query: Affordances as a Framework for Robot Control

positive: The learning and use of traversability affordance using range images on a mobile robot We are interested in how the concept of affordances can affect our view to autonomous robot control, and how the results obtained from autonomous robotics can be reflected back upon the discussion and studies on the concept of affordances. In this paper, we studied how a mobile robot, equipped with a 3D laser scanner, can learn to perceive the traversability affordance and use it to wander in a room tilled with spheres, cylinders and boxes. The results showed that after learning, the robot can wander around avoiding contact with non-traversable objects (i.e. boxes, upright cylinders, or lying cylinders in certain orientation), but moving over traversable objects (such as spheres, and lying cylinders in a rollable orientation with respect to the robot) rolling them out of its way. We have shown that for each action approximately 1% of the perceptual features were relevant to determine whether it is afforded or not and that these relevant features are positioned in certain regions of the range image. The experiments are conducted both using a physics-based simulator and on a real robot.

positive: Action recognition in the premotor cortex. We recorded electrical activity from 532 neurons in the rostral part of inferior area 6 (area F5) of two macaque monkeys. Previous data had shown that neurons of this area discharge during goal-directed hand and mouth movements. We describe here the properties of a newly discovered set of F5 neurons ("mirror neurons', n = 92) all of which became active both when the monkey performed a given action and when it observed a similar action performed by the experimenter. Mirror neurons, in order to be visually triggered, required an interaction between the agent of the action and the object of it. The sight of the agent alone or of the object alone (three-dimensional objects, food) were ineffective. Hand and the mouth were by far the most effective agents. The actions most represented among those activating mirror neurons were grasping, manipulating and placing. In most mirror neurons (92%) there was a clear relation between the visual action they responded to and the motor response they coded. In approximately 30% of mirror neurons the congruence was very strict and the effective observed and executed actions corresponded both in terms of general action (e.g. grasping) and in terms of the way in which that action was executed (e.g. precision grip). We conclude by proposing that mirror neurons form a system for matching observation and execution of motor actions. We discuss the possible role of this system in action recognition and, given the proposed homology between F5 and human Brocca's region, we posit that a matching system, similar to that of mirror neurons exists in humans and could be involved in recognition of actions as well as phonetic gestures.

positive: The Cog Project : Building a Humanoid Robot To explore issues of developmental structure, physical embodiment, integration of multiple sensory and motor systems, and social interaction, we have constructed an upper-torso humanoid robot called Cog. The robot has twenty-one degrees of freedom and a variety of sensory systems, including visual, auditory, vestibular, kinesthetic, and tactile senses. This chapter gives a background on the methodology that we have used in our investigations, highlights the research issues that have been raised during this project, and provides a summary of both the current state of the project and our long-term goals. We report on a variety of implemented visual-motor routines (smooth-pursuit tracking, saccades, binocular vergence, and vestibular-ocular and opto-kinetic reflexes), orientation behaviors, motor control techniques, and social behaviors (pointing to a visual target, recognizing joint attention through face and eye finding, imitation of head nods, and regulating interaction through expressive feedback). We further outline a number of areas for future research that will be necessary to build a complete embodied system.

positive: The role of tutoring in problem solving. THIS PAPER is concerned with the nature of the tutorial process; the means whereby an adult or "expert" helps somebody who is less adult or less expert. Though its aim is general, it is expressed in terms of a particular task: a tutor seeks to teach children aged 3, 4 and 5 yr to build a particular three-dimensional structure that requires a degree of skill that is initially beyond them. It is the usual type of tutoring situation in which one member "knows the answer" and the other does not, rather like a "practical" in which only the instructor "knows how". The changing interaction of tutor and children provide our data. A great deal of early problem solving by the developing child is of this order. Although from the earliest months of life he is a "natural" problem solver in his own right (e.g. Bruner, 1973) it is often the ease that his efforts are assisted and fostered by others who are more skilful than he is (Kaye, 1970). Whether he is learning the procedures that constitute the skills of attending, communicating, manipulating objects, locomoting, or, indeed, a more effective problem solving procedure itself, there are usually others in attendance who help him on his way. Tutorial interactions are, in short, a crucial feature of infancy and childhood. Our species, moreover, appears to be the only one in which any "intentional" tutoring goes on (Bruner, 1972; Hinde, 1971). For although it is true that many of the higher primate species learn by observation of their elders (Hamburg, 1968; van Lawick-Goodall, 1968), there is no evidence that those elders do anything to instruct their charges in the performance of the skill in question. What distinguishes man as a species is not only his capacity for learning, but for teaching as well. It is the main aim of this paper to examine some of the major implications of this interactive, instructional relationship between the developing child and his elders for the study of skill acquisition and problem solving. The acquisition of skill in the human child can be fruitfully conceived as a hierarchical program in which component skills are combined into "higher skills" by appropriate orchestration to meet new, more complex task requirements (Bruner, 1973). The process is analogous to problem solving in which mastery of "lower order" or constituent problems in a sine qua non for success with a larger jjroblcm, each level influencing the other—as with reading where the deciphering of words makes possible the deciphering of sentences, and sentences then aid in the deciphering of particular words (F. Smith, 1971). Given persistent intention in the young learner, given a "lexicon" of constituent skills, the crucial task is often one of com-

negative:
Increasing fruit and vegetable intake by changing environments, policy and pricing: restaurant-based research, strategies, and recommendations. BACKGROUND
Restaurants are among the most important and promising venues for environmental, policy, and pricing initiatives to increase fruit and vegetable (F&V) intake. This article reviews restaurant-based environmental, policy and pricing strategies for increasing intake of fruits and vegetables and identifies promising strategies, research needs, and innovative opportunities for the future.

METHODS
The strategies, examples, and research reported here were identified through an extensive search of published journal articles, government documents, the internet, and inquiries to leaders in the field. Recommendations were expanded based on discussion by participants in the CDC/ACS-sponsored Fruit and Vegetable, Environment Policy and Pricing Workshop held in September of 2002.

RESULTS
Six separate types of restaurant-based interventions were identified: increased availability, increased access, reduced prices and coupons, catering policies, point-of-purchase (POP) information, and promotion and communication. Combination approaches have also been implemented. Evaluation data on these interventions show some significant impact on healthful diets, particularly with point-of-purchase information. However, most published reports emphasize low-fat eating, and there is a need to translate and evaluate interventions focused on increasing fruit and vegetable intake.

CONCLUSIONS
Several models for changing environments, policy and pricing to increase fruit and vegetable availability, access, attractiveness and consumption in restaurants have been tested and found to have some promise. There is a need to evaluate fruit and vegetable-specific strategies; to obtain data from industry; to disseminate promising programs; and to enhance public-private partnerships and collaboration to expand on current knowledge.

negative: Many regression algorithms, one unified model: A review Regression is the process of learning relationships between inputs and continuous outputs from example data, which enables predictions for novel inputs. The history of regression is closely related to the history of artificial neural networks since the seminal work of Rosenblatt (1958). The aims of this paper are to provide an overview of many regression algorithms, and to demonstrate how the function representation whose parameters they regress fall into two classes: a weighted sum of basis functions, or a mixture of linear models. Furthermore, we show that the former is a special case of the latter. Our ambition is thus to provide a deep understanding of the relationship between these algorithms, that, despite being derived from very different principles, use a function representation that can be captured within one unified model. Finally, step-by-step derivations of the algorithms from first principles and visualizations of their inner workings allow this article to be used as a tutorial for those new to regression.

negative: User Models for Adaptive Hypermedia and Adaptive Educational Systems One distinctive feature of any adaptive system is the user model that represents essential information about each user. This chapter complements other chapters of this book in reviewing user models and user modeling approaches applied in adaptive Web systems. The presentation is structured along three dimensions: what is being modeled, how it is modeled, and how the models are maintained. After a broad overview of the nature of the information presented in these various user models, the chapter focuses on two groups of approaches to user model representation and maintenance: the overlay approach to user model representation and the uncertainty-based approach to user

negative: Multimedia Question Answering By surveying recent research in multimedia question answering, this article explores QA's evolution from text to multimedia and identifies the challenges in the field.

negative: Sexual Orientation and Gender Identity Data Collection in Clinical Settings and in Electronic Health Records: A Key to Ending LGBT Health Disparities. The Institute of Medicine's (IOM's) 2011 report on the health of LGBT people pointed out that there are limited health data on these populations and that we need more research. It also described what we do know about LGBT health disparities, including lower rates of cervical cancer screening among lesbians, and mental health issues related to minority stress. Patient disclosure of LGBT identity enables provider-patient conversations about risk factors and can help us reduce and better understand disparities. It is essential to the success of Healthy People 2020's goal of eliminating LGBT health disparities. This is why the IOM's report recommended data collection in clinical settings and on electronic health records (EHRs). The Center for Medicare and Medicaid Services and the Office of the National Coordinator of Health Information Technology rejected including sexual orientation and gender identity (SOGI) questions in meaningful use guidelines for EHRs in 2012 but are considering this issue again in 2013. There is overwhelming community support for the routine collection of SOGI data in clinical settings, as evidenced by comments jointly submitted by 145 leading LGBT and HIV/AIDS organizations in January 2013. Gathering SOGI data in EHRs is supported by the 2011 IOM's report on LGBT health, Healthy People 2020, the Affordable Care Act, and the Joint Commission. Data collection has long been central to the quality assurance process. Preventive health care from providers knowledgeable of their patients' SOGI can lead to improved access, quality of care, and outcomes. Medical and nursing schools should expand their attention to LGBT health issues so that all clinicians can appropriately care for LGBT patients.

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

negative: Collision-free object movement using vector fields The topics of computer animation and object collision detection and resolution have become increasingly desirable in a wide range of applications including autonomous vehicle navigation, robotics, and the movie industry. However, the techniques currently available are typically difficult to use or are restricted to a particular domain. This paper presents a technique for providing automatic animation and collision avoidance of arbitrary objects in a computer graphics system. The underlying construct used in this process is a surrounding volume octree vector field. The system automatically generates these structures around objects in the scene. By judicious creation and use of these vector fields, objects in the scene move and interact, but do not collide. The manner in which these vector fields are created is given. Two applications cloud movement over terrain and autonomous aircraft navigation are presented which show typical usage of this technique.

negative: : Android Deobfuscation via “ Big Code ” This work presents a new approach for deobfuscating Android APKs based on probabilistic learning of large code bases (termed “Big Code”). The key idea is to learn a probabilistic model over thousands of non-obfuscated Android applications and to use this probabilistic model to deobfuscate new, unseen Android APKs. The concrete focus of the paper is on reversing layout obfuscation, a popular transformation which renames key program elements such as classes, packages and methods, thus making it difficult to understand what the program does. Concretely, the paper: (i) phrases the layout deobfuscation problem of Android APKs as structured prediction in a probabilistic graphical model, (ii) instantiates this model with a rich set of features and constraints that capture the Android setting, ensuring both semantic equivalence and high prediction accuracy, and (iii) shows how to leverage powerful inference and learning algorithms to achieve overall precision and scalability of the probabilistic predictions. We implemented our approach in a tool called DeGuard and used it to: (i) reverse the layout obfuscation performed by the popular ProGuard system on benign, open-source applications, (ii) predict third-party libraries imported by benign APKs (also obfuscated by ProGuard), and (iii) rename obfuscated program elements of Android malware. The experimental results indicate that DeGuard is practically effective: it recovers 79.1% of the program element names obfuscated with ProGuard, it predicts third-party libraries with accuracy of 91.3%, and it reveals string decoders and classes that handle sensitive data in Android malware.

negative: Implementation of the State of Charge Estimation with Adaptive Extended Kalman Filter for Lithium-Ion Batteries by Arduino This study considers the use of Arduino to achieve state of charge (SOC) estimation of lithium-ion batteries by adaptive extended Kalman filter (AEKF). To implement a SOC estimator for the lithium-ion battery, we adopt a first-order RC equivalent circuit as the equivalent circuit model (ECM) of the battery. The parameters of the ECM will be identified through the designed experiments, and they will be approximated by the piecewise linear functions and then will be built into Arduino. The AEKF algorithm will also be programed into Arduino to estimate the SOC. To verify the accuracy of the SOC estimation, some lithium-ion batteries are tested at room temperature. Experimental results show that the absolute value of the steady-state SOC estimation error is small.

negative: Detection of Slang Words in e-Data using semi-Supervised Learning The proposed algorithmic approach deals with finding the sense of a word in an electronic data. Now a day, in different communication mediums like internet, mobile services etc. people use few words, which are slang in nature. This approach detects those abusive words using supervised learning procedure. But in the real life scenario, the slang words are not used in complete word forms always. Most of the times, those words are used in different abbreviated forms like sounds alike forms, taboo morphemes etc. This proposed approach can detect those abbreviated forms also using semi supervised learning procedure. Using the synset and concept analysis of the text, the probability of a suspicious word to be a slang word is also evaluated.

negative: FACTORS INFLUENCING CONSUMERS ’ ATTITUDE TOWARDS E-COMMERCE PURCHASES THROUGH ONLINE SHOPPING Online shopping is the process of buying goods and services from merchants who sell on the internet. Shoppers can visit web stores from the comfort of their homes and shop as they sit in front of the computer. The main purpose of this study is to determine the factors influencing consumers’ attitude towards e-commerce purchases through online shopping. The study also investigate how socio-demographic (age, income and occupation), pattern of online buying (types of goods, e-commerce experience and hours use on internet) and purchase perception (product perception, customers’ service and consumers’ risk) affect consumers’ attitude towards online shopping. Convenience sampling method was conducted in this study and the sample comparison of 100 respondents in Taman Tawas Permai, Ipoh. Data were collected via self-administered questionnaire which contains 15 questions in Part A (respondents’ background and their pattern of using internet and online buying), 34 questions in Part B (attitude towards online purchase) and 36 questions in Part C (purchase perception towards online shopping). One-way ANOVA were used to assess the differences between independent variable such as age, income, occupation and pattern of online buying (type of goods) and dependant variable such as attitude towards online shopping. The findings revealed that there is no significant difference in attitude towards online shopping among age group (F = 1.020, p < 0.05) but there is a significant difference in attitude towards online shopping among income group (F = 0.556, p > 0.05). The research finding also showed that there is no significant difference in attitude towards online shopping among occupation group (F = 1.607, p < 0.05) and types of goods group (F = 1.384, p < 0.05). Pearson’s correlation were used to assess the relationship between independent variable such as e-commerce experience, hours spent on internet, product perception, customers’ service and consumers’ risk and dependant variable such as attitude towards online shopping. The findings revealed that there is a significant relationship between e-commerce experience and attitude towards online shopping among the respondents (r = -0.236**, p < 0.05). However, there is no significant relationship between hours spent on internet and attitude towards online shopping among the respondents (r = 0.106, p > 0.05). This study also indicated that there is a significant relationship between product perception and attitude towards online shopping among the respondents (r = 0.471**, p < 0.01) and there is also a significant relationship between customers’ service and attitude towards online shopping among the respondents (r = 0.459**, p < 0.01). Lastly, this result showed that there is no significant relationship between consumers’ risk and attitude towards online shopping among the respondents (r = 0.153, p > 0.05). Further study should explore other factors that influencing consumers’ attitude towards e-commerce purchases through online shopping with a broader range of population and high representative sampling method.

negative: Thermally Stable Enhancement-Mode GaN Metal-Isolator-Semiconductor High-Electron-Mobility Transistor With Partially Recessed Fluorine-Implanted Barrier Al2O3/AlGaN/GaN enhancement-mode metalisolator-semiconductor high-electron-mobility transistor (MIS-HEMT) featuring a partially recessed (Al) GaN barrier was realized by a fluorine plasma implantation/etch technique. By properly adjusting the RF power driving the fluorine plasma, the fluorine plasma is able to produce two desirable results: 1) a well-controlled slow dry etching for gate recess and 2) implanting fluorine ions into the AlGaN barrier. The fluorine ions become negatively charged in the barrier layer and induce a positive shift in the threshold voltage. The proposed MIS-HEMT exhibits a threshold voltage (VTH) of +0.6 V at a drain current of 10 μA/mm, a maximum drive current of 730 mA/mm, an ON-resistance of 7.07 Ω · mm, and an OFF-state breakdown voltage of 703 V at an OFF-state drain leakage current of 1 μA/mm. From room temperature to 200 °C, the device exhibits a small negative shift of VTH (~0.5 V) that is attributed to the high-quality dielectric/F-implanted-(Al) GaN interface and the partially recessed barrier.

negative: Inference and Design in Kuba and Zillij Art with Shape Grammars We present a simple method for structural inference in African Kuba cloth, and Moorish zillij mosaics. Our work is based on Stiny and Gips’ formulation of “Shape Grammars”. It provides art scholars and geometers with a simple yet powerful medium to perform analysis of existing art and draw inspiration for new designs. The analysis involves studying an artwork and capturing its structure as simple shape grammar rules. We then show how interesting families of artworks could be generated using simple variations in their corresponding grammars.

negative: Deep Learning for Fixed Model Reuse Model reuse attempts to construct a model by utilizing existing available models, mostly trained for other tasks, rather than building a model from scratch. It is helpful to reduce the time cost, data amount, and expertise required. Deep learning has achieved great success in various tasks involving images, voices and videos. There are several studies have the sense of model reuse, by trying to reuse pre-trained deep networks architectures or deep model features to train a new deep model. They, however, neglect the fact that there are many other fixed models or features available. In this paper, we propose a more thorough model reuse scheme, FMR (Fixed Model Reuse). FMR utilizes the learning power of deep models to implicitly grab the useful discriminative information from fixed model/features that have been widely used in general tasks. We firstly arrange the convolution layers of a deep network and the provided fixed model/features in parallel, fully connecting to the output layer nodes. Then, the dependencies between the output layer nodes and the fixed model/features are knockdown such that only the raw feature inputs are needed when the model is being used for testing, though the helpful information in the fixed model/features have already been incorporated into the model. On one hand, by the FMR scheme, the required amount of training data can be significantly reduced because of the reuse of fixed model/features. On the other hand, the fixed model/features are not explicitly used in testing, and thus, the scheme can be quite useful in applications where the fixed model/features are protected by patents or commercial secrets. Experiments on five real-world datasets validate the effectiveness of FMR compared with state-of-the-art deep methods.

negative: A mixture model for preferences data analysis A mixture model for preferences data, which adequately represents the composite nature of the elicitation mechanism in ranking processes, is proposed. Both probabilistic features of the mixture distribution and inferential and computational issues arising from the maximum likelihood parameters estimation are addressed. Moreover, empirical evidence from different data sets confirming the goodness of fit of the proposed model to many real preferences data is shown. © 2004 Elsevier B.V. All rights reserved.

negative: Feature Analysis for Fake Review Detection through Supervised Classification Nowadays, review sites are more and more confronted with the spread of misinformation, i.e., opinion spam, which aims at promoting or damaging some target businesses, by misleading either human readers, or automated opinion mining and sentiment analysis systems. For this reason, in the last years, several data-driven approaches have been proposed to assess the credibility of user-generated content diffused through social media in the form of on-line reviews. Distinct approaches often consider different subsets of characteristics, i.e., features, connected to both reviews and reviewers, as well as to the network structure linking distinct entities on the review-site in exam. This article aims at providing an analysis of the main review- and reviewer-centric features that have been proposed up to now in the literature to detect fake reviews, in particular from those approaches that employ supervised machine learning techniques. These solutions provide in general better results with respect to purely unsupervised approaches, which are often based on graph-based methods that consider relational ties in review sites. Furthermore, this work proposes and evaluates some additional new features that can be suitable to classify genuine and fake reviews. For this purpose, a supervised classifier based on Random Forests have been implemented, by considering both well-known and new features, and a large-scale labeled dataset from which all these features have been extracted. The good results obtained show the effectiveness of new features to detect in particular singleton fake reviews, and in general the utility of this study.

negative: Affective outcomes of virtual reality exposure therapy for anxiety and specific phobias: a meta-analysis. Virtual reality exposure therapy (VRET) is an increasingly common treatment for anxiety and specific phobias. Lacking is a quantitative meta-analysis that enhances understanding of the variability and clinical significance of anxiety reduction outcomes after VRET. Searches of electronic databases yielded 52 studies, and of these, 21 studies (300 subjects) met inclusion criteria. Although meta-analysis revealed large declines in anxiety symptoms following VRET, moderator analyses were limited due to inconsistent reporting in the VRET literature. This highlights the need for future research studies that report uniform and detailed information regarding presence, immersion, anxiety and/or phobia duration, and demographics.

negative: A Recurrent Latent Variable Model for Sequential Data In this paper, we explore the inclusion of latent random variables into the dynamic hidden state of a recurrent neural network (RNN) by combining elements of the variational autoencoder. We argue that through the use of high-level latent random variables, our variational RNN (VRNN) is able to learn to model the kind of variability observed in highly-structured sequential data (such as speech). We empirically evaluate the proposed model against related sequential models on five sequential datasets, four of speech and one of handwriting. Our results show the importance of the role that latent random variables can play in the RNN dynamic hidden state.

negative: Least Squares Temporal Difference Learning and Galerkin ’ s Method The problem of estimating the value function underlying a Markovian reward process is considered. As it is well known, the value function underlying a Markovian reward process satisfied a linear fixed point equation. One approach to learning the value function from finite data is to find a good approximation to the value function in a given (linear) subspace of the space of value functions. We review some of the issues that arise when following this approach, as well as some results that characterize the finite-sample performance of some of the algorithms. 1 Markovian Reward Processes Let X be a measurable space and consider a stochastic process (X0, R1, X1, R2, X2, . . .), where Xt ∈ X and Rt+1 ∈ R, t = 0, 1, 2, . . .. The process is called a Markovian Reward process if • (X0, X1, . . .) is a Markov process, and • for any t ≥ 0, given Xt, Xt+1 the distribution of Rt+1 is independent of the history of the process. Here, Xt is called the state of the system at time t, while Rt+1 is the reward associated to transitioning from Xt to Xt+1. We shall denote by P the Markovian kernel underlying the process: Thus, the distribution of (Xt+1, Rt+1) given Xt is given by P(·, ·|Xt), t = 0, 1, . . .. Fix the so-called discount factor 0 ≤ γ ≤ 1 and define the (total discounted) return associated to the process R = ∞ ∑

negative: A Dual Path Deep Network for Single Image Super-Resolution Reconstruction Super-resolution reconstruction based on deep learning has come a long way since the first proposed method in 2015. Numerous methods have been developed for this task using deep learning approaches. Among these methods, residual deep learning algorithms have shown better performance. Although all early proposed deep learning based super-resolution frameworks used bicubic upsampled versions of low resolution images as the main input, most of the current ones use the low resolution images directly by adding up-sampling layers to their networks. In this work, we propose a new method by using both low resolution and bicubic upsampled images as the inputs to our network. The final results confirm that decreasing the depth of the network in lower resolution space and adding the bicubic path lead to almost similar results to those of the deeper networks in terms of PSNR and SSIM, yet making the network computationally inexpensive and more efficient.

negative: BLE Beacon Based Patient Tracking in Smart Care Facilities Patient tracking is an important component toward creating smart cities. In this demo we use Bluetooth Low Energy beacons and single board computers to track patients in the emerging field of smart care facilities. Our model utilizes a fixed scanner moving transmitter method for wireless tracking of patients through the facility. The data collected by all scanners is stored within a central database that is designed to be efficiently queried. We show how inexpensive components in conjunction with free open source software can be used to implement a patient tracking system. We focus on the pipeline between acquisition and display of the location data. Additionally, we also discuss the manipulation of the data required for usability, and optional filtering operations that improve accuracy.

negative: Immediate effect of a slow pace breathing exercise Bhramari pranayama on blood pressure and heart rate. The study was carried out to evaluate the immediate effect Bhramari pranayama, a slow breathing exercise for 5 minutes on heart rate and blood pressure. Heart rate and blood pressure of volunteers were recorded. The subject was directed to inhale slowly up to the maximum for about 5 seconds and then to exhale slowly up to the maximum for about 15 sec keeping two thumbs on two external auditory canal, index and middle finger together on two closed eyes and ring finger on the two sides of the nose. During exhalation the subject must chant the word "O-U-Mmmma" with a humming nasal sound mimicking the sound of a humming wasp, so that the laryngeal walls and the inner walls of the nostril mildly vibrate (Bhramari pranayama, respiratory rate 3/min). After 5 minutes of this exercise, the blood pressure and heart rate were recorded again. Both the systolic and diastolic blood pressure were found to be'decreased with a slight fall in heart rate. Fall of diastolic pressure and mean pressure were significant. The result indicated that slow pace Bhramari pranayama for 5 minutes, induced parasympathetic dominance on cardiovascular system.

negative: Deep Neural Networks for Object Detection Deep Neural Networks (DNNs) have recently shown outstanding performance on image classification tasks [14]. In this paper we go one step further and address the problem of object detection using DNNs, that is not only classifying but also precisely localizing objects of various classes. We present a simple and yet powerful formulation of object detection as a regression problem to object bounding box masks. We define a multi-scale inference procedure which is able to produce high-resolution object detections at a low cost by a few network applications. State-of-the-art performance of the approach is shown on Pascal VOC.

negative: Influence of Some Parameters on the Effectiveness of Induction Heating We have investigated the effectiveness of heating conductive plates by induction in low frequencies analytically and numerically, in relation to the shape of the plate, the area of the region exposed to the magnetic flux, and the position of the exposed region with respect to the center of the plate. We considered both uniform and nonuniform magnetic fields. For plates with equivalent area exposed to the same uniform magnetic flux, the one with the most symmetrical shape is heated most effectively. If a coil is employed for the excitation, the results depend on the shape of the plate, the shape of the coil section, and the coil lift-off. When only the central region of a plate is exposed to a variable magnetic flux, there is a specific value of the exposed area for which the power dissipated in the plate material reaches a maximum. The most effective heating of a plate partially exposed occurs when the axis of the exciting coil is at the plate center.

negative: Point-and-shoot memories: the influence of taking photos on memory for a museum tour. Two studies examined whether photographing objects impacts what is remembered about them. Participants were led on a guided tour of an art museum and were directed to observe some objects and to photograph others. Results showed a photo-taking-impairment effect: If participants took a photo of each object as a whole, they remembered fewer objects and remembered fewer details about the objects and the objects' locations in the museum than if they instead only observed the objects and did not photograph them. However, when participants zoomed in to photograph a specific part of the object, their subsequent recognition and detail memory was not impaired, and, in fact, memory for features that were not zoomed in on was just as strong as memory for features that were zoomed in on. This finding highlights key differences between people's memory and the camera's "memory" and suggests that the additional attentional and cognitive processes engaged by this focused activity can eliminate the photo-taking-impairment effect.

negative: Semantic labeling of 3D point clouds with object affordance for robot manipulation When a robot is deployed it needs to understand the nature of its surroundings. In this paper, we address the problem of semantic labeling 3D point clouds by object affordance (e.g., `pushable', `liftable'). We propose a technique to extract geometric features from point cloud segments and build a classifier to predict associated object affordances. With the classifier, we have developed an algorithm to enhance object segmentation and reduce manipulation uncertainty by iterative clustering, along with minimizing labeling entropy. Our incremental multiple view merging technique shows improved object segmentation. The novel feature of our approach is the semantic labeling that can be directly applied to manipulation planning. In our experiments with 6 affordance labels, an average of 81.8% accuracy of affordance prediction is achieved. We demonstrate refined object segmentation by applying the classifier to data from the PR2 robot using a Microsoft Kinect in an indoor office environment.

negative: Parental scaffolding as a bootstrapping mechanism for learning grasp affordances and imitation skills Parental scaffolding is an important mechanism utilized by infants during their development. Infants, for example, pay stronger attention to the features of objects highlighted by parents and learn the way of manipulating an object while being supported by parents. Parents are known to make modifications in infant-directed actions, i.e. use “motionese”. Motionese is characterized by higher range and simplicity of motion, more pauses between motion segments, higher repetitiveness of demonstration, and more frequent social signals to an infant. In this paper, we extend our previously developed affordances framework to enable the robot to benefit from parental scaffolding and motionese. First, we present our results on how parental scaffolding can be used to guide the robot and modify robot’s crude action execution to speed up learning of complex actions such as grasping. For this purpose, we realize the interactive nature of a human caregiver-infant skill transfer scenario on the robot. During reach and grasp attempts, the movement of the robot hand is modified by the human caregiver’s physical interaction to enable successful grasping. Next, we discuss how parental scaffolding can be used in speeding up imitation learning. The system describes how our robot, by using previously learned affordance prediction mechanisms, can go beyond simple goal-level imitation and become a better imitator using infant-directed modifications of parents.

negative: Weakly Supervised Affordance Detection Localizing functional regions of objects or affordances is an important aspect of scene understanding and relevant for many robotics applications. In this work, we introduce a pixel-wise annotated affordance dataset of 3090 images containing 9916 object instances. Since parts of an object can have multiple affordances, we address this by a convolutional neural network for multilabel affordance segmentation. We also propose an approach to train the network from very few keypoint annotations. Our approach achieves a higher affordance detection accuracy than other weakly supervised methods that also rely on keypoint annotations or image annotations as weak supervision.

negative: Anticipating the Future by Constructing Human Activities using Object Affordances An important aspect of human perception is anticipation and anticipating which activities will a human do next (and how to do them) in useful for many applications, for example, anticipation enables an assistive robot to plan ahead for reactive responses in the human environments. In this work, we present a constructive approach for generating various possible future human activities by reasoning about the rich spatial-temporal relations through object affordances. We represent each possible future using an anticipatory temporal conditional random field (ATCRF) where we sample the nodes and edges corresponding to future object trajectories and human poses from a generative model. We then represent the distribution over the potential futures using a set of constructed ATCRF particles. In extensive evaluation on CAD-120 human activity RGB-D dataset, for new subjects (not seen in the training set), we obtain an activity anticipation accuracy (defined as whether one of top three predictions actually happened) of 75.4%, 69.2% and 58.1% for an anticipation time of 1, 3 and 10 seconds respectively. 1

negative: A whole-body control framework for humanoids operating in human environments Tomorrow's humanoids will operate in human environments, where efficient manipulation and locomotion skills, and safe contact interactions are critical design factors. We report here our recent efforts into these issues, materialized into a whole-body control framework. This framework integrates task-oriented dynamic control and control prioritization allowing to control multiple task primitives while complying with physical and movement-related constraints. Prioritization establishes a hierarchy between control spaces, assigning top priority to constraint-handling tasks, while projecting operational tasks in the null space of the constraints, and controlling the posture within the residual redundancy. This hierarchy is directly integrated at the kinematic level, allowing the program to monitor behavior feasibility at runtime. In addition, prioritization allows us to characterize the dynamic behavior of the individual control primitives subject to the constraints, and to synthesize operational space controllers at multiple levels. To complete this framework, we have developed free-floating models of the humanoid and incorporate the associated dynamics and the effects of the resulting support contacts into the control hierarchy. As part of a long term collaboration with Honda, we are currently implementing this framework into the humanoid robot Asimo

negative: An aspect representation for object manipulation based on convolutional neural networks We propose an intelligent visuomotor system that interacts with the environment and memorizes the consequences of actions. As more memories are recorded and more interactions are observed, the agent becomes more capable of predicting the consequences of actions and is, thus, better at planning sequences of actions to solve tasks. In previous work, we introduced the aspect transition graph (ATG) which represents how actions lead from one observation to another using a directed multi-graph. In this work, we propose a novel aspect representation based on hierarchical CNN features, learned with convolutional neural networks, that supports manipulation and captures the essential affordances of an object based on RGB-D images. In a traditional planning system, robots are given a pre-defined set of actions that take the robot from one symbolic state to another. However symbolic states often lack the flexibility to generalize across similar situations. Our proposed representation is grounded in the robot's observations and lies in a continuous space that allows the robot to handle similar unseen situations. The hierarchical CNN features within a representation also allow the robot to act precisely with respect to the spatial location of individual features. We evaluate the robustness of this representation using the Washington RGB-D Objects Dataset and show that it achieves state of the art results for instance pose estimation. We then test this representation in conjunction with an ATG on a drill grasping task on Robonaut-2. We show that given grasp, drag, and turn demonstrations on the drill, the robot is capable of planning sequences of learned actions to compensate for reachability constraints.

negative: UMAN WAYFINDING IN UNFAMILIAR BUILDINGS : A SIMULATION WITH A COGNIZING AGENT Existing cognitively based computational models for wayfinding focus primarily on the exploration of mental representations rather than the information needs for wayfinding. It is important to consider information needs because people trying to find their ways in unfamiliar environments do not have a previously acquired mental representation but depend on external information. The fundamental tenet of this work is that all information must be presented to the wayfinder at each decision point as “knowledge in the world.” Simulating people’s wayfinding behavior in a cognitively plausible way requires the integration of structures for information perception and cognition in the underlying model. In this work we use a cognizing agent to simulate people’s wayfinding processes in unfamiliar buildings. The agent-based model consists of two tiers: simulated states of the environment and simulated beliefs of the agent. The agent is modeled with state, an observation schema, a specific wayfinding strategy, and commonsense knowledge. The environment is modeled as a graph, where nodes represent decision points and edges represent lines of movement. The perceptual wayfinding model integrates the agent and its environment within a “Sense-Plan-Act” framework. It focuses on knowledge in the world to explain actions of the agent. The concepts of affordance and information are used to describe the kinds of knowledge the agent derives from the world by means of visual perception. Affordances are possibilities for action with reference to the agent. Information is necessary for the agent to decide which affordances to utilize. During the navigation process the agent accumulates beliefs about the environment by observing task-relevant affordances and information at decision points. The utilization of a “go-to” affordance leads the agent from one node to another where it is again provided with percepts. A successful navigation corresponds to the agent’s traversal from a start to a goal node. The proposed formal specifications of the agent-based model can be used to simulate people’s wayfinding behavior in spatial information and design systems in a cognitively plausible way. Such simulation helps to determine where and why people face wayfinding difficulties and what needs to be done to avoid them. The case of wayfinding in an airport, in which the signage in the airport is tested, is employed to demonstrate the perceptual wayfinding model.


query: Social engineering attack framework

positive: Ontology Development 101 : A Guide to Creating Your First Ontology 1 Why develop an ontology? In recent years the development of ontologies—explicit formal specifications of the terms in the domain and relations among them (Gruber 1993)—has been moving from the realm of ArtificialIntelligence laboratories to the desktops of domain experts. Ontologies have become common on the World-Wide Web. The ontologies on the Web range from large taxonomies categorizing Web sites (such as on Yahoo!) to categorizations of products for sale and their features (such as on Amazon.com). The WWW Consortium (W3C) is developing the Resource Description Framework (Brickley and Guha 1999), a language for encoding knowledge on Web pages to make it understandable to electronic agents searching for information. The Defense Advanced Research Projects Agency (DARPA), in conjunction with the W3C, is developing DARPA Agent Markup Language (DAML) by extending RDF with more expressive constructs aimed at facilitating agent interaction on the Web (Hendler and McGuinness 2000). Many disciplines now develop standardized ontologies that domain experts can use to share and annotate information in their fields. Medicine, for example, has produced large, standardized, structured vocabularies such as SNOMED (Price and Spackman 2000) and the semantic network of the Unified Medical Language System (Humphreys and Lindberg 1993). Broad general-purpose ontologies are emerging as well. For example, the United Nations Development Program and Dun & Bradstreet combined their efforts to develop the UNSPSC ontology which provides terminology for products and services (www.unspsc.org). An ontology defines a common vocabulary for researchers who need to share information in a domain. It includes machine-interpretable definitions of basic concepts in the domain and relations among them. Why would someone want to develop an ontology? Some of the reasons are:

positive: Generic Taxonomy of Social Engineering Attack Social engineering is a type of attack that allows unauthorized access to a system to achieve specific objective. Commonly, the purpose is to obtain information for social engineers. Some successful social engineering attacks get victims’ information via human based retrieval approach, example technique terms as dumpster diving or shoulder surfing attack to get access to password. Alternatively, victims’ information also can be stolen using technical-based method such as from pop-up windows, email or web sites to get the password or other sensitive information. This research performed a preliminary analysis on social engineering attack taxonomy that emphasized on types of technical-based social engineering attack. Results from the analysis become a guideline in proposing a new generic taxonomy of Social Engineering Attack (SEA).

positive:
The urgency for effective user privacy-education to counter social engineering attacks on secure computer systems Trusted people can fail to be trustworthy when it comes to protecting their aperture of access to secure computer systems due to inadequate education, negligence, and various social pressures. People are often the weakest link in an otherwise secure computer system and, consequently, are targeted for social engineering attacks. Social Engineering is a technique used by hackers or other attackers to gain access to information technology systems by getting the needed information (for example, a username and password) from a person rather than breaking into the system through electronic or algorithmic hacking methods. Such attacks can occur on both a physical and psychological level. The physical setting for these attacks occurs where a victim feels secure: often the workplace, the phone, the trash, and even on-line. Psychology is often used to create a rushed or officious ambiance that helps the social engineer to cajole information about accessing the system from an employee.
 Data privacy legislation in the United States and international countries that imposes privacy standards and fines for negligent or willful non-compliance increases the urgency to measure the trustworthiness of people and systems. One metric for determining compliance is to simulate, by audit, a social engineering attack upon an organization required to follow data privacy standards. Such an organization commits to protect the confidentiality of personal data with which it is entrusted.
 This paper presents the results of an approved social engineering audit made without notice within an organization where data security is a concern. Areas emphasized include experiences between the Social Engineer and the audited users, techniques used by the Social Engineer, and other findings from the audit. Possible steps to mitigate exposure to the dangers of Social Engineering through improved user education are reviewed.

positive: Ontologies: principles, methods and applications This paper is intended to serve as a comprehensive introduction to the emerging eld concerned with the design and use of ontologies. We observe that disparate backgrounds, languages, tools, and techniques are a major barrier to e ective communication among people, organisations, and/or software systems. We show how the development and implementation of an explicit account of a shared understanding (i.e. an `ontology') in a given subject area, can improve such communication, which in turn, can give rise to greater reuse and sharing, inter-operability, and more reliable software. After motivating their need, we clarify just what ontologies are and what purposes they serve. We outline a methodology for developing and evaluating ontologies, rst discussing informal techniques, concerning such issues as scoping, handling ambiguity, reaching agreement and producing de nitions. We then consider the bene ts of and describe, a more formal approach. We re-visit the scoping phase, and discuss the role of formal languages and techniques in the speci cation, implementation and evaluation of ontologies. Finally, we review the state of the art and practice in this emerging eld, considering various case studies, software tools for ontology development, key research issues and future prospects. AIAI-TR-191 Ontologies Page i

negative: Highly Sensitive, Flexible, and Wearable Pressure Sensor Based on a Giant Piezocapacitive Effect of Three-Dimensional Microporous Elastomeric Dielectric Layer. We report a flexible and wearable pressure sensor based on the giant piezocapacitive effect of a three-dimensional (3-D) microporous dielectric elastomer, which is capable of highly sensitive and stable pressure sensing over a large tactile pressure range. Due to the presence of micropores within the elastomeric dielectric layer, our piezocapacitive pressure sensor is highly deformable by even very small amounts of pressure, leading to a dramatic increase in its sensitivity. Moreover, the gradual closure of micropores under compression increases the effective dielectric constant, thereby further enhancing the sensitivity of the sensor. The 3-D microporous dielectric layer with serially stacked springs of elastomer bridges can cover a much wider pressure range than those of previously reported micro-/nanostructured sensing materials. We also investigate the applicability of our sensor to wearable pressure-sensing devices as an electronic pressure-sensing skin in robotic fingers as well as a bandage-type pressure-sensing device for pulse monitoring at the human wrist. Finally, we demonstrate a pressure sensor array pad for the recognition of spatially distributed pressure information on a plane. Our sensor, with its excellent pressure-sensing performance, marks the realization of a true tactile pressure sensor presenting highly sensitive responses to the entire tactile pressure range, from ultralow-force detection to high weights generated by human activity.

negative: Human-Interactive Subgoal Supervision for Efficient Inverse Reinforcement Learning Humans are able to understand and perform complex tasks by strategically structuring the tasks into incremental steps or subgoals. For a robot attempting to learn to perform a sequential task with critical subgoal states, such states can provide a natural opportunity for interaction with a human expert. This paper analyzes the benefit of incorporating a notion of subgoals into Inverse Reinforcement Learning (IRL) with a Human-In-The-Loop (HITL) framework. The learning process is interactive, with a human expert first providing input in the form of full demonstrations along with some subgoal states. These subgoal states define a set of subtasks for the learning agent to complete in order to achieve the final goal. The learning agent queries for partial demonstrations corresponding to each subtask as needed when the agent struggles with the subtask. The proposed Human Interactive IRL (HI-IRL) framework is evaluated on several discrete path-planning tasks. We demonstrate that subgoal-based interactive structuring of the learning task results in significantly more efficient learning, requiring only a fraction of the demonstration data needed for learning the underlying reward function with the baseline IRL model.

negative: A step toward irrationality: using emotion to change belief Emotions have a powerful impact on behavior and beliefs. The goal of our research is to create general computational models of this interplay of emotion, cognition and behavior to inform the design of virtual humans. Here, we address an aspect of emotional behavior that has been studied extensively in the psychological literature but largely ignored by computational approaches, emotion-focused coping. Rather than motivating external action, emotion-focused coping strategies alter beliefs in response to strong emotions. For example an individual may alter beliefs about the importance of a goal that is being threatened, thereby reducing their distress. We present a preliminary model of emotion-focused coping and discuss how coping processes, in general, can be coupled to emotions and behavior. The approach is illustrated within a virtual reality training environment where the models are used to create virtual human characters in high-stress social situations.

negative: Perching and takeoff of a robotic insect on overhangs using switchable electrostatic adhesion For aerial robots, maintaining a high vantage point for an extended time is crucial in many applications. However, available on-board power and mechanical fatigue constrain their flight time, especially for smaller, battery-powered aircraft. Perching on elevated structures is a biologically inspired approach to overcome these limitations. Previous perching robots have required specific material properties for the landing sites, such as surface asperities for spines, or ferromagnetism. We describe a switchable electroadhesive that enables controlled perching and detachment on nearly any material while requiring approximately three orders of magnitude less power than required to sustain flight. These electroadhesives are designed, characterized, and used to demonstrate a flying robotic insect able to robustly perch on a wide range of materials, including glass, wood, and a natural leaf.

negative: Key parameters influencing the behavior of Steel Plate Shear Walls (SPSW) The complex behavior of Steel Plate Shear Walls (SPSW) is investigated herein through nonlinear FE simulations. A 3D detailed FE model is developed and validated utilizing experimental results available in the literature. The influence of key parameters on the structural behavior is investigated. The considered parameters are: the infill plate thickness, the beam size, the column size, the infill plate material grade and the frame material grade. Several structural responses are used as criteria to quantify their influence on the SPSW behavior. The evaluated structural responses are: yield strength, yield displacement, ultimate strength, initial stiffness and secondary stiffness. The results show that, overall the most influential parameter is the infill plate thickness followed by the beam size. Also, it was found that the least influential parameter is the frame material grade.

negative: Design for Reliability of Low-voltage, Switched-capacitor Circuits Design for Reliability of Low-voltage, Switched-capacitor Circuits by Andrew Masami Abo Doctor of Philosophy in Engineering University of California, Berkeley Professor Paul R. Gray, Chair Analog, switched-capacitor circuits play a critical role in mixed-signal, analogto-digital interfaces. They implement a large class of functions, such as sampling, filtering, and digitization. Furthermore, their implementation makes them suitable for integration with complex, digital-signal-processing blocks in a compatible, low-cost technology–particularly CMOS. Even as an increasingly larger amount of signal processing is done in the digital domain, this critical, analogto-digital interface is fundamentally necessary. Examples of some integrated applications include camcorders, wireless LAN transceivers, digital set-top boxes, and others. Advances in CMOS technology, however, are driving the operating voltage of integrated circuits increasingly lower. As device dimensions shrink, the applied voltages will need to be proportionately scaled in order to guarantee long-term reliability and manage power density. The reliability constraints of the technology dictate that the analog circuitry operate at the same low voltage as the digital circuitry. Furthermore, in achieving low-voltage operation, the reliability constraints of the technology must not be violated. This work examines the voltage limitations of CMOS technology and how analog circuits can maximize the utility of MOS devices without degrading relia-

negative: DWCMM: The Data Warehouse Capability Maturity Model Data Warehouses and Business Intelligence have become popular fields of research in recent years. Unfortunately, in daily practice many Data Warehouse and Business Intelligence solutions still fail to help organizations make better decisions and increase their profitability, due to intransparent complexities and project interdependencies. In addition, emerging application domains such as Mobile Learning & Analytics heavily depend on a wellstructured data foundation with a longitudinally prepared architecture. Therefore, this research presents the Data Warehouse Capability Maturity Model (DWCMM) which encompasses both technical and organizational aspects involved in developing a Data Warehouse environment. The DWCMM can be used to help organizations assess their current Data Warehouse solution and provide them with guidelines for future improvements. The DWCMM consists of a maturity matrix and a maturity assessment questionnaire with 60 questions. The DWCMM has been evaluated empirically through expert interviews and case studies. We conclude that the DWCMM can be successfully applied in practice and that organizations can intelligibly utilize the DWCMM as a quickscan instrument to jumpstart their Data Warehouse and Business Intelligence improvement processes.

negative: Multiple-resonator-based bandpass filters This article describes a class of recently developed multiple-mode-resonator-based bandpass filters for ultra-wide-band (UWB) transmission systems. These filters have many attractive features, including a simple design, compact size, low loss and good linearity in the UWB, enhanced out-of-band rejection, and easy integration with other circuits/antennas. In this article, we present a variety of multiple-mode resonators with stepped-impedance or stub-loaded nonuniform configurations and analyze their properties based on the transmission line theory. Along with the frequency dispersion of parallel-coupled transmission lines, we design and implement various filter structures on planar, uniplanar, and hybrid transmission line geometries.

negative: Enhanced ARP: preventing ARP poisoning-based man-in-the-middle attacks In this letter, an enhanced version of Address Resolution Protocol (ARP) is proposed to prevent ARP poisoning-based Man-in-the-Middle (MITM) attacks. The proposed mechanism is based on the following concept. When a node knows the correct Media Access Control (MAC) address for a given IP address, if it retains the IP/MAC address mapping while that machine is alive, then MITM attack is impossible for that IP address. In order to prevent MITM attacks even for a new IP address, a voting-based resolution mechanism is proposed. The proposed scheme is backward compatible with existing ARP and incrementally deployable.

negative: Time series classification with ensembles of elastic distance measures Several alternative distance measures for comparing time series have recently been proposed and evaluated on time series classification (TSC) problems. These include variants of dynamic time warping (DTW), such as weighted and derivative DTW, and edit distance-based measures, including longest common subsequence, edit distance with real penalty, time warp with edit, and move–split–merge. These measures have the common characteristic that they operate in the time domain and compensate for potential localised misalignment through some elastic adjustment. Our aim is to experimentally test two hypotheses related to these distance measures. Firstly, we test whether there is any significant difference in accuracy for TSC problems between nearest neighbour classifiers using these distance measures. Secondly, we test whether combining these elastic distance measures through simple ensemble schemes gives significantly better accuracy. We test these hypotheses by carrying out one of the largest experimental studies ever conducted into time series classification. Our first key finding is that there is no significant difference between the elastic distance measures in terms of classification accuracy on our data sets. Our second finding, and the major contribution of this work, is to define an ensemble classifier that significantly outperforms the individual classifiers. We also demonstrate that the ensemble is more accurate than approaches not based in the time domain. Nearly all TSC papers in the data mining literature cite DTW (with warping window set through cross validation) as the benchmark for comparison. We believe that our ensemble is the first ever classifier to significantly outperform DTW and as such raises the bar for future work in this area.

negative: Recovering punctuation marks for automatic speech recognition This paper shows results of recovering punctuation over speech transcriptions for a Portuguese broadcast news corpus. The approach is based on maximum entropy models and uses word, part-of-speech, time and speaker information. The contribution of each type of feature is analyzed individually. Separate results for each focus condition are given, making it possible to analyze the differences of performance between planned and spontaneous speech.

negative: Optical character recognition of the Orthodox Hellenic Byzantine Music notation In this paper we present for the first time, the development of a new system for the off-line optical recognition of the characters used in the Orthodox Hellenic Byzantine Music Notation, that has been established since 1814. We describe the structure of the new system and propose algorithms for the recognition of the 71 distinct character classes, based on Wavelets, 4-projections and other structural and statistical features. Using a Nearest Neighbor classifier, combined with a post classification schema and a tree-structured classification philosophy, an accuracy of 99.4 % was achieved, in a database of about 18,000 byzantine character patterns that have been developed for the needs of the system. Optical music recognition Off-line character recognition, Byzantine Music, Byzantine Music Notation, Wavelets, Projections, Neural Networks Contour processing, Nearest Neighbor Classifier Byzantine Music Data Base

negative: DRAW: A Recurrent Neural Network For Image Generation This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural network architecture for image generation. DRAW networks combine a novel spatial attention mechanism that mimics the foveation of the human eye, with a sequential variational auto-encoding framework that allows for the iterative construction of complex images. The system substantially improves on the state of the art for generative models on MNIST, and, when trained on the Street View House Numbers dataset, it generates images that cannot be distinguished from real data with the naked eye.

negative: A Comparison of Features for Android Malware Detection With the increase in mobile device use, there is a greater need for increasingly sophisticated malware detection algorithms. The research presented in this paper examines two types of features of Android applications, permission requests and system calls, as a way to detect malware. We are able to differentiate between benign and malicious apps by applying a machine learning algorithm. The model that is presented here achieved a classification accuracy of around 80% using permissions and 60% using system calls for a relatively small dataset. In the future, different machine learning algorithms will be examined to see if there is a more suitable algorithm. More features will also be taken into account and the training set will be expanded.

negative: CogALex-V Shared Task: GHHH - Detecting Semantic Relations via Word Embeddings This paper describes our system submission to the CogALex-2016 Shared Task on Corpus-Based Identification of Semantic Relations. Our system won first place for Task-1 and second place for Task-2. The evaluation results of our system on the test set is 88.1% (79.0% for TRUE only) f-measure for Task-1 on detecting semantic similarity, and 76.0% (42.3% when excluding RANDOM) for Task-2 on identifying fine-grained semantic relations. In our experiments, we try word analogy, linear regression, and multi-task Convolutional Neural Networks (CNNs) with word embeddings from publicly available word vectors. We found that linear regression performs better in the binary classification (Task-1), while CNNs have better performance in the multi-class semantic classification (Task-2). We assume that word analogy is more suited for deterministic answers rather than handling the ambiguity of one-to-many and many-to-many relationships. We also show that classifier performance could benefit from balancing the distribution of labels in the training data.

negative: A 5-DOF rotation-symmetric parallel manipulator with one unconstrained tool rotation This paper introduces a novel 5-DOF parallel manipulator with a rotation-symmetric arm system. The manipulator is unorthodox since one degree of freedom of its manipulated platform is unconstrained. Such a manipulator is still useful in a wide range of applications utilizing a rotation-symmetric tool. The manipulator workspace is analyzed for singularities and collisions. The rotation-symmetric arm system leads to a large positional workspace in relation to the footprint of the manipulator. With careful choice of structural parameters, the rotational workspace of the tool is also sizeable.

negative: A Framework based on SDN and Containers for Dynamic Service Chains on IoT Gateways In this paper, we describe a new approach for managing service function chains in scenarios where data from Internet of Things (IoT) devices is partially processed at the network edge. Our framework is enabled by two emerging technologies, Software-Defined Networking (SDN) and container based virtualization, which ensure several benefits in terms of flexibility, easy programmability, and versatility. These features are well suitable with the increasingly stringent requirements of IoT applications, and allow a dynamic and automated network service chaining. An extensive performance evaluation, which has been carried out by means of a testbed, seeks to understand how our proposed framework performs in terms of computational overhead, network bandwidth, and energy consumption. By accounting for the constraints of typical IoT gateways, our evaluation tries to shed light on the actual deployability of the framework on low-power nodes.

negative: Diagnostic Accuracy of Clinical Tests for Morton's Neuroma Compared With Ultrasonography. The aim of the present study was to assess the diagnostic accuracy of 7 clinical tests for Morton's neuroma (MN) compared with ultrasonography (US). Forty patients (54 feet) were diagnosed with MN using predetermined clinical criteria. These patients were subsequently referred for US, which was performed by a single, experienced musculoskeletal radiologist. The clinical test results were compared against the US findings. MN was confirmed on US at the site of clinical diagnosis in 53 feet (98%). The operational characteristics of the clinical tests performed were as follows: thumb index finger squeeze (96% sensitivity, 96% accuracy), Mulder's click (61% sensitivity, 62% accuracy), foot squeeze (41% sensitivity, 41% accuracy), plantar percussion (37% sensitivity, 36% accuracy), dorsal percussion (33% sensitivity, 26% accuracy), and light touch and pin prick (26% sensitivity, 25% accuracy). No correlation was found between the size of MN on US and the positive clinical tests, except for Mulder's click. The size of MN was significantly larger in patients with a positive Mulder's click (10.9 versus 8.5 mm, p = .016). The clinical assessment was comparable to US in diagnosing MN. The thumb index finger squeeze test was the most sensitive screening test for the clinical diagnosis of MN.

negative: Why Lurkers Lurk The goal of this paper is to address the question: ‘why do lurkers lurk?’ Lurkers reportedly makeup the majority of members in online groups, yet little is known about them. Without insight into lurkers, our understanding of online groups is incomplete. Ignoring, dismissing, or misunderstanding lurking distorts knowledge of life online and may lead to inappropriate design of online environments. To investigate lurking, the authors carried out a study of lurking using in-depth, semi-structured interviews with ten members of online groups. 79 reasons for lurking and seven lurkers’ needs are identified from the interview transcripts. The analysis reveals that lurking is a strategic activity involving more than just reading posts. Reasons for lurking are categorized and a gratification model is proposed to explain lurker behavior.

negative: Learning Better Embeddings for Rare Words Using Distributional Representations There are two main types of word representations: low-dimensional embeddings and high-dimensional distributional vectors, in which each dimension corresponds to a context word. In this paper, we initialize an embedding-learning model with distributional vectors. Evaluation on word similarity shows that this initialization significantly increases the quality of embeddings for rare words.

negative: Text comparison using word vector representations and dimensionality reduction This paper describes a technique to compare large text sources using word vector representations (word2vec) and dimensionality reduction (tSNE) and how it can be implemented using Python. The technique provides a bird’s-eye view of text sources, e.g. text summaries and their source material, and enables users to explore text sources like a geographical map. Word vector representations capture many linguistic properties such as gender, tense, plurality and even semantic concepts like "capital city of". Using dimensionality reduction, a 2D map can be computed where semantically similar words are close to each other. The technique uses the word2vec model from the gensim Python library and t-SNE from scikit-learn.

negative: Engineering Quality and Reliability in Technology-Assisted Review The objective of technology-assisted review ("TAR") is to find as much relevant information as possible with reasonable effort. Quality is a measure of the extent to which a TAR method achieves this objective, while reliability is a measure of how consistently it achieves an acceptable result. We are concerned with how to define, measure, and achieve high quality and high reliability in TAR. When quality is defined using the traditional goal-post method of specifying a minimum acceptable recall threshold, the quality and reliability of a TAR method are both, by definition, equal to the probability of achieving the threshold. Assuming this definition of quality and reliability, we show how to augment any TAR method to achieve guaranteed reliability, for a quantifiable level of additional review effort. We demonstrate this result by augmenting the TAR method supplied as the baseline model implementation for the TREC 2015 Total Recall Track, measuring reliability and effort for 555 topics from eight test collections. While our empirical results corroborate our claim of guaranteed reliability, we observe that the augmentation strategy may entail disproportionate effort, especially when the number of relevant documents is low. To address this limitation, we propose stopping criteria for the model implementation that may be applied with no additional review effort, while achieving empirical reliability that compares favorably to the provably reliable method. We further argue that optimizing reliability according to the traditional goal-post method is inconsistent with certain subjective aspects of quality, and that optimizing a Taguchi quality loss function may be more apt.

negative: Design of Highly Integrated Mechatronic Gear Selector Levers for Automotive Shift-by-Wire Systems Increased requirements regarding ergonomic comfort, limited space, weight reduction, and electronic automation of functions and safety features are on the rise for future automotive gear levers. At the same time, current mechanical gear levers have restrictions to achieve this. In this paper, we present a monostable, miniaturized mechatronic gear lever to fulfill these requirements for automotive applications. This solution describes a gear lever for positioning in the center console of a car to achieve optimal ergonomics for dynamic driving, which enables both automatic and manual gear switching. In this paper, we describe the sensor and actuator concept, safety concept, recommended shift pattern, mechanical design, and the electronic integration of this shift-by-wire system in a typical automotive bus communication network. The main contribution of this paper is a successful system design and the integration of a mechatronic system in new applications for optimizing the human-machine interface inside road vehicles.

negative: Batch Identification Game Model for Invalid Signatures in Wireless Mobile Networks Secure access is one of the fundamental problems in wireless mobile networks. Digital signature is a widely used technique to protect messages’ authenticity and nodes’ identities. From the practical perspective, to ensure the quality of services in wireless mobile networks, ideally the process of signature verification should introduce minimum delay. Batch cryptography technique is a powerful tool to reduce verification time. However, most of the existing works focus on designing batch verification algorithms for wireless mobile networks without sufficiently considering the impact of invalid signatures, which can lead to verification failures and performance degradation. In this paper, we propose a Batch Identification Game Model (BIGM) in wireless mobile networks, enabling nodes to find invalid signatures with reasonable delay no matter whether the game scenario is complete information or incomplete information. Specifically, we analyze and prove the existence of Nash Equilibriums (NEs) in both scenarios, to select the dominant algorithm for identifying invalid signatures. To optimize the identification algorithm selection, we propose a self-adaptive auto-match protocol which estimates the strategies and states of attackers based on historical information. Comprehensive simulation results in terms of NE reasonability, algorithm selection accuracy, and identification delay are provided to demonstrate that BIGM can identify invalid signatures more efficiently than existing algorithms.

negative: International Journal of Advanced Robotic Systems Design of a Control System for an Autonomous Vehicle Based on Adaptive-PID Regular Paper The  autonomous  vehicle  is  a  mobile  robot integrating  multi‐sensor  navigation  and  positioning, intelligent decision making and control  technology. This paper  presents  the  control  system  architecture  of  the autonomous vehicle, called “Intelligent Pioneer”, and the path  tracking  and  stability  of  motion  to  effectively navigate  in unknown  environments  is discussed.  In  this approach,  a  two  degree‐of‐freedom  dynamic  model  is developed to formulate the path‐tracking problem in state space format. For controlling the instantaneous path error, traditional  controllers  have  difficulty  in  guaranteeing performance and stability over a wide range of parameter changes and disturbances. Therefore, a newly developed adaptive‐PID  controller  will  be  used.  By  using  this approach the flexibility of the vehicle control system will be  increased  and  achieving  great  advantages. Throughout,  we  provide  examples  and  results  from Intelligent Pioneer and the autonomous vehicle using this approach  competed  in  the  2010  and  2011  Future Challenge of China. Intelligent Pioneer finished all of the competition programmes and won  first position  in 2010 and third position in 2011.

negative: Case Study On Social Engineering Techniques for Persuasion T There are plenty of security software in market; each claiming the best, still we daily face problem of viruses and other malicious activities. If we know the basic working principal of such malware then we can very easily prevent most of them even without security software. Hackers and crackers are experts in psychology to manipulate people into giving them access or the information necessary to get access. This paper discusses the inner working of such attacks. Case study of Spyware is provided. In this case study, we got 100% success using social engineering techniques for deception on Linux operating system, which is considered as the most secure operating system. Few basic principal of defend, for the individual as well as for the organization, are discussed here, which will prevent most of such attack if followed.

negative: A Practical Attack to De-anonymize Social Network Users Social networking sites such as Facebook, LinkedIn, and Xing have been reporting exponential growth rates and have millions of registered users. In this paper, we introduce a novel de-anonymization attack that exploits group membership information that is available on social networking sites. More precisely, we show that information about the group memberships of a user (i.e., the groups of a social network to which a user belongs) is sufficient to uniquely identify this person, or, at least, to significantly reduce the set of possible candidates. That is, rather than tracking a user's browser as with cookies, it is possible to track a person. To determine the group membership of a user, we leverage well-known web browser history stealing attacks. Thus, whenever a social network user visits a malicious website, this website can launch our de-anonymization attack and learn the identity of its visitors. The implications of our attack are manifold, since it requires a low effort and has the potential to affect millions of social networking users. We perform both a theoretical analysis and empirical measurements to demonstrate the feasibility of our attack against Xing, a medium-sized social network with more than eight million members that is mainly used for business relationships. Furthermore, we explored other, larger social networks and performed experiments that suggest that users of Facebook and LinkedIn are equally vulnerable.

negative: K-Automorphism: A General Framework For Privacy Preserving Network Publication The growing popularity of social networks has generated interesting data management and data mining problems. An important concern in the release of these data for study is their privacy, since social networks usually contain personal information. Simply removing all identifiable personal information (such as names and social security number) before releasing the data is insufficient. It is easy for an attacker to identify the target by performing different structural queries. In this paper we propose k-automorphism to protect against multiple structural attacks and develop an algorithm (called KM) that ensures k-automorphism. We also discuss an extension of KM to handle “dynamic” releases of the data. Extensive experiments show that the algorithm performs well in terms of protection it provides.

negative: When Social Bots Attack: Modeling Susceptibility of Users in Online Social Networks Social bots are automatic or semi-automatic computer programs that mimic humans and/or human behavior in online social networks. Social bots can attack users (targets) in online social networks to pursue a variety of latent goals, such as to spread information or to influence targets. Without a deep understanding of the nature of such attacks or the susceptibility of users, the potential of social media as an instrument for facilitating discourse or democratic processes is in jeopardy. In this paper, we study data from the Social Bot Challenge 2011 an experiment conducted by the WebEcologyProject during 2011 in which three teams implemented a number of social bots that aimed to influence user behavior on Twitter. Using this data, we aim to develop models to (i) identify susceptible users among a set of targets and (ii) predict users’ level of susceptibility. We explore the predictiveness of three different groups of features (network, behavioral and linguistic features) for these tasks. Our results suggest that susceptible users tend to use Twitter for a conversational purpose and tend to be more open and social since they communicate with many different users, use more social words and show more affection than non-susceptible users.

negative: Mining (Social) Network Graphs to Detect Random Link Attacks Modern communication networks are vulnerable to attackers who send unsolicited messages to innocent users, wasting network resources and user time. Some examples of such attacks are spam emails, annoying tele-marketing phone calls, viral marketing in social networks, etc. Existing techniques to identify these attacks are tailored to certain specific domains (like email spam filtering), but are not applicable to a majority of other networks. We provide a generic abstraction of such attacks, called the Random Link Attack (RLA), that can be used to describe a large class of attacks in communication networks. In an RLA, the malicious user creates a set of false identities and uses them to communicate with a large, random set of innocent users. We mine the social networking graph extracted from user interactions in the communication network to find RLAs. To the best of our knowledge, this is the first attempt to conceptualize the attack definition, applicable to a variety of communication networks. In this paper, we formally define RLA and show that the problem of finding an RLA is NP-complete. We also provide two efficient heuristics to mine subgraphs satisfying the RLA property; the first (GREEDY) is based on greedy set-expansion, and the second (TRWALK) on randomized graph traversal. Our experiments with a real-life data set demonstrate the effectiveness of these algorithms.

negative: TMCoI-SIOT: A trust management system based on communities of interest for the social Internet of Things We propose a trust management system (TMS) for the social Internet of things (SIOT) called TMCoI-SIOT. The proposed system integrates numerous factors such as direct and indirect trust; transaction factors and social modeling of trust. The novelty of our approach can be summed up in three aspects. The first aspect concerns the network architecture which is seen as a set of nodes groups called community of interest (CoI). Each community nodes shares the same interests and is headed by an administrator (admin. The second aspect is the trust management system that prevents On-Off attacks. The third aspect concerns the trust prediction method that uses the Kalman filter technique in order to estimate nodes behaviors and prevent possible attacks. We prove the effectiveness of the proposed system by simulation against TMS attacks.

negative: A Hierarchical Security Framework for Defending Against Sophisticated Attacks on Wireless Sensor Networks in Smart Cities In smart cities, wireless sensor networks (WSNs) act as a type of core infrastructure that collects data from the city to implement smart services. The security of WSNs is one of the key issues of smart cities. In resource-restrained WSNs, dynamic ongoing or unknown attacks usually steer clear of isolated defense components. Therefore, to resolve this problem, we propose a hierarchical framework based on chance discovery and usage control (UCON) technologies to improve the security of WSNs while still taking the low-complexity and high security requirements of WSNs into account. The features of continuous decision and dynamic attributes in UCON can address ongoing attacks using advanced persistent threat detection. In addition, we use a dynamic adaptive chance discovery mechanism to detect unknown attacks. To design and implement a system using the mechanism described above, a unified framework is proposed in which low-level attack detection with simple rules is performed in sensors, and high-level attack detection with complex rules is performed in sinks and at the base station. Moreover, software-defined networking and network function virtualization technologies are used to perform attack mitigation when either low-level or high-level attacks are detected. An experiment was performed to acquire an attack data set for evaluation. Then, a simulation was created to evaluate the resource consumption and attack detection rate. The results demonstrate the feasibility and efficiency of the proposed scheme.