Query: The Ontology Extraction & Maintenance Framework Text-To-Onto

Relevant passages:
From Manual to Semi-Automatic Semantic Annotation: About Ontology-Based Text Annotation Tools <=SEP=> Semantic Annotation is a basic technology for intelligent content and is bene cial in a wide range of content-oriented intelligent applications, esp. in the area of the Semantic Web. In this paper we present our work in ontology-based semantic annotation, which is embedded in a scenario of a knowledge portal application. Starting with seemingly good and bad manual semantic annotation, we describe our experiences made within the KAinitiative. The experiences gave us the starting point for developing an ergonomic and knowledge base-supported annotation tool. Furthermore, the annotation tool described are currently extended with mechanisms for semi-automatic information-extraction based annotation. Supporting the evolving nature of semantic content we additionally describe our idea of evolving ontologies supporting semantic annotation. This paper has been presented at the COLING-2000 Workshop on Semantic Annotation and Intelligent Content, Centre Universitaire, Luxembourg, 5.-6. August, 2000.

Automatic Acquisition of Hyponyms from Large Text Corpora <=SEP=> We describe a method for the automatic acquisition of the hyponymy lexical relation from unrestricted text. Two goals motivate the approach: (i) avoidance of the need for pre-encoded knowledge and (ii) applicability across a wide range of text. We identify a set of lexicosyntactic patterns that are easily recognizable, that occur frequently and across text genre boundaries, and that indisputably indicate the lexical relation of interest. We describe a method for discovering these patterns and suggest that other lexical relations will also be acquirable in this way. A subset of the acquisition algorithm is implemented and the results are used to augment and critique the structure of a large hand-built thesaurus. Extensions and applications to areas such as information retrieval are suggested.

Towards Text Knowledge Engineering <=SEP=> We introduce a methodology for automating the maintenance of domain-specific taxonomies based on natural language text understanding. A given ontology is incrementally updated as new concepts are acquired from real-world texts. The acquisition process is centered around the linguistic and conceptual “quality” of various forms of evidence underlying the generation and refinement of concept hypotheses. On the basis of the quality of evidence, concept hypotheses are ranked according to credibility and the most credible ones are selected for assimilation into the domain knowledge base.

Introduction to WordNet: An On-line Lexical Database <=SEP=> Standard alphabetical procedures for organizing lexical information put together words that are spelled alike and scatter words with similar or related meanings haphazardly through the list. Unfortunately, there is no obvious alternative, no other simple way for lexicographers to keep track of what has been done or for readers to find the word they are looking for. But a frequent objection to this solution is that finding things on an alphabetical list can be tedious and time-consuming. Many people who would like to refer to a dictionary decide not to bother with it because finding the information would interrupt their work and break their train of thought.

WordNet : an electronic lexical database <=SEP=> WordNet is perhaps the most important and widely used lexical resource for natural language processing systems up to now. WordNet: An Electronic Lexical Database, edited by Christiane Fellbaum, discusses the design of WordNet from both theoretical and historical perspectives, provides an up-to-date description of the lexical database, and presents a set of applications of WordNet. The book contains a foreword by George Miller, an introduction by Christiane Fellbaum, seven chapters from the Cognitive Sciences Laboratory of Princeton University, where WordNet was produced, and nine chapters contributed by scientists from elsewhere. Miller's foreword offers a fascinating account of the history of WordNet. He discusses the presuppositions of such a lexical database, how the top-level noun categories were determined, and the sources of the words in WordNet. He also writes about the evolution of WordNet from its original incarnation as a dictionary browser to a broad-coverage lexicon, and the involvement of different people during its various stages of development over a decade. It makes very interesting reading for casual and serious users of WordNet and anyone who is grateful for the existence of WordNet. The book is organized in three parts. Part I is about WordNet itself and consists of four chapters: "Nouns in WordNet" by George Miller, "Modifiers in WordNet" by Katherine Miller, "A semantic network of English verbs" by Christiane Fellbaum, and "Design and implementation of the WordNet lexical database and search software" by Randee Tengi. These chapters are essentially updated versions of four papers from Miller (1990). Compared with the earlier papers, the chapters in this book focus more on the underlying assumptions and rationales behind the design decisions. The description of the information contained in WordNet, however, is not as detailed as in Miller (1990). The main new additions in these chapters include an explanation of sense grouping in George Miller's chapter, a section about adverbs in Katherine Miller's chapter, observations about autohyponymy (one sense of a word being a hyponym of another sense of the same word) and autoantonymy (one sense of a word being an antonym of another sense of the same word) in Fellbaum's chapter, and Tengi's description of the Grinder, a program that converts the files the lexicographers work with to searchable lexical databases. The three papers in Part II are characterized as "extensions, enhancements and

Inferring Query Performance Using Pre-retrieval Predictors <=SEP=> The prediction of query performance is an interesting and important issue in Information Retrieval (IR). Current predictors involve the use of relevance scores, which are time-consuming to compute. Therefore, current predictors are not very suitable for practical applications. In this paper, we study a set of predictors of query performance, which can be generated prior to the retrieval process. The linear and non-parametric correlations of the predictors with query performance are thoroughly assessed on the TREC disk4 and disk5 (minus CR) collections. According to the results, some of the proposed predictors have significant correlation with query performance, showing that these predictors can be useful to infer query performance in practical applications.

Exploring Students' Computational Thinking Skills in Modeling and Simulation Projects: a Pilot Study <=SEP=> Computational Thinking (CT) is gaining a lot of attention in education. We explored how to discern the occurrences of CT in the projects of 12th grade high school students in the computer science (CS) course. Within the projects, they constructed models and ran simulations of phenomena from other (STEM) disciplines. We examined which CT aspects occurred in students' activities and how to assess students' CT accomplishments. For this purpose we employed a framework based on CT characterizations by Wing [14, 15], CSTA [4] and Comer et al. [3]. We analyzed students' project documentation, survey results and interviews with individual students. The findings indicate that this framework is suitable for detection of occurrences of CT aspects in students' data. Moreover, our preliminary results suggest that the framework is useful in assessment of the quality of the students' CT performance.

Influence and passivity in social media <=SEP=> The ever-increasing amount of information flowing through Social Media forces the members of these networks to compete for attention and influence by relying on other people to spread their message. A large study of information propagation within Twitter reveals that the majority of users act as passive information consumers and do not forward the content to the network. Therefore, in order for individuals to become influential they must not only obtain attention and thus be popular, but also overcome user passivity. We propose an algorithm that determines the influence and passivity of users based on their information forwarding activity. An evaluation performed with a 2.5 million user dataset shows that our influence measure is a good predictor of URL clicks, outperforming several other measures that do not explicitly take user passivity into account. We demonstrate that high popularity does not necessarily imply high influence and vice-versa.

Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks <=SEP=> A major challenge in brain tumor treatment planning and quantit ative evaluation is determination of the tumor extent. The noninva s ve magnetic resonance imaging (MRI) technique has emerged as a front-line diag nostic tool for brain tumors without ionizing radiation. Manual segmentation of brain t umor extent from 3D MRI volumes is a very time-consuming task and the performance is highly relied on operator’s experience. In this context, a reliable fully automatic segmentation method for the brain tumor segmentation is necessary for an efficient measurement of the tumor extent. In this study, w e propose a fully automatic method for brain tumor segmentation, which is develo ped using U-Net based deep convolutional networks. Our method was evalu ated on Multimodal Brain Tumor Image Segmentation (BRATS 2015) datasets, which contain 220 high-grade brain tumor and 54 low-grade tumor cases. Cro svalidation has shown that our method can obtain promising segmentation effi-

COMPENDIUM : a text summarisation tool for generating summaries of multiple purposes , domains , and genres <=SEP=> In this paper, we present a Text Summarisation tool, compendium, capable of generating the most common types of summaries. Regarding the input, singleand multi-document summaries can be produced; as the output, the summaries can be extractive or abstractiveoriented; and finally, concerning their purpose, the summaries can be generic, query-focused, or sentiment-based. The proposed architecture for compendium is divided in various stages, making a distinction between core and additional stages. The former constitute the backbone of the tool and are common for the generation of any type of summary, whereas the latter are used for enhancing the capabilities of the tool. The main contributions of compendium with respect to the state-of-the-art summarisation systems are that (i) it specifically deals with the problem of redundancy, by means of textual entailment; (ii) it combines statistical and cognitive-based techniques for determining relevant content; and (iii) it proposes an abstractive-oriented approach for facing the challenge of abstractive summarisation. The evaluation performed in different domains and textual genres, comprising traditional texts, as well as texts extracted from the Web 2.0, shows that compendium is very competitive and appropriate to be used as a tool for generating summaries.

A low noise readout circuit for integrated electrochemical biosensor arrays <=SEP=> This paper presents a low noise electrochemical interface circuit that is tuned to the needs of protein-based biosensor arrays and compatible with the formation of fully integrated biochemical microsystems. The circuit includes an integrated potentiostat and highly sensitive amperometric readout amplifier. It achieves good noise performance while supporting biosensor output currents from 10 pA to 10 /spl mu/A to suit a wide range of sensitivities and electrode areas. Implemented in 0.5 /spl mu/m CMOS process, it is compatible with a post-CMOS process to realize an integrated biosensor array.

A meta-analytic review of gender differences in perceptions of sexual harassment. <=SEP=> Research on gender differences in perceptions of sexual harassment informs an ongoing legal debate regarding the use of a reasonable person standard instead of a reasonable woman standard to evaluate sexual harassment claims. The authors report a meta-analysis of 62 studies of gender differences in harassment perceptions. An earlier quantitative review combined all types of social-sexual behaviors for a single meta-analysis; the purpose of this study was to investigate whether the magnitude of the female-male difference varies by type of behavior. An overall standardized mean difference of 0.30 was found, suggesting that women perceive a broader range of social-sexual behaviors as harassing. However, the meta-analysis also found that the female-male difference was larger for behaviors that involve hostile work environment harassment, derogatory attitudes toward women, dating pressure, or physical sexual contact than sexual propositions or sexual coercion.

Deep Learning and Data Labeling for Medical Applications <=SEP=> This study addresses the recognition problem of the HEp-2 cell using indirect immunofluorescent (IIF) image analysis, which can facilitate the diagnosis of many autoimmune diseases by finding antibodies in the patient serum. Recently, a lot of automatic HEp-2 cell classification strategies including both shallow and deep methods have been developed, wherein the deep Convolutional Neural Networks (CNNs) have been proven to achieve impressive performance. However, the deep CNNs in general requires a fixed size of image as the input. In order to conquer the limitation of the fixed size problem, a spatial pyramid pooling (SPP) strategy has been proposed in general object recognition and detection. The SPP-net usually exploit max pooling strategies for aggregating all activated status of a specific neuron in a predefined spatial region by only taking the maximum activation, which achieved superior performance compared with mean pooling strategy in the traditional state-of-the-art coding methods such as sparse coding, linear locality-constrained coding and so on. However, the max pooling strategy in SPP-net only retains the strongest activated pattern, and would completely ignore the frequency: an important signature for identifying different types of images, of the activated patterns. Therefore, this study explores a generalized spatial pooling strategy, called K-support spatial pooling, in deep CNNs by integrating not only the maximum activated magnitude but also the response magnitude of the relatively activated patterns of a specific neuron together. This proposed K-support spatial pooling strategy in deep CNNs combines the popularly applied mean and max pooling methods, and then avoid awfully emphasizing of the maximum activation but preferring a group of activations in a supported region. The deep CNNs with the proposed K-support spatial pooling is applied for HEp-2 cell classification, and achieve promising performance compared with the state-of-the-art approaches.

Participatory Visualization with Wordle <=SEP=> We discuss the design and usage of ldquoWordle,rdquo a Web-based tool for visualizing text. Wordle creates tag-cloud-like displays that give careful attention to typography, color, and composition. We describe the algorithms used to balance various aesthetic criteria and create the distinctive Wordle layouts. We then present the results of a study of Wordle usage, based both on spontaneous behaviour observed in the wild, and on a large-scale survey of Wordle users. The results suggest that Wordles have become a kind of medium of expression, and that a ldquoparticipatory culturerdquo has arisen around them.

IncludeOS: A Minimal, Resource Efficient Unikernel for Cloud Services <=SEP=> The emergence of cloud computing as a ubiquitous platform for elastically scaling services has generated need and opportunity for new types of operating systems. A service that needs to be both elastic and resource efficient needs A) highly specialized components, and B) to run with minimal resource overhead. Classical general purpose operating systems designed for extensive hardware support are by design far from meeting these requirements. In this paper we present IncludeOS, a single tasking library operating system for cloud services, written from scratch in C++. Key features include: extremely small disk-and memory footprint, efficient asynchronous I/O, OS-library where only what your service needs gets included, and only one device driver by default (virtio). As a test case a bootable disk image consisting of a simple DNS server with OS included is shown to require only 158 kb of disk space and to require 5-20% less CPU-time, depending on hardware, compared to the same binary running on Linux.

Unsupervised Learning without Overfitting: Empirical Risk Approximation as an Induction Principle for Reliable Clustering <=SEP=> Unsupervised learning algorithms are designed to extract structure from data s amples on the basis of a cost function for structures. For a reliable and robust inference proce ss, th unsupervised learning algorithm has to guarantee that the extracted structures ar e typical for the data source. In particular, it has to reject all structures where the infe renc is dominated by the arbitrariness of the sample noise and which, consequently, can be characterize d as overfitting in unsupervised learning. This paper summarizes an inference principle called Empirical Risk Approximationwhich allows us to quantitatively measure the overfitting effect and to derive a criterion as a saveguard against it. The crucial condition for learning is me t if (i) the empirical risk of learning uniformly converges towards the expected risk and if ( ii) the hypothesis class retains a minimal variety for consistent inference. Parameter s l ction of learnable data structures is demonstrated for the case of k-means clustering and Monte Carlo simulations are presented to support the selection principle. 1 PHILOSOPHY OF UNSUPERVISED LEARNING Learning algorithms are designed to extract structure from data. Two class es of algorithms have been widely discussed in the literature – supervised andunsupervised learning. The distinction between the two classes depends on supervision or teacher informati on which is either available to the learning algorithm or missing in the learning process. T hi paper describes a statistical learning theory of unsupervised learning. We summarize an induct ion principle for unsupervised learning which is refered to as Empirical Risk Approximation[1]. This principle is based on the optimization of a quality functional for structures in data and, most i portantly, it contains a safeguard against overfitting of structure to the noise in a sampl e set. The extracted structure of the data is encoded by a loss function and it is assumed to produce a lear ning risk below a predefined risk threshold. This induction principle is summarized by the fol lowing two inference steps: 1. Define a hypothesis class containing loss functions which evaluate candidate structures of the data and which measure their quality. Control the complexity of the hypothesis class by an upper bound on the costs. 2. Select an arbitrary loss function from the smallest subset which still guarantees c onsistent learning (in the sense of Statistical Learning Theory). The reader should note that the learning algorithm has to return a structure with c ostsbounded by a preselected cost threshold, but it is not required to return the structure w ith minimal empirical risk as in Vapnik’s “Empirical Risk Minimization” induction principle for classi fication and regression [2]. All structures in the data with risk smaller than the sel cted risk bound are considered to be equivalent in the approximation process without further distinction. Various cases of unsupervised learning algorithms are discussed in the literature wi h mphasis on the optimization aspect, i.e., data clustering or vector quantization, self-organi zing maps, principal or independent component analysis and principal curves, projection pursuit or algorithms for relational data as multidimensional scaling and pairwise clustering and histogram based clustering methods. How can we select a loss function from a hypothesis class with desired approxim ation quality or, equivalently, find a structure with bounded costs? The nested sequence of hypothesis classe suggests a tracking strategy of solutions which are elements of smaller and sm ller hypothesis classes in that sequence. The sequential sampling can be implemented by soluti on tracking, i.e., admissible solutions are incrementally improved by gradient descent if t he process samples from a subset with smaller risk bound than in the previous sampling step. Candidate procedure for the sampling of structures from the hypothesis class are stochastic search t chniques or continuation methods like simulated or deterministic annealing, although the theory does not refer to any particular learning algorithm. The general framework of Empirical Risk Approximationand its mathematical formulation is presented in Sec. 2, whereas a more detailed discussion can be found in [1]. Thi s theory of unsupervised learning is applied to the case of central clustering in Sec. 3. 2 MATHEMATICAL FRAMEWORK FOR UNSUPERVISED LEARNING The data samples X = xi 2 : IRd; 1 i l which have to be analysed by the unsupervised learning algorithm are elements of a suitable d-dimensional Euclidian space. The data are distributed according to a measure which is assumed to be known for the analysis. A mathematically precise statement of the Empirical Risk Approximationinduction principle requires several definitions which formalize the notion of searching for struct ure in the data. The quality of structures extracted from the data set X is evaluated by a learning cost function R̂( ;X ) = 1l l Xi=1 h(xi; ): (1) R̂( ;X ) denotes theempirical risk of learning a structure for an i.i.d. sample set X . The functionh(x; ) is known asloss function in statistics. It measures the costs for processing a generic datumx and often corresponds to the negative log-likelihood of a stochastic model. For example in vector quantization, the loss function quantifies what the costs are t o ssign a data pointx to a particular prototype or codebook vector. Each value 2 parametrizes an individual loss functions with denoting the parameter set. The parameter characterizes the different structures of the data set which are hypothetically considered candida te structures in the learning process and which have to be validated. Statistical learning t heory distinguishes between different classes of loss functions, i.e., 0 1 functions in classification problems, bounded functions and unbounded non-negative functions in regression problems. This paper is concerned with the class of unbounded non-negative functions since we are particularly interested in polynomially increasing loss function ( O kxkp)) as they occur in vector quantization. Note that the quality measurê R( ;X ) depends on the i.i.d. data set X and, therefore, is itself a random variable. The relevant quality measure for unsupervised learning, howev er, is the expectation value of this random variable, known as the expected risk of learning R( ) = Z h(x; ) d (x): (2) While minima ofR̂( ;X ) or solutions with bounded empirical risk are influenced by fluctuations in the samples, it is the expected risk R( ) which completely assesses the quality of learning results in a sound probabilistic fashion. The distribution is assumed to be known in the following analysis and it has to decay sufficiently fast such that all rth moments ( r > 2) of the loss functionh(x; ) are bounded byE fjh(x; ) R( )jrg r! r 2V fh(x; )g; 8 2 . E f:g andV f:g denote expectation and variance of a random variable, respectively. is a distribution dependent constant. The moment constraint on the distribution h lds for all distributions with exponentially fast decaying tails which include all distri butions with finite support. Empirical Risk Approximationis an induction principle which requires the learning algorithm to sample from the smallest consistently learnable subset of the hypothesis cla s . In the following, the hypothesis class H contains all loss functions h(x; ); 2 and the subsets of risk bounded loss functions HR are defined as HR = fh(x; ) : 2 ^ R( ) R g : (3) The subsetsHR are obviously nested since HR 1 HR 2 H forR 1 R 2 1 andH = limR !1HR . R induces a structure on the hypothesis class in the sense of Vapnik’s “Structural Risk” and essentially controls the complexity of the hypothesis clas . TheEmpirical Risk Approximationinduction principle requires to define a nested sequence of hypothesis classes with bounded expected risk and to sample from the hypothesis clas s with the desired approximation quality. Algorithmically, however, we select t he loss function according to the bounded empirical risk̂ R( ;X ) R . This induction principle is consistent if a bounded empirical risk implies in the asymptotic limit that the loss functi o has bounded expected riskR( ) R and, therefore, is an element of the hypothesis class HR , i.e., 8 2 lim l!1 R̂( ;X ) R =) h(x; ) 2 HR : (4) This implication essentially states that the expected risk asymptotica lly does not exceed the risk bound (R( ) R ) and, therefore, the inferred structure is an approximation of the optimal data structure with risk not worse than R . The consistency assumption (4) for Empirical Risk Approximationholds if the empirical risk uniformly converges towards the expected risk lim l!1P(sup 2 jR( ) R̂( ;X )j pV fh(x; )g > ) = 0; 8 > 0; (5) where denotes the hypothesis set of distinguishable structures. defines an -netH ;R on the set of loss functions HR or coarsens the hypothesis class with an -separated set if there does not exist a finite -net forHR . Using results from the theory of empirical processes the probability of an –deviation of empirical risk from expected risk can be bounded by Bernstein’s inequality ([3], Lemma 2.2.11) P( sup 2 jR( ) R̂( ;X )j pV fh(x; )g > ) 2jH j exp l 2 2(1 + = min) : (6) The minimal variance of all loss functions is denoted by 2 min = inf 2 V fh(x; )g. jH j denotes the cardinality of an -net constructed for the hypothesis class HR under the assumption of the measure . The confidence level limits the probability of large deviations [1]. The large deviation inequality weighs two competing effects in the learning probl em, i.e., the probability of a large deviation exponentially decreases with growing sample si ze l, whereas a large deviation becomes increasingly likely with growing cardinality of the hypothesis class. A compromise between both effects determines how reliable an estimate ac tually is for a given data set X . The sample complexity l0( ; ) is defined as the nec

FPGA-Based Sensorless PMSM Speed Control Using Reduced-Order Extended Kalman Filters <=SEP=> This paper presents the design and implementation of a field-programmable gate array (FPGA)-based architecture for the speed control of sensorless permanent-magnet synchronous motor (PMSM) drives. For the reduction of computation resources, as well as accuracy improvement in the rotor position estimation, a parallel reduced-order extended Kalman filter (EKF) is proposed in this work. Compared with an EKF, the system order is reduced and the iteration process is greatly simplified, resulting in significant savings of resource utility, while maintaining high estimation performance. The whole control system includes a current-control-and-coordinate-transformation unit, a proportional-integral (PI) speed controller, and other accessory modules, all implemented in a single FPGA chip. A hardware description language is adopted to describe advantageous features of the proposed control system. Moreover, the finite-state-machine method is applied with the purpose to reduce logic elements used in the FPGA chip. The validity of the approach is verified through simulation based on the Modelsim/Simulink cosimulation method. Finally, experimental results are obtained on an FPGA platform with an inverter-fed PMSM to show the feasibility and effectiveness of the proposed system-on-programmable-chip for PMSM drives.

Learning to Remember Rare Events <=SEP=> Despite recent advances, memory-augmented deep neural networks are still limited when it comes to life-long and one-shot learning, especially in remembering rare events. We present a large-scale life-long memory module for use in deep learning. The module exploits fast nearest-neighbor algorithms for efficiency and thus scales to large memory sizes. Except for the nearest-neighbor query, the module is fully differentiable and trained end-to-end with no extra supervision. It operates in a life-long manner, i.e., without the need to reset it during training. Our memory module can be easily added to any part of a supervised neural network. To show its versatility we add it to a number of networks, from simple convolutional ones tested on image classification to deep sequence-to-sequence and recurrent-convolutional models. In all cases, the enhanced network gains the ability to remember and do life-long one-shot learning. Our module remembers training examples shown many thousands of steps in the past and it can successfully generalize from them. We set new state-of-the-art for one-shot learning on the Omniglot dataset and demonstrate, for the first time, life-long one-shot learning in recurrent neural networks on a large-scale machine translation task.

A 1.2 V Low-Noise-Amplifier with Double Feedback for High Gain and Low Noise Figure <=SEP=> Faculdade de Ciências e Tecnologia Departamento de Engenharia Eletrotécnica e de Computadores Mestrado Integrado em Engenharia Eletrotécnica e de Computadores por David Jorge Tiago Amoêdo In this thesis we present a balun low noise amplifier (LNA) in which the gain is boosted using a double feedback structure. The circuit is based in a Balun LNA with noise and distortion cancellation. The LNA is based in two basic stages: common-gate (CG) and common-source (CS). We propose to replace the resistors by active loads, which have two inputs that will be used to provide the feedback (in the CG and CS stages). This proposed methodology will boost the gain and reduce the NF (Noise Figure). Simulation results, with a 130 nm CMOS technology, show that the gain is 19.65 dB and the NF is less than 2.17 dB. The total power dissipation is only 5 mW (since no extra blocks are required), leading to an FOM (Figure of Merit) of 3.13 mW from a nominal 1.2 supply.

AdapNet: Adaptive semantic segmentation in adverse environmental conditions <=SEP=> Robust scene understanding of outdoor environments using passive optical sensors is a onerous and essential task for autonomous navigation. The problem is heavily characterized by changing environmental conditions throughout the day and across seasons. Robots should be equipped with models that are impervious to these factors in order to be operable and more importantly to ensure safety in the real-world. In this paper, we propose a novel semantic segmentation architecture and the convoluted mixture of deep experts (CMoDE) fusion technique that enables a multi-stream deep neural network to learn features from complementary modalities and spectra, each of which are specialized in a subset of the input space. Our model adaptively weighs class-specific features of expert networks based on the scene condition and further learns fused representations to yield robust segmentation. We present results from experimentation on three publicly available datasets that contain diverse conditions including rain, summer, winter, dusk, fall, night and sunset, and show that our approach exceeds the state-of-the-art. In addition, we evaluate the performance of autonomously traversing several kilometres of a forested environment using only the segmentation for perception.

Statistical compression-based models for text classification <=SEP=> Text classification is the task of assigning predefined categories to text documents. It is a common machine learning problem. Statistical text classification that makes use of machine learning methods to learn classification rules are particularly known to be successful in this regard. In this research project we are trying to re-invent the text classification problem with a sound methodology based on statistical data compression technique-the Minimum Message Length (MML) principle. To model the data sequence we have used the Probabilistic Finite State Automata (PFSAs). We propose two approaches for text classification using the MML-PFSAs. We have tested both the approaches with the Enron spam dataset and the results of our empirical evaluation has been recorded in terms of the well known classification measures i.e. recall, precision, accuracy and error. The results indicate good classification accuracy that can be compared with the state of art classifiers.

Distributed Mutual Exclusion Algorithms for Intersection Traffic Control <=SEP=> Traffic control at intersections is a key issue and hot research topic in intelligent transportation systems. Existing approaches, including traffic light scheduling and trajectory maneuver, are either inaccurate and inflexible or complicated and costly. More importantly, due to the dynamics of traffic, it is really difficult to obtain the optimal solution in a real-time way. Inspired by the emergence of vehicular ad hoc network, we propose a novel approach to traffic control at intersections. Via vehicle to vehicle or vehicle to infrastructure communications, vehicles can compete for the privilege of passing the intersection, i.e., traffic is controlled via coordination among vehicles. Such an approach is flexible and efficient. To realize the coordination among vehicles, we first model the problem as a new variant of the classic mutual exclusion problem, and then design algorithms to solve new problem. Both centralized and distributed algorithms are. We conduct extensive simulations to evaluate the performance of our proposed algorithms. The results show that, our approach is efficient and outperforms a reference algorithm based on optimal traffic light scheduling. Moreover, our approach does not rely on traffic light or intersection controller facilities, which makes it flexible and applicable to various kinds of intersections.

A SEM-neural network approach for predicting antecedents of m-commerce acceptance <=SEP=> Higher penetration of powerful mobile devices – especially smartphones – and high-speed mobile internet access are leading to better offer and higher levels of usage of these devices in commercial activities, especially among young generations. The purpose of this paper is to determine the key factors that influence consumers’ adoption of mobile commerce. The extended model incorporates basic TAM predictors, such as perceived usefulness and perceived ease of use, but also several external variables, such as trust, mobility, customization and customer involvement. Data was collected from 224 m-commerce consumers. First, structural equation modeling (SEM) was used to determine which variables had signif-

Cortical hubs revealed by intrinsic functional connectivity: mapping, assessment of stability, and relation to Alzheimer's disease. <=SEP=> Recent evidence suggests that some brain areas act as hubs interconnecting distinct, functionally specialized systems. These nexuses are intriguing because of their potential role in integration and also because they may augment metabolic cascades relevant to brain disease. To identify regions of high connectivity in the human cerebral cortex, we applied a computationally efficient approach to map the degree of intrinsic functional connectivity across the brain. Analysis of two separate functional magnetic resonance imaging datasets (each n = 24) demonstrated hubs throughout heteromodal areas of association cortex. Prominent hubs were located within posterior cingulate, lateral temporal, lateral parietal, and medial/lateral prefrontal cortices. Network analysis revealed that many, but not all, hubs were located within regions previously implicated as components of the default network. A third dataset (n = 12) demonstrated that the locations of hubs were present across passive and active task states, suggesting that they reflect a stable property of cortical network architecture. To obtain an accurate reference map, data were combined across 127 participants to yield a consensus estimate of cortical hubs. Using this consensus estimate, we explored whether the topography of hubs could explain the pattern of vulnerability in Alzheimer's disease (AD) because some models suggest that regions of high activity and metabolism accelerate pathology. Positron emission tomography amyloid imaging in AD (n = 10) compared with older controls (n = 29) showed high amyloid-beta deposition in the locations of cortical hubs consistent with the possibility that hubs, while acting as critical way stations for information processing, may also augment the underlying pathological cascade in AD.

Low-power, folded cascode near rail-to-rail OTA for moderate frequency signal processing <=SEP=> This paper presents a gate-driven, folded-cascode operational transconductance amplifier (FCOTA), operating in strong inversion region. The input core utilizes two complementary PMOS and NMOS input pairs to ensure rail-to-rail input common mode range (ICMR). The cascoded load structure at output port performs the job of current summer and provides enhanced gain due to high output impedance for FCOTA. The proposed OTA has been employed to implement universal biquadratic low-pass, high-pass, band-pass and notch transfer functions, simultaneously available at different nodes of the filter. The proposed FCOTA used dual supply voltage of ± 0.9V and dissipates around 93.6 μW power and provides 80.24 dB open loop gain and gain bandwidth (GBW) of 6.03 MHz. The Cadence VIRTUOSO environment using UMC 0.18 μm CMOS process technology has been used to simulate the proposed circuit.

Fast Priority Queues for Cached Memory <=SEP=> The cache hierarchy prevalent in todays high performance processors has to be taken into account in order to design algorithms that perform well in practice. This paper advocates the adaption of external memory algorithms to this purpose. This idea and the practical issues involved are exemplified by engineering a fast priority queue suited to external memory and cached memory that is based on <i>k</i>-way merging. It improves previous external memory algorithms by constant factors crucial for transferring it to cached memory. Running in the cache hierarchy of a workstation the algorithm is at least two times faster than an optimized implementation of binary heaps and 4-ary heaps for large inputs.

Cyber-Crimes and their Impacts : A Review 1 <=SEP=> In the current era of online processing, maximum of the information is online and prone to cyber threats. There are a huge number of cyber threats and their behavior is difficult to early understanding hence difficult to restrict in the early phases of the cyber attacks. Cyber attacks may have some motivation behind it or may be processed unknowingly. The attacks those are processed knowingly can be considered as the cyber crime and they have serious impacts over the society in the form of economical disrupt, psychological disorder, threat to National defense etc. Restriction of cyber crimes is dependent on proper analysis of their behavior and understanding of their impacts over various levels of society. Therefore, the current manuscript provides the understanding of cyber crimes and their impacts over society with the future trends of cyber crimes.

A Literature Review on the Design of Spherical Rolling Robots <=SEP=> There are many advantages to the use of spherical robot designs. The current status of the design of spherical rolling robots is reviewed.

Evolving Personalized Content for Super Mario Bros Using Grammatical Evolution <=SEP=> Adapting game content to a particular player’s needs and expertise constitutes an important aspect in game design. Most research in this direction has focused on adapting game difficulty to keep the player engaged in the game. Dynamic difficulty adjustment, however, focuses on one aspect of the gameplay experience by adjusting the content to increase or decrease perceived challenge. In this paper, we introduce a method for automatic level generation for the platform game Super Mario Bros using grammatical evolution. The grammatical evolution-based level generator is used to generate player-adapted content by employing an adaptation mechanism as a fitness function in grammatical evolution to optimize the player experience of three emotional states: engagement, frustration and challenge. The fitness functions used are models of player experience constructed in our previous work from crowd-sourced gameplay data collected from over 1500 game sessions.

Efficient matrix completion for seismic data reconstruction <=SEP=> Despite recent developments in improved acquisition, seismic data often remains undersampled along source and receiver coordinates, resulting in incomplete data for key applications such as migration and multiple prediction. We interpret the missing-trace interpolation problem in the context of matrix completion and outline three practical principles for using low-rank optimization techniques to recover seismic data. Specifically, we strive for recovery scenarios wherein the original signal is low rank and the subsampling scheme increases the singular values of the matrix. We employ an optimization program that restores this low rank structure to recover the full volume. Omitting one or more of these principles can lead to poor interpolation results, as we show experimentally. In light of this theory, we compensate for the high-rank behavior of data in the source-receiver domain by employing the midpoint-offset transformation for 2D data and a source-receiver permutation for 3D data to reduce the overall singular values. Simultaneously, in order to work with computationally feasible algorithms for large scale data, we use a factorization-based approach to matrix completion, which significantly speeds up the computations compared to repeated singular value decompositions without reducing the recovery quality. In the context of our theory and experiments, we also show that windowing the data too aggressively can have adverse effects on the recovery quality. To overcome this problem, we carry out our interpolations for each frequency independently while working with the entire frequency slice. The result is a computationally efficient, theoretically motivated framework for interpolating missing-trace data. Our tests on realistic twoand three-dimensional seismic data sets show that our method compares favorably, both in terms of computational speed and recovery quality, to existing curvelet-based and tensor-based techniques. Geophysics This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Copyright c ©Mitsubishi Electric Research Laboratories, Inc., 2015 201 Broadway, Cambridge, Massachusetts 02139