title: Applying clustering and ensemble clustering approaches to phishing profiling

abstract: This paper describes a novel approach to profiling phishing emails based on the combination of multiple independent clusterings of the email documents. Each clustering is motivated by a natural representation of the emails. A data set of 2048 phishing emails provided by a major Australian financial institution was pre-processed to extract features describing the textual content, hyperlinks and orthographic structure of the emails. Independent clusterings using different techniques were performed on each representation, and these clusterings were then ensembled using a variety of consensus functions. This paper concentrates on using several clustering approaches to determine the most likely number of phishing groups and explores ways in which individual and combined results relate. The approach suggests a number of phishing groups and the structure of the approach can aid the development of profiles based on the individual clusters. The actual profiling is not carried out in this paper.


A Hierarchical Model for Universal Schema Relation Extraction


title: Anonymization of moving objects databases by clustering and perturbation

abstract: Preserving individual privacy when publishing data is a problem that is receiving increasing attention. Thanks to its simplicity the concept of k-anonymity, introduced by Samarati and Sweeney [1], established itself as one fundamental principle for privacy preserving data publishing. According to the k-anonymity principle, each release of data must be such that each individual is indistinguishable from at least k-1 other individuals. In this article we tackle the problem of anonymization of moving objects databases. We propose a novel concept of k-anonymity based on co-localization, that exploits the inherent uncertainty of the moving object's whereabouts. Due to sampling and imprecision of the positioning systems (e.g., GPS), the trajectory of a moving object is no longer a polyline in a three-dimensional space, instead it is a cylindrical volume, where its radius @d represents the possible location imprecision: we know that the trajectory of the moving object is within this cylinder, but we do not know exactly where. If another object moves within the same cylinder they are indistinguishable from each other. This leads to the definition of (k,@d)-anonymity for moving objects databases. We first characterize the (k,@d)-anonymity problem, then we recall NWA (NeverWalkAlone), a method that we introduced in [2] based on clustering and spatial perturbation. Starting from a discussion on the limits of NWA we develop a novel clustering method that, being based on EDR distance [3], has the important feature of being time-tolerant. As a consequence it perturbs trajectories both in space and time. The novel method, named W4M (WaitforMe), is empirically shown to produce higher quality anonymization than NWA, at the price of higher computational requirements. Therefore, in order to make W4M scalable to large datasets, we introduce two variants based on a novel (and computationally cheaper) time-tolerant distance function, and on chunking. All the variants of W4M are empirically evaluated in terms of data quality and efficiency, and thoroughly compared to their predecessor NWA. Data quality is assessed both by means of objective measures of information distortion, and by more usability oriented measure, i.e., by comparing the results of (i) spatio-temporal range queries and (ii) frequent pattern mining, executed on the original database and on the (k,@d)-anonymized one. Experimental results over both real-world and synthetic mobility data confirm that, for a wide range of values of @d and k, the relative distortion introduced by our anonymization methods is kept low. Moreover, the techniques introduced to make W4M scalable to large datasets, achieve their goal without giving up data quality in the anonymization process.


title: Commutative -Algebras of Toeplitz Operators via the Moment Map on the Polydisk

abstract: We found that in the polydisk there exist different classes of commutative -algebras generated by Toeplitz operators whose symbols are invariant under the action of maximal Abelian subgroups of biholomorphisms. On the other hand, using the moment map associated with each (not necessary maximal) Abelian subgroup of biholomorphism we introduced a family of symbols given by the moment map such that the -algebra generated by Toeplitz operators with this kind of symbol is commutative. Thus we relate to each Abelian subgroup of biholomorphisms a commutative -algebra of Toeplitz operators.


title: Individual differences and usage behavior: revisiting a technology acceptance model assumption

abstract: A critical assumption of the Technology Acceptance Model (TAM) is that its belief constructs - perceived ease of use (PEOU) and perceived usefulness (PU) - fully mediate the influence of external variables on IT usage behavior. If this assumption is true, researchers can effectively "assume away" the effects of broad categories of external variables, those relating to the specific task, the technology, and user differences. One recent study did indeed find that belief constructs fully mediated individual differences, and its authors suggest that further studies with similar results could pave the way for simpler acceptance models that ignore such differences. To test the validity of these authors' results, we conducted a similar study to determine the effect of staff seniority, age, and education level on usage behavior. Our study involved 106 professional and administrative staff in the IT division of a large manufacturing company who voluntarily use email and word processing. We found that these individual user differences have significant direct effects on both the frequency and volume of usage. These effects are beyond the indirect effects as mediated through the TAM belief constructs. Thus, rather than corroborating the recent study, our findings underscore the importance of users' individual differences and suggest that TAM's belief constructs are accurate but incomplete predictors of usage behavior.


title: Using a 6LoWPAN smart meter mesh network for event-driven monitoring of power quality

abstract: Power quality monitoring is one of the key issues of managing an electrical grid, which is becoming even more important with more distributed and more variable generation. Today expensive equipment allows monitoring of the power network at key points, but for cost reasons this can not reach the residential end-user. To prevent an excessive need for specialized monitoring hardware, e.g. network analysers, it is proposed to engage the capabilities of modern smart meters which can monitor and report power quality events (e.g. voltage deviations). Subsequently a grid operator can follow up with actions in an affected area in order to analyse problems e.g. by increasing the sampling rate. Although the smart meter precision is not comparable to the precision of a commercial network analyser, in large numbers distributed smart meters forming a mesh network can provide sufficient information for power quality in an area while keeping the monitoring overhead and the cost low. It is shown that by using modern interoperable wireless communication protocols and Internet services, the proposed system has a high degree of flexibility, and good potential for scalability and resilience. The preliminary evaluation shows that the smart metering infrastructure, if coupled with suitable information and communication tools, can offer innovative value-added services and enhance existing business processes.


Named Data Networking (NDN) Project NDN-0001


title: Synchronized gesture and speech production for humanoid robots

abstract: We present a model that is capable of synchronizing expressive gestures with speech. The model, implemented on a Honda humanoid robot, can generate a full range of gesture types, such as emblems, iconic and metaphoric gestures, deictic pointing and beat gestures. Arbitrary input text is analyzed with a part-of-speech tagger and a text-to-speech engine for timing information of spoken words. In addition, style tags can be optionally added to specify the level of excitement or topic changes. The text, combined with any tags, is then processed by several grammars, one for each gesture type to produce several candidate gestures for each word of the text. The model then selects probabilistically amongst the gesture types based on the desired degree of expressivity. Once a gesture type is selected, it coincides with a particular gesture template, consisting of trajectory curves that define the gesture. Speech timing patterns and style parameters are used to modulate the shape of the curve before it sent to the whole body control system on the robot. Evaluation of the model's parameters were performed, demonstrating the ability of observers to differentiate varying levels of expressiveness, excitement and speech synchronization. Modification of gesture speed for trajectory tracking found that positive associations like happiness and excitement accompanied faster speeds, with negative associations like sadness or tiredness occurred at slower speeds.


title: Methodology for sustainability strategic planning and management

abstract: Purpose – The purpose of this paper is to propose a framework that intends to help organisations achieve the sustainability goal by means of a methodology that integrates sustainability in both the planning and management tasks of the organisation and that serves as a base for the implementation of an information system aligned with the business strategy.Design/methodology/approach – After an exhaustive review of literature about corporate social responsibility, strategic planning of organisations and balanced scorecards (BSCs), a methodology has been developed that describes the process of designing and implementing a sustainability BSC for sustainability strategic planning and management.Findings – The methodology can be easily implemented at companies with a minimum of computer resources, but managers play a key role in its success, since they are the responsible for providing the necessary environment for overcoming such an important change.Research limitations/implications – The methodology has only ...


title: Mining customer knowledge for electronic catalog marketing

abstract: Abstract Direct marketing is the use of the telephone and non-personal media to communicate product and organizational information to customers, who then can purchase products via mail, telephone, or the Internet. In contrast, catalog marketing is a type of marketing in which an organization provides a catalog from which customers make selections and place orders by mail or telephone. However, most catalogs for retailing firms are presented to customers in the format of paper catalogs without strategic segmentation design and implementation. In this regard, electronic catalog design and marketing could be a method to integrate the Internet and catalog marketing using market segmentation in order to enhance the effectiveness of direct marketing and sales management in retailing. This paper uses data mining based on association rules from relational database design and implementation for mining customer knowledge. As result, marketing knowledge patterns and rules are extracted for the electronic catalog marketing and sales management of a retailing mall in Taiwan.


title: A new optimizer using particle swarm theory

abstract: The optimization of nonlinear functions using particle swarm methodology is described. Implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm. Benchmark testing of both paradigms is described, and applications, including neural network training and robot task learning, are proposed. Relationships between particle swarm optimization and both artificial life and evolutionary computation are reviewed.


title: Epidermal growth factor receptor (EGFR) in oral squamous cell carcinomas: overexpression, localization and therapeutic implications.

abstract: Dysregulation of oncogenes, overproduction of growth factor receptors and their ligands, and loss of function of tumor suppressor genes are thought to contribute to multi-step process of carcinogenesis. It is suggested that proliferation markers like epidermal growth factor receptor (EGFR) actively participate in oral carcinogenesis, during initiation or promotion stage of the process. Potent mitogens such as epidermal growth factor (EGF) and transforming growth factor-alpha (TGF-a) mediate their growth responses through the common transmembrane glycoprotein receptor, EGFR. Current data suggest that a good number of epithelial cancers including oral squamous cell carcinomas (OSCC) overexpress EGFR and that monoclonal antibodies directed against EGFR may provide valuable information that would be useful in planning proper palliative treatment of certain premalignant and malignant lesions derived from squamous epithelium.


title: Automatic spine and pelvis detection in frontal X-rays using deep neural networks for patch displacement learning

abstract: This paper proposes a method to automatically detect the spine and pelvis structures from a postero-anterior radiograph. From a training dataset, a non-linear regression model was trained using a deep neural network (DNN) in order to predict the displacement that recovers the optimal location of an anatomical landmark from an input image patch. Using a DNN for each landmark of a 2D simplified model of the spine, a detection sequence was able to localize the vertebral body centers and femoral heads. The whole process is regularized using a statistical shape model of a simplified model of the spine. The quantitative assessment on a set of 121 radiographs of scoliotic patients presented a mean localization errors of 3.5 ± 3.6 mm and 5.7 ± 6 mm respectively for the femoral heads and the vertebral body centers (vertebral levels T1 to L5). The mean error for the spinal curve automatic detection was 2 ± 2.8 mm, which is accurate enough to determine a first estimate of the spine 3D reconstruction in a 3D biplanar reconstruction scheme.


title: Measuring Individuals' Concerns over Collective Privacy on Social Networking Sites.

abstract: With the rise of social networking sites (SNSs), individuals not only disclose personal information but also share private information concerning others online. While shared information is co-constructed by self and others, personal and collective privacy boundaries become blurred. Thus there is an increasing concern over information privacy beyond the individual perspective. However, limited research has empirically examined if individuals are concerned about privacy loss not only of their own but their social ties’; nor is there an established instrument for measuring the collective aspect of individuals’ privacy concerns. In order to address this gap in existing literature, we propose a conceptual framework of individuals’ collective privacy concerns in the context of SNSs. Drawing on the Communication Privacy Management (CPM) theory (Petronio, 2002), we suggest three dimensions of collective privacy concerns, namely, collective information access, control and diffusion. This is followed by the development and empirical validation of a preliminary scale of SNS collective privacy concerns (SNSCPC). Structural model analyses confirm the three-dimensional conceptualization of SNSCPC and reveal antecedents of SNS users’ concerns over violations of the collective privacy boundaries. This paper serves as a starting point for theorizing privacy as a collective notion and for understanding online information disclosure as a result of social interaction and group influence.


title: IR and visible light face recognition

abstract: This paper presents the results of several large-scale studies of face recognition employing visible-light and infrared (IR) imagery in the context of principal component analysis. We find that in a scenario involving time lapse between gallery and probe, and relatively controlled lighting, (1) PCA-based recognition using visible-light images outperforms PCA-based recognition using infrared images, (2) the combination of PCA-based recognition using visible-light and infrared imagery substantially outperforms either one individually. In a same-session scenario (i.e., near-simultaneous acquisition of gallery and probe images) neither modality is significantly better than the other. These experimental results reinforce prior research that employed a smaller data set, presenting a convincing argument that, even across a broad experimental spectrum, the behaviors enumerated above are valid and consistent.


title: Automated Template Generation for Question Answering over Knowledge Graphs

abstract: Templates are an important asset for question answering over knowledge graphs, simplifying the semantic parsing of input utterances and generating structured queries for interpretable answers. State-of-the-art methods rely on hand-crafted templates with limited coverage. This paper presents QUINT, a system that automatically learns utterance-query templates solely from user questions paired with their answers. Additionally, QUINT is able to harness language compositionality for answering complex questions without having any templates for the entire question. Experiments with different benchmarks demonstrate the high quality of QUINT.


title: Intentional modulation of emotional responding to unpleasant pictures: An ERP study

abstract: Intentionally altering responses to unpleasant stimuli affects physiological and hemodynamic activity associated with emotional and cognitive processing. In the present experiment, we measured the late-positive potential (LPP) of the visually evoked event-related brain potential to examine the effects of intentional emotion modulation on electrophysiological correlates of emotional and cognitive processing. Seventeen participants received instructions to view, suppress, and enhance emotional responses to unpleasant stimuli. Results revealed significantly decreased electrophysiological activity during suppression of emotional responses beginning around 250 ms poststimulus and lasting several hundred milliseconds. These data suggest that ERPs are sensitive to emotion modulation/regulation processes. Descriptors: Late-positive potential, LPP, Unpleasant pictures, Emotion modulation/regulation, Attention Researchers have begun to examine the effects of instructions to alter responding to emotional stimuli on physiological and neural indices of emotional and cognitive processing (cf. Ochsner &


title: Efficient Exact Gradient Update for training Deep Networks with Very Large Sparse Targets

abstract: An important class of problems involves training deep neural networks with sparse prediction targets of very high dimension D. These occur naturally in e.g. neural language models or the learning of word-embeddings, often posed as predicting the probability of next words among a vocabulary of size D (e.g. 200 000). Computing the equally large, but typically non-sparse D-dimensional output vector from a last hidden layer of reasonable dimension d (e.g. 500) incurs a prohibitive O(Dd) computational cost for each example, as does updating the D x d output weight matrix and computing the gradient needed for backpropagation to previous layers. While efficient handling of large sparse network inputs is trivial, the case of large sparse targets is not, and has thus so far been sidestepped with approximate alternatives such as hierarchical softmax or sampling-based approximations during training. In this work we develop an original algorithmic approach which, for a family of loss functions that includes squared error and spherical softmax, can compute the exact loss, gradient update for the output weights, and gradient for backpropagation, all in O(d^2) per example instead of O(Dd), remarkably without ever computing the D-dimensional output. The proposed algorithm yields a speedup of D/4d , i.e. two orders of magnitude for typical sizes, for that critical part of the computations that often dominates the training time in this kind of network architecture.


title: An optimal algorithm for minimizing run-time reconfiguration delay

abstract: Reconfiguration delay is one of the major barriers in the way of dynamically adapting a system to its application requirements. The run-time reconfiguration delay is quite comparable to the application latency for many classes of applications and might even dominate the application run-time. In this paper, we present an efficient optimal algorithm for minimizing the run-time reconfiguration (context switching) delay of executing an application on a dynamically adaptable system. The system is composed of a number of cameras with embedded reconfigurable resources collaborating in order to track an object. The operations required to execute in order to track the object are revealed to the system at run-time and can change according to a number of parameters, such as the target shape and proximity. Similarly, we can assume that the applications comprising tasks are already scheduled and each of them has to be realized on the reconfigurable fabric in order to be executed.The modeling and the algorithm are both applicable to partially reconfigurable platforms as well as multi-FPGA systems. The algorithm can be directly applied to minimize the application run-time for the typical classes of applications, where the actual execution delay of the basic operations is negligible compared to the reconfiguration delay. We prove the optimality and the efficiency of our algorithm. We report the experimental results, which demonstrate a 2.5--40p improvement on the total run-time reconfiguration delay as compared to other heuristics.


title: EEG-based driver fatigue detection using hybrid deep generic model

abstract: Classification of electroencephalography (EEG)-based application is one of the important process for biomedical engineering. Driver fatigue is a major case of traffic accidents worldwide and considered as a significant problem in recent decades. In this paper, a hybrid deep generic model (DGM)-based support vector machine is proposed for accurate detection of driver fatigue. Traditionally, a probabilistic DGM with deep architecture is quite good at learning invariant features, but it is not always optimal for classification due to its trainable parameters are in the middle layer. Alternatively, Support Vector Machine (SVM) itself is unable to learn complicated invariance, but produces good decision surface when applied to well-behaved features. Consolidating unsupervised high-level feature extraction techniques, DGM and SVM classification makes the integrated framework stronger and enhance mutually in feature extraction and classification. The experimental results showed that the proposed DBN-based driver fatigue monitoring system achieves better testing accuracy of 73.29 % with 91.10 % sensitivity and 55.48 % specificity. In short, the proposed hybrid DGM-based SVM is an effective method for the detection of driver fatigue in EEG.


title: Bayesian Quadratic Network Game Filters

abstract: A repeated network game where agents have quadratic utilities that depend on information externalities-an unknown underlying state-as well as payoff externalities-the actions of all other agents in the network-is considered. Agents play Bayesian Nash Equilibrium strategies with respect to their beliefs on the state of the world and the actions of all other nodes in the network. These beliefs are refined over subsequent stages based on the observed actions of neighboring peers. This paper introduces the Quadratic Network Game (QNG) filter that agents can run locally to update their beliefs, select corresponding optimal actions, and eventually learn a sufficient statistic of the network's state. The QNG filter is demonstrated on a Cournot market competition game and a coordination game to implement navigation of an autonomous team.


title: The extreme male brain revisited: gender coherence in adults with autism spectrum disorder

abstract: Background ::: ::: The ‘extreme male brain’ theory suggests that autism spectrum disorder (ASD) is an extreme variant of male intelligence. However, somewhat paradoxically, many individuals with ASD display androgynous physical features regardless of gender. ::: ::: Aims ::: ::: To assess physical measures, supposedly related to androgen influence, in adults with and without ASD. ::: ::: Method ::: ::: Serum hormone levels, anthropometry, the ratio of 2nd to 4th digit length (2D:4D) and psychiatric symptomatology were measured in 50 adults with high-functioning ASD and age- and gender-matched neurotypical controls. Photographs of face and body, as well as voice recordings, were obtained and assessed with respect to gender coherence, blindly and independently, by eight assessors. ::: ::: Results ::: ::: Women with ASD had higher total and bioactive testosterone levels, less feminine facial features and a larger head circumference than female controls. Men in the ASD group were assessed as having less masculine body characteristics and voice quality, and displayed higher (i.e. less masculine) 2D:4D ratios, but similar testosterone levels to controls. Androgynous facial features correlated strongly and positively with autistic traits measured with the Autism-Spectrum Quotient in the total sample. In males and females with ASD dehydroepiandrosterone sulfate did not decrease with age, in contrast to the control group. ::: ::: Conclusions ::: ::: Women with ASD had elevated testosterone levels and several masculinised characteristics compared with controls, whereas men with ASD displayed several feminised characteristics. Our findings suggest that ASD, rather than being characterised by masculinisation in both genders, may constitute a gender defiant disorder.


title: To have or not to have Internet at home: Implications for online shopping

abstract: This paper analyzes the individual decision of online shopping, in terms of socioeconomic characteristics, Internet related variables and location factors. Since online shopping is only observed for Internet users, we use the Heckman’s two-step model to correct sample selection. We argue that one of the relevant variables to explain online shopping, the existence of a home Internet connection, can be endogenous. To account for this potential endogeneity, we jointly estimate the probability of online shopping and the probability of having Internet at home. The dataset used in this paper comes from the Household Survey of ICT Equipment and Usage, conducted by the Spanish Statistical Office on an annual basis. Our analysis covers the period 2004–2009. Our results show that not accounting for the endogeneity of having Internet at home, leads to an overestimate of that variable’s effect on the probability of buying online. We also show that, compared to other variables that are also relevant for online shopping, the effect of Internet at home is quite small. This finding can be important in the design of public policies aimed at enhancing e-commerce through providing households with Internet connection at home.


title: Pose estimation for a partially observable human body from RGB-D cameras

abstract: Human pose estimation in realistic world conditions raises multiple challenges such as foreground extraction, background update and occlusion by scene objects. Most of existing approaches were demonstrated in controlled environments. In this paper, we propose a framework to improve the performance of existing tracking methods to cope with these problems. To this end, a robust and scalable framework is provided composed of three main stages. In the first one, a probabilistic occupancy grid updated with a Hidden Markov Model used to maintain an up-to-date background and to extract moving persons. The second stage uses component labelling to identify and track persons in the scene. The last stage uses an hierarchical particle filter to estimate the body pose for each moving person. Occlusions are handled by querying the occupancy grid to identify hidden body parts so that they can be discarded from the pose estimation process. We provide a parallel implementation that runs on CPU and GPU at 4 frames per second. We also validate the approach on our own dataset that consists of synchronized motion capture with a single RGB-D camera data of a person performing actions in challenging situations with severe occlusions generated by scene objects. We make this dataset available online.


title: Decoupling Strategy and Generation in Negotiation Dialogues

abstract: We consider negotiation settings in which two agents use natural language to bargain on goods. Agents need to decide on both high-level strategy (e.g., proposing \$50) and the execution of that strategy (e.g., generating"The bike is brand new. Selling for just \$50."). Recent work on negotiation trains neural models, but their end-to-end nature makes it hard to control their strategy, and reinforcement learning tends to lead to degenerate solutions. In this paper, we propose a modular approach based on coarse di- alogue acts (e.g., propose(price=50)) that decouples strategy and generation. We show that we can flexibly set the strategy using supervised learning, reinforcement learning, or domain-specific knowledge without degeneracy, while our retrieval-based generation can maintain context-awareness and produce diverse utterances. We test our approach on the recently proposed DEALORNODEAL game, and we also collect a richer dataset based on real items on Craigslist. Human evaluation shows that our systems achieve higher task success rate and more human-like negotiation behavior than previous approaches.


title: Operator entanglement of two-qubit joint unitary operations revisited: Schmidt number approach

abstract: The operator entanglement of two-qubit joint unitary operations is revisited. The Schmidt number, an important attribute of a two-qubit unitary operation, may have connection with the entanglement measure of the unitary operator. We find that the entanglement measure of a two-qubit unitary operators is classified by the Schmidt number of the unitary operators. We also discuss the exact relation between the operator entan- glement and the parameters of the unitary operator.


title: Checking models based on an iterative co-specification process of a critical system

abstract: Recurrent incident reports indicate that critical systems such as power plants exhibit unintended emergent behaviors outside of acceptable limits, despite of the increasing development of dependable automation technologies as well as of a lot of techniques related to dependability issues. Among many causes, the role of human for operating technical artefacts is of importance, but also for designing them from the early stages of specification in order to check the basic property of wholeness of any system. A mean is to ensure a continuum of compliant models to component integration through an iterative process between all the disciplines involved to engineer these systems as whole all along their life cycle. However, a human-based process to check the “right-system requirements-right” remains not fully adequate at the scale of real systems engineering projects and in any case to critical issues. This paper explores the formal checking compliance of architecting models with dependability requirements. These models are refined iteratively by specialist and specialty engineers interoperating with a system engineer through a co-specification process on a particular case study of a critical power-plant sub-system.


title: Transition to Adulthood for High-Functioning Individuals with Autism Spectrum Disorders

abstract: Adolescence and young adulthood appear to form the most difficult period in the lives of high-functioning individuals with autism spectrum disorders (ASD; Tantam, 2003). The challenges often faced by those without intellectual disabilities appear to result from the demands of social relationships, academics, employment, and independent living in those with ASD, which may exacerbate core ASD deficits or co-occurring conditions (Barnhill, 2007; Howlin, 2000). The features of these developmental periods, combined with the general challenges with transitions in this population, may explain why the reduction in ASD symptoms that individuals experience throughout life (Seltzer et al., 2003) tend to slow in the transition to adulthood (Taylor & Lounds, 2010). This population appears to be growing despite a shortfall in services and knowledge about these individuals, posing the need for more attention to this area (Barnard et al., 2001; Gerhardt & Lainer, 2011; Shattuck et al., 2011). Adulthood marks a transfer of legal responsibility for individuals with ASD from parental support to self-advocacy. Young adults are often faced with needs to make decisions about their lives, yet they may not fully understand their own unique profile of strengths and weaknesses, or how to advocate for services to meet their needs (Geller & Greenberg, 2010; Townson et al., 2007). In addition, these young adults often feel socially alienated during this period of transition, as though they do not belong or fit in with their peer group (Portway & Johnson, 2003; Ryan & Raisanen, 2008; Simmeborn Fleischer, in press), while sensing others’ may be underestimating their competences (Ashby & Causton-Theoharis, 2009). These struggles may not only relate to social deficits, but may also be associated with cognitive, emotional, and sensory information processing deficits (Chamak et al., 2008). For example, sensory overload may compromise the cognitive performance of individuals with ASD or lead to withdrawal or absence of participation from various social situations (Madriaga, 2010). Yet for adults with ASD, quality of life or subjective wellbeing appears positively related to perceived informal support and inversely related to unmet formal support needs. Disability characteristics such as ASD symptoms and IQ appear to be unrelated to subjective wellbeing (Renty & Roeyers, 2006). These findings have implications for the provision of accommodations and formal support for relationships and daytime activities for adults with ASD. Yet services tend to decline for individuals with ASD after they leave high school, which appears related to reduced abatement of ASD symptoms, the frequent absence of daytime activities such as higher education or work, and strains on the mother-child


title: A survey on nature inspired metaheuristic algorithms for partitional clustering

abstract: Abstract The partitional clustering concept started with K-means algorithm which was published in 1957. Since then many classical partitional clustering algorithms have been reported based on gradient descent approach. The 1990 kick started a new era in cluster analysis with the application of nature inspired metaheuristics. After initial formulation nearly two decades have passed and researchers have developed numerous new algorithms in this field. This paper embodies an up-to-date review of all major nature inspired metaheuristic algorithms employed till date for partitional clustering. Further, key issues involved during formulation of various metaheuristics as a clustering problem and major application areas are discussed.


title: Design concept of a new bio-inspired tactile sensor based on main pulvinus motor organ cells distribution of Mimosa Pudica plant

abstract: This paper proposed a novel concept for a new tactile sensor that is inspired by the seismonastic movement of plants. The movement of Mimosa Pudica leaf is due to the change of pressure between the upper and lower motor organ of main pulvinus. From an engineering point of view, the turgidity changes capable of reversible shape changes, thus bringing the idea of sensing and actuating concept of a new tactile sensor. The idea is to fusion the artificial cell of Mimosa Pudica as the sensing mechanism for the new bio-inspired tactile sensor. Experiments have been conducted in order to determine the volume of the upper n lower motor organ cells of main pulvinus. The aim is to view the cross section of the main pulvinus between before and after the stimulation applied which led to two experiment procedures. Experimental results show that area of upper motor organ cell is approximately 50% larger than lower part for all four samples when mechanical stimulation applied. The design concept of the bio-inspired tactile sensor is proposed.


title: CARDIAC: An Intelligent Conversational Assistant for Chronic Heart Failure

abstract: We describe CARDIAC, a prototype for an intelligent conversational assistant that provides health monitoring for chronic heart failure patients. CARDIAC supports user initiative through its ability to understand natural language and connect it to intention recognition. The natural language interface allows patients to interact with CARDIAC without special training. The system is designed to understand information that arises spontaneously in the course of the interview. If the patient gives more detail than necessary for answering a question, the system updates the user model accordingly. CARDIAC is a first step towards developing cost-effective, customizable, automated in-home conversational assistants that help patients manage their care and monitor their health using natural language.


title: Creating false memories: Remembering words not presented in lists.

abstract: False memories—either remembering events that never happened, or remembering them quite differently from the way they happened—have recently captured the attention of both psychologists and the public at large. The primary impetus for this recent surge of interest is the increase in the number of cases in which memories of previously unrecognized abuse are reported during the course of therapy. Some researchers have argued that certain therapeutic practices can cause the creation of false memories, and therefore, the apparent "recovery" of memories during the course of therapy may actually represent the creation of memories (Lindsay & Read, 1994; Loftus, 1993). Although the concept of false memories is currently enjoying an increase in publicity, it is not new; psychologists have been studying false memories in several laboratory paradigms for years. Schacter (in press) provides an historical overview of the study of memory distortions. Bartlett (1932) is usually credited with conducting the first experimental investigation of false memories; he had subjects read an Indian folktale, "The War of the Ghosts," and recall it repeatedly. Although he reported no aggregate data, but only sample protocols, his results seemed to show distortions in subjects' memories over repeated attempts to recall the story. Interestingly, Bartlett's repeated reproduction results never have been successfully replicated by later researchers (see Gauld & Stephenson, 1967; Roediger, Wheeler, & Rajaram, 1993); indeed, Wheeler and Roediger (1992) showed that recall of prose passages (including "The War of the Ghosts")


title: Multi-Objective Model Selection via Racing

abstract: Model selection is a core aspect in machine learning and is, occasionally, multi-objective in nature. For instance, hyper-parameter selection in a multi-task learning context is of multi-objective nature, since all the tasks’ objectives must be optimized simultaneously. In this paper, a novel multi-objective racing algorithm (RA), namely S-Race, is put forward. Given an ensemble of models, our task is to reliably identify Pareto optimal models evaluated against multiple objectives, while minimizing the total computational cost. As a RA, S-Race attempts to eliminate non-promising models with confidence as early as possible, so as to concentrate computational resources on promising ones. Simultaneously, it addresses the problem of multi-objective model selection (MOMS) in the sense of Pareto optimality. In S-Race, the nonparametric sign test is utilized for pair-wise dominance relationship identification. Moreover, a discrete Holm’s step-down procedure is adopted to control the family-wise error rate of the set of hypotheses made simultaneously. The significance level assigned to each family is adjusted adaptively during the race. In order to illustrate its merits, S-Race is applied on three MOMS problems: 1) selecting support vector machines for classification; 2) tuning the parameters of artificial bee colony algorithms for numerical optimization; and 3) constructing optimal hybrid recommendation systems for movie recommendation. The experimental results confirm that S-Race is an efficient and effective MOMS algorithm compared to a brute-force approach. 1 , 2 1 A preliminary version of this paper appeared in [1] .


title: A Case for Memory-Centric HPC System Architecture for Training Deep Neural Networks

abstract: As the models and the datasets to train deep learning (DL) models scale, system architects are faced with new challenges, one of which is the memory capacity bottleneck, where the limited physical memory inside the accelerator device constrains the algorithm that can be studied. We propose a memory-centric deep learning system that can transparently expand the memory capacity accessible to the accelerators while also providing fast inter-device communication for parallel training. Our proposal aggregates a pool of memory modules locally within the device-side interconnect, which are decoupled from the host interface and function as a vehicle for transparent memory capacity expansion. Compared to conventional systems, our proposal achieves an average $2.1\times$ speedup on eight DL applications and increases the system-wide memory capacity to tens of TBs.


title: Evaluating neural network explanation methods using hybrid documents and morphological agreement

abstract: The behavior of deep neural networks (DNNs) is hard to understand. This makes it necessary to explore post hoc explanation methods. We conduct the first comprehensive evaluation of explanation methods for NLP. To this end, we design two novel evaluation paradigms that cover two important classes of NLP problems: small context and large context problems. Both paradigms require no manual annotation and are therefore broadly applicable. We also introduce LIMSSE, an explanation method inspired by LIME that is designed for NLP. We show empirically that LIMSSE, LRP and DeepLIFT are the most effective explanation methods and recommend them for explaining DNNs in NLP.


title: Taskonomy: Disentangling Task Transfer Learning

abstract: Do visual tasks have a relationship, or are they unrelated? For instance, could having surface normals simplify estimating the depth of an image? Intuition answers these questions positively, implying existence of a structure among visual tasks. Knowing this structure has notable values; it is the concept underlying transfer learning and provides a principled way for identifying redundancies across tasks, e.g., to seamlessly reuse supervision among related tasks or solve many tasks in one system without piling up the complexity. We proposes a fully computational approach for modeling the structure of space of visual tasks. This is done via finding (first and higher-order) transfer learning dependencies across a dictionary of twenty six 2D, 2.5D, 3D, and semantic tasks in a latent space. The product is a computational taxonomic map for task transfer learning. We study the consequences of this structure, e.g. nontrivial emerged relationships, and exploit them to reduce the demand for labeled data. For example, we show that the total number of labeled datapoints needed for solving a set of 10 tasks can be reduced by roughly 2/3 (compared to training independently) while keeping the performance nearly the same. We provide a set of tools for computing and probing this taxonomical structure including a solver that users can employ to devise efficient supervision policies for their use cases.


title: A Reinforcement Learning Based Approach for Automated Lane Change Maneuvers

abstract: Lane change is a crucial vehicle maneuver which needs coordination with surrounding vehicles. Automated lane changing functions built on rule-based models may perform well under pre-defined operating conditions, but they may be prone to failure when unexpected situations are encountered. In our study, we proposed a Reinforcement Learning based approach to train the vehicle agent to learn an automated lane change behavior such that it can intelligently make a lane change under diverse and even unforeseen scenarios. Particularly, we treated both state space and action space as continuous, and designed a Q-function approximator that has a closed- form greedy policy, which contributes to the computation efficiency of our deep Q-learning algorithm. Extensive simulations are conducted for training the algorithm, and the results illustrate that the Reinforcement Learning based vehicle agent is capable of learning a smooth and efficient driving policy for lane change maneuvers.


title: Dynamic-MUSIC: accurate device-free indoor localization

abstract: Device-free passive indoor localization is playing a critical role in many applications such as elderly care, intrusion detection, smart home, etc. However, existing device-free localization systems either suffer from labor-intensive offline training or require dedicated special-purpose devices. To address the challenges, we present our system named MaTrack, which is implemented on commodity off-the-shelf Intel 5300 Wi-Fi cards. MaTrack proposes a novel Dynamic-MUSIC method to detect the subtle reflection signals from human body and further differentiate them from those reflected signals from static objects (furniture, walls, etc.) to identify the human target's angle for localization. MaTrack does not require any offline training compared to existing signature-based systems and is insensitive to changes in environment. With just two receivers, MaTrack is able to achieve a median localization accuracy below 0.6 m when the human is walking, outperforming the state-of-the-art schemes.


title: Visualizing Object Detection Features

abstract: We introduce algorithms to visualize feature spaces used by object detectors. Our method works by inverting a visual feature back to multiple natural images. We found that these visualizations allow us to analyze object detection systems in new ways and gain new insight into the detector's failures. For example, when we visualize the features for high scoring false alarms, we discovered that, although they are clearly wrong in image space, they often look deceptively similar to true positives in feature space. This result suggests that many of these false alarms are caused by our choice of feature space, and supports that creating a better learning algorithm or building bigger datasets is unlikely to correct these errors without improving the features. By visualizing feature spaces, we can gain a more intuitive understanding of recognition systems.


title: Path planning for autonomous vehicles using model predictive control

abstract: Path planning for autonomous vehicles in dynamic environments is an important but challenging problem, due to the constraints of vehicle dynamics and existence of surrounding vehicles. Typical trajectories of vehicles involve different modes of maneuvers, including lane keeping, lane change, ramp merging, and intersection crossing. There exist prior arts using the rule-based high-level decision making approaches to decide the mode switching. Instead of using explicit rules, we propose a unified path planning approach using Model Predictive Control (MPC), which automatically decides the mode of maneuvers. To ensure safety, we model surrounding vehicles as polygons and develop a type of constraints in MPC to enforce the collision avoidance between the ego vehicle and surrounding vehicles. To achieve comfortable and natural maneuvers, we include a lane-associated potential field in the objective function of the MPC. We have simulated the proposed method in different test scenarios and the results demonstrate the effectiveness of the proposed approach in automatically generating reasonable maneuvers while guaranteeing the safety of the autonomous vehicle.


title: Secure cross-domain cookies for HTTP

abstract: Cookies represent an important element of HTTP providing state management to an otherwise stateless protocol. HTTP cookies currently in use are governed by the same origin policy that directs Web browsers to allow cookie sharing only between Web sites in the same DNS domain. As Web applications get richer, data sharing across domain boundaries becomes more important. While practical solutions to cross-domain data sharing exist, in many cases they increase complexity and cost. In this paper we propose a simple mechanism to share cookies using authorizations based on X.509 attribute and public key certificates. In addition to supporting secure cookie sharing between unrelated domains, it can be beneficial for hosts in the same domain when the currently used same origin policy is deemed too permissive, exposing cookies to leakage and spoofing.


Comparison of Volatile Compounds Profile and Antioxydant Activity of Allium sativum Essential Oils Extracted using Hydrodistillation, Ultrasound- Assisted and Sono-Hydrodistillation Processes


title: Extracting grammar from programs: evolutionary approach

abstract: The paper discusses context-free grammar (CFG) inference using genetic-programming with application to inducing grammars from programs written in simple domain-specific languages. Grammar-specific heuristic operators and non-random construction of the initial population are proposed to achieve this task. Suitability of the approach is shown by small examples where the underlying CFG's are successfully inferred.


title: A combined approach for evaluating papers, authors and scientific journals

abstract: An integrated model for ranking scientific publications together with authors and journals recently presented in [Bini, Del Corso, Romani, ETNA 2008] is closely analyzed. The model, which relies on certain adjacency matrices H,K and F obtained from the relations of citation, authorship and publication, provides the ranking by means of the Perron vector of a stochastic matrix obtained by combining H,K and F. Some perturbation theorems concerning the Perron vector previously introduced by the authors are extended to more general cases and a counterexample to a property previously addressed by the authors is presented. The theoretical results confirm the consistency and effectiveness of our model. Some paradigmatic examples are reported together with some results obtained on a real set of data.


title: Design of High-Directivity Compact-Size Conical Horn Lens Antenna

abstract: The design of lightweight, compact-size, and high-directivity conical horn lens antenna that covers both 71-76 and 81-86 GHz bands is presented in this letter. The conventional conical moderate-gain horn antenna is integrated with a planar inhomogeneous dielectric flat transmit-array lens having diameter of 35 mm and thickness of 6.35 mm by covering its aperture. The transmit-array lens is designed based on the perforating dielectric technique that is based on the drilling of numerous holes of variable diameters through a dielectric host material. The unit cell size is about λ0/2, which allows for easier fabrication. The proposed lens consists of eight concentric zones of same width of 2.32 mm and sharing the same host dielectric material. A directivity of 26.4 dBi, 9 dBi improvement, sidelobe level (SLL) of -15.7 dB at 74 GHz and directivity of 27 dBi, 9.9 dBi improvement, and SLL of -18.4 dB at 86 GHz were achieved. The proposed horn antenna is 44.46% shorter than the conventional horn antenna producing the same directivity, where the horn antenna length reduction is a result of using the proposed transmit-array lens.


title: QoS-Aware Adaptive Routing in Multi-layer Hierarchical Software Defined Networks: A Reinforcement Learning Approach

abstract: Software-defined networks (SDNs) have been recognized as the next-generation networking paradigm that decouples the data forwarding from the centralized control. To realize the merits of dedicated QoS provisioning and fast route (re-)configuration services over the decoupled SDNs, various QoS requirements in packet delay, loss, and throughput should be supported by an efficient transportation with respect to each specific application. In this paper, a QoS-aware adaptive routing (QAR) is proposed in the designed multi-layer hierarchical SDNs. Specifically, the distributed hierarchical control plane architecture is employed to minimize signaling delay in large SDNs via three-levels design of controllers, i.e., the super, domain (or master), and slave controllers. Furthermore, QAR algorithm is proposed with the aid of reinforcement learning and QoS-aware reward function, achieving a time-efficient, adaptive, QoS-provisioning packet forwarding. Simulation results confirm that QAR outperforms the existing learning solution and provides fast convergence with QoS provisioning, facilitating the practical implementations in large-scale software service-defined networks.


title: MobileFusion: Real-Time Volumetric Surface Reconstruction and Dense Tracking on Mobile Phones

abstract: We present the first pipeline for real-time volumetric surface reconstruction and dense 6DoF camera tracking running purely on standard, off-the-shelf mobile phones. Using only the embedded RGB camera, our system allows users to scan objects of varying shape, size, and appearance in seconds, with real-time feedback during the capture process. Unlike existing state of the art methods, which produce only point-based 3D models on the phone, or require cloud-based processing, our hybrid GPU/CPU pipeline is unique in that it creates a connected 3D surface model directly on the device at 25Hz. In each frame, we perform dense 6DoF tracking, which continuously registers the RGB input to the incrementally built 3D model, minimizing a noise aware photoconsistency error metric. This is followed by efficient key-frame selection, and dense per-frame stereo matching. These depth maps are fused volumetrically using a method akin to KinectFusion, producing compelling surface models. For each frame, the implicit surface is extracted for live user feedback and pose estimation. We demonstrate scans of a variety of objects, and compare to a Kinect-based baseline, showing on average ∼ 1.5 cm error. We qualitatively compare to a state of the art point-based mobile phone method, demonstrating an order of magnitude faster scanning times, and fully connected surface models.


title: Patient predictors of response to cognitive behaviour therapy and interpersonal psychotherapy in a randomised clinical trial for depression.

abstract: Abstract Objectives This study examined patient predictors of response to interpersonal psychotherapy (IPT) and cognitive behaviour therapy (CBT). Method Participants were 177 adults with a primary diagnosis of major depressive disorder randomised to 16 weekly sessions of either IPT or CBT. Pre and post treatment depressive symptomatology was assessed by an independent clinician with the Montgomery Asberg Depression Rating Scale. Results General predictors of response were perceived logicalness of therapy, recurrent depression and childhood reasons for depression (r 2 = .21). Only one differential predictor of treatment response was identified. Increasing comorbid personality disorder symptoms was associated with decreases in response to IPT but not CBT. Conclusion The results indicate that attention to specific pretreatment patient factors may enhance response to psychotherapy.


title: Immersive FPS games: user experience and performance

abstract: Computer games are ideally placed to form the content of future Immersive Media, but this prospect is faced with both technical and usability issues. This paper describes an experiment in immersive gaming using a state-of-the-art computer First Person Shooter (FPS) game, in which we analyze user experience and performance through a combination of in-game metrics, questionnaires and subjective reports. We describe the evaluation of a major commercial computer game as a real-time immersive stereoscopic experience based on a four-screen CAVE-like installation. The implementation is based on a bespoke VR middleware developed on top of the game's own engine. Our results show an overwhelming subjective preference for the immersive version despite a decrease in performance attributed to a more realistic aiming mechanism. More importantly, metrics suggest that users took advantage of the immersive context rather than simply transposing their desktop gaming skills.


title: Event Schema Induction with a Probabilistic Entity-Driven Model

abstract: Event schema induction is the task of learning high-level representations of complex events (e.g., a bombing) and their entity roles (e.g., perpetrator and victim) from unlabeled text. Event schemas have important connections to early NLP research on frames and scripts, as well as modern applications like template extraction. Recent research suggests event schemas can be learned from raw text. Inspired by a pipelined learner based on named entity coreference, this paper presents the first generative model for schema induction that integrates coreference chains into learning. Our generative model is conceptually simpler than the pipelined approach and requires far less training data. It also provides an interesting contrast with a recent HMM-based model. We evaluate on a common dataset for template schema extraction. Our generative model matches the pipeline’s performance, and outperforms the HMM by 7 F1 points (20%).


title: Integrated affordance detection and humanoid locomotion planning

abstract: A humanoid robot navigating in an unstructured environment requires knowledge of the affordances which allow it to make contact with the environment. This knowledge often comes from a perception system, which processes data from 3D sensors such as LIDAR and extracts available areas for the robot to make contact. Because perception systems run independently of the robot's planner, without knowledge of the robot's goal, they must process the entire visible area. In large environments, or those with complex geometry, the perception system may spend significant time processing areas of the environment that the planner will never consider visiting. By integrating the perception process with the planner, we are able to improve the speed with which the robot can compute a motion plan by only processing those areas of the environment which are considered by the planner for navigation. Two experiments with simulated and real-world point cloud data suggest that our framework can produce comparable plans up to seven times faster than the perceive-then-plan approach.


title: Imaging With Nature: Compressive Imaging Using a Multiply Scattering Medium

abstract: The recent theory of compressive sensing leverages upon the structure of signals to acquire them with much fewer measurements than was previously thought necessary, and certainly well below the traditional Nyquist-Shannon sampling rate. However, most implementations developed to take advantage of this framework revolve around controlling the measurements with carefully engineered material or acquisition sequences. Instead, we use the natural randomness of wave propagation through multiply scattering media as an optimal and instantaneous compressive imaging mechanism. Waves reflected from an object are detected after propagation through a well-characterized complex medium. Each local measurement thus contains global information about the object, yielding a purely analog compressive sensing method. We experimentally demonstrate the effectiveness of the proposed approach for optical imaging by using a 300-micrometer thick layer of white paint as the compressive imaging device. Scattering media are thus promising candidates for designing efficient and compact compressive imagers.


title: A Modified Sierpinski Fractal Antenna for Multiband Application

abstract: A broadband planar Sierpinski fractal antenna for multiband application is proposed, designed, and tested. The perturbed Sierpinski fractal patch and slotted ground plane are employed to achieve broadband characteristics. The implemented antenna including the ground plane has a total dimension of 100 times 53.7 times 0.8 mm3. The measured 10-dB return loss bandwidths are 808-1008 MHz (22%) and 1581-2760 MHz (54.3%), which cover the GSM/DCS/PCS/IMT-2000/ISM/satellite DMB bands. The measured return loss, radiation patterns, and gain of the proposed antenna are presented and compared with simulated results.


title: Forward and inverse models in motor control and cognitive control

abstract: It is now commonly accepted that the motor system ::: makes use of so-called forward and inverse models in order to ::: control the musculoskeletal system during rapid, skilled, motor ::: behaviour. Inverse models are held to allow the system to ::: determine the motor commands necessary to achieve a desired ::: state, while forward models are held to allow the system to ::: predict the expected sensory feedback of a motor command, ::: allowing rapid error detection when actual and predicted ::: feedback do not match. It has recently been suggested that these ::: ideas from control theory might also be applied to the control of ::: cognitive processes, allowing (for example) the cognitive system ::: to anticipate processing conflict and pre-emptively minimise it ::: by adjusting processing strategies or the allocation of processing ::: resources. ::: 12 ::: This paper reviews theories of cognitive control that ::: are broadly consistent with the use of complementary forward ::: and inverse models. It is argued that there is indeed a role for ::: such models in cognitive control – particularly in relation to a ::: putative monitoring function – but that the models involved are ::: likely to be somewhat impoverished.


title: Divisive Normalization in Olfactory Population Codes

abstract: Summary In many regions of the visual system, the activity of a neuron is normalized by the activity of other neurons in the same region. Here we show that a similar normalization occurs during olfactory processing in the Drosophila antennal lobe. We exploit the orderly anatomy of this circuit to independently manipulate feedforward and lateral input to second-order projection neurons (PNs). Lateral inhibition increases the level of feedforward input needed to drive PNs to saturation, and this normalization scales with the total activity of the olfactory receptor neuron (ORN) population. Increasing total ORN activity also makes PN responses more transient. Strikingly, a model with just two variables (feedforward and total ORN activity) accurately predicts PN odor responses. Finally, we show that discrimination by a linear decoder is facilitated by two complementary transformations: the saturating transformation intrinsic to each processing channel boosts weak signals, while normalization helps equalize responses to different stimuli. Video Abstract


abstract: Graphical abstractDisplay Omitted Propose a Cloud-based bioinformatics workflow platform for next-generation sequencing analyses.Propose a method for automatically deploying Galaxy workflow system on Amazon Cloud.Integrate Galaxy with Globus Transfer for high-performance and reliable data transfer.Integrate Galaxy with HTCondor scheduler for auto-scaling and parallel computing.Two bioinformatics workflow use cases and performance evaluation are presented. Due to the upcoming data deluge of genome data, the need for storing and processing large-scale genome data, easy access to biomedical analyses tools, efficient data sharing and retrieval has presented significant challenges. The variability in data volume results in variable computing and storage requirements, therefore biomedical researchers are pursuing more reliable, dynamic and convenient methods for conducting sequencing analyses. This paper proposes a Cloud-based bioinformatics workflow platform for large-scale next-generation sequencing analyses, which enables reliable and highly scalable execution of sequencing analyses workflows in a fully automated manner. Our platform extends the existing Galaxy workflow system by adding data management capabilities for transferring large quantities of data efficiently and reliably (via Globus Transfer), domain-specific analyses tools preconfigured for immediate use by researchers (via user-specific tools integration), automatic deployment on Cloud for on-demand resource allocation and pay-as-you-go pricing (via Globus Provision), a Cloud provisioning tool for auto-scaling (via HTCondor scheduler), and the support for validating the correctness of workflows (via semantic verification tools). Two bioinformatics workflow use cases as well as performance evaluation are presented to validate the feasibility of the proposed approach.


title: EP-SPARQL: A Unified Language for Event Processing and Stream Reasoning

abstract: Streams of events appear increasingly today in various Web applications such as blogs, feeds, sensor data streams, geospatial information, on-line financial data, etc. Event Processing (EP) is concerned with timely detection of compound events within streams of simple events. State-of-the-art EP provides on-the-fly analysis of event streams, but cannot combine streams with background knowledge and cannot perform reasoning tasks. On the other hand, semantic tools can effectively handle background knowledge and perform reasoning thereon, but cannot deal with rapidly changing data provided by event streams. To bridge the gap, we propose Event Processing SPARQL (EP-SPARQL) as a new language for complex events and Stream Reasoning. We provide syntax and formal semantics of the language and devise an effective execution model for the proposed formalism. The execution model is grounded on logic programming, and features effective event processing and inferencing capabilities over temporal and static knowledge. We provide an open-source prototype implementation and present a set of tests to show the usefulness and effectiveness of our approach.


title: Logistic Regression, AdaBoost and Bregman Distances

abstract: We give a unified account of boosting and logistic regression in which each learning problem is cast in terms of optimization of Bregman distances. The striking similarity of the two problems in this framework allows us to design and analyze algorithms for both simultaneously, and to easily adapt algorithms designed for one problem to the other. For both problems, we give new algorithms and explain their potential advantages over existing methods. These algorithms are iterative and can be divided into two types based on whether the parameters are updated sequentially (one at a time) or in parallel (all at once). We also describe a parameterized family of algorithms that includes both a sequential- and a parallel-update algorithm as special cases, thus showing how the sequential and parallel approaches can themselves be unified. For all of the algorithms, we give convergence proofs using a general formalization of the auxiliary-function proof technique. As one of our sequential-update algorithms is equivalent to AdaBoost, this provides the first general proof of convergence for AdaBoost. We show that all of our algorithms generalize easily to the multiclass case, and we contrast the new algorithms with the iterative scaling algorithm. We conclude with a few experimental results with synthetic data that highlight the behavior of the old and newly proposed algorithms in different settings.


title: Participatory Design: Issues and Concerns

abstract: We characterize Participatory Design (PD) as a maturing area of research and as an evolving practice among design professionals. Although PD has been applied outside of technology design, here we focus on PD in relation to the introduction of computer-based systems at work. We discuss three main issues addressed by PD researchers; the politics of design; the nature of participation; and method, tools and techniques for participation. We also report on the conditions for the transfer of ’’PD results‘‘ to workers, user groups, and design professionals that have characterized PD over time and across geopolitical terrains. The topic of the sustainability of PD within an organizational context is also considered. The article concludes with a discussion of common issues explored within PD and CSCW and frames directions for a continuing dialogue between researchers and practitioners from the two fields. The article draws on a review of PD and CSCW literatures as well as on our own research and practical experiences.


title: Edge Detection and Ridge Detection with Automatic Scale Selection

abstract: When computing descriptors of image data, the type of information that can be extracted may be strongly dependent on the scales at which the image operators are applied. This article presents a systematic methodology for addressing this problem. A mechanism is presented for automatic selection of scale levels when detecting one-dimensional image features, such as edges and ridges. ::: ::: A novel concept of a scale-space edge is introduced, defined as a connected set of points in scale-space at which: (i) the gradient magnitude assumes a local maximum in the gradient direction, and (ii) a normalized measure of the strength of the edge response is locally maximal over scales. An important consequence of this definition is that it allows the scale levels to vary along the edge. Two specific measures of edge strength are analyzed in detail, the gradient magnitude and a differential expression derived from the third-order derivative in the gradient direction. For a certain way of normalizing these differential descriptors, by expressing them in terms of so-called γ-normalized derivatives, an immediate consequence of this definition is that the edge detector will adapt its scale levels to the local image structure. Specifically, sharp edges will be detected at fine scales so as to reduce the shape distortions due to scale-space smoothing, whereas sufficiently coarse scales will be selected at diffuse edges, such that an edge model is a valid abstraction of the intensity profile across the edge. ::: ::: Since the scale-space edge is defined from the intersection of two zero-crossing surfaces in scale-space, the edges will by definition form closed curves. This simplifies selection of salient edges, and a novel significance measure is proposed, by integrating the edge strength along the edge. Moreover, the scale information associated with each edge provides useful clues to the physical nature of the edge. ::: ::: With just slight modifications, similar ideas can be used for formulating ridge detectors with automatic selection, having the characteristic property that the selected scales on a scale-space ridge instead reflect the width of the ridge. ::: ::: It is shown how the methodology can be implemented in terms of straightforward visual front-end operations, and the validity of the approach is supported by theoretical analysis as well as experiments on real-world and synthetic data.


title: The Secret Is To Follow Your Nose Route Path Selection and Angularity

abstract: This article presents an experiment in which route choice decisions made at road junctions are recorded. Such routes can be expressed as the sum of individual decisions made or potential decisions available throughout a journey. Relationships between these aggregate values are assessed statistically, indicating that participants’ decisions correlate more strongly with maximum angles of incidence of road center lines (leading from a junction) than to mean or minimum angles. One interpretation is that participants appear to be attempting to conserve linearity throughout their journey. However, informal observations of participants traversing urban grids cast doubt on the proposed theories of the conservation of angular linearity, requiring the theory’s modification. The resultant hypothesis combines principles of a conservation of linearity while also minimizing the angular difference between pairs of bearings; the key bearings are the directions of potential route choices and a perceived bearing of the way...


title: Dynamic analysis of Reaction Force sensing Series Elastic Actuator as unlumped two mass system

abstract: Recently, the dynamic model of Series Elastic Actuator (SEA) has been researched intensively, and most of the approaches ignore the dynamics of the load part and model SEA as a simple mass damper spring system, which is valid only when SEAs contact with high impedance environments. To consider the dynamic model of SEA as a mass damper spring system can be erroneous when SEA does not contact high impedance environment and makes free motions. In this paper, a novel dynamic model of Reaction Force Series Elastic Actuator is proposed, which is unlumped two mass system model. The dynamic model is derived using the Lagrangian mechanics so as to successfully include all degrees of freedom of the SEA. The proposed model is verified through system identification experiments. The results indicate the actual dynamic model of SEA is valid throughout various load conditions.


title: Happy and less inhibited? Effects of positive mood on inhibitory control during an antisaccade task revealed using topographic evoked potential mapping

abstract: Abstract Affective states might influence inhibitory control, a cognitive process fundamental for goal adaptive behavior. Here, we recorded high-density EEG while participants performed an antisaccade task, after the induction of a happy ( n = 20) or neutral ( n = 20) mood, to compare the same inhibition-related processes across these two affective contexts. Topographical evoked potential mapping methods were used to characterize changes in the electric field depending on mood and saccade type (pro vs. anti) concurrently. Results showed that prior to target onset, the CNV component was enhanced for anti- compared to prosaccades, selectively in the neutral mood group. Following target onset, the topography of the N2 was more strongly expressed in the happy mood group, and was also altered by saccade type. The subsequent P3 components were not modulated by mood. We discuss these new findings in light of recent neurobiological and neuropsychological models that posit that positive affect dynamically changes cognitive control.


title: An effective support vector machines (SVMs) performance using hierarchical clustering

abstract: The training time for SVMs to compute the maximal marginal hyper-plane is at least O(N/sup 2/) with the data set size N, which makes it nonfavorable for large data sets. This work presents a study for enhancing the training time of SVMs, specifically when dealing with large data sets, using hierarchical clustering analysis. We use the dynamically growing self-organizing tree (DGSOT) algorithm for clustering because it has proved to overcome the drawbacks of traditional hierarchical clustering algorithms. Clustering analysis helps find the boundary points, which are the most qualified data points to train SVMs, between two classes. We present a new approach of combination of SVMs and DGSOT, which starts with an initial training set and expands it gradually using the clustering structure produced by the DGSOT algorithm. We compare our approach with the Rocchio Bundling technique in terms of accuracy loss and training time gain using two benchmark real data sets.


Uneven Image Point Distribution in Camera Pre-Calibration and its Effects on 3 D Reconstruction Errors


title: Attribute-Based Partial Geo-Replication System

abstract: Existing partial geo-replication systems do not always provide optimal cost or latency, because their replication decisions are based on statically established data access popularity metrics, regardless of the application types. We demonstrate that additional reduction in cost and latency can be achieved by 1) using the right object attributes for making replication decisions for each type of application, 2) using multi-attribute-based replications, and 3) combining the popularity-based but reactive approach with the more random but proactive approach to data replication. Toward this end, we propose Acorn, an Attribute-based COntinuous partial geo-ReplicatioN system, and its prototype implementation based on Apache Cassandra. Experiments with two types of global-scale, data-sharing applications demonstrate up to 54% and 90% cost overhead reduction over existing systems or 38% and 91% latency overhead reduction.


title: Grounding Interactive Machine Learning Tool Design in How Non-Experts Actually Build Models

abstract: Machine learning (ML) promises data-driven insights and solutions for people from all walks of life, but the skill of crafting these solutions is possessed by only a few. Emerging research addresses this issue by creating ML tools that are easy and accessible to people who are not formally trained in ML (non-experts). This work investigated how non-experts build ML solutions for themselves in real life. Our interviews and surveys revealed unique potentials of non-expert ML, as well several pitfalls that non-experts are susceptible to. For example, many perceived percentage accuracy as a sole measure of performance, thus problematic models proceeded to deployment. These observations suggested that, while challenging, making ML easy and robust should both be important goals of designing novice-facing ML tools. To advance on this insight, we discuss design implications and created a sensitizing concept to demonstrate how designers might guide non-experts to easily build robust solutions.


title: Magnetic resonance imaging of multifunctional pluronic stabilized iron-oxide nanoparticles in tumor-bearing mice.

abstract: We are investigating the magnetic resonance imaging characteristics of magnetic nanoparticles (MNPs) that consist of an iron-oxide magnetic core coated with oleic acid (OA), then stabilized with a pluronic or tetronic block copolymer. Since pluronics and tetronics vary structurally, and also in the ratio of hydrophobic (poly[propylene oxide]) and hydrophilic (poly[ethylene oxide]) segments in the polymer chain and in molecular weight, it was hypothesized that their anchoring to the OA coating around the magnetic core could significantly influence the physical properties of MNPs, their interactions with biological environment following intravenous administration, and ability to localize to tumors. The amount of block copolymer associated with MNPs was seen to depend upon their molecular structures and influence the characteristics of MNPs. Pluronic F127-modified MNPs demonstrated sustained and enhanced contrast in the whole tumor, whereas that of Feridex IV was transient and confined to the tumor periphery. In conclusion, our pluronic F127-coated MNPs, which can also be loaded with anticancer agents for drug delivery, can be developed as an effective cancer theranostic agent, i.e. an agent with combined drug delivery and imaging properties.


The Turkish Adaptation of 15-Item Version of the Need for Cognitive Closure Scale and Relationship between Thinking and Decision-making Styles


Natural Language Processing in Information Retrieval


title: Kanthaka: Big Data Caller Detail Record (CDR) Analyzer for Near Real Time Telecom Promotions

abstract: With the competitiveness that is growing in the telecom industry, the operators seek for ways to attract and keep the subscribers in their network. Giving away attractive promotions to their subscribers is a powerful and commonly used approach in that context. With the technical limitations and the scales of the rising subscriber base, these promotions have been limited to a very narrow pattern. In order to gain a competitive advantage, the operators need new technologies and methodologies to support more attractive newer patterns of promotions, despite the challenges involved in complex analyzing procedures. In this research study, we have come up with a system to select eligible users for a particular predefined promotion, analyzing the Caller Detail Records, while being scalable for a rapidly escalating subscriber base.


abstract: Security is a major problem in networking. Corporates and Service providers spend much money on highly priced firewalls to implement security. Software-Defined Networking (SDN) is a new structure that can save Corporates and service providers money, reduce provisioning time from days to minutes, provides centralized management, allow programmability in the implicit Network. SDN accomplishes this by decoupling the control plane from the data plane. This paper demonstrates the benefits of implementing a Cloud based firewall on OpenFlow SDN network, which is a way of implementing SDN. The firewall has the capability of identifying application layer traffic, like social Medias and other websites and excluding some Denial-of-Service Attacks. This paper handles the implementation information and give a performance analysis of the firewall component.


title: Experience sampling during fMRI reveals default network and executive system contributions to mind wandering

abstract: Although mind wandering occupies a large proportion of our waking life, its neural basis and relation to ongoing behavior remain controversial. We report an fMRI study that used experience sampling to provide an online measure of mind wandering during a concurrent task. Analyses focused on the interval of time immediately preceding experience sampling probes demonstrate activation of default network regions during mind wandering, a finding consistent with theoretical accounts of default network functions. Activation in medial prefrontal default network regions was observed both in association with subjective self-reports of mind wandering and an independent behavioral measure (performance errors on the concurrent task). In addition to default network activation, mind wandering was associated with executive network recruitment, a finding predicted by behavioral theories of off-task thought and its relation to executive resources. Finally, neural recruitment in both default and executive network regions was strongest when subjects were unaware of their own mind wandering, suggesting that mind wandering is most pronounced when it lacks meta-awareness. The observed parallel recruitment of executive and default network regions—two brain systems that so far have been assumed to work in opposition—suggests that mind wandering may evoke a unique mental state that may allow otherwise opposing networks to work in cooperation. The ability of this study to reveal a number of crucial aspects of the neural recruitment associated with mind wandering underscores the value of combining subjective self-reports with online measures of brain function for advancing our understanding of the neurophenomenology of subjective experience.


title: Thermal Facial Analysis for Deception Detection

abstract: Thermal imaging technology can be used to detect stress levels in humans based on the radiated heat from their face. In this paper, we use thermal imaging to monitor the periorbital region's thermal variations and test whether it can offer a discriminative signature for detecting deception. We start by presenting an overview on automated deception detection and propose a novel methodology, which we validate experimentally on 492 thermal responses (249 lies and 243 truths) extracted from 25 participants. The novelty of this paper lies in scoring a larger number of questions per subject, emphasizing a within-person approach for learning from data, proposing a framework for validating the decision making process, and correct evaluation of the generalization performance. A $k$ -nearest neighbor classifier was used to classify the thermal responses using different strategies for data representation. We report an 87% ability to predict the lie/truth responses based on a within-person methodology and fivefold cross validation. Our results also show that the between-person approach for modeling deception does not generalize very well across the training data.


title: A joint encryption and error correction method used in satellite communications

abstract: Due to the ubiquitous open air links and complex electromagnetic environment in the satellite communications, how to ensure the security and reliability of the information through the satellite communications is an urgent problem. This paper combines the AES(Advanced Encryption Standard) with LDPC(Low Density Parity Check Code) to design a secure and reliable error correction method ? SEEC(Satellite Encryption and Error Correction). This method selects the LDPC codes, which is suitable for satellite communications, and uses the AES round key to control the encoding process, at the same time, proposes a new algorithm of round key generation. Based on a fairly good property in error correction in satellite communications, the method improves the security of the system, achieves a shorter key size, and then makes the key management easier. Eventually, the method shows a great error correction capability and encryption effect by the MATLAB simulation.


title: An Integrated 122-GHz Antenna Array With Wire Bond Compensation for SMT Radar Sensors

abstract: This paper shows the antenna design of an integrated 122-GHz radar sensor. The sensor consists of a SiGe IC with a complete radar circuit and two antennas for transmitting and receiving the 122-GHz signal. The IC and the antennas are integrated into a low-cost plastic package that is assembled using wire bond technology. The antenna design was specifically optimized regarding the integration into the package and the compensation of the parasitic effects of the wire bond interconnect. First, the antenna design and its novel feeding technique based on a GCPW transmission line are explained. Then, measurement results including the wire bond interconnect are presented. Finally, details of the complete radar sensor are given.


title: Children with Unmet Healthcare Needs under the State Children Health Insurance Program

abstract: The Balanced Budget Act of 1997 established the State Children’s Health Insurance Program (SCHIP), which makes health insurance available to children under the age of eighteen who are members of low income families that do not qualify for Medicaid. The recent development in the government outreach strategies of the SCHIP program has contributed to reduce the disparity in healthcare service accessibility to children. Purpose: The objective of this study is to examine children with special healthcare needs and the unmet healthcare needs under state health insurance programs for children. Two major purposes of the study are: 1. to assess children with healthcare needs between SCHIP single (SCHIP-S), SCHIP combination (SCHI-C) and SCHIP Medicaid expansion (SCHIP-M) programs, and Medicaid; and 2. to examine children with unmet healthcare needs under the SCHIP-S, SCHIP-C, and SCHIP-M programs. Data: The data source comes from the Center for Disease Control and Prevention, the National Center for Health Statistics, the State and Local Area Integrated Telephone Survey, and the National Survey of Children with Special Healthcare Needs, 2001. Results: The study shows that regardless of the types of the SCHIP, the children under three types of the SCHIP programs have more healthcare needs than Medicaid children. Another thing that has been uncovered is that parents who do not speak English and who are without regular healthcare sources are more likely to decline their access to services allowing them to address the healthcare needs of their children. Based on this study, children under SCHIP single are more likely to be associated with a higher probability of unmet healthcare needs than children under SCHIP combination and SCHIP Medicaid expansion programs. The study illustrates that financial barriers (cost, health plan coverage, and financial hardships), indirect barriers (time constraints), and practice-level barriers (treatment issues of doctors, and the difficulty of getting an appointment) would raise children with unmet healthcare needs. Conclusion: The variation in coverage of a state’s SCHIP program depends on the particular state’s baseline and criteria. This variation introduces an inequality of health accessibility, which in turn, leads to health inequality in children among and within states. In order for SCHIP programs to reduce the number of children with unmet healthcare needs and improve the health of children, financial and practice-level barriers for healthcare services must be diminished.


title: A Fast, Compact Approximation of the Exponential Function

abstract: Neural network simulations often spend a large proportion of their time computing exponential functions. Since the exponentiation routines of typical math libraries are rather slow, their replacement with a fast approximation can greatly reduce the overall computation time. This article describes how exponentiation can be approximated by manipulating the components of a standard (IEEE-754) floating-point representation. This models the exponential function as well as a lookup table with linear interpolation, but is significantly faster and more compact.


title: Governing Big Data: Principles and practices

abstract: As data-intensive decision making is being increasingly adopted by businesses, governments, and other agencies around the world, most organizations encountering a very large amount and variety of data are still contemplating and assessing their readiness to embrace "Big Data." While these organizations devise various ways to deal with the challenges such data brings, the impact and importance of Big Data to information quality and governance programs should not be underestimated. Drawing upon implementation experiences of early adopters of Big Data technologies across multiple industries, this paper explores the issues and challenges involved in the management of Big Data, highlighting the principles and best practices for effective Big Data governance.


title: SARA: Security Automotive Risk Analysis Method

abstract: Connected and automated vehicles aim to improve the comfort and the safety of the driver and passengers. To this end, car manufacturers continually improve actual standardized methods to ensure their customers safety, privacy, and vehicles security. However, these methods do not support fully autonomous vehicles, linkability and confusion threats. To address such gaps, we propose a systematic threat analysis and risk assessment framework, SARA, which comprises an improved threat model, a new attack method/asset map, the involvement of the attacker in the attack tree, and a new driving system observation metric. Finally, we demonstrate its feasibility in assessing risk with two use cases: Vehicle Tracking and Comfortable Emergency Brake Failure.


title: Human Action Generation with Generative Adversarial Networks

abstract: Inspired by the recent advances in generative models, we introduce a human action generation model in order to generate a consecutive sequence of human motions to formulate novel actions. We propose a framework of an autoencoder and a generative adversarial network (GAN) to produce multiple and consecutive human actions conditioned on the initial state and the given class label. The proposed model is trained in an end-to-end fashion, where the autoencoder is jointly trained with the GAN. The model is trained on the NTU RGB+D dataset and we show that the proposed model can generate different styles of actions. Moreover, the model can successfully generate a sequence of novel actions given different action labels as conditions. The conventional human action prediction and generation models lack those features, which are essential for practical applications.


title: Electric-field visualization technique for evaluating radiation from automotive millimeter-wave radar

abstract: Pushing accuracy and reliability of radar systems to higher levels is a requirement to realize autonomous driving. To maximize its performance, the millimeter-wave radar has to be designed in consideration of its surroundings such as emblems, bumpers and so on, because the electric-field distortion will degrade the performance. We propose electro-optic (EO) measurement system to visualize amplitude and phase distribution of millimeter waves, aiming at the evaluation of the disturbance of car components with the radar module equipped inside a vehicle. Visualization of 76-GHz millimeter waves passing through plastic plates is presented to demonstrate our system's capability of diagnosing a local cause of the field disturbance.


title: An overview of online trust: Concepts, elements, and implications

abstract: Lack of trust has been repeatedly identified as one of the most formidable barriers to people for engaging in e-commerce, involving transactions in which financial and personal information is submitted to merchants via the Internet. The future of e-commerce is tenuous without a general climate of online trust. Building consumer trust on the Internet presents a challenge for online merchants and is a research topic of increasing interest and importance. This paper provides an overview of the nature and concepts of trust from multi-disciplinary perspectives, and it reviews relevant studies that investigate the elements of online trust. Also, a framework of trust-inducing interface design features articulated from the existing literature is presented. The design features were classified into four dimensions, namely (1) graphic design, (2) structure design, (3) content design, and (4) social-cue design. By applying the design features identified within this framework to e-commerce web site interfaces, online merchants might then anticipate fostering optimal levels of trust in their customers.


title: Seamless Mosaicing of Image-Based Texture Maps

abstract: Image-based object modeling has emerged as an important computer vision application. Typically, the process starts with the acquisition of the image views of an object. These views are registered within the global coordinate system using structure-and-motion techniques, while on the next step the geometric shape of an object is recovered using stereo and/or silhouette cues. This paper considers the final step, which creates the texture map for the recovered geometry model. The approach proposed in the paper naturally starts by backprojecting original views onto the obtained surface. A texture is then mosaiced from these back projections, whereas the quality of the mosaic is maximized within the process of Markov random field energy optimization. Finally, the residual seams between the mosaic components are removed via seam levelling procedure, which is similar to gradient-domain stitching techniques recently proposed for image editing. Unlike previous approaches to the same problem, intensity blending as well as image resampling are avoided on all stages of the process, which ensures that the resolution of the produced texture is essentially the same as that of the original views. Importantly, due to restriction to non-greedy energy optimization techniques, good results are produced even in the presence of significant errors on image registration and geometric estimation steps.


title: Pelvic tilt and truncal inclination: two key radiographic parameters in the setting of adults with spinal deformity.

abstract: STUDY DESIGN ::: Prospective radiographic and clinical analysis. ::: ::: ::: OBJECTIVE ::: Investigate the relationship between spino-pelvic parameters and patient self reported outcomes on adult subjects with spinal deformities. ::: ::: ::: SUMMARY OF BACKGROUND DATA ::: It is becoming increasingly recognized that the study of spinal alignment should include pelvic position. While pelvic incidence determines lumbar lordosis, pelvic tilt (PT) is a positional parameter reflecting compensation to spinal deformity. Correlation between plumbline offset (sagittal vertical axis [SVA]) and Health Related Quality of Life (HRQOL) measures has been demonstrated, but such a study is lacking for PT. ::: ::: ::: METHODS ::: This prospective study was carried out on 125 adult patients suffering from spinal deformity (mean age: 57 years). Full-length free-standing radiographs including the spine and pelvis were available for all patients. HRQOL instruments included: Oswestry Disability Index, Short Form-12, Scoliosis Research Society. Correlation analysis between radiographic spinopelvic parameters and HRQOL measures was pursued. ::: ::: ::: RESULTS ::: Correlation analysis revealed no significance pertaining to coronal plane parameters. Significant sagittal plane correlations were identified. SVA and truncal inclination measured by T1 spinopelvic inclination (T1-SPI) (angle between T1-hip axis and vertical) correlated with: Scoliosis Research Society (appearance, activity, total score), Oswestry Disability Index, and Short Form-12 (physical component score). Correlation coefficients ranged from 0.42 < r < 0.55 (P < 0.0001). T1-SPI revealed greater correlation with HRQOL compared to SVA. PT showed correlation with HRQOL (0.28 < r < 0.42) and with SVA (r = 0.64, P < 0.0001). ::: ::: ::: CONCLUSION ::: This study confirms that pelvic position measured via PT correlates with HRQOL in the setting of adult deformity. High values of PT express compensatory pelvic retroversion for sagittal spinal malalignment. This study also demonstrates significant T1-SPI correlation with HRQOL measures and outperforms SVA. This parameter carries the advantage of being an angular measurement which avoids the error inherent in measuring offsets in noncalibrated radiographs.