Query: Twin Learning for Similarity and Clustering: A Unified Kernel Approach

Relevant passages:
Nonlinear dimensionality reduction by locally linear embedding. <=SEP=> Many areas of science depend on exploratory data analysis and visualization. The need to analyze large amounts of multivariate data raises the fundamental problem of dimensionality reduction: how to discover compact representations of high-dimensional data. Here, we introduce locally linear embedding (LLE), an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs. Unlike clustering methods for local dimensionality reduction, LLE maps its inputs into a single global coordinate system of lower dimensionality, and its optimizations do not involve local minima. By exploiting the local symmetries of linear reconstructions, LLE is able to learn the global structure of nonlinear manifolds, such as those generated by images of faces or documents of text.

Robust Subspace Clustering via Smoothed Rank Approximation <=SEP=> Matrix rank minimizing subject to affine constraints arises in many application areas, ranging from signal processing to machine learning. Nuclear norm is a convex relaxation for this problem which can recover the rank exactly under some restricted and theoretically interesting conditions. However, for many real-world applications, nuclear norm approximation to the rank function can only produce a result far from the optimum. To seek a solution of higher accuracy than the nuclear norm, in this letter, we propose a rank approximation based on Logarithm-Determinant. We consider using this rank approximation for subspace clustering application. Our framework can model different kinds of errors and noise. Effective optimization strategy is developed with theoretical guarantee to converge to a stationary point. The proposed method gives promising results on face clustering and motion segmentation tasks compared to the state-of-the-art subspace clustering algorithms.

Sparse subspace clustering <=SEP=> We propose a method based on sparse representation (SR) to cluster data drawn from multiple low-dimensional linear or affine subspaces embedded in a high-dimensional space. Our method is based on the fact that each point in a union of subspaces has a SR with respect to a dictionary formed by all other data points. In general, finding such a SR is NP hard. Our key contribution is to show that, under mild assumptions, the SR can be obtained `exactly' by using l1 optimization. The segmentation of the data is obtained by applying spectral clustering to a similarity matrix built from this SR. Our method can handle noise, outliers as well as missing data. We apply our subspace clustering algorithm to the problem of segmenting multiple motions in video. Experiments on 167 video sequences show that our approach significantly outperforms state-of-the-art methods.

Clustering and projected clustering with adaptive neighbors <=SEP=> Many clustering methods partition the data groups based on the input data similarity matrix. Thus, the clustering results highly depend on the data similarity learning. Because the similarity measurement and data clustering are often conducted in two separated steps, the learned data similarity may not be the optimal one for data clustering and lead to the suboptimal results. In this paper, we propose a novel clustering model to learn the data similarity matrix and clustering structure simultaneously. Our new model learns the data similarity matrix by assigning the adaptive and optimal neighbors for each data point based on the local distances. Meanwhile, the new rank constraint is imposed to the Laplacian matrix of the data similarity matrix, such that the connected components in the resulted similarity matrix are exactly equal to the cluster number. We derive an efficient algorithm to optimize the proposed challenging problem, and show the theoretical analysis on the connections between our method and the K-means clustering, and spectral clustering. We also further extend the new clustering model for the projected clustering to handle the high-dimensional data. Extensive empirical results on both synthetic data and real-world benchmark data sets show that our new clustering methods consistently outperforms the related clustering approaches.

Feature Selection and Kernel Learning for Local Learning-Based Clustering <=SEP=> The performance of the most clustering algorithms highly relies on the representation of data in the input space or the Hilbert space of kernel methods. This paper is to obtain an appropriate data representation through feature selection or kernel learning within the framework of the Local Learning-Based Clustering (LLC) (Wu and Schölkopf 2006) method, which can outperform the global learning-based ones when dealing with the high-dimensional data lying on manifold. Specifically, we associate a weight to each feature or kernel and incorporate it into the built-in regularization of the LLC algorithm to take into account the relevance of each feature or kernel for the clustering. Accordingly, the weights are estimated iteratively in the clustering process. We show that the resulting weighted regularization with an additional constraint on the weights is equivalent to a known sparse-promoting penalty. Hence, the weights of those irrelevant features or kernels can be shrunk toward zero. Extensive experiments show the efficacy of the proposed methods on the benchmark data sets.

Embedded Robust Visual Obstacle Detection on Autonomous Lawn Mowers <=SEP=> Currently, the only mass-market service robots are floor cleaners and lawn mowers. Although available for more than 20 years, they mostly lack intelligent functions from modern robot research. In particular, the obstacle detection and avoidance is typically a simple physical collision detection. In this work, we discuss a prototype autonomous lawn mower with camera-based non-contact obstacle avoidance. We devised a low-cost compact module consisting of color cameras and an ARM-based processing board, which can be added to an autonomous lawn mower with minimal effort. For testing our system, we conducted a field test with 20 prototype units distributed in eight European countries with a total mowing time of 3,494 hours. The results show that our proposed system is able to work without expert interaction for a full season and strongly reduces collision events while still keeping the good mowing performance. Furthermore, a questionnaire with the testers revealed that most people would favor the camera-based mower over a non-camera-based mower.

Message Passing Inference for Large Scale Graphical Models with High Order Potentials <=SEP=> To keep up with the Big Data challenge, parallelized algorithms based on dual decomposition have been proposed to perform inference in Markov random fields. Despite this parallelization, current algorithms struggle when the energy has high order terms and the graph is densely connected. In this paper we propose a partitioning strategy followed by a message passing algorithm which is able to exploit pre-computations. It only updates the high-order factors when passing messages across machines. We demonstrate the effectiveness of our approach on the task of joint layout and semantic segmentation estimation from single images, and show that our approach is orders of magnitude faster than current methods.

Map Matching with Inverse Reinforcement Learning <=SEP=> We study map-matching, the problem of estimating the route that is traveled by a vehicle, where the points observed with the Global Positioning System are available. A state-of-the-art approach for this problem is a Hidden Markov Model (HMM). We propose a particular transition probability between latent road segments by the use of the number of turns in addition to the travel distance between the latent road segments. We use inverse reinforcement learning to estimate the importance of the number of turns relative to the travel distance. This estimated importance is incorporated in the transition probability of the HMM. We show, through numerical experiments, that the error of map-matching can be reduced substantially with the proposed transition probability.

Online Object Tracking Based on CNN with Metropolis-Hasting Re-Sampling <=SEP=> Tracking-by-learning strategies have been effective in solving many challenging problems in visual tracking, in which the learning sample generation and labeling play important roles for final performance. Since the concern of deep learning based approaches has shown an impressive performance in different vision tasks, how to properly apply the learning model, such as CNN, to an online tracking framework is still challenging. In this paper, to overcome the overfitting problem caused by straight-forward incorporation, we propose an online tracking framework by constructing a CNN based adaptive appearance model to generate more reliable training data over time. With a reformative Metropolis-Hastings re-sampling scheme to reshape particles for a better state posterior representation during online learning, the proposed tracking outperforms most of the state-of-art trackers on challenging benchmark video sequences.

Avoiding revascularization with lifestyle changes: The Multicenter Lifestyle Demonstration Project. <=SEP=> The Multicenter Lifestyle Demonstration Project was designed to determine if comprehensive lifestyle changes can be a direct alternative to revascularization for selected patients without increasing cardiac events. A total of 333 patients completed this demonstration project (194 in the experimental group and 139 in the control group). We found that experimental group patients were able to avoid revascularization for at least 3 years by making comprehensive lifestyle changes at substantially lower cost without increasing cardiac morbidity and mortality. These patients reported reductions in angina comparable with what can be achieved with revascularization.

Security risk assessment framework for cloud computing environments <=SEP=> Cloud computing has become today’s most common technology buzzword. Despite the promises of cloud computing to decrease computing implementation costs and deliver computing as a service, which allows clients to pay only for what they need and use, cloud computing also raises many security concerns. Most popular risk assessment standards, such as ISO27005, NIST SP800-30, and AS/NZS 4360, assume that an organization’s assets are fully managed by the organization itself and that all security management processes are imposed by the organization. These assumptions, however, do not apply to cloud computing environments. Hence, this paper proposes a security risk assessment framework that can enable cloud service providers to assess security risks in the cloud computing environment and allow cloud clients to contribute in risk assessment. The proposed framework provides a more realistic and accurate risk assessment outcome by considering the cloud clients’ evaluation of security risk factors and avoiding the complexity that can result from the involvement of clients in whole risk assessment process. Copyright © 2014 John Wiley & Sons, Ltd.

A Sentence Interaction Network for Modeling Dependence between Sentences <=SEP=> Modeling interactions between two sentences is crucial for a number of natural language processing tasks including Answer Selection, Dialogue Act Analysis, etc. While deep learning methods like Recurrent Neural Network or Convolutional Neural Network have been proved to be powerful for sentence modeling, prior studies paid less attention on interactions between sentences. In this work, we propose a Sentence Interaction Network (SIN) for modeling the complex interactions between two sentences. By introducing “interaction states” for word and phrase pairs, SIN is powerful and flexible in capturing sentence interactions for different tasks. We obtain significant improvements on Answer Selection and Dialogue Act Analysis without any feature engineering.

On-road vehicle detection: a review <=SEP=> Developing on-board automotive driver assistance systems aiming to alert drivers about driving environments, and possible collision with other vehicles has attracted a lot of attention lately. In these systems, robust and reliable vehicle detection is a critical step. This paper presents a review of recent vision-based on-road vehicle detection systems. Our focus is on systems where the camera is mounted on the vehicle rather than being fixed such as in traffic/driveway monitoring systems. First, we discuss the problem of on-road vehicle detection using optical sensors followed by a brief review of intelligent vehicle research worldwide. Then, we discuss active and passive sensors to set the stage for vision-based vehicle detection. Methods aiming to quickly hypothesize the location of vehicles in an image as well as to verify the hypothesized locations are reviewed next. Integrating detection with tracking is also reviewed to illustrate the benefits of exploiting temporal continuity for vehicle detection. Finally, we present a critical overview of the methods discussed, we assess their potential for future deployment, and we present directions for future research.

Multi-Player Alpha-Beta Pruning <=SEP=> Korf, R.E., Multi-player alpha-beta pruning (Research Note), Artificial Intelligence 48 (1991) 99-111. We consider the generalization of minimax search with alpha-beta pruning to non-cooperative, perfect-information games with more than two players. The minimax algorithm was generalized in [2] to the maxn algorithm applied to vectors of n-tuples representing the evaluations for each of the players. If we assume an upper bound on the sum of the evaluations for each player, and a lower bound on each individual evaluation, then shallow alpha-beta pruning is possible, but not deep pruning. In the best case, the asymptotic branching factor is reduced to (1 + 4bv'~b-"s~-3)/2. In the average case, however, pruning does not reduce the asymptotic branching factor. Thus, alpha-beta pruning is found to be effective only in the special case of two-player games. In addition, we show that it is an optimal directional algorithm for two players.

LogicBlox, Platform and Language: A Tutorial <=SEP=> The modern enterprise software stack—a collection of applications supporting bookkeeping, analytics, planning, and forecasting for enterprise data—is in danger of collapsing under its own weight. The task of building and maintaining enterprise software is tedious and laborious; applications are cumbersome for end-users; and adapting to new computing hardware and infrastructures is difficult. We believe that much of the complexity in today’s architecture is accidental, rather than inherent. This tutorial provides an overview of the LogicBlox platform, a ambitious redesign of the enterprise software stack centered around a unified declarative programming model, based on an extended version of Datalog. 1 The Enterprise Hairball Modern enterprise applications involve an enormously complex technology stack composed of many disparate systems programmed in a hodgepodge of programming languages. We refer to this stack, depicted in Figure 1, as “the enterprise hairball.” First, there is an online transaction processing (OLTP) layer that performs bookkeeping of the core business data for an enterprise. Such data could include the current product catalog, recent sales figures, current outstanding invoices, customer account balances, and so forth. This OLTP layer typically includes a relational DBMS—programmed in a combination of a query language (SQL), a stored procedure language (like PL/SQL or TSQL), and a batch programming language like Pro*C—an application server, such as Oracle WebLogic [35], IBM WebSphere [36], or SAP NetWeaver [26]—programmed in an object-oriented language like Java, C#, or ABAP—and a web browser front-end, programmed using HTML and Javascript. In order to track the performance of the enterprise over time, a second business intelligence (BI) layer typically holds five to ten years of historical information that was originally recorded in the OLTP layer and performs read-only analyses on this information. This layer typically includes another DBMS (or, more commonly, a BI variant like Teradata [33] or IBM Netezza [25]) along with a BI application server such as Microstrategy [23], SAP BusinessObjects [5], or IBM Cognos [7], programmed using a vendor-specific declarative language. Fig. 1: Enterprise software components and technology stack example. Data is moved from the transaction layer to the BI layer via so-called extracttransform-load (ETL) tools that come with their own tool-specific programming language. Finally, in order to plan the future actions of an enterprise, there is a planning layer, which supports a class of read-write use cases for which the BI layer is unsuitable. This layer typically includes a planning application server, like Oracle Hyperion [13] or IBM Cognos TM1 [34], that are programmed using a vendor-specific declarative language or a standard language like MDX [21], and spreadsheets like Microsoft Excel that are programmed in a vendor-specific formula language (e.g. A1 = B17 D12) and optionally a scripting language like VBA. In order to enhance or automate decisions made in the planning layer, statistical predictive models are often prototyped using modeling tools like SAS, Matlab, SPSS, or R, and then rewritten for production in C++ or Java so they can be embedded in the OLTP or OLAP layers. In summary, enterprise software developed according to the hairball model must be programmed using a dozen or so different programming languages, running in almost as many system-scale components. Some of the languages are imperative (both object-oriented and not) and some are declarative (every possible flavor). Most of the languages used are vendor-specific and tied to the component in which they run (e.g. ABAP, Excel, R, OPL, etc.). Even the languages that are based on open standards are not easily ported from one component to another because of significant variations in performance or because of vendor specific extensions (e.g., the same SQL query on Oracle will perform very differently on DB2). Recent innovations in infrastructure technology for supporting much larger numbers of users (Web applications) and big data (predictive analytics), including NoSQL [28], NewSQL [27] and analytic databases, have introduced even more components into the technology stack described above, and have not helped reduce its overall complexity. This complexity makes enterprise software hard to build, hard to implement, and hard to change. Simple changes—like extending a product identifier by 3 characters or an employee identifier by 1 digit—often necessitate modifications to most of the different components in the stack, requiring thousands of days of person effort and costing many millions of dollars. An extreme example of the problems caused by this accidental complexity occurred in the lead-up to the year 2000, where the simple problem of adding two digits to the year field (the Y2K problem) cost humanity over $300 billion dollars [24]. Moreover, as a result of the time required for such changes, individuals within an enterprise often resort to ad hoc, error-prone methods to perform the calculations needed in order to make timely business decisions. For example, they often roll their own extensions using spreadsheets, where they incompletely or incorrectly implement such calculations based on copies of the data that is not kept in sync with the OLTP database and thus may no longer accurately reflect the state of their enterprise.

Attitudes toward Vegans and Vegetarians : The Role of Anticipated Moral Reproach and Dissonance <=SEP=> This study attempted to determine if anticipated moral reproach and cognitive dissonance explain negative attitudes toward vegans and vegetarians, and if vegans are subject to more negative attitudes than vegetarians. The sample consisted of 65 participants (women =41, men =24) who were randomly assigned into one of four conditions. Each participant read a vignette about a person who consumed either a vegan, vegetarian, plant-based or omnivore diet. They then completed a series of questionnaires that recorded their attitudes towards the person in the vignette, anticipated moral reproach, and cognitive dissonance. The results indicated that for the observed general attitudes for the vegan, vegetarian and plant-based diet conditions were more negative than for the omnivore condition, with plant-based diet being the most negative. For moral reproach the results indicated that higher levels of moral reproach contributed to more negative attitudes, but only in the omnivore condition. Whereas the liner regression indicated that cognitive dissonance was only significant predictor of attitudes in the vegan condition. The findings of this study indicated that diet can have an impact on attitudes, and that the diet its self may have a larger impact than the labels applied. DIET AND ATTITUDES 3 Attitudes Toward Vegans and Vegetarians: The Role of Anticipated Moral Reproach and Dissonance Although the majority of North Americans eat an omnivore diet, veganism and vegetarianism have in recent years experienced an increase in popularity (Duchene & Jackson, 2017). As a result, omnivores are believed to be more likely to discuss their attitudes towards vegans and vegetarians, due to the increased exposure to the groups, both in public dialogue and interpersonally among acquaintances. Cole (2011) examined all British newspapers published in the year 2007 that mentioned the words vegan, vegans or veganism. The results showed that the majority of papers published expressed a negative attitude towards veganism. Additionally, any positive papers published looked at only food suggesting that there is a limited perspective provided by the media on veganism. Cole (2011) provides evidence in support that there are systematic negative associations against vegans. The purpose of this study will be to look at possible explanatory theories that could explain why veganism is observed to be viewed more negatively then vegetarianism. The theories that will be used are moral reproach (Minson & Monin, 2012) and cognitive dissonance (Loughnan, Haslam, & Bastian, 2010). This is an important topic to research as veganism and vegetarianism are theorized to be concepts that are strongly tied to the individual’s identity and are seen to be as important to the individual as their sexual orientation, religion, and/ or ethnicity (Wright, 2015). Furthermore, this study may also help to explain why strategies that attempt to encourage people to adopt a vegetarian or vegan diet are often unsuccessful (Duchene & Jackson, 2017). It has been found that personal factors are the major contributor to choosing to become a vegetarian. These personal factors are altered by the perceptions held by the dominant group, omnivores (Hussar & Harris, 2010). By understanding why omnivores have these DIET AND ATTITUDES 4 perceptions about vegans and vegetarians, will lead to insight into why these negative attitudes are occurring. Comparing Veganism and Vegetarianism in Terms of Attitudes and Diet To explore the potential difference in attitudes toward vegetarians and vegans it is important to clarify what the difference is between the two groups. Vegetarianism is defined as a diet that doesn't involve the consumption of meat or by-products of animal slaughter (Wright, 2015). In comparison, veganism consists of a diet without any animal-based products, or any item containing an ingredient derived from an animal (Wright, 2015). Furthermore, vegans place more stress on the ethical tie to animal rights then vegetarians. Animal rights scholar, Carol Adams defines veganism as "an ethical stance based on compassion for all beings" (Adams, 2010, pp.113) and argues that being vegan is about more than adhering to a specific diet. Further evidence to support that veganism is distinctly different from vegetarianism can be found by looking at the groups’ origins. Historically, veganism was founded from a group of vegetarians, who no longer wanted to be seen as part of vegetarianism but rather as their own group (Adams, 2010). This supports the notion that veganism is distinctly different from vegetarianism, as it was created with the purpose to be different then vegetarianism. Evidently there are some clear differences between vegetarians and vegans. It is important to translate how these differences are believed to lead to differing attitudes towards vegans and vegetarians. To understand why vegetarianism may be viewed more positivity then veganism, one commonly offered explanation is that because vegetarians still consume dairy products, the group is viewed as having less polarizing views (Adams, 2010). Elaborating on this statement, Rothgerber found that 38% of people who identified as vegetarian reported consuming meat DIET AND ATTITUDES 5 products to some capacity (2014a). It has been theorised that the emergence of semivegetarianism has lead to vegetarianism being perceived as less negative then veganism. Semivegetarianism is a term that refers to an individual who identifies as being a vegetarian but still consumes meat (Adams, 2010; Rothgerber, 2014a; Ruby, 2012). The amount of meat consumed, and the degree of willingness varies among semi-vegetarians. The presence of semivegetarianism has lead to a blurred conceptualization of vegetarianism resulting in an increase in the perceived homogeneity between vegetarianism and omnivores (Adams, 2010). In support of this, Rothgerber found that semi-vegetarians were closer to omnivores than strict vegetarians regarding the level of disgust shown towards meat (2014a). In summary, this difference could alter the perception that the similarity between omnivores and vegetarians is greater than the similarity between omnivores and vegans. Previous research has been conducted looking at attitude differences between vegetarians and omnivores. The reasoning that a vegetarian gives for maintaining the diet has been found to influence omnivore attitudes towards the group. Generally, the reasons for vegetarianism have been grouped into one of two categories. Health vegetarians tend to cite personal reasons for their membership, such as citing the health benefits or a food allergy. In contrast, ethical vegetarians cite external reasons, such as animal welfare. It has been found that when vegetarians reported being morally motivated, it resulted in a harsher judgement then when the reason reported was personal motivation (Hussar & Harris, 2010). In summary, it is likely that the observed differences between health and ethical vegetarians may translate to comparing vegetarians and vegans, as veganism is tied more to morality then vegetarianism. Another area that is important to look at is difference between vegetarianism veganism, and conscious omnivores. As it is important to address if it is the association to DIET AND ATTITUDES 6 environmentalism or if it is the act of not eating meat that effects attitudes. Rothgerber (2015) states that vegetarianism is a specific form of environmentalism. As well, Bashir et. al (2013) found that individuals were less likely to affiliate with someone if they engaged in a stereotypical environmental activity such as engaging in environmental activism. However, research has shown that the diet that a person eats has a larger impact on attitudes then environmentalism. Rothgerber (2015) compared conscious omnivores to vegans and vegetarians to see how much of an effect the diet had on attitudes towards animals. Conscious omnivores are omnivores who limit their animal consumption and attempt to avoid consuming commercial meat and instead choose animals that are grass feed, or raised more humanely (Rothgerber,2015). The findings of the study were that conscious omnivores scored significantly lower than vegetarianism on both animal favourability and disgust towards meat. The findings also suggest that conscious omnivores conceptually aren’t a group of transitioning vegetarians but rather a separate group. Also, conscious omnivores were found not to put as much emphasis on their eating habits as being a part of their social identity, aligning with the attitudes of normal omnivores (Rothgerber, 2015). In conclusion, it is evident that there are some characteristics of vegetarianism and veganism that are different then omnivore environmentalists. These differences likely contribute to the differences in perceptions that are experienced by omnivores. To summarise it is evident that there is significant evidence to support that there is a difference in the perceptions of vegans and vegetarians that could contribute to a difference in attitudes between the two groups. This paper will now move to discuss some theories that have been applied to these groups in the past to potentially explain this phenomenon. Anticipated Moral Reproach Towards Vegans and Vegetarians DIET AND ATTITUDES 7 Anticipated moral reproach is defined as believing that an individual who holds a different moral stance is judging your own stance as being immoral (Minson & Monin, 2012). This perception of being judged leads to the individual developing a negative attitude towards the other group. In terms of group attitudes, anticipated moral reproach has been used to explain why, when a group stresses overly moral behaviour, it is given the derogatory label of a dogooder or goody-two-shoes. Though not inherently negative, terms such as do-gooder are applied to the individual in a derogatory manner to express their dislike of the group (Minson & Monin, 2012

Dual Band Microstrip Patch Antenna for Sar Applications <=SEP=> Microstrip patch antennas offer the advantages of thin profile, light weight, low cost, ease of fabrication and compatibility with integrated circuitry, so the antennas are widely used to satisfy demands for polarization diversity and dual-frequency. This paper presents a coaxilly-fed single-layer compact dual band (Cand X-band) microstrip patch antenna for achieving dual-polarized radiation suitable for applications in airborne synthetic aperture radar (SAR) systems. The designed antenna consists of three rectangular patches which are overlapped along their diagonals. Full-wave electromagnetic simulations are performed to accurately predict the frequency response of the antenna. The fabricated antenna achieves an impedance bandwidth of 154 MHz (f0 = 6.83 GHz) and 209 MHz (f0 = 9.73 GHz) for VSWR < 2. Simultaneous use of both frequencies should drastically improve data collection and knowledge of the targets in the SAR system.

Epidemiological features of chronic low-back pain <=SEP=> Although the literature is filled with information about the prevalence and incidence of back pain in general, there is less information about chronic back pain, partly because of a lack of agreement about definition. Chronic back pain is sometimes defined as back pain that lasts for longer than 7-12 weeks. Others define it as pain that lasts beyond the expected period of healing, and acknowledge that chronic pain may not have well-defined underlying pathological causes. Others classify frequently recurring back pain as chronic pain since it intermittently affects an individual over a long period. Most national insurance and industrial sources of data include only those individuals in whom symptoms result in loss of days at work or other disability. Thus, even less is known about the epidemiology of chronic low-back pain with no associated work disability or compensation. Chronic low-back pain has also become a diagnosis of convenience for many people who are actually disabled for socioeconomic, work-related, or psychological reasons. In fact, some people argue that chronic disability in back pain is primarily related to a psychosocial dysfunction. Because the validity and reliability of some of the existing data are uncertain, caution is needed in an assessment of the information on this type of pain.

Human-Inspired Neurorobotic System for Classifying Surface Textures by Touch <=SEP=> Giving robots the ability to classify surface textures requires appropriate sensors and algorithms. Inspired by the biology of human tactile perception, we implement a neurorobotic texture classifier with a recurrent spiking neural network, using a novel semisupervised approach for classifying dynamic stimuli. Input to the network is supplied by accelerometers mounted on a robotic arm. The sensor data are encoded by a heterogeneous population of neurons, modeled to match the spiking activity of mechanoreceptor cells. This activity is convolved by a hidden layer using bandpass filters to extract nonlinear frequency information from the spike trains. The resulting high-dimensional feature representation is then continuously classified using a neurally implemented support vector machine. We demonstrate that our system classifies 18 metal surface textures scanned in two opposite directions at a constant velocity. We also demonstrate that our approach significantly improves upon a baseline model that does not use the described feature extraction. This method can be performed in real-time using neuromorphic hardware, and can be extended to other applications that process dynamic stimuli online.

Determination of differential leakage factors in electrical machines with non-symmetrical full and dead-coil windings <=SEP=> In this paper Görges polygons are used in conjunction with masses geometry to find an easy and affordable way to compute the differential leakage factor of non symmetrical full and dead coil winding. By following the traditional way, the use of the Ossanna's infinite series which has to be obviously truncated under the bound of a predetermined accuracy is mandatory. In the presented method no infinite series is instead required. An example is then shown and discussed to demonstrate practically the effectiveness of the proposed method.

RUM Extractor: A Facebook Extractor for Data Analysis <=SEP=> Social Network Analysis (SNA) is a field of study that focuses on analyzing user profiles and participations on social network channels in order to model relationships between people and to predict certain behaviors or knowledge. To achieve their goals, researchers, interested in SNA, have to extract content and structure from the numerous social networks available today. Existing tools, which help in this task, often require substantial pre-processing or good programming skills which may not be available for all SNA researchers. This paper describes RUM, a data extraction tool which allows researchers to easily extract several types of content and structure that are available on Facebook pages. Consequently, the extracted data can be saved and analyzed. RUM Extractor is easy to set up and use, and it gives flexible options to users to specify the type and amount of content and structure they want to retrieve. The paper also demonstrates how RUM can be exploited by collecting and further analyzing data collected from two popular Arabic news pages.

Decentralized frequency control of a DDG-PV Microgrid in islanded mode <=SEP=> This paper presents an innovative approach to control the frequency in Diesel-Driven Generator (DDG)-Photovoltaic (PV) Microgrid (MG) in islanded mode. The common approach is based on hierarchical control: primary control and secondary control. The conventional primary controller is a P-controller. Compared to this, we propose a novel primary controller for PV to mimic the dynamic behavior of the conventional synchronous generator (SG) so that more inertia in the MG can be obtained. The proposed secondary control is accomplished decentralized, too, so that no communication links for the real-time control are required and the single point of failure can be avoided. Although in this approach every controller works locally, the controllers and their parameters are designed centrally. This means, the controllers can be appropriately adjusted, when the parameters or the topology of the system change. The parameters of the controllers are optimized by minimizing the H-inf-norm using a known non-smooth method.

TinyECC: A Configurable Library for Elliptic Curve Cryptography in Wireless Sensor Networks <=SEP=> Public Key Cryptography (PKC) has been the enabling technology underlying many security services and protocols in traditional networks such as the Internet. In the context of wireless sensor networks, elliptic curve cryptography (ECC), one of the most efficient types of PKC, is being investigated to provide PKC supportin sensor network applications so that the existing PKC-based solutions can be exploited. This paper presents the design, implementation, and evaluation of TinyECC, a configurable library for ECC operations in wireless sensor networks. The primary objective of TinyECC is to provide a ready-to-use, publicly available software package for ECC-based PKC operations that can be flexibly configured and integrated into sensor network applications. TinyECC provides a number of optimization switches, which can turn specific optimizations on or off based on developers' needs. Different combinations of the optimizations have different execution time andresource consumptions, giving developers great flexibility in integrating TinyECC into sensor network applications. This paperalso reports the experimental evaluation of TinyECC on several common sensor platforms, including MICAz, Tmote Sky, and Imote2. The evaluation results show the impacts of individual optimizations on the execution time and resource consumptions, and give the most computationally efficient and the most storage efficient configuration of TinyECC.

A meta-analysis of human-system interfaces in unmanned aerial vehicle (UAV) swarm management. <=SEP=> A meta-analysis was conducted to systematically evaluate the current state of research on human-system interfaces for users controlling semi-autonomous swarms composed of groups of drones or unmanned aerial vehicles (UAVs). UAV swarms pose several human factors challenges, such as high cognitive demands, non-intuitive behavior, and serious consequences for errors. This article presents findings from a meta-analysis of 27 UAV swarm management papers focused on the human-system interface and human factors concerns, providing an overview of the advantages, challenges, and limitations of current UAV management interfaces, as well as information on how these interfaces are currently evaluated. In general allowing user and mission-specific customization to user interfaces and raising the swarm's level of autonomy to reduce operator cognitive workload are beneficial and improve situation awareness (SA). It is clear more research is needed in this rapidly evolving field.

Sarcasm Detection on Czech and English Twitter <=SEP=> This paper presents a machine learning approach to sarcasm detection on Twitter in two languages – English and Czech. Although there has been some research in sarcasm detection in languages other than English (e.g., Dutch, Italian, and Brazilian Portuguese), our work is the first attempt at sarcasm detection in the Czech language. We created a large Czech Twitter corpus consisting of 7,000 manually-labeled tweets and provide it to the community. We evaluate two classifiers with various combinations of features on both the Czech and English datasets. Furthermore, we tackle the issues of rich Czech morphology by examining different preprocessing techniques. Experiments show that our language-independent approach significantly outperforms adapted state-of-the-art methods in English (F-measure 0.947) and also represents a strong baseline for further research in Czech (F-measure 0.582).

Beyond the 'digital natives' debate: Towards a more nuanced understanding of students' technology experiences <=SEP=> The idea of the ‘digital natives’, a generation of tech-savvy young people immersed in digital technologies for which current education systems cannot cater, has gained widespread popularity on the basis of claims rather than evidence. Recent research has shown flaws in the argument that there is an identifiable generation or even a single type of highly adept technology user. For educators, the diversity revealed by these studies provides valuable insights into students’ experiences of technology inside and outside formal education. While this body of work provides a preliminary understanding, it also highlights subtleties and complexities that require further investigation. It suggests, for example, that we must go beyond simple dichotomies evident in the digital natives debate to develop a more sophisticated understanding of our students’ experiences of technology. Using a review of recent research findings as a starting point, this paper identifies some key issues for educational researchers, offers new ways of conceptualizing key ideas using theoretical constructs from Castells, Bourdieu and Bernstein, and makes a case for how we need to develop the debate in order to advance our understanding.

A Frame of Mind: Using Statistical Models for Detection of Framing and Agenda Setting Campaigns <=SEP=> Framing is a sophisticated form of discourse in which the speaker tries to induce a cognitive bias through consistent linkage between a topic and a specific context (frame). We build on political science and communication theory and use probabilistic topic models combined with time series regression analysis (autoregressive distributed-lag models) to gain insights about the language dynamics in the political processes. Processing four years of public statements issued by members of the U.S. Congress, our results provide a glimpse into the complex dynamic processes of framing, attention shifts and agenda setting, commonly known as ‘spin’. We further provide new evidence for the divergence in party discipline in U.S. politics.

Deterministic compressed-sensing matrices: Where Toeplitz meets Golay <=SEP=> Recently, the statistical restricted isometry property (STRIP) has been formulated to analyze the performance of deterministic sampling matrices for compressed sensing. In this paper, a class of deterministic matrices which satisfy STRIP with overwhelming probability are proposed, by taking advantage of concentration inequalities using Stein's method. These matrices, called orthogonal symmetric Toeplitz matrices (OSTM), guarantee successful recovery of all but an exponentially small fraction of K-sparse signals. Such matrices are deterministic, Toeplitz, and easy to generate. We derive the STRIP performance bound by exploiting the specific properties of OSTM, and obtain the near-optimal bound by setting the underlying sign sequence of OSTM as the Golay sequence. Simulation results show that these deterministic sensing matrices can offer reconstruction performance similar to that of random matrices.

Perceptual Control Theory 1 Perceptual Control Theory A Model for Understanding the Mechanisms and Phenomena of Control <=SEP=> Perceptual Control Theory (PCT) provides a general theory of functioning for organisms. At the conceptual core of the theory is the observation that living things control the perceived environment by means of their behavior. Consequently, the phenomenon of control takes center stage in PCT, with observable behavior playing an important but supporting role. The first part of the paper explains how the PCT model works. This explanation includes a definition of “control” as well as the basic equations from which one can see what is required for control to be possible. The second part of the paper describes demonstrations that the reader can download from the Internet and run, so as to learn the basics of control by experiencing and verifying the phenomenon directly. The third part of the paper shows examples of the application of PCT to different areas of psychological research including learning, developmental psychology, social psychology, and psychotherapy. This summary of the current state of the field celebrates the 50th Anniversary of the first major publication in PCT (Powers, Clark & MacFarland, 1960).

Intelligent Wireless Patient Monitoring and Tracking System (Using Sensor Network and Wireless Communication). <=SEP=> * Corresponding author: Ch.Sandeep Kumar Subudhi Abstract Aim of our work is to monitor the human body temperature, blood pressure (BP), Pulse Rate and ECG and tracking the patient location. The human body temperature, BP, Pulse Rate and ECG are detected in the working environment; this can be sensed by using respective sensors. The sensed information is send to the PIC16F877 microcontroller through signal conditioning circuit in the patient unit. A desired amount of sensor value is set and if it is exceeded preliminary steps should be taken by the indicating by buzzer.The sensor information will be transmitted from the patient unit to the main controller unit with the help of Zigbee communication system which is connected with the microcontrollers in the both units. The main controller unit will send those sensed data as well as the location of that patient by the help of GPS Module to the observer/doctor. The observer/doctor can receive the SMS sent by GSM module and further decision can be taken. The message is sent to a mobile phone using Global system mobile (GSM) Modem. MAX232 was a driver between microcontroller and modem.