Query: Evolutionary mining of relaxed dependencies from big data collections

Relevant passages:
DFD: Efficient Functional Dependency Discovery <=SEP=> The discovery of unknown functional dependencies in a dataset is of great importance for database redesign, anomaly detection and data cleansing applications. However, as the nature of the problem is exponential in the number of attributes none of the existing approaches can be applied on large datasets. We present a new algorithm DFD for discovering all functional dependencies in a dataset following a depth-first traversal strategy of the attribute lattice that combines aggressive pruning and efficient result verification. Our approach is able to scale far beyond existing algorithms for up to 7.5 million tuples, and is up to three orders of magnitude faster than existing approaches on smaller datasets.

Database Dependency Discovery: A Machine Learning Approach <=SEP=> Database dependencies, such as functional and multivalued dependencies, express the presence of structure in databas e relations, that can be utilised in the database design proce ss. The discovery of database dependencies can be viewed as an induction problem, in which general rules (dependencies) a re obtained from specific facts (the relation). This viewpoint has the advantage of abstracting away as much as possible from the particulars of the dependencies. The algorithms in this paper are designed such that they can easily be generalised to other kinds of dependencies. Like in current approaches to computational induction such as inductive logic programming, we distinguish between top down algorithms and bottom-up algorithms. In a top-down approach, hypotheses are generated in a systematic way and then tested against the given relation. In a bottom-up approach, the relation is inspected in order to see what dependencies it may satisfy or violate. We give algorithms for bot h approaches.

GORDIAN: Efficient and Scalable Discovery of Composite Keys <=SEP=> Identification of (composite) key attributes is of fundamental importance for many different data management tasks such as data modeling, data integration, anomaly detection, query formulation, query optimization, and indexing. However, information about keys is often missing or incomplete in many real-world database scenarios. Surprisingly, the fundamental problem of automatic key discovery has received little attention in the existing literature. Existing solutions ignore composite keys, due to the complexity associated with their discovery. Even for simple keys, current algorithms take a brute-force approach; the resulting exponential CPU and memory requirements limit the applicability of these methods to small datasets. In this paper, we describe GORDIAN, a scalable algorithm for automatic discovery of keys in large datasets, including composite keys. GORDIAN can provide exact results very efficiently for both real-world and synthetic datasets. GORDIAN can be used to find (composite) key attributes in any collection of entities, e.g., key column-groups in relational data, or key leaf-node sets in a collection of XML documents with a common schema. We show empirically that GORDIAN can be combined with sampling to efficiently obtain high quality sets of approximate keys even in very large datasets.

Fast Algorithms for Mining Association Rules in Large Databases <=SEP=> 

Logic and Databases: A Deductive Approach <=SEP=> ion, Databases and Conceptual Modeling (Pingree Park, Colo., June), pp. 112-114; ACM SZGMOD Rec. 11, 2 (Feb.). CODD, E. F. 1982. Relational database: A practical foundation for productivity. Commun. ACM 25, 2 (Feb.), 109-117. COLMERAUER, A. 1973. Un systeme de communication homme-machine en francais. Rapport, Groupe Intelligence Artificielle, Universite d’AixMarseille-Luminy, Marseilles, France. COLMERAUER, A., AND PIQUE, J. F. 1981. About natural logic. In Advances in Database Theory vol. 1, H. Gallaire, J. Minker, and J.-M. Nicolas, Eds. Plenum, New York, pp. 343-365. COOPER, E. C. 1980. On the expressive power of query languages for relational databases. Tech. Rep. 14-80, Computer Science Dept., Harvard Univ., Cambridge, Mass. DAHL, V. 1982. On database systems development through logic. ACM Trans. Database Syst. 7, 1 (Mar.), 102-123. DATE, C. J. 1977. An Introduction to Database Systems. Addison-Wesley, Reading, Mass. DATE, C. J. 1981. An Introduction to Database Systems, 3rd ed. Addison-Wesley, Reading, Mass. DELIYANNI, A., AND KOWALSKI, R. A. 1979. Logic and semantic networks. Commun. ACM 22, 3

Regional cerebral blood flow correlates of visuospatial tasks in Alzheimer's disease. <=SEP=> This study investigated the role of visuospatial tasks in identifying cognitive decline in patients with Alzheimer's disease (AD), by correlating neuropsychological performance with cerebral perfusion measures. There were 157 participants: 29 neurologically healthy controls (age: 70.3 +/- 6.6, MMSE > or = 27), 86 patients with mild AD (age: 69.18 +/- 8.28, MMSE > or = 21) and 42 patients moderate/severe AD (age: 68.86 +/- 10.69, MMSE 8-20). Single Photon Emission Computerized Tomography (SPECT) was used to derive regional perfusion ratios, and correlated using partial least squares (PLS) with neuropsychological test scores from the Benton Line Orientation (BLO) and the Rey-Osterrieth Complex Figure (RO). Cross-sectional analysis demonstrated that mean scores differed in accordance with disease status: control group (BLO 25.5, RO 33.3); mild AD (BLO 20.1, RO 25.5); moderate/severe AD (BLO 10.7, RO 16). Correlations were observed between BLO/RO and right parietal SPECT regions in the AD groups. Visuospatial performance, often undersampled in cognitive batteries for AD, is clearly impaired even in mild AD and correlates with functional deficits as indexed by cerebral perfusion ratios on SPECT implicating right hemisphere circuits. Furthermore, PLS reveals that usual spatial tasks probe a distributed brain network in both hemispheres including many areas targeted by early AD pathology.

The Critical Success Factors for Effective ICT Governance in Malaysian Public Sector : A Delphi Study <=SEP=> The fundamental issues in ICT Governance (ICTG) implementation for Malaysian Public Sector (MPS) is how ICT be applied to support improvements in productivity, management effectiveness and the quality of services offered to its citizens. Our main concern is to develop and adopt a common definition and framework to illustrate how ICTG can be used to better align ICT with government’s operations and strategic focus. In particular, we want to identify and categorize factors that drive a successful ICTG process. This paper presents the results of an exploratory study to identify, validate and refine such Critical Success Factors (CSFs) and confirmed seven CSFs and nineteen sub-factors as influential factors that fit MPS after further validated and refined. The Delphi method applied in validation and refining process before being endorsed as appropriate for MPS. The identified CSFs reflect the focus areas that need to be considered strategically to strengthen ICT Governance implementation and ensure business success. Keywords—IT Governance, Critical Success Factors.

Articulatory , positional and coarticulatory characteristics for clear / l / and dark / l / : evidence from two Catalan dialects <=SEP=> Electropalatographic and acoustic data reported in this study show differences in closure location and degree, dorsopalatal contact size, closure duration, relative timing of events and formant frequency between clear /l/ and dark /l/ in two dialects of Catalan (Valencian and Majorcan). The two Catalan dialects under investigation differ also regarding degree of darkness but essentially not regarding coarticulatory resistance at the word edges, i.e. the alveolar lateral is equally dark word-initially and word-finally in Majorcan, and clearer in the former position vs. than the latter in Valencian, and more resistant to vowel effects in the two positions than intervocalically in both dialects. With reference to data from the literature, it appears that languages and dialects may differ as to whether /l/ is dark or clear in all word positions or whether or not initial /l/ is clearer than final /l/, and that articulatory strengthening occurs not only wordand utterance-initially but wordand utterance-finally as well. These and other considerations confirm the hypothesis that degree of darkness in /l/ proceeds gradually rather than categorically from one language to another.

Risk factors for involvement in cyber bullying : Victims , bullies and bully – victims ☆ <=SEP=> The use of online technology is exploding worldwide and is fast becoming a preferred method of interacting. While most online interactions are neutral or positive the Internet provides a new means through which children and youth are bullied. The aim of this grounded theory approach was to explore technology, virtual relationships and cyber bullying from the perspectives of students. Seven focus groups were held with 38 students between fifth and eighth grades. The participants considered cyber bullying to be a serious problem and some characterized online bullying as more serious than  ̳traditional‘ bullying because of the associated anonymity. Although the students depicted anonymity as integral to cyber bullying, the findings suggest that much of the cyber bullying occurred within the context of their social groups and relationships. Findings revealed five major themes: technology embraced at younger ages and becoming the dominant medium for communication; definitions and views of cyber bullying; factors unique to cyber bullying; types of cyber bullying; and telling adults. The findings highlight the complexity of the perceived anonymity provided by the Internet and how AC C EP TE D M AN U SC R IP T ACCEPTED MANUSCRIPT 3 this may impact cyber bullying. The study offers greater awareness of the meanings of online relationships for children and youth.

The Gut Microbiota and Autism Spectrum Disorders <=SEP=> Gastrointestinal (GI) symptoms are a common comorbidity in patients with autism spectrum disorder (ASD), but the underlying mechanisms are unknown. Many studies have shown alterations in the composition of the fecal flora and metabolic products of the gut microbiome in patients with ASD. The gut microbiota influences brain development and behaviors through the neuroendocrine, neuroimmune and autonomic nervous systems. In addition, an abnormal gut microbiota is associated with several diseases, such as inflammatory bowel disease (IBD), ASD and mood disorders. Here, we review the bidirectional interactions between the central nervous system and the gastrointestinal tract (brain-gut axis) and the role of the gut microbiota in the central nervous system (CNS) and ASD. Microbiome-mediated therapies might be a safe and effective treatment for ASD.

Compiling Knowledge into Decomposable Negation Normal Form <=SEP=> We propose a method for compiling proposit ional theories into a new tractable form that we refer to as decomposable negation normal form (DNNF). We show a number of results about our compilation approach. First, we show that every propositional theory can be compiled into D N N F and present an algorithm to this effect. Second, we show that if a clausal form has a bounded treewidth, then its DNNF compilation has a linear size and can be computed in linear t ime — treewidth is a graphtheoretic parameter which measures the connectivity of the clausal form. Th i rd , we show that once a propositional theory is compiled into DNNF, a number of reasoning tasks, such as satisfiability and forgett ing, can be performed in linear t ime. Finally, we propose two techniques for approximating the DNNF compilat ion of a theory when the size of such compilation is too large to be practical. One of the techniques generates a sound but incomplete compilation, while the other generates a complete but unsound compilation. Together, these approximations bound the exact compilation from below and above in terms for their abil i ty to answer queries.

Object volume estimation based on 3D point cloud <=SEP=> An approach to estimating the volume of an object based on 3D point cloud is proposed in this paper. Firstly, 3D point cloud is cut into slices of equal thickness along the z-axis. Bisect each slice along the y-axis and cut each slice into sub-intervals along the x-axis. Using the minimum and maximum coordinates in each sub-interval, two surface curve functions can be fitted accordingly for the bisected slices. Then, the curves are integrated to estimate the area of each slice and further integrated along the z-axis for an estimated volume of the object.

Auto-Encoding User Ratings via Knowledge Graphs in Recommendation Scenarios <=SEP=> In the last decade, driven also by the availability of an unprecedented computational power and storage capabilities in cloud environments, we assisted to the proliferation of new algorithms, methods, and approaches in two areas of artificial intelligence: knowledge representation and machine learning. On the one side, the generation of a high rate of structured data on the Web led to the creation and publication of the so-called knowledge graphs. On the other side, deep learning emerged as one of the most promising approaches in the generation and training of models that can be applied to a wide variety of application fields. More recently, autoencoders have proven their strength in various scenarios, playing a fundamental role in unsupervised learning. In this paper, we instigate how to exploit the semantic information encoded in a knowledge graph to build connections between units in a Neural Network, thus leading to a new method, SEM-AUTO, to extract and weight semantic features that can eventually be used to build a recommender system. As adding content-based side information may mitigate the cold user problems, we tested how our approach behaves in the presence of a few ratings from a user on the Movielens 1M dataset and compare results with BPRSLIM.

3D Deep Learning for Efficient and Robust Landmark Detection in Volumetric Data <=SEP=> Recently, deep learning has demonstrated great success in computer vision with the capability to learn powerful image features from a large training set. However, most of the published work has been confined to solving 2D problems, with a few limited exceptions that treated the 3D space as a composition of 2D orthogonal planes. The challenge of 3D deep learning is due to a much larger input vector, compared to 2D, which dramatically increases the computation time and the chance of over-fitting, especially when combined with limited training samples (hundreds to thousands), typical for medical imaging applications. To address this challenge, we propose an efficient and robust deep learning algorithm capable of full 3D detection in volumetric data. A two-step approach is exploited for efficient detection. A shallow network (with one hidden layer) is used for the initial testing of all voxels to obtain a small number of promising candidates, followed by more accurate classification with a deep network. In addition, we propose two approaches, i.e., separable filter decomposition and network sparsification, to speed up the evaluation of a network. To mitigate the over-fitting issue, thereby increasing detection robustness, we extract small 3D patches from a multi-resolution image pyramid. The deeply learned image features are further combined with Haar wavelet features to increase the detection accuracy. The proposed method has been quantitatively evaluated for carotid artery bifurcation detection on a head-neck CT dataset from 455 patients. Compared to the state-ofthe-art, the mean error is reduced by more than half, from 5.97 mm to 2.64 mm, with a detection speed of less than 1 s/volume.

Residual Memory Networks in Language Modeling: Improving the Reputation of Feed-Forward Networks <=SEP=> We introduce the Residual Memory Network (RMN) architecture to language modeling. RMN is an architecture of feedforward neural networks that incorporates residual connections and time-delay connections that allow us to naturally incorporate information from a substantial time context. As this is the first time RMNs are applied for language modeling, we thoroughly investigate their behaviour on the well studied Penn Treebank corpus. We change the model slightly for the needs of language modeling, reducing both its time and memory consumption. Our results show that RMN is a suitable choice for small-sized neural language models: With test perplexity 112.7 and as few as 2.3M parameters, they out-perform both a much larger vanilla RNN (PPL 124, 8M parameters) and a similarly sized LSTM (PPL 115, 2.08M parameters), while being only by less than 3 perplexity points worse than twice as big LSTM.

Author Disambiguation in PubMed: Evidence on the Precision and Recall of Author-ity among NIH-Funded Scientists <=SEP=> We examined the usefulness (precision) and completeness (recall) of the Author-ity author disambiguation for PubMed articles by associating articles with scientists funded by the National Institutes of Health (NIH). In doing so, we exploited established unique identifiers-Principal Investigator (PI) IDs-that the NIH assigns to funded scientists. Analyzing a set of 36,987 NIH scientists who received their first R01 grant between 1985 and 2009, we identified 355,921 articles appearing in PubMed that would allow us to evaluate the precision and recall of the Author-ity disambiguation. We found that Author-ity identified the NIH scientists with 99.51% precision across the articles. It had a corresponding recall of 99.64%. Precision and recall, moreover, appeared stable across common and uncommon last names, across ethnic backgrounds, and across levels of scientist productivity.

Motion planning based on learning models of pedestrian and driver behaviors <=SEP=> Autonomous driving has shown the capability of providing driver convenience and enhancing safety. While introducing autonomous driving into our current traffic system, one significant issue is to make the autonomous vehicle be able to react in the same way as real human drivers. In order to ensure that an autonomous vehicle of the future will perform like human drivers, this paper proposes a vehicle motion planning model, which can represent how drivers control vehicles based on the assessment of traffic environments in the real signalized intersection. The proposed motion planning model comprises functions of pedestrian intention detection, gap detection and vehicle dynamic control. The three functions are constructed based on the analysis of actual data collected from real traffic environments. Finally, this paper demonstrates the performance of the proposed method by comparing the behaviors of our model with the behaviors of real pedestrians and human drivers. The experimental results show that our proposed model can achieve 85% recognition rate for the pedestrian crossing intention. Moreover, the vehicle controlled by the proposed motion planning model and the actual human-driven vehicle are highly similar with respect to the gap acceptance in intersections.

Facing prejudice: implicit prejudice and the perception of facial threat. <=SEP=> We propose that social attitudes, and in particular implicit prejudice, bias people's perceptions of the facial emotion displayed by others. To test this hypothesis, we employed a facial emotion change-detection task in which European American participants detected the offset (Study 1) or onset (Study 2) of facial anger in both Black and White targets. Higher implicit (but not explicit) prejudice was associated with a greater readiness to perceive anger in Black faces, but neither explicit nor implicit prejudice predicted anger perceptions regarding similar White faces. This pattern indicates that European Americans high in implicit racial prejudice are biased to perceive threatening affect in Black but not White faces, suggesting that the deleterious effects of stereotypes may take hold extremely early in social interaction.

Recurrent networks with attention and convolutional networks for sentence representation and classification <=SEP=> In this paper, we propose a bi-attention, a multi-layer attention and an attention mechanism and convolution neural network based text representation and classification model (ACNN). The bi-attention have two attention mechanism to learn two context vectors, forward RNN with attention to learn forward context vector c⃗ $\overrightarrow {\mathbf {c}}$ and backward RNN with attention to learn backward context vector c⃖ $\overleftarrow {\mathbf {c}}$, and then concatenation c⃗ $\overrightarrow {\mathbf {c}}$ and c⃖ $\overleftarrow {\mathbf {c}}$ to get context vector c. The multi-layer attention is the stack of the bi-attention. In the ACNN, the context vector c is obtained by the bi-attention, then the convolution operation is performed on the context vector c, and the max-pooling operation is used to reduce the dimension. After max-pooling operation the text is converted to low-dimensional sentence vector m. Finally, the Softmax classifier be used for text classification. We test our model on 8 benchmarks text classification datasets, and our model achieved a better or the same performance compare with the state-of-the-art methods.

Modeling changing dependency structure in multivariate time series <=SEP=> We show how to apply the efficient Bayesian changepoint detection techniques of Fearnhead in the multivariate setting. We model the joint density of vector-valued observations using undirected Gaussian graphical models, whose structure we estimate. We show how we can exactly compute the MAP segmentation, as well as how to draw perfect samples from the posterior over segmentations, simultaneously accounting for uncertainty about the number and location of changepoints, as well as uncertainty about the covariance structure. We illustrate the technique by applying it to financial data and to bee tracking data.

Habits — A Repeat Performance <=SEP=> Habits are response dispositions that are activated automatically by the context cues that co-occurred with responses during past performance. Experiencesampling diary studies indicate that much of everyday action is characterized by habitual repetition. We consider various mechanisms that could underlie the habitual control of action, and we conclude that direct cuing and motivated contexts best account for the characteristic features of habit responding—in particular, for the rigid repetition of action that can be initiated without intention and that runs to completion with minimal conscious control. We explain the utility of contemporary habit research for issues central to psychology, especially for behavior prediction, behavior change, and self-regulation. KEYWORDS—habit; automaticity; motivation; goals; behavior change; behavior prediction; self-regulation From self-help guru Anthony Robbins to the religion of Zen Buddhism, received wisdom exhorts people to be mindful, deliberative, and conscious in all they do. In contrast, contemporary research in psychology shows that it is actually people’s unthinking routines—or habits—that form the bedrock of everyday life. Without habits, people would be doomed to plan, consciously guide, and monitor every action, from making that first cup of coffee in the morning to sequencing the finger movements in a Chopin piano concerto. But what is a habit? The cognitive revolution radically reshaped the behaviorist view that habits rely on simple stimulus– response associations devoid of mental representation. Emerging is a more nuanced construct that includes roles for consciousness, goals, and motivational states. Fundamental questions persist, however, especially when comparing evidence across neuropsychology, animal-learning, and social-cognition literatures. Data from these fields support three views of habit, which we term the direct-context-cuing, implicit-goal, and motivated-context models. In this article, we consider these models and explain the relevance for psychology of a reinvigorated habit construct. HABITS AFTER BEHAVIORISM Within current theorizing, habits are automated response dispositions that are cued by aspects of the performance context (i.e., environment, preceding actions). They are learned through a process in which repetition incrementally tunes cognitive processors in procedural memory (i.e., the memory system that supports the minimally conscious control of skilled action). The relatively primitive associative learning that promotes habits is shared in some form across mammalian species. Our own interest in habits has been fueled by the recognition that much of everyday action is characterized by repetition. In experience-sampling diary studies using both student and community samples, approximately 45% of everyday behaviors tended to be repeated in the same location almost every day (Quinn & Wood, 2005; Wood, Quinn, & Kashy, 2002). In these studies, people reported a heterogeneous set of actions that varied in habit strength, including reading the newspaper, exercising, and eating fast food. Although a consensual perspective on habit mechanisms has yet to develop, common to all views is the idea that many behavioral sequences (e.g., one’s morning coffee-making routine) are performed repeatedly in similar contexts. When responses and features of context occur in contiguity, the potential exists for associations to form between them, such that contexts come to cue responses. In what follows, we outline three views of habitual control that build on this understanding. Direct Context Cuing According to the direct-context-cuing model, repeated coactivation forges direct links in memory between context and response representations. Once these links are formed via associative learning, merely perceiving a context triggers associated responses. Supporting evidence comes from research in which merely activating a construct, such as the elderly stereotype, influences the performance of relevant behaviors, such as a slow speed of walking (e.g., Bargh, Chen, & Burrows, 1996). Address correspondence to David Neal, Department of Psychology, Box 90085, Duke University, Durham, NC 27708; e-mail: dneal@ duke.edu. CURRENT DIRECTIONS IN PSYCHOLOGICAL SCIENCE 198 Volume 15—Number 4 Copyright r 2006 Association for Psychological Science Readers might wonder if it is realistic that contexts cue responses through this simple mechanism in the absence of an implicit or explicit goal. The answer is not clear, given that social-cognition research has thus far demonstrated only a limited version of direct-cuing effects. For example, activating the elderly stereotype influences walking speed, but it remains to be demonstrated whether such activation can initiate walking itself. However, the direct cuing of repeated action by contexts is suggested by myriad findings in cognitive neuroscience that reveal reduced involvement of goal-related neural structures, such as the prefrontal cortex, when behaviors have come under habitual control (see Daw, Niv, & Dayan, 2005). Furthermore, animal-learning research using a clever paradigm in which reinforcers are devalued suggests direct control by context. When rats initially perform an instrumental behavior (e.g., pressing a bar for a food pellet), they appear to be guided by specific goal expectations; they cease the behavior if the reward is devalued (e.g., by pairing it with a toxin; Dickinson & Balleine, 1995). In contrast, when rats have extensively repeated a behavior, their responses appear to be cued directly by contextual stimuli (e.g., the bar); reward devaluation has little impact on continued performance. These data are commonly interpreted as indicating that habit formation involves a shift to direct context cuing. Implicit Goals Associative learning explains not only the direct binding of contexts and actions but also the binding of contexts and goals. In implicit-goal models, habits develop when people repeatedly pursue a goal via a specific behavior in a given context. An indirect association then forms between the context and behavior within the broader goal system. In support, Aarts and Dijksterhuis (2000) found in several experiments that the automatic activation of habitual responses (e.g., bicycle riding) only occurs when a relevant goal has first been made accessible (e.g., the thought of attending class). These studies did not measure people’s real-world behavior, however, but focused instead on judgments about behavior. It remains to be seen whether such judgments tap the cognitive processes that actually drive behavior. In addition, there is good reason to think that habit performance itself does not depend on goal activation. Goaldriven responses tend to be dynamic and flexible, as evidenced by people sometimes substituting behaviors that serve a common goal. In contrast, habits emerge in a rigid pattern such that, for example, a habitual runner is unlikely to substitute a cycling class for running. Thus, although implicit goals provide potentially powerful guides to action, they do not plausibly explain the context cuing of habits. Motivated Contexts In another framework for understanding context-cued responses, contexts can acquire diffuse motivational value when they have preceded rewards in the past. When contexts predict rewards in this way, they energize associated responses without activating specific goals. Evidence of the motivating quality of contexts comes from animal studies of the neurotransmitters that mediate reward learning. For example, when monkeys first learn that a feature of the environment (e.g., a light) predicts a reward (e.g., a drop of juice) when a response is made (e.g., a lever press), neurotransmitter activity (i.e., dopamine release) occurs just after the reward (see Schultz, Dayan, & Montague, 1997). After repeated practice, the animal reaches for the lever when the light is illuminated. Furthermore, the neurotransmitter response is no longer elicited by the juice but instead by the light. In this way, environmental cues can acquire motivational value. Reward-predicting environments are thought to signal the cached (or long-run future) value of an action without signaling a specific outcome (e.g., juice; Daw et al., 2005). This diffuse motivation may explain the rigid nature of context cuing, given that cached values do not convey a specific desired outcome that could be met by substitutable means. Contributing further to the rigidity of habits, neural evidence indicates that, with repetition, whole sequences of responses become chunked or integrated in memory with the contexts that predict them (Barnes, Kubota, Hu, Jin, & Graybiel, 2005). Chunked responses are cued and implemented as a unit, consistent with the idea that habits require limited conscious control to proceed to completion. This quality of habitual responding is frustratingly evident when, for example, trying to fix a well-practiced but badly executed golf swing or dance-step sequence. As yet, the motivated-context idea has been tested primarily with animals. Its promise as a model of human habits comes from evidence that reward-related neurotransmitter systems are shared across species (e.g., in humans, dopamine is elicited by monetary reward). Multiple Habit Mechanisms The high degree of repetition in daily life observed in the diary research of Wood et al. (2002) is likely to be a product of multiple habit-control mechanisms that draw, in various cases, on direct context associations as well as on diffuse motivations. Although we consider implicit goals to be an implausible mediator of habitual behavior, they undoubtedly contribute to some types of repetition. Whether habits are cued directly or are diffusely motivated, they are triggered automatically by contexts and performed in a relatively rigid way. These features of responding have important implications for theories of behavior prediction, behavior cha

Digital Enterprise Architecture - Transformation for the Internet of Things <=SEP=> Excellence in IT is both a driver and a key enabler of the digital transformation. The digital transformation changes the way we live, work, learn, communicate, and collaborate. The Internet of Things (IoT) fundamentally influences today's digital strategies with disruptive business operating models and fast changing markets. New business information systems are integrating emerging Internet of Things infrastructures and components. With the huge diversity of Internet of Things technologies and products organizations have to leverage and extend previous Enterprise Architecture efforts to enable business value by integrating Internet of Things architectures. Both architecture engineering and management of current information systems and business models are complex and currently integrating beside the Internet of Things synergistic subjects, like Enterprise Architecture in context with services & cloud computing, semantic-based decision support through ontologies and knowledge-based systems, big data management, as well as mobility and collaboration networks. To provide adequate decision support for complex business/IT environments, we have to make transparent the impact of business and IT changes over the integral landscape of affected architectural capabilities, like directly and transitively impacted IoT-objects, business categories, processes, applications, services, platforms and infrastructures. The paper describes a new metamodel-based approach for integrating Internet of Things architectural objects, which are semi-automatically federated into a holistic Digital Enterprise Architecture environment.

A novel bridgeless buck-boost PFC converter <=SEP=> Conventional cascade buck-boost PFC (CBB-PFC) converter suffers from the high conduction loss in the input rectifier bridge. To resolve the above problem, a novel bridgeless buck-boost PFC topology is proposed in this paper. The proposed PFC converter which removes the input rectifier bridge has three conduction semiconductors at every moment. Comparing with CBB-PFC topology, the proposed topology reduces the conduction semiconductors, reduces conduction losses effectively, improves the efficiency of converter and is suitable for use in the wide input voltage range. In this paper, the average current mode control was implemented with UC3854, the theoretical analysis and design of detection circuits was presented. The experimental prototype with 400 V/600 W output and line input voltage range from 220 VAC to 380 VAC was built. Experimental results show that the proposed converter can improve 0.8% efficiency comparing CBB-PFC converter.

Scene text recognition and tracking to identify athletes in sport videos <=SEP=> We present an athlete identification module forming part of a system for the personalization of sport video broadcasts. The aim of this module is the localization of athletes in the scene, their identification through the reading of names or numbers printed on their uniforms, and the labelling of frames where athletes are visible. Building upon a previously published algorithm we extract text from individual frames and read these candidates by means of an optical character recognizer (OCR). The OCR-ed text is then compared to a known list of athletes’ names (or numbers), to provide a presence score for each athlete. Text regions are tracked in subsequent frames using a template matching technique. In this way blurred or distorted text, normally unreadable by the OCR, is exploited to provide a denser labelling of the video sequences. Extensive experiments show that the method proposed is fast, robust and reliable, out-performing results of other systems in the literature.

A Knowledge Graph based Bidirectional Recurrent Neural Network Method for Literature-based Discovery <=SEP=> In this paper, we present a model which incorporates biomedical knowledge graph, graph embedding and deep learning methods for literature-based discovery. Firstly, the relations between entities are extracted from biomedical abstracts and then a knowledge graph is constructed by using these obtained relations. Secondly, the graph embedding technologies are applied to convert the entities and relations in the knowledge graph into a low-dimensional vector space. Thirdly, a bidirectional Long Short-Term Memory network is trained based on the entity associations represented by the pre-trained graph embeddings. Finally, the learned model is used for open and closed literature-based discovery tasks. The experimental results show that our method could not only effectively discover hidden associations between entities, but also reveal the corresponding mechanism of interactions. It suggests that incorporating knowledge graph and deep learning methods is an effective way for capturing the underlying complex associations between entities hidden in the literature.

A system for generating and injecting indistinguishable network decoys <=SEP=> We propose a novel trap-based architecture for detecting passive, “silent”, attackers who are eavesdropping on enterprise networks. Motivated by the increasing number of incidents where attackers sniff the local network for interesting information, such as credit card numbers, account credentials, and passwords, we introduce a methodology for building a trap-based network that is designed to maximize the realism of bait-laced traffic. Our proposal relies on a “record, modify, replay” paradigm that can be easily adapted to different networked environments. The primary contributions of our architecture are the ease of automatically injecting large amounts of believable bait, and the integration of different detection mechanisms in the back-end. We demonstrate our methodology in a prototype platform that uses our decoy injection API to dynamically create and dispense network traps on a subset of our campus wireless network. Our network traps consist of several types of monitored passwords, authentication cookies, credit cards, and documents containing beacons to alarm when opened. The efficacy of our decoys against a model attack program is also discussed, along with results obtained from experiments in the field. In addition, we present a user study that demonstrates the believability of our decoy traffic, and finally, we provide experimental results to show that our solution causes only negligible interference to ordinary users.

Reflect , and React : A Culturally Responsive Model for Pre-service Secondary Social Studies Teachers <=SEP=> The purpose of this qualitative study was to design and implement a model of cultural-responsiveness within a social studies teacher education program. Specifically, we sought to understand how pre-service grades 6-12 social studies practitioners construct culturally responsive teaching (CRT) in their lesson planning. In addition, we examined the professional barriers that prevented teacher-candidates from actualizing culturally responsive pedagogy. Incorporating a conceptual model of Review, Reflect, and React, 20 teacher candidates in a social studies methods course engaged CRT theory and practice. Thematic analysis of lesson plans and clinical reflections indicated successful proponents of CRT critically analyzed their curriculum, explored the diverse needs of their students, and engaged learners in culturally appropriate social studies pedagogy. Findings also showed that unsuccessful CRT was characterized by a lack of content knowledge, resistance from the cooperating teacher, and a reliance on the textbook materials.

Deep and Broad Learning on Content-Aware POI Recommendation <=SEP=> POI recommendation has attracted lots of research attentions recently. There are several key factors that need to be modeled towards effective POI recommendation - POI properties, user preference and sequential momentum of check- ins. The challenge lies in how to synergistically learn multi-source heterogeneous data. Previous work tries to model multi-source information in a flat manner, using either embedding based methods or sequential prediction models in a cross-related space, which cannot generate mutually reinforce results. In this paper, a deep and broad learning approach based on a Deep Context- aware POI Recommendation (DCPR) model was proposed to structurally learn POI and user characteristics. The proposed DCPR model includes three collaborative layers, a CNN layer for POI feature mining, an RNN layer for sequential dependency and user preference modeling, and an interactive layer based on matrix factorization to jointly optimize the overall model. Experiments over three data sets demonstrate that DCPR model achieves significant improvement over state-of-the-art POI recommendation algorithms and other deep recommendation models.

A Rule Based Approach to Discourse Parsing <=SEP=> In this paper we present an overview of recent developments in discourse theory and parsing under the Linguistic Discourse Model (LDM) framework, a semantic theory of discourse structure. We give a novel approach to the problem of discourse segmentation based on discourse semantics and sketch a limited but robust approach to symbolic discourse parsing based on syntactic, semantic and lexical rules. To demonstrate the utility of the system in a real application, we briefly describe the architecture of the PALSUMM system, a symbolic summarization system being developed at FX Palo Alto Laboratory that uses discourse structures constructed using the theory outlined to summarize written English prose texts. 1

Emerging Principles of Gene Expression Programs and Their Regulation. <=SEP=> Many mechanisms contribute to regulation of gene expression to ensure coordinated cellular behaviors and fate decisions. Transcriptional responses to external signals can consist of many hundreds of genes that can be parsed into different categories based on kinetics of induction, cell-type and signal specificity, and duration of the response. Here we discuss the structure of transcription programs and suggest a basic framework to categorize gene expression programs based on characteristics related to their control mechanisms. We also discuss possible evolutionary implications of this framework.