Evaluation, Particle Swarm Optimization and Genetic algorithms are applied as optimization techniques. The simulation results of Genetic algorithm, Particle Swarm Optimization and Differential Evaluation algorithms reveal that the output results of tunes controllers with Particle Swarm Optimization and Differential Evaluation algorithms are more similar than Genetic algorithm and the processing time of Differential Evaluation is less than Genetic algorithm and Particle Swarm Optimization. Moreover, performance of the Particle Swarm Optimization and Differential Evaluation algorithms are better than Genetic algorithm for tuning the controllers parameters.


Accurate Linear-Time Chinese Word Segmentation via Embedding Matching

This paper proposes an embedding matching approach to Chinese word segmentation, which generalizes the traditional sequence labeling framework and takes advantage of distributed representations. The training and prediction algorithms have linear-time complexity. Based on the proposed model, a greedy segmenter is developed and evaluated on benchmark corpora. Experiments show that our greedy segmenter achieves improved results over previous neural network-based word segmenters, and its performance is competitive with state-of-the-art methods, despite its simple feature set and the absence of external resources for training.


Handover Performance in 3GPP Long Term Evolution (LTE) Systems

The specification of the long term evolution (LTE) of 3G systems is currently ongoing in 3GPP with a target date of ready specification at the end of 2007. The evolved radio access network (RAN) involves a new radio interface based on OFDM technology and a radically different RAN architecture, where radio functionality is distributed into the base stations. The distributed nature of the RAN architecture calls for new radio control algorithms and procedures that operate in a distributed manner, including a distributed handover scheme as well. The most important aspects of the handover procedure in LTE has been already settled in 3GPP except a few details. In this paper we give an overview of the LTE intra-access handover procedure and evaluate its performance focusing on the user perceived performance aspects of it. We investigate the necessity of packet forwarding from a TCP throughput point of view, we analyse the problem of out of order packet delivery during handover and propose a simple solution for it. Finally, we investigate the impact of HARQ/ARQ state discard at handover on the radio efficiency. The results show that neither the user perceived performance nor the radio efficiency are compromised by the relocation based handover procedure of LTE.


A bandwidth-tunable bioamplifier with voltage-controlled symmetric pseudo-resistors

This paper describes a bioamplifier that employs a voltage-controlled-pseudo-resistor to achieve tunable bandwidth and wide operating voltage range for biomedical applications. The versatile pseudo-resistor employed provides ultra-high resistance for ac coupling to cancel the dc offset from electrode–tissue interface. The voltage-controlled-pseudo-resistor consists of serial-connected PMOS transistors working at the subthreshold region and an auto-tuning circuit that makes sure the constant (time-invariant) control-voltage of the pseudo-resistor. This bandwidth-tunable bioamplifier is designed in a 0.18-μm standard CMOS process, achieving a gain of 40.2 dB with 10.35-μW power consumption. The designed chip was also used to develop the proof-of-concept prototype. An operation bandwidth of 9.5 kHz, inputreferred noise of 5.2 μVrms from 6.3 Hz to 9.5 kHz and 5.54 μVrms from 250 Hz to 9.5 kHz, and a tunable cutoff-frequency from 6.3–600 Hz were demonstrated to prove our design. & 2015 Elsevier Ltd. All rights reserved.


Broadband Planar Fully Integrated 8 $\, \times \,$8 Butler Matrix Using Coupled-Line Directional Couplers

An innovative approach that allows for realization of broadband planar fully integrated 8 × 8 Butler matrices is presented. Coupled-line directional couplers have been utilized, which enable broad operational band. A novel arrangement of the network has been proposed that allows the creation of an entirely planar design having two metallization layers and no interconnections between these layers. Four selected crossovers have been realized as a tandem connection of two 3-dB/90° coupled-line directional couplers, which, together with reference lines having appropriate electrical lengths, perform simultaneously crossovers of signal lines, and all needed broadband constant value phase shifters. Moreover, two of the needed 3-dB/90° directional couplers have been designed as tandem connections of two 8.34-dB directional couplers, acting as 3-dB/90° directional couplers having crossed outputs. With such a modification, a fully planar design with no inter-layer connections is possible. The proposed network arrangement has been experimentally tested with the design of an 8 × 8 Butler matrix operating at the center frequency of 3 GHz. The obtained measurement results fully confirm the validity of the proposed approach.


Design and Development of GPS-GSM Based Tracking System with Google Map Based Monitoring

GPS is one of the technologies that are used in a huge number of applications today. One of the applications is tracking your vehicle and keeps regular monitoring on them. This tracking system can inform you the location and route travelled by vehicle, and that information can be observed from any other remote location. It also includes the web application that provides you exact location of target. This system enables us to track target in any weather conditions. This system uses GPS and GSM technologies. The paper includes the hardware part which comprises of GPS, GSM, Atmega microcontroller MAX 232, 16x2 LCD and software part is used for interfacing all the required modules and a web application is also developed at the client side. Main objective is to design a system that can be easily installed and to provide platform for further enhancement.


The DRAM Latency PUF: Quickly Evaluating Physical Unclonable Functions by Exploiting the Latency-Reliability Tradeoff in Modern Commodity DRAM Devices

Physically Unclonable Functions (PUFs) are commonly used in cryptography to identify devices based on the uniqueness of their physical microstructures. DRAM-based PUFs have numerous advantages over PUF designs that exploit alternative substrates: DRAM is a major component of many modern systems, and a DRAM-based PUF can generate many unique identiers. However, none of the prior DRAM PUF proposals provide implementations suitable for runtime-accessible PUF evaluation on commodity DRAM devices. Prior DRAM PUFs exhibit unacceptably high latencies, especially at low temperatures (e.g., >125.8s on average for a 64KiB memory segment below 55C), and they cause high system interference by keeping part of DRAM unavailable during PUF evaluation. In this paper, we introduce the DRAM latency PUF, a new class of fast, reliable DRAM PUFs. The key idea is to reduce DRAM read access latency below the reliable datasheet specications using software-only system calls. Doing so results in error patterns that reect the compound eects of manufacturing variations in various DRAM structures (e.g., capacitors, wires, sense ampli- ers). Based on a rigorous experimental characterization of 223 modern LPDDR4 DRAM chips, we demonstrate that these error patterns 1) satisfy runtime-accessible PUF requirements, and 2) are quickly generated (i.e., at 88.2ms) irrespective of operating temperature using a real system with no additional hardware modications. We show that, for a constant DRAM capacity overhead of 64KiB, our implementation of the DRAM latency PUF enables an average (minimum, maximum) PUF evaluation time speedup of 152x (109x, 181x) at 70C and 1426x (868x, 1783x) at 55C when compared to a DRAM retention PUF and achieves greater speedups at even lower temperatures.


CrackIT — An image processing toolbox for crack detection and characterization

This paper presents a comprehensive set of image processing algorithms for detection and characterization of road pavement surface crack distresses, which is being made available to the research community. The toolbox, in the Matlab environment, includes algorithms to preprocess images, to detect cracks and characterize them into types, based on image processing and pattern recognition techniques, as well as modules devoted to the performance evaluation of crack detection and characterization solutions. A sample database of 84 pavement surface images taken during a traditional road survey is provided with the toolbox, since no pavement image databases are publicly available for crack detection and characterization evaluation purposes. Results achieved applying the proposed toolbox to the sample database are discussed, illustrating the potential of the available algorithms.


Local-Learning-Based Feature Selection for High-Dimensional Data Analysis

This paper considers feature selection for data classification in the presence of a huge number of irrelevant features. We propose a new feature-selection algorithm that addresses several major issues with prior work, including problems with algorithm implementation, computational complexity, and solution accuracy. The key idea is to decompose an arbitrarily complex nonlinear problem into a set of locally linear ones through local learning, and then learn feature relevance globally within the large margin framework. The proposed algorithm is based on well-established machine learning and numerical analysis techniques, without making any assumptions about the underlying data distribution. It is capable of processing many thousands of features within minutes on a personal computer while maintaining a very high accuracy that is nearly insensitive to a growing number of irrelevant features. Theoretical analyses of the algorithm's sample complexity suggest that the algorithm has a logarithmical sample complexity with respect to the number of features. Experiments on 11 synthetic and real-world data sets demonstrate the viability of our formulation of the feature-selection problem for supervised learning and the effectiveness of our algorithm.


Nasal bone length throughout gestation: normal ranges based on 3537 fetal ultrasound measurements.

OBJECTIVE
To establish normal ranges for nasal bone length measurements throughout gestation and to compare measurements in two subsets of patients of different race (African-American vs. Caucasian) to determine whether a different normal range should be used in these populations.

METHOD
Normal nasal bone length reference ranges were generated using prenatal measurements by a standardized technique in 3537 fetuses.

RESULTS
The nasal bone lengths were found to correlate positively with advancing gestation (R(2) = 0.77, second-order polynomial). No statistical difference was found between African-American and Caucasian subjects.

CONCLUSION
These reference ranges may prove to be useful in prenatal screening and diagnosis of syndromes known to be associated with nasal hypoplasia. Different normal ranges for African-American and Caucasian women are not required.


A Laminated Waveguide Magic-T With Bandpass Filter Response in Multilayer LTCC

A laminated waveguide magic-T with imbedded Chebyshev filter response is developed in a multilayer low-temperature co-fired ceramic technology by vertically stacked rectangular cavity resonators with highly symmetric coupling structures. The cavities provide even- and odd-symmetric field distributions by simultaneously resonating TE102 and TE201 modes at the center frequency of the magic-T. Thus, the in-phase and out-of-phase responses of the magic-T function are accomplished according to the induced mode. Meanwhile, the filter frequency response is realized by the cascaded cavities with proper coupling strengths according to the filter specification. With the degenerate, but orthogonal cavity modes, a fewer number of cavities are required and the circuit size is further reduced. A third-order bandpass magic-T is designed and fabricated to operate at 24 GHz with 6% fractional bandwidth. The highly symmetric structure of the implemented magic-T provides a low in-band magnitude imbalance ( ±0.25 dB) and phase imbalance (0°-6°). The sum and difference ports also provide an isolation greater than 30 dB in the operation frequency range. Measurement and simulated results are in good agreement, both validating the design concept.


Demand Side Management in Smart Grids

Smart grids are considered to be the next generation electric grids. Drivers for this development are among others: an increased electricity demand, an ageing network infrastructure and an increased share of renewable energy sources. Sustainability and the European 20/20/20 climate and energy targets are also main drivers for this development. Some of the important aspects of smart grids are that they allow two-way communication between the actors in the grid and enable optimisation of electricity use and production and customer participation. Demand side management is associated to smart grids and means adapting the electricity demand to the electricity production and the available electricity in the grid. It is considered that smart grids and demand side management hold potential to facilitate an increased share of renewable energy sources and to reduce the need for the current power reserve, which is based on fossil energy sources. The aim of this study was to compile and analyse some of the work that has been done and is being implemented around the world concerning demand side management in smart grids with a focus on market rules and business models in relation to Swedish conditions. The study includes a review of selected research and demonstration projects. Success factors and knowledge gaps have been identified through an analysis of the project findings and recommendations. A description of the current Nordic market conditions is also given. The conclusions regarding the conditions on the Swedish electricity market is that it appears to be relatively well adjusted to demand side management with the possibility for hourly meter readings and a full deployment of smart meters. The Nord Pool electricity market allows both day-ahead trading and intra-day trading on the elspot and the elbas markets. The review of the projects show that there is a potential for achieving flexible load from private customers. Hourly metering is seen as a prerequisite for demand side management and active customers is also considered crucial. Visualisation of electricity consumption and current costs is shown to be an important tool for encouraging customers to take an active part in their electricity consumption. Some of the other methods for achieving flexibility are for example: different types of pricing models, a signal for renewable energy sources and direct and indirect control of smart appliances and heating systems. The aggregation concept is an example of direct control that have been used and show potential for achieving flexibility in the grid. The concept means that a so called aggregator, which for example could be the supplier or the distribution system operator, compiles and controls the electrical load for several customers. A key challenge that has been identified is the issue of standardisation. Exchange of experiences from various research and demonstration projects is also considered desirable to support the development of smart grid and demand response.


Usability Engineering for Augmented Reality: Employing User-Based Studies to Inform Design

A major challenge, and thus opportunity, in the field of human-computer interaction and specifically usability engineering (UE) is designing effective user interfaces for emerging technologies that have no established design guidelines or interaction metaphors or introduce completely new ways for users to perceive and interact with technology and the world around them. Clearly, augmented reality (AR) is one such emerging technology. We propose a UE approach that employs user-based studies to inform design by iteratively inserting a series of user-based studies into a traditional usability-engineering life cycle to better inform initial user interface designs. We present an exemplar user-based study conducted to gain insight into how users perceive text in outdoor AR settings and to derive implications for design in outdoor AR. We also describe "lessons learned" from our experiences, conducting user-based studies as part of the design process.


Dataless Text Classification: A Topic Modeling Approach with Document Manifold

Recently, dataless text classification has attracted increasing attention. It trains a classifier using seed words of categories, rather than labeled documents that are expensive to obtain. However, a small set of seed words may provide very limited and noisy supervision information, because many documents contain no seed words or only irrelevant seed words. In this paper, we address these issues using document manifold, assuming that neighboring documents tend to be assigned to a same category label. Following this idea, we propose a novel Laplacian seed word topic model (LapSWTM). In LapSWTM, we model each document as a mixture of hidden category topics, each of which corresponds to a distinctive category. Also, we assume that neighboring documents tend to have similar category topic distributions. This is achieved by incorporating a manifold regularizer into the log-likelihood function of the model, and then maximizing this regularized objective. Experimental results show that our LapSWTM significantly outperforms the existing dataless text classification algorithms and is even competitive with supervised algorithms to some extent. More importantly, it performs extremely well when the seed words are scarce.


The Cambridge Face Memory Test: Results for neurologically intact individuals and an investigation of its validity using inverted face stimuli and prosopagnosic participants

The two standardized tests of face recognition that are widely used suffer from serious shortcomings [Duchaine, B. & Weidenfeld, A. (2003). An evaluation of two commonly used tests of unfamiliar face recognition. Neuropsychologia, 41, 713-720; Duchaine, B. & Nakayama, K. (2004). Developmental prosopagnosia and the Benton Facial Recognition Test. Neurology, 62, 1219-1220]. Images in the Warrington Recognition Memory for Faces test include substantial non-facial information, and the simultaneous presentation of faces in the Benton Facial Recognition Test allows feature matching. Here, we present results from a new test, the Cambridge Face Memory Test, which builds on the strengths of the previous tests. In the test, participants are introduced to six target faces, and then they are tested with forced choice items consisting of three faces, one of which is a target. For each target face, three test items contain views identical to those studied in the introduction, five present novel views, and four present novel views with noise. There are a total of 72 items, and 50 controls averaged 58. To determine whether the test requires the special mechanisms used to recognize upright faces, we conducted two experiments. We predicted that controls would perform much more poorly when the face images are inverted, and as predicted, inverted performance was much worse with a mean of 42. Next we assessed whether eight prosopagnosics would perform poorly on the upright version. The prosopagnosic mean was 37, and six prosopagnosics scored outside the normal range. In contrast, the Warrington test and the Benton test failed to classify a majority of the prosopagnosics as impaired. These results indicate that the new test effectively assesses face recognition across a wide range of abilities.


(s|qu)eries: Visual Regular Expressions for Querying and Exploring Event Sequences

Many different domains collect event sequence data and rely on finding and analyzing patterns within it to gain meaningful insights. Current systems that support such queries either provide limited expressiveness, hinder exploratory workflows or present interaction and visualization models which do not scale well to large and multi-faceted data sets. In this paper we present (s|qu)eries (pronounced "Squeries"), a visual query interface for creating queries on sequences (series) of data, based on regular expressions. (s|qu)eries is a touch-based system that exposes the full expressive power of regular expressions in an approachable way and interleaves query specification with result visualizations. Being able to visually investigate the results of different query-parts supports debugging and encourages iterative query-building as well as exploratory work-flows. We validate our design and implementation through a set of informal interviews with data scientists that analyze event sequences on a daily basis.


SnipSuggest: Context-Aware Autocompletion for SQL

In this paper, we present SnipSuggest, a system that provides onthe-go, context-aware assistance in the SQL composition process. SnipSuggest aims to help the increasing population of non-expert database users, who need to perform complex analysis on their large-scale datasets, but have difficulty writing SQL queries. As a user types a query, SnipSuggest recommends possible additions to various clauses in the query using relevant snippets collected from a log of past queries. SnipSuggest’s current capabilities include suggesting tables, views, and table-valued functions in the FROM clause, columns in the SELECT clause, predicates in the WHERE clause, columns in the GROUP BY clause, aggregates, and some support for sub-queries. SnipSuggest adjusts its recommendations according to the context: as the user writes more of the query, it is able to provide more accurate suggestions. We evaluate SnipSuggest over two query logs: one from an undergraduate database class and another from the Sloan Digital Sky Survey database. We show that SnipSuggest is able to recommend useful snippets with up to 93.7% average precision, at interactive speed. We also show that SnipSuggest outperforms naı̈ve approaches, such as recommending popular snippets.


Profiler: integrated statistical analysis and visualization for data quality assessment

Data quality issues such as missing, erroneous, extreme and duplicate values undermine analysis and are time-consuming to find and fix. Automated methods can help identify anomalies, but determining what constitutes an error is context-dependent and so requires human judgment. While visualization tools can facilitate this process, analysts must often manually construct the necessary views, requiring significant expertise. We present Profiler, a visual analysis tool for assessing quality issues in tabular data. Profiler applies data mining methods to automatically flag problematic data and suggests coordinated summary visualizations for assessing the data in context. The system contributes novel methods for integrated statistical and visual analysis, automatic view suggestion, and scalable visual summaries that support real-time interaction with millions of data points. We present Profiler's architecture --- including modular components for custom data types, anomaly detection routines and summary visualizations --- and describe its application to motion picture, natural disaster and water quality data sets.


Wired for Speech: How Voice Activates and Advances the Human-Computer Relationship

Spoken dialogue systems have received increased interest because they are potentially much more natural and powerful methods of communicating with machines than are current graphics-based interfaces. Wired for Speech presents basic research in the psychological and sociological aspects of voice synthesis and recognition. Its major lesson is that people attribute human characteristics to spoken dialogue systems for reasons related to human evolution. But although it contains interesting basic research, the book is mainly aimed at giving technological or marketing advice to those seeking to use voice interfaces when creating commercial applications. The book is oriented around a series of simple experiments designed to show just how pervasive psychological and social influences can be on the opinions and behaviors of people confronted with voice interfaces. Each chapter describes a basic research hypothesis, introduces an experiment to test it, and discusses its implications for designing voice interfaces: gender, personality, accent, ethnicity, emotion, number of distinct voices, use of " I " by the system, voices in concert with faces, mixed synthetic and recorded voices, context, and the effects of errors in human–computer cooperation. Although Wired for Speech is very accessible , especially to the non-scientist, it is written with an unusual bibliography style for an academic book: All references and details are given in a notes section at the end of the book, making up one third of the content. This narrative exposition style will probably not satisfy either type of reader: Scientists will be frustrated at the imprecision in argumen-tation, lack of detail in the book itself, and continually having to refer to the notes. The lack of detail also prevents the book from serving as a reference work. Meanwhile those needing advice when implementing voice interfaces will be puzzled at references to Chomsky, Grice, and Zemlin. Thus the book seems to suffer from not knowing its audience well, which is odd as this is precisely the lesson that the book tries to impart. Another complaint from the scientific point of view is the obsession this particular book has with casting every experimental result as advice for the business and marketing side of voice interfaces, typically concentrating on Web-based e-marketing examples such as buying books on line. Most of the experiments have sample sizes of between 40 and 50, but the authors seem ready to invite multi-billion-dollar businesses to immediately base their deployed systems on these results. Finally, and fundamentally, this …


Characterizing the usability of interactive applications through query log analysis

People routinely rely on Internet search engines to support their use of interactive systems: they issue queries to learn how to accomplish tasks, troubleshoot problems, and otherwise educate themselves on products. Given this common behavior, we argue that search query logs can usefully augment traditional usability methods by revealing the primary tasks and needs of a product's user population. We term this use of search query logs CUTS - characterizing usability through search. In this paper, we introduce CUTS and describe an automated process for harvesting, ordering, labeling, filtering, and grouping search queries related to a given product. Importantly, this data set can be assembled in minutes, is timely, has a high degree of ecological validity, and is arguably less prone to self-selection bias than data gathered via traditional usability methods. We demonstrate the utility of this approach by applying it to a number of popular software and hardware systems.


Multithreaded Sliding Window Approach to Improve Exact Pattern Matching Algorithms

In this paper an efficient pattern matching approach, based on a multithreading sliding window technique, is proposed to improve the efficiency of the common sequential exact pattern matching algorithms including: (i) Brute Force, (ii) Knuth-Morris-Pratt and (iii) Boyer-Moore. The idea is to divide the text under-search into blocks, each block is allocated one or two threads running concurrently. Reported experimental results indicated that the proposed approach improves the performance of the well-known pattern matching algorithms, in terms of search time, especially when the searched patterns are located at the middle or at the end of the text. Keywords—pattern matching; multithreading; sliding window; Brute Force; Knuth-Morris-Pratt; Boyer-Moore


Techniques of EMG signal analysis: detection, processing, classification and applications

Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. The purpose of this paper is to illustrate the various methodologies and algorithms for EMG signal analysis to provide efficient and effective ways of understanding the signal and its nature. We further point up some of the hardware implementations using EMG focusing on applications related to prosthetic hand control, grasp recognition, and human computer interaction. A comparison study is also given to show performance of various EMG signal analysis methods. This paper provides researchers a good understanding of EMG signal and its analysis procedures. This knowledge will help them develop more powerful, flexible, and efficient applications.


A particle swarm optimization algorithm for flexible job shop scheduling problem

The classical job shop scheduling problem (JSP) is the most popular machine scheduling model in practice and is well known as NP-hard. The formulation of the JSP is based on the assumption that for each part type or job there is only one process plan that prescribes the sequence of operations and the machine on which each operation has to be performed. Flexible job shop scheduling problem (FJSP) is an extension of the JSP, which allows an operation to be processed by any machine from a given set. Since FJSP requires an additional decision of machine allocation during scheduling, therefore it is much more complex problem than JSP. To solve such NP-hard problems, heuristic approaches are commonly preferred over the traditional mathematical techniques. This paper proposes a particle swarm optimization (PSO) based heuristic for solving the FJSP for minimum makespan time criterion. The performance of the proposed PSO is evaluated by comparing its results with the results obtained using ILOG Solver, a constraint-programming tool. The comparison of the results proves the effectiveness of the proposed PSO for solving FJSP instances.