Query: A framework for 3D visualisation and manipulation in an immersive space using an untethered bimanual gestural interface

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Asymmetric division of labor in human skilled bimanual action: the kinematic chain as a model. <=SEP=> This article presents a tentative theoretical framework for the study of asymmetry in the context of human bimanual action. It is emphasized that in man most skilled manual activities involve two hands playing different roles, a fact that has been often overlooked in the experimental study of human manual lateralization. As an alternative to the current concepts of manual preference and manual superiority-whose relevance is limited to the particular case of unimanual actions-the more general concept of lateral preference is proposed to denote preference for one of the two possible ways of assigning two roles to two hands. A simple model describing man's favored intermanual division of labor in the model are the following. 1) The two hands represent two motors, that is, decomplexity is ignored in the suggested approach. 2) In man, the two manual motors cooperate with one another as if they were assembled in series, thereby forming a kinematic chain: In a right-hander allowed to follow his or her lateral preferences, motion produced by the right hand tends to articulate with motion produced by the left. It is suggested that the kinematic chain model may help in understanding the adaptive advantage of human manual specialization.

CONDENSATION—Conditional Density Propagation for Visual Tracking <=SEP=> The problem of tracking curves in dense visual clutter is challenging. Kalman filtering is inadequate because it is based on Gaussian densities which, being unimo dal, cannot represent simultaneous alternative hypotheses. The Condensation algorithm uses “factored sampling”, previously applied to the interpretation of static images, in which the probability distribution of possible interpretations is represented by a randomly generated set. Condensation uses learned dynamical models, together with visual observations, to propagate the random set over time. The result is highly robust tracking of agile motion. Notwithstanding the use of stochastic methods, the algorithm runs in near real-time.

Surround-screen projection-based virtual reality: the design and implementation of the CAVE <=SEP=> Several common systems satisfy some but not all of the VR definition above. Flight simulators provide vehicle tracking, not head tracking, and do not generally operate in binocular stereo. Omnimax theaters give a large angle of view [8], occasionally in stereo, but are not interactive. Head-tracked monitors [4][6] provide all but a large angle of view. Head-mounted displays (HMD) [7][13] and BOOMs [9] use motion of the actual display screens to achieve VR by our definition. Correct projection of the imagery on large screens can also create a VR experience, this being the subject of this paper. This paper describes the CAVE (CAVE Automatic Virtual Environment) virtual reality/scientific visualization system in detail and demonstrates that projection technology applied to virtual-reality goals achieves a system that matches the quality of workstation screens in terms of resolution, color, and flicker-free stereo. In addition, this format helps reduce the effect of common tracking and system latency errors. The off-axis perspective projection techniques we use are shown to be simple and straightforward. Our techniques for doing multi-screen stereo vision are enumerated, and design barriers, past and current, are described. Advantages and disadvantages of the projection paradigm are discussed, with an analysis of the effect of tracking noise and delay on the user. Successive refinement, a necessary tool for scientific visualization, is developed in the virtual reality context. The use of the CAVE as a one-to-many presentation device at SIGGRAPH '92 and Supercomputing '92 for computational science data is also mentioned. Previous work in the VR area dates back to Sutherland [12], who in 1965 wrote about the “Ultimate Display.” Later in the decade at the University of Utah, Jim Clark developed a system that allowed wireframe graphics VR to be seen through a headmounted, BOOM-type display for his dissertation. The common VR devices today are the HMD and the BOOM. Lipscomb [4] showed a monitor-based system in the IBM booth at SIGGRAPH '91 and Deering [6] demonstrated the Virtual Portal, a closets ized three-wal l project ion-based system, in the Sun Microsystems' booth at SIGGRAPH '92. The CAVE, our projection-based VR display [3], also premiered at SIGGRAPH '92. The Virtual Portal and CAVE have similar intent, but different implementation schemes.

The Representation and Matching of Pictorial Structures <=SEP=> The primary problem dealt with in this paper is the following. Given some description of a visual object, find that object in an actual photograph. Part of the solution to this problem is the specification of a descriptive scheme, and a metric on which to base the decision of "goodness" of matching or detection.

Research directions in virtual environments: report of an NSF Invitational Workshop, March 23-24, 1992, University of North Carolina at Chapel Hill <=SEP=> At the request of NSF's Interactive Systems Program, a two-day invitational workshop was held March 23-24, 1992 at UNC Chapel Hill to identify and recommend future research directions in the area of "virtual environments" (VE) *. Workshop participants included some 18 experts (plus 4 NSF officials) from universities, industry, and other leading technical organizations. The two-day schedule alternated between sessions of the entire group and sessions in the following specialty areas, around which the recommendations came to be organized: 1) Perception , 2) Human-Machine Software Interface , 3) Software, 4) Hardware, and 5) Applications. Also, two participants developed a taxonomy of VE applications that is included as an appendix to the report. 1. Collaborative science-technology development programs should be established at several sites around the country to encourage closer collaboration between developers and scientists. 2. Theoretical research should focus on development of metrics of performance and task demands in VE. 3. Paradigmatic applications and theoretical questions that illustrate the science-technology synergy need identification. Audition Spatial Sound 1.Theoretical research should emphasize the role of individual differences in Head-Related Transfer Functions (HRTF's), critical cues for distance and externalization, spectral cues for enhancing elevation and disambiguating the cone-of-confusion, head-motion, and intersensory interaction and adaptation in the accurate perception of virtual acoustic sources. The notion of artificially enhanced localization cues is also a promising area. 2. A fruitful area for joint basic and applied research is the development of perceptually-viable methods of simplifying the synthesis technique to maximize the efficiency of algorithms for complex room modeling. 3. Future effort should still be devoted to developing more realistic models of acoustic environments with implementation on more powerful hardware platforms. Nonspeech Audio 1. Theoretical research should focus on lower-level sensory and higher-level cognitive determinants of acoustic perceptual organization, with particular emphasis on how acoustic parameters interact to determine the identification, segregation, and localization of multiple, simultaneous sources. 2. Technology development should focus on hardware and software systems specifically aimed at real-time generation and control for acoustic information display. Haptics 1. Development should be encouraged of a variety of computer-controlled mechanical devices for either basic scientific investigation of the human haptic system or to serve as haptic interfaces for virtual environments and teleloperation. 2. Research programs should be initiated to encourage collaboration among engineers who are capable of building high precision robotic devices and scientists who can conduct biomechanical and perceptual experiments with the devices. 3. Research …

Bilingualism, Biliteracy, and Learning to Read: Interactions Among Languages and Writing Systems <=SEP=> Four groups of children in first grade were compared on early literacy tasks. Children in three of the groups were bilingual, each group representing a different combination of language and writing system, and children in the fourth group were monolingual speakers of English. All the bilingual children used both languages daily and were learning to read in both languages. The children solved decoding and phonological awareness tasks, and the bilinguals completed all tasks in both languages. Initial differences between the groups in factors that contribute to early literacy were controlled in an analysis of covariance, and the results showed a general increment in reading ability for all the bilingual children but a larger advantage for children learning two alphabetic systems. Similarly, bilinguals transferred literacy skills across languages only when both languages were written in the same system. Therefore, the extent of the bilingual facilitation for early reading depends on the relation between the two languages and writing systems.

A Live-User Study of Opinionated Explanations for Recommender Systems <=SEP=> This paper describes an approach for generating rich and compelling explanations in recommender systems, based on opinions mined from user-generated reviews. The explanations highlight the features of a recommended item that matter most to the user and also relate them to other recommendation alternatives and the user's past activities to provide a context.

Position Sensorless Control for Four-Switch Three-Phase Brushless DC Motor Drives <=SEP=> This paper proposes a position sensorless control scheme for four-switch three-phase (FSTP) brushless DC (BLDC) motor drives using a field programmable gate array (FPGA). A novel sensorless control with six commutation modes and novel pulsewidth modulation scheme is developed to drive FSTP BLDC motors. The low cost BLDC driver is achieved by the reduction of switch device count, cost down of control, and saving of hall sensors. The feasibility of the proposed sensorless control for FSTP BLDC motor drives is demonstrated by analysis and experimental results.

Social Media Use and Mental Health among Young Adults <=SEP=> In recent years many parents, advocates and policy makers have expressed concerns regarding the potential negative impact of social media use. Some studies have indicated that social media use may be tied to negative mental health outcomes, including suicidality, loneliness and decreased empathy. Other studies have not found evidence for harm, or have indicated that social media use may be beneficial for some individuals. The current correlational study examined 467 young adults for their time spent using social media, importance of social media in their lives and tendency to engage in vaguebooking (posting unclear but alarming sounding posts to get attention). Outcomes considered included general mental health symptoms, suicidal ideation, loneliness, social anxiety and decreased empathy. Results indicated that social media use was not predictive of impaired mental health functioning. However, vaguebooking was predictive of suicidal ideation, suggesting this particular behavior could be a warning sign for serious issues. Overall, results from this study suggest that, with the exception of vaguebooking, concerns regarding social media use may be misplaced.

Evaluation of games for teaching computer science <=SEP=> Well-designed educational games (both online and offline) can be a valuable way to engage students with a topic. However, games can also be a time-consuming distraction, so it is useful to be able to identify which are suitable. This paper provides guidelines for evaluating games, and applies them to identify promising games. We begin by defining what is meant by a game, ruling out simple animations or quizzes, and focusing on a more playful approach to education. Based on this definition, we have identified games that are available for teaching topics in computer science, and classified them. We have developed criteria for evaluating games based on existing research on educational games, combined with educational theory and considering them in the context of CS. The criteria developed cover the topics taught, how easy it is to install and run, how engaging the game is, and how much time students might invest to use the game. We find that although there are a number of worth-while games in the field of CS and most are free, the coverage of topics is very patchy, with most focused on programming concepts and binary numbers.

Discriminative and consistent similarities in instance-level Multiple Instance Learning <=SEP=> In this paper we present a bottom-up method to instance-level Multiple Instance Learning (MIL) that learns to discover positive instances with globally constrained reasoning about local pairwise similarities. We discover positive instances by optimizing for a ranking such that positive (top rank) instances are highly and consistently similar to each other and dissimilar to negative instances. Our approach takes advantage of a discriminative notion of pairwise similarity coupled with a structural cue in the form of a consistency metric that measures the quality of each similarity. We learn a similarity function for every pair of instances in positive bags by how similarly they differ from instances in negative bags, the only certain labels in MIL. Our experiments demonstrate that our method consistently outperforms state-of-the-art MIL methods both at bag-level and instance-level predictions in standard benchmarks, image category recognition, and text categorization datasets.

Polyhedral Extraction Tool <=SEP=> We present a new library for extracting a polyhedral model from C source. The library is based on clang, the LLVM C frontend, and isl, a library for manipulating quasi-affine sets and relations. The use of clang for parsing the C code brings advanced diagnostics and full support for C99. The use of isl allows for an easy construction and a powerful and compact representation of the polyhedral model. Besides allowing arbitrary piecewise quasi-affine index expressions and conditions, the library also supports some data dependent constructs and has special treatment for unsigned integers. The library has been successfully used to obtain polyhedral models for use in an equivalence checker, a tool for constructing polyhedral process networks, a parallelizer targeting GPUs and an interactive polyhedral environment.

Printed Rectangular Monopole Antenna with Slots for UWB Applications <=SEP=> This paper presents various slotted rectangular Monopole Antenna. The proposed antenna is designed in order to meet ultra wideband (UWB) requirements and applications. This antenna is small in size enabling it to be fixed into wireless Personal Area Network (PAN) devices. There are 3 types of proposed slots; T-slot, Π-slot and I-slot for patch. The T & I slots produce more vertical current, thus improve the matching impedance performance at higher frequencies. The Π-slot on rectangular patch has shown dual frequency performance. For all proposed antennas, their measured return loss and radiation pattern meet the UWB requirements.

Cultivating Competence , Self-Efficacy , and Intrinsic Interest Through Proximal Self-Motivation <=SEP=> The present experiment tested the hypothesis that self-motivation through proximal goal setting serves as an effective mechanism for cultivating competencies, self-percepts of efficacy, and intrinsic interest. Children who exhibited gross deficits and disinterest in mathematical tasks pursued a program of self-directed learning under conditions involving either proximal subgoals, distal goals, or no goals. Results of the multifaceted assessment provide support for the superiority of proximal self-influence. Under proximal subgoals, children progressed rapidly in self-directed learning, achieved substantial mastery of mathematical operations, and developed a sense of personal efficacy and intrinsic interest in arithmetic activities that initially held little attraction for them. Distal goals had no demonstrable effects. In addition to its other benefits, goal proximity fostered veridical self-knowledge of capabilities as reflected in high congruence between judgments of mathematical self-efficacy and subsequent mathematical performance. Perceived self-efficacy was positively related to accuracy of mathematical performance and to intrinsic interest in arithmetic activities. Article: Much human behavior is directed and sustained over long periods, even though the external inducements for it may be few and far between. Under conditions in which external imperatives are minimal and discontinuous, people must partly serve as agents of their own motivation and action. In social learning theory (Bandura, 1977b, in press), self-directedness operates through a self system that comprises cognitive structures and subfunctions for perceiving, evaluating, motivating, and regulating behavior. An important, cognitively based source of self-motivation relies on the intervening processes of goal setting and self-evaluative re-actions to one's own behavior. This form of self-motivation, which operates largely through internal comparison processes, re-quires personal standards against which to evaluate ongoing performance. By making self-satisfaction conditional on a certain level of performance, individuals create self-inducements to persist in their efforts until their performances match internal standards. Both the anticipated satisfactions for matching attainments and the dissatisfactions with insufficient ones provide incentives for self-directed actions. Personal goals or standards do not automatically activate the evaluative processes that affect the level and course of one's behavior. Certain properties of goals, such as their specificity and level, help to provide clear standards of adequacy (Latham & Yukl, 1975; Locke, 1968; Steers & Porter, 1974). Hence, explicit goals are more likely than vague intentions to engage self-reactive influences in any given activity. Goal proximity, a third property, is especially critical because the more closely referential standards are related to ongoing behavior, the greater the likelihood that self-influences will be activated during the process. Some suggestive evidence exists that the impact of goals on behavior is indeed determined by how far into the future they are projected (Bandura & Simon, 1977; Jeffery, 1977). In the social learning view, adopting proximal subgoals for one's own behavior can have at least three major psychological effects. As already alluded to, such goals have motivational effects. One of the propositions tested in the present experiment is that self-motivation can be best created and sustained by attainable subgoals that lead to larger future ones. Proximal subgoals provide immediate incentives and guides for performance, whereas distal goals are too far re-moved in time to effectively mobilize effort or to direct what one does in the here and now. Focus on the distant future makes it easy to temporize and to slacken efforts in the present. Proximal subgoals can also serve as an important vehicle in the development of self-percepts of efficacy. Competence in dealing with one's environment is not a fixed act or simply knowing what to do. Rather, it involves a generative capability in which component skills must be selected and organized into integrated courses of action to manage changing task demands. Operative competence thus requires flexible orchestration of multiple subskills. Self-efficacy is concerned with judgments about how well one can organize and execute courses of action required to deal with prospective situations containing many ambiguous, unpredictable, and often stressful elements. Self-percepts of efficacy can affect people's choice of activities, how much effort they expend, and how long they will persist in the face of difficulties (Bandura, 1977a; Brown & Inouye, 1978; Schunk, 1981). Without standards against which to measure their performances, people have little basis for judging how they are doing or for gauging their capabilities. Subgoal attainments provide indicants of mastery for enhancing selfefficacy. By contrast, distal goals are too far removed in time to provide sufficiently clear markers of progress along the way to ensure a growing sense of self-efficacy. The processes underlying the development of intrinsic interest may similarly be governed, at least in part, by goal proximity. Most of the activities that people enjoy doing for their own sake originally had little or no interest for them. Young children are not innately interested in singing operatic arias, playing tubas, deriving mathematical equations, writing sonnets, or propelling heavy shotput balls through the air. However, through favorable continued involvement, almost any activity can become imbued with consuming significance. One can posit at least two ways in which proximal goals might contribute to enhancement of interest in activities. When people aim for, and master, desired levels of performance, they experience a sense of satisfaction (Locke, Cartledge, & Knerr, 1970). The satisfactions derived from subgoal attainments can build intrinsic interest. When performances are gauged against lofty, distal goals, the large negative disparities between standards and attainments are likely to attenuate the level of self-satisfaction experienced along the way. Conceptual analyses of intrinsic interest within the framework of both self-efficacy theory (Bandura, 1981) and intrinsic motivation theory (Deci, 1975; Lepper & Greene, 1979) assign perceived competence a mediating role. A sense of personal efficacy in mastering challenges is apt to generate greater interest in the activity than is selfperceived inefficacy in producing competent performances. To the extent that proximal subgoals promote and authenticate a sense of causal agency, they can heighten interest through their effects on perception of personal causation. Investigations of intrinsic interest have been concerned almost exclusively with the effects of extrinsic rewards on interest when it is already highly developed. Although results are somewhat variable, the usual finding is that rewards given regardless of quality of performance tend to reduce interest, whereas rewards for performances signifying competence sustain high interest (Boggiano & Ruble, 1979; Enzle & Ross, 1978; Lepper & Greene, 1979; Ross, 1976). The controversy over the effects of extrinsic rewards on preexisting high interest has led to a neglect of the issue of how intrinsic interest is developed when it is lacking. One of the present study's aims was to test the notion that proximal subgoals enlist the type of sustained involvement in activities that build selfefficacy and interest when they are absent. Children who displayed gross deficits in mathematical skills and strong disinterest in such activities engaged in self-directed learning over a series of sessions. They pursued the self-paced learning under conditions involving either proximal subgoals, distal goals, or bids to work actively without any reference to goals. It was predicted that self-motivation through proximal subgoals would prove most effective in cultivating mathematical competencies, self-percepts of efficacy, and intrinsic interest in mathematical activities. For reasons given earlier, distal goals were not expected to exceed bids to work actively in promoting changes. It was hypothesized further that strength of self-efficacy would predict subsequent accuracy on mathematical tasks and level of intrinsic interest.

Efficient segmentation and surface classification of range images <=SEP=> Derivation of geometric structures from point clouds is an important step towards scene understanding for mobile robots. In this paper, we present a novel method for segmentation and surface classification of ordered point clouds. Data from RGB-D cameras are used as input. Normal based region growing segments the cloud and point feature descriptors classify each segment. Not only planar segments can be described but also curved surfaces. In an evaluation on indoor scenes we show the performance of our approach as well as give a comparison to state of the art methods.

Design of a Dual-Band Microstrip Antenna With Enhanced Gain for Energy Harvesting Applications <=SEP=> In this letter, a dual-band circular patch antenna is introduced. The antenna consists of a circular patch with a direct feed through microstrip feedline designed to radiate at 2.45 GHz with fractional bandwidth of 4.5%. A circular slot is inserted into the ground plane that radiates by capacitive coupling between the patch and the ground plane. This slot radiates at 1.95 GHz with fractional impedance bandwidth of 5%. The antenna achieves good radiation characteristics by inserting a reflecting plane at optimum position behind it. The antenna has gain of 8.3 and 7.8 dBi at 1.95 and 2.45 GHz, respectively. This antenna is proposed for the rectenna; then it is designed to direct the main beam in a certain direction by increasing front-to-back (F/B) ratio with low cross-polarization levels by using defected reflector structure in the reflecting plane. The equivalent circuit of the proposed antenna is introduced to model the electrical behavior of the antenna. The proposed antenna can be used to harvest the energy from Wi-Fi and widely spread mobile networks. The proposed antenna was designed using CST Microwave Studio. The simulated and measured results show good agreement.

Trend analysis model: trend consists of temporal words, topics, and timestamps <=SEP=> This paper presents a topic model that identifies interpretable low dimensional components in time-stamped data for capturing the evolution of trends. Unlike other models for time-stamped data, our proposal, the trend analysis model (TAM), focuses on the difference between temporal words and other words in each document to detect topic evolution over time. TAM introduces a latent trend class variable into each document and a latent switch variable into each token for handling these differences. The trend class has a probability distribution over temporal words, topics, and a continuous distribution over time, where each topic is responsible for generating words. The latter class uses a document specific probabilistic distribution to judge which variable each word comes from for generating words in each token. Accordingly, TAM can explain which topic co-occurrence pattern will appear at any given time, and represents documents of similar content and timestamp as sharing the same trend class. Therefore, TAM projects them on a latent space of trend dimensionality and allows us to predict the temporal evolution of words and topics in document collections. Experiments on various data sets show that the proposed model can capture interpretable low dimensionality sets of topics and timestamps, take advantage of previous models, and is useful as a generative model in the analysis of the evolution of trends.

Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation <=SEP=> In this work, we propose to apply trust region optimization to deep reinforcement learning using a recently proposed Kronecker-factored approximation to the curvature. We extend the framework of natural policy gradient and propose to optimize both the actor and the critic using Kronecker-factored approximate curvature (K-FAC) with trust region; hence we call our method Actor Critic using Kronecker-Factored Trust Region (ACKTR). To the best of our knowledge, this is the first scalable trust region natural gradient method for actor-critic methods. It is also the method that learns non-trivial tasks in continuous control as well as discrete control policies directly from raw pixel inputs. We tested our approach across discrete domains in Atari games as well as continuous domains in the MuJoCo environment. With the proposed methods, we are able to achieve higher rewards and a 2to 3-fold improvement in sample efficiency on average, compared to previous state-of-the-art on-policy actor-critic methods. Code is available at https://github.com/openai/baselines.

Exploring Deep Learning and Transfer Learning for Colonic Polyp Classification <=SEP=> Recently, Deep Learning, especially through Convolutional Neural Networks (CNNs) has been widely used to enable the extraction of highly representative features. This is done among the network layers by filtering, selecting, and using these features in the last fully connected layers for pattern classification. However, CNN training for automated endoscopic image classification still provides a challenge due to the lack of large and publicly available annotated databases. In this work we explore Deep Learning for the automated classification of colonic polyps using different configurations for training CNNs from scratch (or full training) and distinct architectures of pretrained CNNs tested on 8-HD-endoscopic image databases acquired using different modalities. We compare our results with some commonly used features for colonic polyp classification and the good results suggest that features learned by CNNs trained from scratch and the "off-the-shelf" CNNs features can be highly relevant for automated classification of colonic polyps. Moreover, we also show that the combination of classical features and "off-the-shelf" CNNs features can be a good approach to further improve the results.

User Based Aggregation for Biterm Topic Model <=SEP=> Biterm Topic Model (BTM) is designed to model the generative process of the word co-occurrence patterns in short texts such as tweets. However, two aspects of BTM may restrict its performance: 1) user individualities are ignored to obtain the corpus level words co-occurrence patterns; and 2) the strong assumptions that two co-occurring words will be assigned the same topic label could not distinguish background words from topical words. In this paper, we propose Twitter-BTM model to address those issues by considering user level personalization in BTM. Firstly, we use user based biterms aggregation to learn user specific topic distribution. Secondly, each user’s preference between background words and topical words is estimated by incorporating a background topic. Experiments on a large-scale real-world Twitter dataset show that Twitter-BTM outperforms several stateof-the-art baselines.

Network medicine: a network-based approach to human disease <=SEP=> Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular and intercellular network that links tissue and organ systems. The emerging tools of network medicine offer a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships among apparently distinct (patho)phenotypes. Advances in this direction are essential for identifying new disease genes, for uncovering the biological significance of disease-associated mutations identified by genome-wide association studies and full-genome sequencing, and for identifying drug targets and biomarkers for complex diseases.

Development of a capacitive-type 6-axis force-torque sensor <=SEP=> Force sensing is a crucial task for robots, especially when end effectors such as fingers and hands need to interact with unknown environments; to sense such forces, a force-torque (F/T) sensor is an essential component. In this paper, we propose a small-sized 6-axis F/T sensor with a novel arrangement of 12 transducers using the force transducer we have previously developed. The copper beryllium used in our sensor reduces hysteresis in each transducer. Additionally, the sensor provides digital output via I2C bus to reduce the susceptibility to noise, and reduce the number of required wires. Sensor characteristics such as its sensitivity, signal-to-noise ratio, linearity, and hysteresis are determined. More importantly, we showed that our sensor can detect and measure the 6-axis F/T.

Service-Oriented Computing: State of the Art and Research Challenges <=SEP=> Service-oriented computing promotes the idea of assembling application components into a network of services that can be loosely coupled to create flexible, dynamic business processes and agile applications that span organizations and computing platforms. An SOC research road map provides a context for exploring ongoing research activities.

An Edge Detection Algorithm for Online Image Analysis <=SEP=> Online image analysis is used in a wide variety of applications. Edge detection is a fundamental tool used to obtain features of objects as a prerequisite step to object segmentation. This paper presents a simple and relatively fast online edge detection algorithm based on second derivative. The proposed edge detector is less sensitive to noise and may be applied on color, gray and binary images without preprocessing requirements. The merits of the algorithm are demonstrated by comparison with Canny’s and Sobel’s edge detectors.

An implementation of the 802.1AE MAC Security Standard for in-car networks <=SEP=> The continuous increase in complexity in automotive electronics has led to cars that include up to 80 Electronic Control Units (ECUs). As a consequence, in-car networks are currently up to their limit in terms of data load, flexibility and bandwidth. The Ethernet backbone is thus considered as the best performing solution. On the other hand, the growing interconnection of cars with the external world requires high security standards in order to prevent safety risks for car passengers. The IEEE 802.1AE MAC Security Standard (MACSec) solves the security issues of Ethernet networks by providing confidentiality, authenticity and integrity of data. This paper presents an efficient hardware implementation of the MACSec standard for the automotive world. The system was synthesized on a Stratix V FPGA and on a 28nm standard-cell CMOS technology. In terms of maximum throughput, the FPGA results in 1.1 Gbps while the standard-cell technology reaches 3.9 Gbps. The FPGA implementation occupies 4.5% of the Adaptive Logic Modules (ALMs) while the standard-cell one gives a 285 kgate size. The proposed architecture represents a suitable implementation for a low area and high-performance solution as usually required by in-car network controllers.

Contextual correlates of synonymy <=SEP=> Experimentol corroboration was obtained for the hypothesis that the proportion of words common to the contexts of word A and to the contexts of word B is a function of the degree to which A and B are similar in meaning. The tests were carried out for variously defined contexts. The shapes of the functions, however, indicate that similarity of context is reliable as criterion only for detecting pairs of words that are very similar in meaning.

Stock market prediction exploiting microblog sentiment analysis <=SEP=> This paper proposes a stock market prediction method exploiting sentiment analysis using financial microblogs (Sina Weibo). We analyze the microblog texts to find the financial sentiments, then combine the sentiments and the historical data of the Shanghai Composite Index (SH000001) to predict the stock market movements. Our framework includes three modules: Microblog Filter (MF), Sentiment Analysis (SA), and Stock Prediction (SP). The MF module is based on LDA to get the financial microblogs. The SA module first sets up a financial lexicon, then gets the sentiments of the microblogs obtained from the MF module. The SP module proposes a user-group model which adjusts the importance of different people, and combines it with stock historical data to predict the movement of the SH000001. We use about 6.1 million microblogs to test our method, and the result demonstrates our method is effective.

A taxonomy for user-healthcare robot interaction <=SEP=> This paper evaluates existing taxonomies aimed at characterizing the interaction between robots and their users and modifies them for health care applications. The modifications are based on existing robot technologies and user acceptance of robotics. Characterization of the user, or in this case the patient, is a primary focus of the paper, as they present a unique new role as robot users. While therapeutic and monitoring-related applications for robots are still relatively uncommon, we believe they will begin to grow and thus it is important that the spurring relationship between robot and patient is well understood.

A Critical Review of Recurrent Neural Networks for Sequence Learning <=SEP=> Countless learning tasks require dealing with sequential data. Image captioning, speech synthesis, and music generation all require that a model produce outputs that are sequences. In other domains, such as time series prediction, video analysis, and musical information retrieval, a model must learn from inputs that are sequences. Interactive tasks, such as translating natural language, engaging in dialogue, and controlling a robot, often demand both capabilities. Recurrent neural networks (RNNs) are connectionist models that capture the dynamics of sequences via cycles in the network of nodes. Unlike standard feedforward neural networks, recurrent networks retain a state that can represent information from an arbitrarily long context window. Although recurrent neural networks have traditionally been difficult to train, and often contain millions of parameters, recent advances in network architectures, optimization techniques, and parallel computation have enabled successful large-scale learning with them. In recent years, systems based on long short-term memory (LSTM) and bidirectional (BRNN) architectures have demonstrated ground-breaking performance on tasks as varied as image captioning, language translation, and handwriting recognition. In this survey, we review and synthesize the research that over the past three decades first yielded and then made practical these powerful learning models. When appropriate, we reconcile conflicting notation and nomenclature. Our goal is to provide a selfcontained explication of the state of the art together with a historical perspective and references to primary research.

The Expressive Power of SPARQL <=SEP=> This paper studies the expressive power of SPARQL. The main result is that SPARQL and non-recursive safe Datalog with negation have equivalent expressive power, and hence, by classical results, SPARQL is equivalent from an expressive point of view to Relational Algebra. We present explicit generic rules of the transformations in both directions. Among other findings of the paper are the proof that negation can be simulated in SPARQL, that non-safe filters are superfluous, and that current SPARQL W3C semantics can be simplified to a standard