Query: Parser Extraction of Triples in Unstructured Text

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DBpedia: A Nucleus for a Web of Open Data <=SEP=> DBpedia is a community effort to extract structured information from Wikipedia and to make this information available on the Web. DBpedia allows you to ask sophisticated queries against datasets derived from Wikipedia and to link other datasets on the Web to Wikipedia data. We describe the extraction of the DBpedia datasets, and how the resulting information is published on the Web for humanand machineconsumption. We describe some emerging applications from the DBpedia community and show how website authors can facilitate DBpedia content within their sites. Finally, we present the current status of interlinking DBpedia with other open datasets on the Web and outline how DBpedia could serve as a nucleus for an emerging Web of open data.

ClausIE: clause-based open information extraction <=SEP=> We propose ClausIE, a novel, clause-based approach to open information extraction, which extracts relations and their arguments from natural language text. ClausIE fundamentally differs from previous approaches in that it separates the detection of ``useful'' pieces of information expressed in a sentence from their representation in terms of extractions. In more detail, ClausIE exploits linguistic knowledge about the grammar of the English language to first detect clauses in an input sentence and to subsequently identify the type of each clause according to the grammatical function of its constituents. Based on this information, ClausIE is able to generate high-precision extractions; the representation of these extractions can be flexibly customized to the underlying application. ClausIE is based on dependency parsing and a small set of domain-independent lexica, operates sentence by sentence without any post-processing, and requires no training data (whether labeled or unlabeled). Our experimental study on various real-world datasets suggests that ClausIE obtains higher recall and higher precision than existing approaches, both on high-quality text as well as on noisy text as found in the web.

Introduction to Automata Theory, Languages and Computation <=SEP=> Let n be the pumping-lemma constant (note this n is unrelated to the n that is a local variable in the definition of the language L). Pick w = 0^n10^n. Then when we write w = xyz, we know that |xy| <= n, and therefore y consists of only 0's. Thus, xz, which must be in L if L is regular, consists of fewer than n 0's, followed by a 1 and exactly n 0's. That string is not in L, so we contradict the assumption that L is regular.

Global Learning of Typed Entailment Rules <=SEP=> Extensive knowledge bases of entailment rules between predicates are crucial for applied semantic inference. In this paper we propose an algorithm that utilizes transitivity constraints to learn a globally-optimal set of entailment rules for typed predicates. We model the task as a graph learning problem and suggest methods that scale the algorithm to larger graphs. We apply the algorithm over a large data set of extracted predicate instances, from which a resource of typed entailment rules has been recently released (Schoenmackers et al., 2010). Our results show that using global transitivity information substantially improves performance over this resource and several baselines, and that our scaling methods allow us to increase the scope of global learning of entailment-rule graphs.

Accurate Unlexicalized Parsing <=SEP=> We demonstrate that an unlexicalized PCFG can parse much more accurately than previously shown, by making use of simple, linguistically motivated state splits, which break down false independence assumptions latent in a vanilla treebank grammar. Indeed, its performance of 86.36% (LP/LR F 1) is better than that of earlylexicalizedPCFG models, and surprisingly close to the current state-of-theart. This result has potential uses beyond establishing a strong lower bound on the maximum possible accuracy of unlexicalized models: an unlexicalized PCFG is much more compact, easier to replicate, and easier to interpret than more complex lexical models, and the parsing algorithms are simpler, more widely understood, of lower asymptotic complexity, and easier to optimize. In the early 1990s, as probabilistic methods swept NLP, parsing work revived the investigation of probabilistic context-free grammars ( PCFGs) (Booth and Thomson, 1973; Baker, 1979). However, early results on the utility ofPCFGs for parse disambiguation and language modeling were somewhat disappointing. A conviction arose that lexicalizedPCFGs (where head words annotate phrasal nodes) were the key tool for high performancePCFG parsing. This approach was congruent with the great success of word n-gram models in speech recognition, and drew strength from a broader interest in lexicalized grammars, as well as demonstrations that lexical dependencies were a key tool for resolving ambiguities such asPPattachments (Ford et al., 1982; Hindle and Rooth, 1993). In the following decade, great success in terms of parse disambiguation and even language modeling was achieved by various lexicalized PCFG models (Magerman, 1995; Charniak, 1997; Collins, 1999; Charniak, 2000; Charniak, 2001). However, several results have brought into question how large a role lexicalization plays in such parsers. Johnson (1998) showed that the performance of anunlexicalizedPCFGover the Penn treebank could be improved enormously simply by annotating each node by its parent category. The Penn treebank coveringPCFGis a poor tool for parsing because the context-freedom assumptions it embodies are far too strong, and weakening them in this way makes the model much better. More recently, Gildea (2001) discusses how taking the bilexical probabilities out of a good current lexicalized PCFG parser hurts performance hardly at all: by at most 0.5% for test text from the same domain as the training data, and not at all for test text from a different domain. 1 But it is precisely these bilexical dependencies that backed the intuition that lexicalized PCFGs should be very successful, for example in Hindle and Rooth’s demonstration fromPPattachment. We take this as a reflection of the fundamental sparseness of the lexical dependency information available in the Penn Treebank. As a speech person would say, one million words of training data just isn’t enough. Even for topics central to the treebank’s Wall Street Journal text, such as stocks, many very plausible dependencies occur only once, for example stocks stabilized, while many others occur not at all, for example stocks skyrocketed .2 The best-performing lexicalized PCFGs have increasingly made use of subcategorization 3 of the 1There are minor differences, but all the current best-known lexicalized PCFGs employ bothmonolexicalstatistics, which describe the phrasal categories of arguments and adjuncts t hat appear around a head lexical item, and bilexicalstatistics, or dependencies, which describe the likelihood of a head word tak ing as a dependent a phrase headed by a certain other word. 2This observation motivates various classor similaritybased approaches to combating sparseness, and this remains a promising avenue of work, but success in this area has proven somewhat elusive, and, at any rate, current lexicalized PCFGs do simply use exact word matches if available, and interpola te with syntactic category-based estimates when they are not. 3In this paper we use the term subcategorizationin the original general sense of Chomsky (1965), for where a syntactic ca tcategories appearing in the Penn treebank. Charniak (2000) shows the value his parser gains from parentannotation of nodes, suggesting that this information is at least partly complementary to information derivable from lexicalization, and Collins (1999) uses a range of linguistically motivated and carefully hand-engineered subcategorizations to break down wrong context-freedom assumptions of the naive Penn treebank covering PCFG, such as differentiating “baseNPs” from noun phrases with phrasal modifiers, and distinguishing sentences with empty subjects from those where there is an overt subject NP. While he gives incomplete experimental results as to their efficacy, we can assume that these features were incorporated because of beneficial effects on parsing that were complementary to lexicalization. In this paper, we show that the parsing performance that can be achieved by an unlexicalized PCFG is far higher than has previously been demonstrated, and is, indeed, much higher than community wisdom has thought possible. We describe several simple, linguistically motivated annotations which do much to close the gap between a vanilla PCFG and state-of-the-art lexicalized models. Specifically, we construct anunlexicalizedPCFG which outperforms the lexicalized PCFGs of Magerman (1995) and Collins (1996) (though not more recent models, such as Charniak (1997) or Collins (1999)). One benefit of this result is a much-strengthened lower bound on the capacity of an unlexicalized PCFG. To the extent that no such strong baseline has been provided, the community has tended to greatly overestimate the beneficial effect of lexicalization in probabilistic parsing, rather than looking critically at where lexicalized probabilities are both neededto make the right decision and availablein the training data. Secondly, this result affirms the value of linguistic analysis for feature discovery. The result has other uses and advantages: an unlexicalized PCFGis easier to interpret, reason about, and improve than the more complex lexicalized models. The grammar representation is much more compact, no longer requiring large structures that store lexicalized probabilities. The parsing algorithms have lower asymptotic complexity4 and have much smaller grammar egory is divided into several subcategories, for example di viding verb phrases into finite and non-finite verb phrases, rath e than in the modern restricted usage where the term refers onl y to the syntactic argument frames of predicators. 4O(n3) vs. O(n5) for a naive implementation, or vs. O(n4) if using the clever approach of Eisner and Satta (1999). constants. An unlexicalizedPCFG parser is much simpler to build and optimize, including both standard code optimization techniques and the investigation of methods for search space pruning (Caraballo and Charniak, 1998; Charniak et al., 1998). It is not our goal to argue against the use of lexicalized probabilities in high-performance probabilistic parsing. It has been comprehensively demonstrated that lexical dependencies are useful in resolving major classes of sentence ambiguities, and a parser should make use of such information where possible. We focus here on using unlexicalized, tructural context because we feel that this information has been underexploited and underappreciated. We see this investigation as only one part of the foundation for state-of-the-art parsing which employsboth lexical and structural conditioning. 1 Experimental Setup To facilitate comparison with previous work, we trained our models on sections 2–21 of the WSJsection of the Penn treebank. We used the first 20 files (393 sentences) of section 22 as a development set (devset ). This set is small enough that there is noticeable variance in individual results, but it allowed rapid search for good features via continually reparsing the devset in a partially manual hill-climb. All of section 23 was used as a test set for the final model. For each model, input trees were annotated or transformed in some way, as in Johnson (1998). Given a set of transformed trees, we viewed the local trees as grammar rewrite rules in the standard way, and used (unsmoothed) maximum-likelihood estimates for rule probabilities. 5 To parse the grammar, we used a simple array-based Java implementation of a generalizedCKY parser, which, for our final best model, was able to exhaustively parse all sentences in section 23 in 1GB of memory, taking approximately 3 sec for average length sentences. 6 5The tagging probabilities were smoothed to accommodate unknown words. The quantityP(tag|word) was estimated as follows: words were split into one of several categories wordclass, based on capitalization, suffix, digit, and other character features. For each of these categories, we took th e maximum-likelihood estimate of P(tag|wordclass). This distribution was used as a prior against which observed tagging s, if any, were taken, givingP(tag|word) = [c(tag, word) + κ P(tag|wordclass)]/[c(word)+κ]. This was then inverted to give P(word|tag). The quality of this tagging model impacts all numbers; for example the raw treebank grammar’s devset F 1 is 72.62 with it and 72.09 without it. 6The parser is available for download as open source at: http://nlp.stanford.edu/downloads/lex-parser.shtml

TUNABLE AND SWITCHABLE BANDPASS FILTERS USING SLOT-LINE RESONATORS <=SEP=> In this paper, novel uniplanar tunable and switchable bandpass filters are designed by using the centrally-loaded slot-line resonator. From the voltage-wave distribution along the resonator, the appropriate location for the loading element is determined to be the center of the slot-line resonator, where the voltages of the fundamental signal and second harmonic are maximum and zero, respectively. As a result, the fundamental frequency can be tuned while the second harmonic remains almost unchanged. For the first time, the properties of the centrally-loaded slot-line resonator are analyzed by using the evenand odd-mode method, and their respective resonant frequencies are derived. The demonstrated tunable bandpass filter can give a 30.9% frequency tuning range with acceptable insertion loss when a varactor is used as the loading element. By replacing the loading varactors with PIN diodes, a switchable bandpass filter is realized in which the attenuation in the fundamental passband can be controlled. In experiments, the switchable bandpass filter exhibits a 2.13 dB insertion loss in the fundamental passband when the PIN diodes are off and more than 49 dB isolation across the passband when the PIN diodes are on. Received 8 October 2010, Accepted 5 November 2010, Scheduled 27 November 2010 Corresponding author: Zhi-Hua Bao (bao.zh@ntu.edu.cn).

ViziQuer: A Tool to Explore and Query SPARQL Endpoints <=SEP=> The presented tool uses a novel approach to explore and query a SPARQL endpoint. The tool is simple to use as a user needs only to enter an address of a SPARQL endpoint of one’s interest. The tool will extract and visualize graphically the data schema of the endpoint. The user will be able to overview the data schema and use it to construct a SPARQL query according to the data schema. The tool can be downloaded from http://viziquer.lumii.lv. There is also additional information and help on how to use it in practice.

Knowledge Discovery in Manufacturing Simulations <=SEP=> Discrete event simulation studies in a manufacturing context are a powerful instrument when modeling and evaluating processes of various industries. Usually simulation experts conduct simulation experiments for a predetermined system specification by manually varying parameters through educated assumptions and according to a prior defined goal. Moreover, simulation experts try to reduce complexity and number of simulation runs by excluding parameters that they consider as not influential regarding the simulation project scope. On the other hand, today's world of big data technology enables us to handle huge amounts of data. We therefore investigate the potential benefits of designing large scale experiments with a much broader coverage of possible system behavior. In this paper, we propose an approach for applying data mining methods on simulation data in combination with suitable visualization methods in order to uncover relationships in model behavior to discover knowledge that otherwise would have remained hidden. For a prototypical demonstration we used a clustering algorithm to divide large amounts of simulation output datasets into groups of similar performance values and depict those groups through visualizations to conduct a visual investigation process of the simulation data.

Toward Delay-Efficient Game-Aware Data Centers for Cloud Gaming <=SEP=> Gaming on demand is an emerging service that has recently started to garner prominence in the gaming industry. Cloud-based video games provide affordable, flexible, and high-performance solutions for end-users with constrained computing resources and enables them to play high-end graphic games on low-end thin clients. Despite its advantages, cloud gaming's Quality of Experience (QoE) suffers from high and varying end-to-end delay. Since the significant part of computational processing, including game rendering and video compression, is performed in data centers, controlling the transfer of information within the cloud has an important impact on the quality of cloud gaming services. In this article, a novel method for minimizing the end-to-end latency within a cloud gaming data center is proposed. We formulate an optimization problem for reducing delay, and propose a Lagrangian Relaxation (LR) time-efficient heuristic algorithm as a practical solution. Simulation results indicate that the heuristic method can provide close-to-optimal solutions. Also, the proposed model reduces end-to-end delay and delay variation by almost 11% and 13.5%, respectively, and outperforms the existing server-centric and network-centric models. As a byproduct, our proposed method also achieves better fairness among multiple competing players by almost 45%, on average, in comparison with existing methods.

The Application of Non-parametric Method and Time Series Analysis to Predict the Machine Repair Times <=SEP=> This study focuses on the utilization of non-parametric to assess the distribution of repair times of a machine part as well as the prediction of the future values. There are two folds of objectives, namely, the distribution assessment of the repair times. The diagnostic graph, i.e., histogram and normal probability plot, as well as a non-parametric test, Kolmogorov-Smirnov (KS) method, is utilized to assess the distribution of data. According to the KS test, it can be used effectively to test the distribution of the repair times of a machine which are selected as a case study. Another objective is the prediction of the future repair time required to fix a designated part. The time series analysis based on autoregressive integrated moving average (ARIMA) model is deployed in order to forecast the repair times. It turned out that one of the simplest models of ARIMA, ARIMA (0, 1, 0) or random walk, is the most appropriate model for the prediction and this indicates that the pattern of repair times is non-stationary.

Towards an automated system for intelligent screening of candidates for recruitment using ontology mapping (EXPERT) <=SEP=> Many e-recruitment tools for recruiting candidates for jobs have significantly spread in recent years. Companies often receive resumes from candidates using e-recruitment tools or via job portal for each job posting and manually short list qualified applicants. The existing e-recruitment tools have been mainly used for the storage of applicant contact data. In this paper, we present EXPERT, an intelligent tool for screening candidates for recruitment using ontology mapping. EXPERT has three phases in screening candidates for recruitment. In first phase, the system collects candidates’ resumes and constructs ontology document for the features of the candidates. Job openings/job requirements are represented as ontology in the second phase and in third phase, EXPERT maps the job requirement ontology onto the candidate ontology document and retrieves the eligible candidates. Experiment results show that this model improves the accuracy of matching candidates with job requirement.

Social Internet of Things: The Potential of the Internet of Things for Defining Human Behaviours <=SEP=> The potential of the artificial intelligence, data mining and the so-called Big Data to build solutions based on the understanding of human behaviours is being extended with the capabilities of the Future Internet and the Internet of Things (IoT). The described potential of data and networks have been analyzed by the complex network theory, offering a specialized branch, the defined as Human Dynamics. The goal with the Internet of Things in the social area is to describe in real-time the human behaviours and activities. These goals are starting to be feasible through the quantity of data provided by the personal devices such as smart phone, and the smart environments such as Smart cities that makes more intelligent the environment and offer a smart space that sense our movements, actions, and the evolution of the ecosystem. This work analyses the ecosystem defined through the triangle formed by Big Data, Smart Cities and Personal/Wearable Computing to determinate human behaviours and human dynamics. For this purpose, the life-cycle of human dynamics determinations in Smart Cities have been analysed in order to determinate the current status of the technology, challenges, and opportunities.

The State of Solving Large Incomplete-Information Games, and Application to Poker <=SEP=> ion algorithms take as input a description of the game and output a smaller but strategically similar — or even equivalent — game. The abstraction algorithms discussed here work with any finite number of players and do not assume a zerosum game. Information Abstraction The most popular kind of abstraction is information abstraction. The game is abstracted so that the agents cannot distinguish some of the states that they can distinguish in the actual game. For example in an abstracted poker hand, an agent is not able to observe all the nuances of the cards that she would normally observe. Lossless Information Abstraction. It turns out that it is possible to do lossless information abstraction, which may seem like an oxymoron at first. The method I will describe (Gilpin and Sandholm 2007b) is for a class of games that we call games with ordered signals. It is structured, but still general enough to capture a wide range of strategic situations. A game with ordered signals consists of a finite number of rounds. Within a round, the players play a game on a directed tree (the tree can be different in different rounds). The only uncertainty players face stems from private signals the other players have received and from the unknown future signals. In other words, players observe each Articles 14 AI MAGAZINE Nash equilibrium Nash equilibrium Original game

A case study on propagating and updating provenance information using the CIDOC CRM <=SEP=> Provenance information of digital objects maintained by digital libraries and archives is crucial for authenticity assessment, reproducibility and accountability. Such information is commonly stored on metadata placed in various Metadata Repositories (MRs) or Knowledge Bases (KBs). Nevertheless, in various settings it is prohibitive to store the provenance of each digital object due to the high storage space requirements that are needed for having complete provenance. In this paper, we introduce provenance-based inference rules as a means to complete the provenance information, to reduce the amount of provenance information that has to be stored, and to ease quality control (e.g., corrections). Roughly, we show how provenance information can be propagated by identifying a number of basic inference rules over a core conceptual model for representing provenance. The propagation of provenance concerns fundamental modelling concepts such as actors, activities, events, devices and information objects, and their associations. However, since a MR/KB is not static but changes over time due to several factors, the question that arises is how we can satisfy update requests while still supporting the aforementioned inference rules. Towards this end, we elaborate on the specification of the required add/delete operations, consider two different semantics for deletion of information, and provide the corresponding update algorithms. Finally, we report extensive comparative results for different repository policies regarding the derivation of new knowledge, in datasets containing up to one million RDF triples. The results allow us to understand the tradeoffs related to the use of inference rules on storage space and performance of queries and updates.

Development of a Whole-Sensitive Teleoperated Robot Arm using Torque Sensing Technique <=SEP=> In this paper, we concentrate on the design of a new whole-sensitive robot arm enabling torque measurement in each joint by means of developed optical torque sensors. When the contact of arm with an object occurs, local impedance algorithm provides active compliance of corresponding robot arm joint. Thus, the whole structure of the manipulator can safely interact with unstructured environment. The detailed design procedure of the 4-DOF robot arm and optical torque sensors is described in the paper. The gravity compensation algorithm was elaborated and verified by implementation of the local impedance control of a simple two-link arm manipulator

The kNN-TD Reinforcement Learning Algorithm <=SEP=> A reinforcement learning algorithm called kNN-TD is introduced. This algorithm has been developed using the classical formulation of temporal difference methods and a k-nearest neighbors scheme as its expectations memory. By means of this kind of memory the algorithm is able to generalize properly over continuous state spaces and also take benefits from collective action selection and learning processes. Furthermore, with the addition of probability traces, we obtain the kNN-TD(λ) algorithm which exhibits a state of the art performance. Finally the proposed algorithm has been tested on a series of well known reinforcement learning problems and also at the Second Annual RL Competition with excellent results.

The Evolution and Future of National Customer Satisfaction Index Models <=SEP=> A number of both national and international customer satisfaction barometers or indices have been introduced in the last decade. For the most part, these satisfaction indices are embedded within a system of cause and effect relationships or satisfaction model. Yet there has been little in the way of model development. Of critical importance to the validity and reliability of such indices is that the models and methods used to measure customer satisfaction and related constructs continue to learn, adapt, and improve over time. The primary goal of this research is to propose and test a number of modifications and improvements to the national index models. Using survey data from the Norwegian Customer Satisfaction Barometer (NCSB), we find general support for the proposed modifications.

Virtual Reality Exposure Therapy for Anxiety and Specific Phobias <=SEP=> There is a growing body of research indicating the multiple ways that affective dysregulation (e.g., anxiety disorders, specific phobias, panic disorder, and posttraumatic stress disorder (PTSD)) may lead to significant impairments in normal life functioning. Anxiety and fear are concentrated emotional experiences that serve critical functions in organizing necessary survival responses (Fendt & Fanselow, 1999). In properly functioning affective systems, the responses are adaptive. LeDoux (2012a) posits survival circuits that enable humans to adapt to feared stimuli by organizing brain functions. The fear induced arousal and activation of survival circuits allows for adaptive responses to take priority and other responses are inhibited. Further, attentional processing focuses on pertinent environmental stimuli and learning occurs (Scherer, 2000; LeDoux, 2012b). Hence, adaptive survival circuits are optimized to detect threatening stimuli and relay the information as environmental challenges and opportunities. The adaptive survival circuits use this information to adjust behavioral and psychophysiological responses for appropriate adaptation and resolution. Excessive fear responses, however, can be restrictive and may be a sign of dysregulated anxiety. When exposure to stress occurs early in development and is repeated in persons with a particular genetic disposition, a decreased threshold for developing anxiety may result (Heim & Nemeroff, 1999). Further, over-excitation and deprivation can influence the affective system and may induce changes in the emotional circuitry of the brain that can contribute to stress-related psychopathology (Davidson, Jackson, & Kalin, 2000). A good deal of research has shown that exposure therapy is effective for reducing negative affective symptoms (Rothbaum & Schwartz, 2002). In vivo exposure therapy has been found to have greater efficacy when compared to imaginal exposure, especially in the treatment of specific phobias (Emmelkamp, 2003). Exposure to emotional situations and prolonged rehearsal result in the regular activation of cerebral metabolism in brain areas associated with inhibition of maladaptive associative processes (Schwartz, 1998). Identical neural circuits have been found to be involved in affective regulation across affective disorders (De Raedt, 2006; Mineka, Watson, & Clark, 1998). Systematic and controlled therapeutic exposure to phobic stimuli may enhance emotional regulation through adjustments of inhibitory processes on the amygdala by the medial prefrontal cortex during exposure and structural changes in the hippocampus after successful therapy (Hariri, Bookheimer, & Mazziotta, 2000). A novel tool for conducting exposure therapy is virtual reality exposure therapy (VRET), in which users are immersed within a computer-generated simulation or virtual environment (VE) that updates in a natural way to the user’s psychophysiological arousal, head and/ or body motion (Parsons and Courtney, 2011, Parsons and Reinebold, 2012). Virtual environment applications that focus on treatment of affective (see Powers & Emmelkamp, 2008; Parsons et al., 2008a; Opris et al., 2012) and cognitive disorders (see Rose et al., 2005; Parsons 2009a) as well as assessment of component cognitive processes are now being developed and tested: attention (Parsons, et al., 2007, 2011) spatial abilities (Beck et al., 2010; Goodrich-Hunsaker and Hopkins, 2010; Parsons, et al., 2013), memory (Moffat, 2009; Parsons & Rizzo, 2008b; Parsons et al., 2013; Knight & Titov, 2009), spatial memory (Parsons et al., 2013); and executive functions (Armstrong et al., 2013; Henry et al., 2012; Parsons et al., 2012, 2013, 2014). The increased ecological validity of virtual scenarios may aid differential diagnosis and treatment planning. Within a virtual world, it is possible to systematically present cognitive tasks targeting neuropsychological performance beyond what are currently available using traditional methods (Parsons, 2011, 2012). Thomas D. Parsons University of North Texas, USA

BYOD security challenges: control and protect your most sensitive data <=SEP=> According to a recent survey by Harris Interactive and ESET, more than 80% of employed adults use some kind of personally owned electronic device for work-related functions.1 One of the primary advantages in allowing employees to bring and use their personal devices for work is that it increases efficiency and flexibility. By giving access to the corporate network, and therefore corporate information via unmanaged devices, work can be done from anywhere, at any time, using any endpoint. It also provides cost savings to organisations, since personal device usage means they don’t need to provide or manage mobile devices for their employees. The Harris survey also determined that 47% of employees use personal desktop computers to access or store company information, while 41% do this with personal laptops, 24% with smartphones and 10% with tablets. However, with less than half of these devices being protected by basic security measures, some organisations may start to worry that the security challenges associated with BYOD far outweigh the benefits.

Enabling the IoT Paradigm in E-health Solutions through the VIRTUS Middleware <=SEP=> In Europe, in a context of growing population and decreasing resources, ageing related diseases represent one of the most relevant challenges in terms of healthcare organization complexity, plus care levels and system financing balancing. Nowadays there are several researches that are studying the application of the IoT (Internet of Things) paradigm in the e-health field. This paper explains how a solution, built on the top of the VIRTUS IoT middleware, provides a valid alternative to current IoT solutions, which are mainly based on SOA (Service Oriented Architecture). VIRTUS leverage an Instant Messaging protocol (XMPP) to guarantee a (near) real-time, secure and reliable communication channel among heterogeneous devices. The presented development has been exploited in a healthcare case study: an implementation of a cost-savvy remote body movement monitoring system, aimed at classify daily patients' activities, designed as a modular architecture and deployed in a large scale scenario. The paper analyzes the features offered by the VIRTUS middleware, if used within a e-health solution, providing a comparison with other well-known systems.

Feasibility Study on the Use of Biometrics in an Entitlement Scheme <=SEP=> This report examines the feasibility of using biometrics as a means of establishing a unique identity, to support the proposed entitlement scheme under development by the United Kingdom Passport Service (UKPS) and Driver and Vehicle Licensing Agency (DVLA). Biometrics identification systems measure physiological and behavioural characteristics of a person, and use these measurements to reliably distinguish one person from another. The main examples considered in this study are fingerprint, iris and face image recognition. Biometric identification can assist in the issue of entitlement cards, passports, driving licences and other identity documents in two ways. On the initial issue of such documents, biometrics can be used to check that applicants are not erroneously issued documents using two different identity details; this is the main focus of this study. Secondly, when an entitlement card, passport or driving licence is being used, biometrics can help confirm that it is being used by the correct person. The purpose of the study is to assess the feasibility of fingerprint, iris and face recognition technologies for these applications, to identify unknowns and the risks associated with the use of biometrics in such a national identity scheme, and to make recommendations for how some of these risks might be addressed should such a scheme proceed. Biometric methods do not offer 100% certainty of authentication of individuals. The success of any deployment of a system using biometric methods depends, therefore, on many factors such as: the degree of the 'uniqueness' of the biometric measure, technical and social factors, user interfaces, and provision of secure backup systems for those situations and individual s where the biometric will not work effectively. The main findings of the study are: a In principle, fingerprint or iris recognition can provide the identification performance required for unique identification over the entire UK adult population. In the case of fingerprint recognition, the system would require the enrolment of at least four fingers, whereas for iris recognition both irises should be registered. However, the practicalities of deploying either iris or fingerprint recognition in such a scheme are far from straightforward. b Such a system would be a groundbreaking deployment for this kind of biometric application. Not only would it be one of the largest deployments to date, but aspects of its performance would be far more demanding than those of similarly sized systems; such existing systems are either not applied in the civil sector, or operate …

Model predictive hovering-translation control of an unmanned Tri-TiltRotor <=SEP=> The experimental translational hovering control of a Tri-TiltRotor Unmanned Aerial Vehicle is the subject of this paper. This novel UAV is developed to possess the capability to perform autonomous conversion between the Vertical Take-Off and Landing, and the Fixed-Wing flight modes. Via this design's implemented features however, the capability for additional control authority on the UAV's longitudinal translational motion arises: The rotor-tilting servos are utilized in performing thrust vectoring of the main rotors, thus exploiting their fast response characteristics in directly providing translation-controlling forces. The system's hovering translation is handled by a Model Predictive Control scheme, following the aforementioned actuation principles. While performing experimental studies of the overall controlled system's efficiency, the advantageous effects of this novel control authority are clearly noted. Additionally, in this article the considerations and requirements for operational autonomy-related on-board-only state estimation are addressed.

Regularized Off-Policy TD-Learning <=SEP=> We present a novel l1 regularized off-policy convergent TD-learning method (termed RO-TD), which is able to learn sparse representations of value functions with low computational complexity. The algorithmic framework underlying ROTD integrates two key ideas: off-policy convergent gradient TD methods, such as TDC, and a convex-concave saddle-point formulation of non-smooth convex optimization, which enables first-order solvers and feature selection using online convex regularization. A detailed theoretical and experimental analysis of RO-TD is presented. A variety of experiments are presented to illustrate the off-policy convergence, sparse feature selection capability and low computational cost of the RO-TD algorithm.

Understanding neurocognitive developmental disorders can improve education for all. <=SEP=> Specific learning disabilities (SLDs) are estimated to affect up to 10% of the population, and they co-occur far more often than would be expected, given their prevalences. We need to understand the complex etiology of SLDs and their co-occurrences in order to underpin the training of teachers, school psychologists, and clinicians, so that they can reliably recognize SLDs and optimize the learning contexts for individual learners.

A Fast String Searching Algorithm <=SEP=> An algorithm is presented that searches for the location, “<italic>i</italic>l” of the first occurrence of a character string, “<italic>pat</italic>,” in another string, “<italic>string</italic>.” During the search operation, the characters of <italic>pat</italic> are matched starting with the last character of <italic>pat</italic>. The information gained by starting the match at the end of the pattern often allows the algorithm to proceed in large jumps through the text being searched. Thus the algorithm has the unusual property that, in most cases, not all of the first <italic>i</italic> characters of <italic>string</italic> are inspected. The number of characters actually inspected (on the average) decreases as a function of the length of <italic>pat</italic>. For a random English pattern of length 5, the algorithm will typically inspect <italic>i</italic>/4 characters of <italic>string</italic> before finding a match at <italic>i</italic>. Furthermore, the algorithm has been implemented so that (on the average) fewer than <italic>i</italic> + <italic>patlen</italic> machine instructions are executed. These conclusions are supported with empirical evidence and a theoretical analysis of the average behavior of the algorithm. The worst case behavior of the algorithm is linear in <italic>i</italic> + <italic>patlen</italic>, assuming the availability of array space for tables linear in <italic>patlen</italic> plus the size of the alphabet.
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An efficient k-means clustering algorithm <=SEP=> In this paper, we present a novel algorithm for performing k-means clustering. It organizes all the patterns in a k-d tree structure such that one can find all the patterns which are closest to a given prototype efficiently. The main intuition behind our approach is as follows. All the prototypes are potential candidates for the closest prototype at the root level. However, for the children of the root node, we may be able to prune the candidate set by using simple geometrical constraints. This approach can be applied recursively until the size of the candidate set is one for each node. Our experimental results demonstrate that our scheme can improve the computational speed of the direct k-means algorithm by an order to two orders of magnitude in the total number of distance calculations and the overall time of computation.

Miniaturized Wilkinson power dividers utilizing capacitive loading <=SEP=> The authors report the miniaturization of a planar Wilkinson power divider by capacitive loading of the quarter wave transmission lines employed in conventional Wilkinson power dividers. Reduction of the transmission line segments from /spl lambda//4 to between /spl lambda//5 and /spl lambda//12 are reported here. The input and output lines at the three ports and the lines comprising the divider itself are coplanar waveguide (CPW) and asymmetric coplanar stripline (ACPS), respectively. The 10 GHz power dividers are fabricated on high resistivity silicon (HRS) and alumina wafers. These miniaturized dividers are 74% smaller than conventional Wilkinson power dividers, and have a return loss better than +30 dB and an insertion loss less than 0.55 dB. Design equations and a discussion about the effect of parasitic reactance on the isolation are presented for the first time.

A Randomized Singular Value Decomposition Algorithm for Image Processing Applications <=SEP=> The main contribution of this paper is to demonstrate that a new randomized SVD algorithm, proposed by Drineas et. al. in [4], is not only of theoretical interest but also a viable and fast alternative to traditional SVD algorithms in applications (e.g. image processing). This algorithm samples a constant number of rows (or columns) of the matrix, scales them appropriately to form a small matrix, say S, and then computes the SVD of S (which is a good approximation to the SVD of the original matrix). We experimentally evaluate the accuracy and speed of this algorithm for image matrices, using various probability distributions to perform the sampling.

SoftGUESS: Visualization and Exploration of Code Clones in Context <=SEP=> We introduce SoftGUESS, a code clone exploration system. SoftGUESS is built on the more general GUESS system which provides users with a mechanism to interactively explore graph structures both through direct manipulation as well as a domain-specific language. We demonstrate SoftGUESS through a number of mini-applications to analyze evolutionary code-clone behavior in software systems. The mini-applications of SoftGUESS represent a novel way of looking at code-clones in the context of many system features. It is our hope that SoftGUESS will form the basis for other analysis tools in the software-engineering domain.

Design and experimental evaluation of the hydraulically actuated prototype leg of the HyQ robot <=SEP=> This paper focuses on the design and experimental evaluation of a hydraulically actuated robot leg. The evaluation of the leg prototype is an important milestone in the development of HyQ, a Hydraulically actuated Quadruped robot. The prototype features two rotary joints actuated by hydraulic cylinders and has a mass of 4.5kg. We performed several experiments with the leg prototype attached to a vertical slider to tests the robustness of the mechanical design and the hydraulic actuation system. Besides the experimental evaluation of the hydraulic components, we also extensively studied the sensor data of the leg during periodic hopping. The results show that hydraulic actuation is suitable for legged robots because of its high power-to-weight ratio, fast response and ability to cope with high impact force peaks. Furthermore, we compare the cylinder force data obtained by the load cell with the calculated value based on the cylinder pressures to analyze if it is possible to eliminate this sensory system redundancy in the future. Through these studies, weaknesses of the design were identified and suggestions on how to improve them are presented.