title: Stripmap phase gradient autofocus

abstract: Current sonar autofocus techniques for blur removal originate in the radar community but have not provided a complete solution for Synthetic Aperture Sonar (SAS) imagery. The wide-beam, wide-band nature of SAS imagery makes implementation of Synthetic Aperture Radar (SAR) autofocus techniques difficult. This paper describes a generalisation of the standard Phase Gradient Autofocus (PGA) algorithm used in spotlight SAR that allows operation with stripmap SAS geometries. PGA uses prominent points within the target scene to estimate image blurring and phase errors. We show how PGA can be generalised to work with wide-band, wide-beam stripmap geometries. The SPGA method works by employing wave number domain 2D phase estimation techniques. The 2D phase errors are related to aperture position errors using the wave number transform. Robust sway estimates are obtained by using redundancy over a number of target points. We also present an improved Phase Curvature Autofocus (PCA) algorithm using the wave number transform. Preliminary results from the two algorithms (both on field-collected and simulated data sets) are presented and related to those obtained using previous methods. A discussion of SPGA's benefits over traditional algorithms and the limitations of the SPGA algorithm is presented. The SPGA algorithm was found to perform better than 2-D PCA on both simulated and field-collected data sets. Further testing on a variety of target scenes and imagery is required to investigate avenues of autofocus improvement.


title: Learning sparse graphs under smoothness prior

abstract: In this paper, we are interested in learning the underlying graph structure behind training data. Solving this basic problem is essential to carry out any graph signal processing or machine learning task. To realize this, we assume that the data is smooth with respect to the graph topology, and we parameterize the graph topology using an edge sampling function. That is, the graph Laplacian is expressed in terms of a sparse edge selection vector, which provides an explicit handle to control the sparsity level of the graph. We solve the sparse graph learning problem given some training data in both the noiseless and noisy settings. Given the true smooth data, the posed sparse graph learning problem can be solved optimally and is based on simple rank ordering. Given the noisy data, we show that the joint sparse graph learning and denoising problem can be simplified to designing only the sparse edge selection vector, which can be solved using convex optimization.


title: A lane-departure identification based on LBPE, Hough transform, and linear regression

abstract: This paper presents a lane-departure identification (LDI) system of a traveling vehicle on a structured road with lane marks. As is the case with modified version of the previous EDF-based LDI approach [J.W. Lee, A machine vision system for lane-departure detection, CVIU 86 (2002) 52-78], the new system increases the number of lane-related parameters and introduces departure ratios to determine the instant of lane departure and a linear regression (LR) to minimize wrong decisions due to noise effects. To enhance the robustness of LDI, we conceive of a lane boundary pixel extractor (LBPE) capable of extracting pixels expected to be on lane boundaries. Then, the Hough transform utilizes the pixels from the LBPE to provide the lane-related parameters such as an orientation and a location parameter. The fundamental idea of the proposed LDI is based on an observation that the ratios of orientations and location parameters of left- and right-lane boundaries are equal to one as far as the optical axis of a camera mounted on a vehicle is coincident with the center of lane. The ratios enable the lane-related parameters and the symmetrical property of both lane boundaries to be connected. In addition, the LR of the lane-related parameters of a series of successive images plays the role of determining the trend of a vehicle's traveling direction and the error of the LR is used to avoid a wrong LDI. We show the efficiency of the proposed LDI system with some real images.


title: Improving Deep Image Clustering With Spatial Transformer Layers

abstract: Image clustering is an important but challenging task in machine learning. As in most image processing areas, the latest improvements came from models based on the deep learning approach. However, classical deep learning methods have problems to deal with spatial image transformations like scale and rotation. In this paper, we propose the use of visual attention techniques to reduce this problem in image clustering methods. We evaluate the combination of a deep image clustering model called Deep Adaptive Clustering (DAC) with the Spatial Transformer Networks (STN). The proposed model is evaluated in the datasets MNIST and FashionMNIST and outperformed the baseline model.


Synergistic Design of a Bio-Inspired Micro Aerial Vehicle with Articulated Wings


title: Some New Parallel Fast Fourier Transform Algorithms

abstract: Discrete Fourier transform (DFT) has many applications in digital signal and image processing and other scientific and technological domains, but its time complexity of direct computation is O(n2), limiting greatly its application range. Thus many people have developed fast Fourier transform (FFT) algorithms, reducing the complexity from O(n2) to O(nlogn)(In this paper logn denotes log2n).But for large n, O(nlogn) is still very high. So multiprocessor systems have been used to speed up the computation of DFT. This paper first introduces a new general method to deduce FFT algorithms, then transforms the deduced second radix-2 decimation-in-time FFT algorithm into another parallelizable sequential form, and finally transforms the latter algorithm into a new parallel FFT algorithm, reducing the time complexity of DFT to O(nlogn/p) (where p is the number of processors). Using similar methods, the authors can also design other new parallel 1-D and 2-D FFT algorithms.


title: Achieving Secure, Universal, and Fine-Grained Query Results Verification for Secure Search Scheme over Encrypted Cloud Data

abstract: Secure search techniques over encrypted cloud data allow an authorized user to query data files of interest by submitting encrypted query keywords to the cloud server in a privacy-preserving manner. However, in practice, the returned query results may be incorrect or incomplete in the dishonest cloud environment. For example, the cloud server may intentionally omit some qualified results to save computational resources and communication overhead. Thus, a well-functioning secure query system should provide a query results verification mechanism that allows the data user to verify results. In this paper, we design a secure, easily integrated, and fine-grained query results verification mechanism, by which, given an encrypted query results set, the query user not only can verify the correctness of each data file in the set but also can further check how many or which qualified data files are not returned if the set is incomplete before decryption. The verification scheme is loose-coupling to concrete secure search techniques and can be very easily integrated into any secure query scheme. We achieve the goal by constructing secure verification object for encrypted cloud data. Furthermore, a short signature technique with extremely small storage cost is proposed to guarantee the authenticity of verification object and a verification object request technique is presented to allow the query user to securely obtain the desired verification object. Performance evaluation shows that the proposed schemes are practical and efficient.


title: Semantic similarity based evaluation for C programs through the use of symbolic execution

abstract: Automatic grading of programs has existed in various fields for many years ago. Within this paper, we propose a method for evaluating C programs. Two approaches are distinguished in this context: static and dynamic analysis methods. Unlike the dynamic analysis that requires an executable program to be evaluated, static analysis could evaluate a program even if it is not totally correct. The proposed method is based on static analysis of programs. It consists of comparing the evaluated program with the evaluator-provided program through their Control Flow Graphs. Here, the great challenge is to deal with the multiplicity of solutions that exists for the same programming problem. As a solution to this weakness, we propose an innovative similarity measure that compares two programs according to their semantic executions. In fact, the evaluated program is compared to the evaluator-provided program called model program by using the symbolic execution technique. The experimentations presented in this work are performed by using a basic implementation of the proposed method. The obtained results reveal a promising realization in the field of automated evaluation of programs. They also show that the proposed method guarantees a considerable approximation to the human program evaluation.


title: A Tutorial on Logistic Regression

abstract: Many procedures in SAS/STAT can be used to perform logistic regression analysis: CATMOD, GENMOD,LOGISTIC, and PROBIT. Each procedure has special features that make it useful for certain applications. For most applications, PROC LOGISTIC is the preferred choice. It fits binary response or proportional odds models, provides various model-selection methods to identify important prognostic variables from a large number of candidate variables, and computes regression diagnostic statistics. This tutorial discusses some of the problems users encountered when they used the LOGISTIC procedure. INTRODUCTION PROC LOGISTIC can be used to analyze binary response as well as ordinal response data. Binary Response The response, Y, of a subject can take one of two possible values, denoted by 1 and 2 (for example, Y=1 if a disease is present; otherwise Y=2). Let x = (x1; : : :; xk)0 be the vector of explanatory variables. The logistic regression model is used to explain the effects of the explanatory variables on the binary response. logitfPr(Y = 1jx)g = log Pr(Y = 1jx) 1 Pr(Y = 1jx) = 0+x 0 where 0 is the intercept parameter, and is the vector of slope parameters (Hosmer and Lameshow, 1989). Ordinal Response The response, Y, of a subject can take one of m ordinal values, denoted by 1;2; : : :;m. PROC LOGISTIC fits the following cumulative logit model: logitfPr(Y rjx)g = r + x0 1 r < m where 1; : : :; m 1 are (m-1) intercept parameters. This model is also called the proportional odds model because the odds of making response r are exp( 0(x1 x2)) times higher at x = x1 than at x = x2 (Agresti, 1990). This ordinal model is especially appropriate if the ordinal nature of the response is due to methodological limitations in collecting the data in which the researchers are forced to logit of the cumulative probabilities lump together and identify various portions of an otherwise continuous variable. Let T be the underlying continuous variable and suppose that


title: AUTOMATIC PLANT PEST DETECTION AND RECOGNITION USING k-MEANS CLUSTERING ALGORITHM AND CORRESPONDENCE FILTERS

abstract: Plant pest recognition and detection is vital for f ood security, quality of life and a stable agricult ural economy. This research demonstrates the combination of the k -means clustering algorithm and the correspondence filter to achieve pest detection and recognition. The detecti on of the dataset is achieved by partitioning the d ata space into Voronoi cells, which tends to find clusters of comparable spatial extents, thereby separating the objects (pests) from the background (pest habitat). The det ection is established by extracting the variant dis tinctive attributes between the pest and its habitat (leaf, stem) and using the correspondence filter to identi fy the plant pests to obtain correlation peak values for differe nt datasets. This work further establishes that the recognition probability from the pest image is directly proport ional to the height of the output signal and invers ely proportional to the viewing angles, which further c onfirmed that the recognition of plant pests is a f unction of their position and viewing angle. It is encouraging to note that the correspondence filter can achieve rotational invariance of pests up to angles of 360 degrees, wh ich proves the effectiveness of the algorithm for t he detection and recognition of plant pests.


title: End-to-End Goal-Driven Web Navigation

abstract: We propose a goal-driven web navigation as a benchmark task for evaluating an agent with abilities to understand natural language and plan on partially observed environments. In this challenging task, an agent navigates through a website, which is represented as a graph consisting of web pages as nodes and hyperlinks as directed edges, to find a web page in which a query appears. The agent is required to have sophisticated high-level reasoning based on natural languages and efficient sequential decision-making capability to succeed. We release a software tool, called WebNav, that automatically transforms a website into this goal-driven web navigation task, and as an example, we make WikiNav, a dataset constructed from the English Wikipedia. We extensively evaluate different variants of neural net based artificial agents on WikiNav and observe that the proposed goal-driven web navigation well reflects the advances in models, making it a suitable benchmark for evaluating future progress. Furthermore, we extend the WikiNav with question-answer pairs from Jeopardy! and test the proposed agent based on recurrent neural networks against strong inverted index based search engines. The artificial agents trained on WikiNav outperforms the engined based approaches, demonstrating the capability of the proposed goal-driven navigation as a good proxy for measuring the progress in real-world tasks such as focused crawling and question-answering.


title: Exploiting drug–disease relationships for computational drug repositioning

abstract: Finding new uses for existing drugs, or drug repositioning, has been used as a strategy for decades to get drugs to more patients. As the ability to measure molecules in high-throughput ways has improved over the past decade, it is logical that such data might be useful for enabling drug repositioning through computational methods. Many computational predictions for new indications have been borne out in cellular model systems, though extensive animal model and clinical trial-based validation are still pending. In this review, we show that computational methods for drug repositioning can be classified in two axes: drug based, where discovery initiates from the chemical perspective, or disease based, where discovery initiates from the clinical perspective of disease or its pathology. Newer algorithms for computational drug repositioning will likely span these two axes, will take advantage of newer types of molecular measurements, and will certainly play a role in reducing the global burden of disease.


title: The Glasgow Face Matching Test

abstract: We describe a new test for unfamiliar face matching, the Glasgow Face Matching Test (GFMT). Viewers are shown pairs of faces, photographed in full-face view but with different cameras, and are asked to make same/different judgments. The full version of the test comprises 168 face pairs, and we also describe a shortened version with 40 pairs. We provide normative data for these tests derived from large subject samples. We also describe associations between the GFMT and other tests of matching and memory. The new test correlates moderately with face memory but more strongly with object matching, a result that is consistent with previous research highlighting a link between object and face matching, specific to unfamiliar faces. The test is available free for scientific use.


title: Development of infants’ attention to faces during the first year

abstract: In simple tests of preference, infants as young as newborns prefer faces and face-like stimuli over distractors. Little is known, however, about the development of attention to faces in complex scenes. We recorded eye-movements of 3-, 6-, and 9-month-old infants and adults during free-viewing of clips from A Charlie Brown Christmas (an animated film). The tendency to look at faces increased with age. Using novel computational tools, we found that 3-month-olds were less consistent (across individuals) in where they looked than were older infants. Moreover, younger infants’ fixations were best predicted by low-level image salience, rather than the locations of faces. Between 3 and 9 months of age, infants gradually focused their attention on faces. We discuss several possible interpretations of this shift in terms of social development, cross-modal integration, and attentional/executive control.


title: A general metric for identifying adversarial images

abstract: It is well known that a determined adversary can fool a neural network by making imperceptible adversarial perturbations to an image. Recent studies have shown that these perturbations can be detected even without information about the neural network if the strategy taken by the adversary is known beforehand. Unfortunately, these studies suffer from the generalization limitation -- the detection method has to be recalibrated every time the adversary changes his strategy. In this study, we attempt to overcome the generalization limitation by deriving a metric which reliably identifies adversarial images even when the approach taken by the adversary is unknown. Our metric leverages key differences between the spectra of clean and adversarial images when an image is treated as a matrix. Our metric is able to detect adversarial images across different datasets and attack strategies without any additional re-calibration. In addition, our approach provides geometric insights into several unanswered questions about adversarial perturbations.


title: Deep Learning-Based Intrusion Detection With Adversaries

abstract: Deep neural networks have demonstrated their effectiveness in most machine learning tasks, with intrusion detection included. Unfortunately, recent research found that deep neural networks are vulnerable to adversarial examples in the image classification domain, i.e., they leave some opportunities for an attacker to fool the networks into misclassification by introducing imperceptible changes to the original pixels in an image. The vulnerability raises some concerns in applying deep neural networks in security-critical areas, such as intrusion detection. In this paper, we investigate the performances of the state-of-the-art attack algorithms against deep learning-based intrusion detection on the NSL-KDD data set. The vulnerabilities of neural networks employed by the intrusion detection systems are experimentally validated. The roles of individual features in generating adversarial examples are explored. Based on our findings, the feasibility and applicability of the attack methodologies are discussed.


title: Inverse Watkins–Johnson Topology-Based Inverter

abstract: A Z-source inverter (ZSI) uses an L-C impedance network between the source and the voltage source inverter (VSI). It has the property of stepping down or stepping up the input voltage, as a result, the output can be either higher or lower than the input voltage as per requirement. This topology also possesses robust electromagnetic interference noise immunity, which is achieved by allowing shoot through of the inverter leg switches. This letter proposes an inverter circuit based on the inverse Watkins-Johnson (IWJ) topology that can achieve similar advantages as that of a ZSI. The proposed circuit requires two switches and one pair of an LC filter apart from the VSI. The systematic development of this inverter topology is described starting from the basic IWJ circuit. Steady-state analysis and implementation of the proposed topology are also described. The pulse width modulation control strategy of the inverter is explained. An experimental prototype is used to validate the proposed circuit.


title: Sentiment Analysis of Movie Reviews Using Heterogeneous Features

abstract: Human disposition has always influenced by others suggestion and reviews. People are always eager to know other's reviews for their profit but, every website contains a very large amount of review text, the average human reader will have trouble in identifying relevant sites, extracting and abstracting the reviews so they cannot reach to the right decision in less time that is why automated sentiment analysis systems are required. In the proposed approach, heterogeneous features such as machine learning based and Lexicon based features and supervised learning algorithms like Naive Bayes (NB) and Linear Support Vector Machine (LSVM) used to build the system model. From implementation and observation, conclude that using proposed heterogeneous features and hybrid approach can get an accurate sentiment analysis system compared to other baseline system. In future for big data, we can use these heterogeneous features for bulding advance and more accurate models using Deep Learning (DL) algorithms.


title: Delight: An Efficient Descriptor for Global Localisation Using LiDAR Intensities

abstract: Place recognition is a key element of mobile robotics. It can assist with the “wake-up” and “kidnapped robot” problems, where the robot position needs to be estimated without prior information. Among the different sensors that can be used for the task (e.g., camera, GPS, LiDAR), LiDAR has the advantage of operating in the dark and in GPS-denied areas. We propose a new method that uses solely the LiDAR data and that can be performed without robot motion. In contrast to other methods, our system leverages intensity information (as opposed to only range information) which is encoded into a novel descriptor of LiDAR intensities as a group of histograms, named DELIGHT. The descriptor encodes the distributed histograms of intensity of the surroundings which are compared using chi-squared tests. Our pipeline is a two-stage solution consisting of an intensity-based prior estimation and a geometry-based verification. For a map of 220k square meters, the method achieves localisation in around 3s with a success rate of 97 %, illustrating the applicability of the method in real environments.


title: Visual-Inertial Navigation Algorithm Development Using Photorealistic Camera Simulation in the Loop

abstract: The development of fast, agile micro Unmanned Aerial Vehicles (UAVs) has been limited by (i) on-board computing hardware restrictions, (ii) the lack of sophisticated vision-based perception and vision-in-the-loop control algorithms, and (iii) the absence of development environments where such systems and algorithms can be rapidly and easily designed, implemented, and validated. Here, we first present a new micro UAV platform that integrates high-rate cameras, inertial sensors, and an NVIDIA Jetson Tegra X1 system-on-chip compute module that boasts 256 GPU cores. The UAV mechanics and electronics were designed and built in house, and are described in detail. Second, we present a novel “virtual reality” development environment, in which photorealistically-rendered synthetic on-board camera images are generated in real time while the UAV is in flight. This development environment allows us to rapidly prototype computing and sensing hardware as well as perception and control algorithms, using real physics, real interoceptive sensor data (e.g., from the on-board inertial measurement unit), and synthetic exteroceptive sensor data (e.g., from synthetic cameras). Third, we demonstrate repeated agile maneuvering with closed-loop vision-based perception and control algorithms, which we have developed using this environment.


title: Indoor Coverage Path Planning: Survey, Implementation, Analysis

abstract: Coverage Path Planning (CPP) describes the process of generating robot trajectories that fully cover an area or volume. Applications are, amongst many others, mobile cleaning robots, lawn mowing robots or harvesting machines in agriculture. Many approaches and facets of this problem have been discussed in literature but despite the availability of several surveys on the topic there is little work on quantitative assessment and comparison of different coverage path planning algorithms. This paper analyzes six popular off-line coverage path planning methods, applicable to previously recorded maps, in the setting of indoor coverage path planning on room-sized units. The implemented algorithms are thoroughly compared on a large dataset of over 550 rooms with and without furniture.


title: Factorizing Scene Albedo and Depth from a Single Foggy Image

abstract: Atmospheric conditions induced by suspended particles, such as fog and haze, severely degrade image quality. Restoring the true scene colors (clear day image) from a single image of a weather-degraded scene remains a challenging task due to the inherent ambiguity between scene albedo and depth. In this paper, we introduce a novel probabilistic method that fully leverages natural statistics of both the albedo and depth of the scene to resolve this ambiguity. Our key idea is to model the image with a factorial Markov random field in which the. scene albedo and depth are. two statistically independent latent layers. We. show that we may exploit natural image and depth statistics as priors on these hidden layers and factorize a single foggy image via a canonical Expectation Maximization algorithm with alternating minimization. Experimental results show that the proposed method achieves more accurate restoration compared to state-of-the-art methods that focus on only recovering scene albedo or depth individually.


title: Multi-attribute decision making: A simulation comparison of select methods

abstract: Abstract Several methods have been proposed for solving multi-attribute decision making problems (MADM). A major criticism of MADM is that different techniques may yield different results when applied to the same problem. The problem considered in this study consists of a decision matrix input of N criteria weights and ratings of L alternatives on each criterion. The comparative performance of some methods has been investigated in a few, mostly field, studies. In this simulation experiment we investigate the performance of eight methods: ELECTRE, TOPSIS, Multiplicative Exponential Weighting (MEW), Simple Additive Weighting (SAW), and four versions of AHP (original vs. geometric scale and right eigenvector vs. mean transformation solution). Simulation parameters are the number of alternatives, criteria and their distribution. The solutions are analyzed using twelve measures of similarity of performance. Similarities and differences in the behavior of these methods are investigated. Dissimilarities in weights produced by these methods become stronger in problems with few alternatives; however, the corresponding final rankings of the alternatives vary across methods more in problems with many alternatives. Although less significant, the distribution of criterion weights affects the methods differently. In general, all AHP versions behave similarly and closer to SAW than the other methods. ELECTRE is the least similar to SAW (except for closer matching the top-ranked alternative), followed by MEW. TOPSIS behaves closer to AHP and differently from ELECTRE and MEW, except for problems with few criteria. A similar rank-reversal experiment produced the following performance order of methods: SAW and MEW (best), followed by TOPSIS, AHPs and ELECTRE. It should be noted that the ELECTRE version used was adapted to the common MADM problem and therefore it did not take advantage of the method's capabilities in handling problems with ordinal or imprecise information.


title: The sequential auction problem on eBay: an empirical analysis and a solution

abstract: Bidders on eBay have no dominant bidding strategy when faced with multiple auctions each offering an item of interest. As seen through an analysis of 1,956 auctions on eBay for a Dell E193FP LCD monitor, some bidders win auctions at prices higher than those of other available auctions, while others never win an auction despite placing bids in losing efforts that are greater than the closing prices of other available auctions. These misqueues in strategic behavior hamper the efficiency of the system, and in so doing limit the revenue potential for sellers. This paper proposes a novel options-based extension to eBay's proxy-bidding system that resolves this strategic issue for buyers in commoditized markets. An empirical analysis of eBay provides a basis for computer simulations that investigate the market effects of the options-based scheme, and demonstrates that the options-based scheme provides greater efficiency than eBay, while also increasing seller revenue.


title: Lnc2Cancer: a manually curated database of experimentally supported lncRNAs associated with various human cancers

abstract: Lnc2Cancer (http://www.bio-bigdata.net/lnc2cancer) is a manually curated database of cancer-associated long non-coding RNAs (lncRNAs) with experimental support that aims to provide a high-quality and integrated resource for exploring lncRNA deregulation in various human cancers. LncRNAs represent a large category of functional RNA molecules that play a significant role in human cancers. A curated collection and summary of deregulated lncRNAs in cancer is essential to thoroughly understand the mechanisms and functions of lncRNAs. Here, we developed the Lnc2Cancer database, which contains 1057 manually curated associations between 531 lncRNAs and 86 human cancers. Each association includes lncRNA and cancer name, the lncRNA expression pattern, experimental techniques, a brief functional description, the original reference and additional annotation information. Lnc2Cancer provides a user-friendly interface to conveniently browse, retrieve and download data. Lnc2Cancer also offers a submission page for researchers to submit newly validated lncRNA-cancer associations. With the rapidly increasing interest in lncRNAs, Lnc2Cancer will significantly improve our understanding of lncRNA deregulation in cancer and has the potential to be a timely and valuable resource.


title: The Leiden Ranking 2011/2012: Data collection, indicators, and interpretation

abstract: The Leiden Ranking 2011/2012 is a ranking of universities based on bibliometric indicators of publication output, citation impact, and scientific collaboration. The ranking includes 500 major universities from 41 different countries. This paper provides an extensive discussion of the Leiden Ranking 2011/2012. The ranking is compared with other global university rankings, in particular the Academic Ranking of World Universities (commonly known as the Shanghai Ranking) and the Times Higher Education World University Rankings. The comparison focuses on the methodological choices underlying the different rankings. Also, a detailed description is offered of the data collection methodology of the Leiden Ranking 2011/2012 and of the indicators used in the ranking. Various innovations in the Leiden Ranking 2011/2012 are presented. These innovations include (1) an indicator based on counting a university's highly cited publications, (2) indicators based on fractional rather than full counting of collaborative publications, (3) the possibility of excluding non-English language publications, and (4) the use of stability intervals. Finally, some comments are made on the interpretation of the ranking and a number of limitations of the ranking are pointed out. © 2012 Wiley Periodicals, Inc.


title: Is the brain really a small-world network?

abstract: It is commonly assumed that the brain is a small-world network (e.g., Sporns and Honey 2006). Indeed, one of the present authors claimed as much 15 years ago (Hilgetag et al. 2000). The small-worldness is believed to be a crucial aspect of efficient brain organization that confers significant advantages in signal processing (e.g., LagoFernandez et al. 2000). Correspondingly, the small-world organization is deemed essential for healthy brain function, as alterations of small-world features are observed in patient groups with Alzheimer’s disease (Stam et al. 2007), autism (Barttfeld et al. 2011) or schizophrenia spectrum diseases (Liu et al. 2008; Wang et al. 2012; Zalesky et al. 2011). While the colloquial idea of a small, interconnected world has a long tradition (e.g., Klemperer 1938), the present concept of small-world features of networks is frequently associated with the Milgram experiment (Milgram 1967) that demonstrated surprisingly short paths across social networks (‘six degrees of separation’). The concept was formalized by Watts and Strogatz (1998), who derived small-world networks from regular networks by including a small proportion of random network shortcuts. Such an organization results in short paths across the whole network—almost as small as in random networks—combined with local ‘cliquishness’ (or clustering) of neighboring nodes, due to dense local interconnections. These features can be mathematically summarized by the smallworld coefficient (Humphries et al. 2006), which is defined as the clustering coefficient of a given network (normalized by the clustering coefficient of a same-size random network) divided by the network’s normalized average shortest pathlength. While any network that has a smallworld coefficient larger than one is formally a small-world network, for many researchers, the term has become associated with the specific Watts and Strogatz model that is based on the partial random rewiring of a regular network (Fig. 1a). Indeed, the estimation of the rewiring probability has been used to directly associate real-world networks with the Watts and Strogatz model (Humphries and Gurney 2008). Incidentally, the small-world coefficient might not faithfully capture the small-world property as originally described by Watts and Strogatz (1998). Therefore, an alternative coefficient has been proposed that compares the clustering of the network to a lattice instead of a random network (Telesford et al. 2011). A large number of empirical network data conform to the small-world features of short paths combined with high clustering, including many neural networks—but do these Electronic supplementary material The online version of this article (doi:10.1007/s00429-015-1035-6) contains supplementary material, which is available to authorized users.


title: Ship Detection From Optical Satellite Images Based on Saliency Segmentation and Structure-LBP Feature

abstract: Automatic ship detection from optical satellite imagery is a challenging task due to cluttered scenes and variability in ship sizes. This letter proposes a detection algorithm based on saliency segmentation and the local binary pattern (LBP) descriptor combined with ship structure. First, we present a novel saliency segmentation framework with flexible integration of multiple visual cues to extract candidate regions from different sea surfaces. Then, simple shape analysis is adopted to eliminate obviously false targets. Finally, a structure-LBP feature that characterizes the inherent topology structure of ships is applied to discriminate true ship targets. Experimental results on numerous panchromatic satellite images validate that our proposed scheme outperforms other state-of-the-art methods in terms of both detection time and detection accuracy.


title: Up the down staircase : Wayfinding strategies in multi-level buildings

abstract: The intention of this article is to create a link between human spatial cognition research and architectural design. We conducted an empirical study with human subjects in a complex multi-level building and compared thinking aloud protocols and performance measures of experienced and inexperienced participants in different wayfinding tasks. Three specific strategies for navigation in multi-level buildings were compared. The central point strategy relies on well-known parts of the building; the direction strategy relies on routes that first head towards the horizontal position of the goal, while the floor strategy relies on routes that first head towards the vertical position of the goal. We show that the floor strategy was preferred by experienced participants over the other strategies and was overall tied to better wayfinding performance. Route knowledge showed a greater impact on wayfinding performance compared to survey knowledge. A cognitive-architectural analysis of the building revealed seven possible causes for navigation problems. Especially the staircase design was identified as a major wayfinding obstacle. Finally we address the benefits of cognitive approaches for the architectural design process and describe some open issues for further research.


title: A potential field approach to dexterous tactile exploration of unknown objects

abstract: Haptic exploration of unknown objects is of great importance for acquiring multi-modal object representations, which enable a humanoid robot to autonomously execute grasping and manipulation tasks. In this paper we present a tactile exploration strategy to guide an anthropomorphic five-finger hand along the surface of previously unknown objects and build a 3D object representation based on acquired tactile point clouds. The proposed strategy makes use of the dynamic potential field approach suggested in the context of mobile robot navigation. To demonstrate the capabilities of this strategy, we conduct experiments in a detailed physics simulation using a model of the five-finger hand. Exploration results of several test objects are given.


title: Traffic in the Smart City: Exploring City-Wide Sensing for Traffic Control Center Augmentation

abstract: Smart city technologies can provide substantial benefits that improve people's daily lives. Here, the authors investigate how ubiquitous traffic sensing technologies and techniques can be incorporated with conventional traffic control and monitoring practices in the city of Oulu, Finland. In collaboration with the city's traffic control center and traffic planners, they exploit inductive magnetic sensing and Wi-Fi scanning across the city center, and develop tools to assist traffic operators in their tasks.


title: Current mismatch during switching due to the self-turn-off effect in paralleled IGBT

abstract: IGBT are the most important power semiconductor in high power converter [1,2,3,4] up to the MW range. To reduce the switching losses, Field-Stop-IGBTs are used in high voltage applications in comparison with Non-Punch-Through-IGBTs [5]. To increase the current carrying capacity of converters, for example in high power traction applications, it is common to connect IGBTs in parallel. Gate drive circuits for paralleled IGBTs often use separate gate resistors for each IGBT [6,7,8] to avoid oscillations between the gates of these IGBTs. This decoupling of the gates may lead to a complete current mismatch during turn-off of field-stop IGBTs. The reason for this is the self-turn-off of the IGBT, whose space-charge-region (SCR) reaches the field-stop layer at first. To explain the effect of current mismatch of paralleled IGBTs it is important to understand the self-turn-off process and what happens, if an IGBT reaches the field-stop layer during turn-off. Under some conditions it is possible that the IGBT gets a parasitic turn-on behavior if the electrical field becomes trapezoidal. This paper explains these two effects and translate the results to the current mismatch of paralleled IGBTs.


title: Distinguishing users with capacitive touch communication

abstract: As we are surrounded by an ever-larger variety of post-PC devices, the traditional methods for identifying and authenticating users have become cumbersome and time-consuming. In this paper, we present a capacitive communication method through which a device can recognize who is interacting with it. This method exploits the capacitive touchscreens, which are now used in laptops, phones, and tablets, as a signal receiver. The signal that identifies the user can be generated by a small transmitter embedded into a ring, watch, or other artifact carried on the human body. We explore two example system designs with a low-power continuous transmitter that communicates through the skin and a signet ring that needs to be touched to the screen. Experiments with our prototype transmitter and tablet receiver show that capacitive communication through a touchscreen is possible, even without hardware or firmware modifications on a receiver. This latter approach imposes severe limits on the data rate, but the rate is sufficient for differentiating users in multiplayer tablet games or parental control applications. Controlled experiments with a signal generator also indicate that future designs may be able to achieve datarates that are useful for providing less obtrusive authentication with similar assurance as PIN codes or swipe patterns commonly used on smartphones today.


title: GPU simulator of multilayer neural network based on multi-valued neurons

abstract: In this paper, we consider principles of design and basic fundamentals of a GPU simulator of the multilayer neural network with multi-valued neurons (MLMVN). Slowing down a learning process due to a big learning dataset and/or a big neural network needed for solving a certain problem is a potential bottleneck preventing the use of neural networks for solving some challenging problems. The same is related to deep learning. MLMVN is a feedforward complex-valued neural network, which has a number of advantages when compared to real-valued neural networks. These advantages include derivative-free learning and significantly better generalization capability. To extend applicability of MLMVN, its GPU-based software implementation shall be considered. We present basic principles of the GPU simulator of MLMVN and how matrix algebra operations are specifically employed there. It is shown that the bigger the network is, the more beneficial is its GPU implementation. It is shown that up to 32× acceleration can be achieved for the MLMVN learning process. Some applications, which could not be even considered without a GPU simulator, are also presented.


title: Unequal Wilkinson Power Divider With Reduced Arm Length For Size Miniaturization

abstract: This paper presents a compact unequal Wilkinson power divider (WPD) with a reduced arm length. The unequal power divider consists of four arms and the isolation elements, including a compensating capacitor and isolation resistor. Compared with the conventional WPD, the electric length θ of the arms between the input port and the isolation elements is much shorter, and thus, compact size can be obtained. A theoretical analysis is carried out, and design equations are obtained. In addition, a study is carried out to characterize the isolation bandwidth and output port return loss bandwidth with different values of θ. For validation, two power dividers operating at 1.5 GHz are implemented with various size and isolation bandwidths. Around 50% size reduction and good performance are obtained.


title: Failsafe Drive Performance of FRID Electric Vehicles With the Structure Driven by the Front and Rear Wheels Independently

abstract: Failsafe performance is clarified for electric vehicles (EVs) with the drive structure driven by front and rear wheels independently, i.e., front and rear wheel independent drive type (FRID) EV. A simulator based on the four-wheel vehicle model, which can be applied to various types of drive systems like four in-wheel motor-drive-type EVs, is used for the clarification. Yaw rate and skid angle, which are related to drivability and steerability of vehicles and which further influence the safety of vehicles during runs, are analyzed under the condition that one of the motor drive systems fails while cornering on wet roads. In comparison with the four in-wheel motor-drive-type EVs, it is confirmed that the EVs with the structure focused in this paper have little change of the yaw rate and that hardly any dangerous phenomena appear, which would cause an increase in the skid angle of vehicles even if the front or rear wheel drive systems fail when running on wet roads with low friction coefficient. Moreover, the failsafe drive performance of the FRID EVs with the aforementioned structure is verified through experiments using a prototype EV.


title: A Language Modeling Approach to Information Retrieval

abstract: In today's world, there is no shortage of information. However, for a specific information need, only a small subset of all of the available information will be useful. The field of information retrieval (IR) is the study of methods to provide users with that small subset of information relevant to their needs and to do so in a timely fashion. Information sources can take many forms, but this thesis will focus on text based information systems and investigate problems germane to the retrieval of written natural language documents. ::: ::: Central to these problems is the notion of "topic." In other words, what are documents about? However, topics depend on the semantics of documents and retrieval systems are not endowed with knowledge of the semantics of natural language. The approach taken in this thesis will be to make use of probabilistic language models to investigate text based information retrieval and related problems. ::: ::: One such problem is the prediction of topic shifts in text, the topic segmentation problem. It will be shown that probabilistic methods can be used to predict topic changes in the context of the task of new event detection. Two complementary sets of features are studied individually and then combined into a single language model. The language modeling approach allows this problem to be approached in a principled way without complex semantic modeling. ::: ::: Next, the problem of document retrieval in response to a user query will be investigated. Models of document indexing and document retrieval have been extensively studied over the past three decades. The integration of these two classes of models has been the goal of several researchers but it is a very difficult problem. Much of the reason for this is that the indexing component requires inferences as to the semantics of documents. Instead, an approach to retrieval based on probabilistic language modeling will be presented. Models are estimated for each document individually. The approach to modeling is non-parametric and integrates the entire retrieval process into a single model. One advantage of this approach is that collection statistics, which are used heuristically for the assignment of concept probabilities in other probabilistic models, are used directly in the estimation of language model probabilities in this approach. The language modeling approach has been implemented and tested empirically and performs very well on standard test collections and query sets. ::: ::: In order to improve retrieval effectiveness, IR systems use additional techniques such as relevance feedback, unsupervised query expansion and structured queries. These and other techniques are discussed in terms of the language modeling approach and empirical results are given for several of the techniques developed. These results provide further proof of concept for the use of language models for retrieval tasks.


title: Tensor completion in hierarchical tensor representations

abstract: Compressed sensing extends from the recovery of sparse vectors from undersampled measurements via efficient algorithms to the recovery of matrices of low rank from incomplete information. Here we consider a further extension to the reconstruction of tensors of low multi-linear rank in recently introduced hierarchical tensor formats from a small number of measurements. Hierarchical tensors are a flexible generalization of the well-known Tucker representation, which have the advantage that the number of degrees of freedom of a low rank tensor does not scale exponentially with the order of the tensor. While corresponding tensor decompositions can be computed efficiently via successive applications of (matrix) singular value decompositions, some important properties of the singular value decomposition do not extend from the matrix to the tensor case. This results in major computational and theoretical difficulties in designing and analyzing algorithms for low rank tensor recovery. For instance, a canonical analogue of the tensor nuclear norm is NP-hard to compute in general, which is in stark contrast to the matrix case. In this book chapter we consider versions of iterative hard thresholding schemes adapted to hierarchical tensor formats. A variant builds on methods from Riemannian optimization and uses a retraction mapping from the tangent space of the manifold of low rank tensors back to this manifold. We provide first partial convergence results based on a tensor version of the restricted isometry property (TRIP) of the measurement map. Moreover, an estimate of the number of measurements is provided that ensures the TRIP of a given tensor rank with high probability for Gaussian measurement maps.


title: Blockchain Radio Access Network (B-RAN): Towards Decentralized Secure Radio Access Paradigm

abstract: The relentless growth of wireless applications and data traffic continues to accentuate the long felt need for decentralized, self-managed, and cooperative network architectures. Enlightened by the power of blockchain technology, we propose a blockchain radio access network (B-RAN) architecture and develop decentralized, secure, and efficient mechanisms to manage network access and authentication among inherently trustless network entities. We further identify promising advanced functions made possible by adopting blockchain for open radio access networks. Our test results demonstrate the benefits of the B-RAN architecture. We also present a number of challenges and future research directions.


cplint on SWISH: Probabilistic Logical Inference with a Web Browser


title: 17.6 Envelope modulator for multimode transmitters with AC-coupled multilevel regulators

abstract: Modern wireless communication systems, such as high-speed uplink packet access (HSUPA) or long term evolution (LTE), employ highly spectral-efficient modulations with inherent non-constant envelope signals having high peak-to-average power ratio (PAPR). These signals require the RF Power Amplifiers (PAs) to be backed off from saturation to satisfy the stringent linearity requirements. Unfortunately, linear operation leads to very low overall system efficiency. The envelope tracking (ET) technique has been identified as one possible solution to improve the global efficiency of the RF transmission chain. This technique has raised interest in the optimization of the fast supply envelope modulator (EM), the most critical component in the system. Several topologies for the EM exist such as fast buck converters, multilevel buck converters [1] and parallel hybrid structures with a class-AB amplifier AC-coupled to a buck converter [2].


title: A novel active gate driver for silicon carbide MOSFET

abstract: A novel active gate driver (AGD) for silicon carbide (SiC) MOSFET is studied in this paper. The gate driver (GD) increases the gate resistance value during the voltage plateau area of the gate-source voltage, in both turn-on and turn-off transitions. The proposed AGD is validated in both simulation and experimental environments and in hard-switching conditions. The simulation is evaluated in MATLAB/Simulink with 100 kHz of switching frequency and 600 V of dc-bus, whereas, the experimental part was realised at 100 kHz and 100 V of dc-bus. The results show that the gate driver can reduce the over-voltage and ringing, with low switching losses.


title: Multi-source Deep Learning for Human Pose Estimation

abstract: Visual appearance score, appearance mixture type and deformation are three important information sources for human pose estimation. This paper proposes to build a multi-source deep model in order to extract non-linear representation from these different aspects of information sources. With the deep model, the global, high-order human body articulation patterns in these information sources are extracted for pose estimation. The task for estimating body locations and the task for human detection are jointly learned using a unified deep model. The proposed approach can be viewed as a post-processing of pose estimation results and can flexibly integrate with existing methods by taking their information sources as input. By extracting the non-linear representation from multiple information sources, the deep model outperforms state-of-the-art by up to 8.6 percent on three public benchmark datasets.


title: How Is Pneumonia Diagnosed in Clinical Stroke Research?: A Systematic Review and Meta-Analysis

abstract: BACKGROUND AND PURPOSE ::: Diagnosis of pneumonia complicating stroke is challenging, and there are currently no consensus diagnostic criteria. As a first step in developing such consensus-based diagnostic criteria, we undertook a systematic review to identify the existing diagnostic approaches to pneumonia in recent clinical stroke research to establish the variation in diagnosis and terminology. ::: ::: ::: METHODS ::: Studies of ischemic stroke, intracerebral hemorrhage, or both, which reported occurrence of pneumonia from January 2009 to March 2014, were considered and independently screened for inclusion by 2 reviewers after multiple searches using electronic databases. The primary analysis was to identify existing diagnostic approaches for pneumonia. Secondary analyses explored potential reasons for any heterogeneity where standard criteria for pneumonia had been applied. ::: ::: ::: RESULTS ::: Sixty-four studies (56% ischemic stroke, 6% intracerebral hemorrhage, 38% both) of 639 953 patients were included. Six studies (9%) reported no information on the diagnostic approach, whereas 12 (19%) used unspecified clinician-reported diagnosis or initiation of antibiotics. The majority used objective diagnostic criteria: 20 studies (31%) used respiratory or other published standard criteria; 26 studies (41%) used previously unpublished ad hoc criteria. The overall occurrence of pneumonia was 14.3% (95% confidence interval 13.2%-15.4%; I(2)=98.9%). Occurrence was highest in studies applying standard criteria (19.1%; 95% confidence interval 15.1%-23.4%; I(2)=98.5%). The substantial heterogeneity observed was not explained by stratifying for other potential confounders. ::: ::: ::: CONCLUSIONS ::: We found considerable variation in terminology and the diagnostic approach to pneumonia. Our review supports the need for consensus development of operational diagnostic criteria for pneumonia complicating stroke.


title: Virtual-Impedance-Based Control for Voltage-Source and Current-Source Converters

abstract: The virtual impedance concept is increasingly used for the control of power electronic systems. Generally, the virtual impedance loop can either be embedded as an additional degree of freedom for active stabilization and disturbance rejection, or be employed as a command reference generator for the converters to provide ancillary services. This paper presents an overview of the virtual-impedance-based control strategies for voltage-source and current-source converters. The control output impedance shaping attained by the virtual impedances is generalized first using the impedance-based models. Different virtual impedances and their implementation issues are then discussed. A number of practical examples are demonstrated to illustrate the feasibility of virtual impedances. Emerging applications and future trends of virtual impedances in power electronic systems conclude this paper.


title: Predicting recurrent stroke after minor stroke and transient ischemic attack

abstract: The risk of a subsequent stroke following an acute transient ischemic attack or minor stroke is high, with 90-day risk at approximately 10%. Identification of those patients at the highest risk for recurrent stroke following a transient ischemic attack or minor stroke may allow risk-specific management strategies to be implemented, such as hospital admission with expedited work-up for those at high risk and emergency room discharge for those at low risk. Predictors of recurrent stroke, including the ABCD2 score, brain imaging and the stroke mechanism, are reviewed in this article, with a focus on recent literature. An emphasis is placed on the importance of early imaging of the brain parenchyma (diffusion-weighted imaging) and vascular imaging to identify patients at high risk for recurrence. The need for identification of the cause of the initial event, allowing therapies to be tailored to the individual patient, is discussed.


title: Combined static and dynamic automated test generation

abstract: In an object-oriented program, a unit test often consists of a sequence of method calls that create and mutate objects, then use them as arguments to a method under test. It is challenging to automatically generate sequences that are legal and behaviorally-diverse, that is, reaching as many different program states as possible. This paper proposes a combined static and dynamic automated test generation approach to address these problems, for code without a formal specification. Our approach first uses dynamic analysis to infer a call sequence model from a sample execution, then uses static analysis to identify method dependence relations based on the fields they may read or write. Finally, both the dynamically-inferred model (which tends to be accurate but incomplete) and the statically-identified dependence information (which tends to be conservative) guide a random test generator to create legal and behaviorally-diverse tests. Our Palus tool implements this testing approach. We compared its effectiveness with a pure random approach, a dynamic-random approach (without a static phase), and a static-random approach (without a dynamic phase) on several popular open-source Java programs. Tests generated by Palus achieved higher structural coverage and found more bugs. Palus is also internally used in Google. It has found 22 previously-unknown bugs in four well-tested Google products.


title: Recommender Systems Based on Evolutionary Computing: A Survey

abstract: Data mining techniques and information personalization have made significant growth in the past decade. Enormous volume of data is generated every day. Recommender systems can help users to find their specific information in the extensive volume of information. Several techniques have been presented for development of Recommender System (RS). One of these techniques is the Evolutionary Computing (EC), which can optimize and improve RS in the various applications. This study investigates the number of publications, focusing on some aspects such as the recommendation techniques, the evaluation methods and the datasets which are used.


title: Police officer in-vehicle discomfort: Appointments carriage method and vehicle seat features

abstract: Musculoskeletal pain is commonly reported by police officers. A potential cause of officer discomfort is a mismatch between vehicle seats and the method used for carrying appointments. Twenty-five police officers rated their discomfort while seated in: (1) a standard police vehicle seat, and (2) a vehicle seat custom-designed for police use. Discomfort was recorded in both seats while wearing police appointments on: (1) a traditional appointments belt, and (2) a load-bearing vest/belt combination (LBV). Sitting in the standard vehicle seat and carrying appointments on a traditional appointments belt were both associated with significantly elevated discomfort. Four vehicle seat features were most implicated as contributing to discomfort: back rest bolster prominence; lumbar region support; seat cushion width; and seat cushion bolster depth. Authorising the carriage of appointments using a LBV is a lower cost solution with potential to reduce officer discomfort. Furthermore, the introduction of custom-designed vehicle seats should be considered.


title: Ultra-low-power 10 to 28.5 Gb/s CMOS-driven VCSEL-based optical links [Invited]

abstract: We report three 850 nm, vertical-cavity surface-emitting laser (VCSEL)-based optical links using 90 nm complementary metal-oxide-semiconductor (CMOS) circuits that achieve new records in speed and power efficiency. Two receiver (RX) circuit designs explore the tradeoffs between speed, power, and silicon area. The two RXs are first tested with a simple transmitter (TX), delivering full-link power efficiencies of 1.37 pJ/bit at 15 Gb/s and 3.6 pJ/bit at 25 Gb/s. An improved TX using feed-forward equalization (FFE) and an advanced VCSEL is then introduced. The link using the FFE TX achieves full-link power efficiencies of 1.5, 2.0, and 3.8 pJ/bit at 22.5, 25, and 28.5 Gb/s, respectively.


title: Students' perceptions on game-based classroom response system in a computer programming course

abstract: The use of game-based learning in the classroom has turned out to be a trend nowadays. Most game-based learning tools and platforms are based on a quiz concept where the students can score points if they can choose the correct answer among multiple answers. Based on our experience in Faculty of Electrical Engineering, Universiti Teknologi MARA, most undergraduate students have difficulty to appreciate the Computer Programming course thus demotivating them in learning any programming related courses. Game-based learning approach using a Game-based Classroom Response System (GCRS) tool known as Kahoot is used to address this issue. This paper presents students' perceptions on Kahoot activity that they experienced in the classroom. The study was carried out by distributing a survey form to 120 students. Based on the feedback, majority of students enjoyed the activity and able to attract their interest in computer programming.


title: Economic Organization in the Construction Industry: A Case Study of Collaborative Production Under High Uncertainty

abstract: A central question of law and economics is how complex productive activity is initiated, organized, and carried out successfully without central planning. What are some of the most important organizational devices and what is their function? The effort to respond to this type of inquiry has led, among other things, to the dichotomy between transactions within firms and transactions across markets - also referred to as the make-or-buy decision or the outsourcing decision. This dichotomy, leading to explanations of the functions of firms and markets, has proved to be a powerful tool in analysis of economic organization. As with most simple descriptions of complex reality, however, it emphasizes some aspects of reality at the expense of others and is not a good fit in certain settings. One such setting is construction, where the organization of the economic activity (the construction project) is mostly contractual (technically, across markets), but where vital organizational ingredients are networks of relationships as well as collaboration and teamwork, generated in large part by pride, commitment, and reputation. The present paper is a case study that examines those ingredients and others that play, at most, a minor role in traditional thinking about firms and markets. This study also illustrates the notion that bilateral contracts are part of a mosaic of such contracts, with the performance of each dependent on the performance of the others, and contractual relationships exist within an industry in which individual projects are of limited duration but the participants are in for the long haul. Perhaps an even more interesting and important observation is that in construction, and no doubt in other economic activities as well, it is not the firm that is the locus for production. Nor does the idea of market exchanges between firms properly describe the productive process. Instead, production is in the hands of teams of people who are associated with various firms but who operate autonomously with respect to their firms. The teams may perform the functions of firms but they lack the critical firm attribute of hierarchical control. Related to this and also important in at least some settings is the manner of selection of team members: the client/owner may contract with, say, an architectural firm but expects to be working with particular, identified individuals within that firm. This raises the question: when a person contracts for services, what is the role of the individual (e.g., an architect or a lawyer) and what is expected of the firm of which that individual is a member? And what does this tell us about the nature and the boundaries of firms? We also offer some observations about fixed fees versus hourly rates and other contingent compensation.


Survey on Clustering Techniques in Data Mining


title: 3D reconstruction of a smooth articulated trajectory from a monocular image sequence

abstract: An articulated trajectory is defined as a trajectory that remains at a fixed distance with respect to a parent trajectory. In this paper, we present a method to reconstruct an articulated trajectory in three dimensions given the two dimensional projection of the articulated trajectory, the 3D parent trajectory, and the camera pose at each time instant. This is a core challenge in reconstructing the 3D motion of articulated structures such as the human body because endpoints of each limb form articulated trajectories. We simultaneously apply activity-independent spatial and temporal constraints, in the form of fixed 3D distance to the parent trajectory and smooth 3D motion. There exist two solutions that satisfy each instantaneous 2D projection and articulation constraint (a ray intersects a sphere at up to two locations) and we show that resolving this ambiguity by enforcing smoothness is equivalent to solving a binary quadratic programming problem. A geometric analysis of the reconstruction of articulated trajectories is also presented and a measure of the reconstructibility of an articulated trajectory is proposed.


title: New resampling algorithm for particle filter localization for mobile robot with 3 ultrasonic sonar sensor

abstract: This paper present a particle filter also known as Monte Carlo Localization (MCL) to solve the localization problem presented before. A new resampling mechanism is proposed. This new resampling mechanism enables the particle filter to converge quicker and more robust to kidnaping problem. This particle filter is simulated in MATLAB and also experimented physically using a simple autonomous mobile robot built with Lego Mindstorms NXT with 3 ultrasonic sonar and RWTH Mindstorms NXT Toolbox for MATLAB to connect the robot to MATLAB. The particle filter with the new resampling algorithm can perform very well in the physical experiments.


title: Modeling Trust Relationships for Developing Trustworthy Information Systems

abstract: Developing a trustworthy information system is a challenging task. The overall trustworthiness of an information system depends on trust relationships that are generally assumed without adequate justification. However, lack of appropriate analysis of such relationships and of appropriate justification of relevant trust assumptions might lead to systems that fail to fully achieve their functionalities. Existing literature does not provide adequate guidelines for a systematic process or an appropriate modeling language to support such trust-focused analysis. This paper fills this gap by introducing a process that allows developers to capture possible trust relationships and to reason about them. The process is supported by a modeling language based on a set of concepts relating to trust and control and a CASE tool. An illustrative example from the UK health care domain is used to demonstrate the applicability and usefulness of the approach.


title: Performance Evaluation of Video Streaming Using MPEG DASH, RTSP, and RTMP in Mobile Networks

abstract: Nearly three-fourths of the world's mobile data traffic will be video in the next 5 years. Video is one of the most demanding services in terms of network efficiency, reliability, and quality. In this work, we present a comparative performance evaluation of three different video streaming protocols, namely MPEG-DASH, RTSP, and RTMP for both on-demand and live video streaming over 4G and Wi-Fi (under different network conditions) in terms of Quality of user Experience (QoE). QoE measurements have been done (i) applying the recently standardized ITU-T Rec P-1201.1, which specifies the model algorithm for non-intrusive monitoring of video quality of IP-based video services based on packet header information for the lower resolution application area, and (ii) an extended non-standardized parametric model for comparative purposes. Results suggest that RTSP is more efficient than MPEG DASH for starting the video playback, but at the expense of decreasing QoE due to packet losses. We have also detected that PLR has a bigger influence over re-buffering events than end-to-end delay both in 4G/LTE and Wi-Fi, and that a slightly best quality is achieved by using QPSK at 20 MHz in 4G/LTE. QoE is noticeably higher with MPEG DASH than that attained by using RTSP, but slightly worse than that obtained with RTMP. Finally, our findings suggest that the use of parametric models for video QoE evaluation should be carefully review in terms of the weight that packet losses should have when streaming protocols based on reliable transport protocols (e.g., TCP) are used.


title: Pop-up book MEMS

abstract: We present a design methodology and manufacturing process for the construction of articulated three-dimensional microstructures with features on the micron to centimeter scale. Flexure mechanisms and assembly folds result from the bulk machining and lamination of alternating rigid and compliant layers, similar to rigid-flex printed circuit board construction. Pop-up books and other forms of paper engineering inspire designs consisting of one complex part with a single assembly degree of freedom. Like an unopened pop-up book, mechanism links reside on multiple interconnected layers, reducing interference and allowing folding mechanisms of greater complexity than achievable with a single folding layer. Machined layers are aligned using dowel pins and bonded in parallel. Using mechanical alignment that persists during bonding allows device layers to be anisotropically pre-strained, a feature we exploit to create self-assembling structures. These methods and three example devices are presented.


title: Targeted Applications of Unmanned Aerial Vehicles (Drones) in Telemedicine

abstract: Abstract Introduction: Advances in technology have revolutionized the medical field and changed the way healthcare is delivered. Unmanned aerial vehicles (UAVs) are the next wave of technological advancements that have the potential to make a huge splash in clinical medicine. UAVs, originally developed for military use, are making their way into the public and private sector. Because they can be flown autonomously and can reach almost any geographical location, the significance of UAVs are becoming increasingly apparent in the medical field. Materials and Methods: We conducted a comprehensive review of the English language literature via the PubMed and Google Scholar databases using search terms “unmanned aerial vehicles,” “UAVs,” and “drone.” Preference was given to clinical trials and review articles that addressed the keywords and clinical medicine. Results: Potential applications of UAVs in medicine are broad. Based on articles identified, we grouped UAV application in medicine into three categories: ...


title: Characterizing Guidance in Visual Analytics

abstract: Visual analytics (VA) is typically applied in scenarios where complex data has to be analyzed. Unfortunately, there is a natural correlation between the complexity of the data and the complexity of the tools to study them. An adverse effect of complicated tools is that analytical goals are more difficult to reach. Therefore, it makes sense to consider methods that guide or assist users in the visual analysis process. Several such methods already exist in the literature, yet we are lacking a general model that facilitates in-depth reasoning about guidance. We establish such a model by extending van Wijk's model of visualization with the fundamental components of guidance. Guidance is defined as a process that gradually narrows the gap that hinders effective continuation of the data analysis. We describe diverse inputs based on which guidance can be generated and discuss different degrees of guidance and means to incorporate guidance into VA tools. We use existing guidance approaches from the literature to illustrate the various aspects of our model. As a conclusion, we identify research challenges and suggest directions for future studies. With our work we take a necessary step to pave the way to a systematic development of guidance techniques that effectively support users in the context of VA.


title: A Corpus-Based Approach For Building Semantic Lexicons

abstract: Semantic knowledge can be a great asset to natural language processing systems, but it is usually hand-coded for each application. Although some semantic information is available in general-purpose knowledge bases such as WordNet and Cyc, many applications require domain-specific lexicons that represent words and categories for a particular topic. In this paper, we present a corpus-based method that can be used to build semantic lexicons for specific categories. The input to the system is a small set of seed words for a category and a representative text corpus. The output is a ranked list of words that are associated with the category. A user then reviews the top-ranked words and decides which ones should be entered in the semantic lexicon. In experiments with five categories, users typically found about 60 words per category in 10-15 minutes to build a core semantic lexicon.


title: Autoassociative neural network for speech processing

abstract: Connectionist architectures are being studied from various perspectives and applied in diverse domains with promising performance. In the paper, the authors study MultiLayer Perceptron (MLP), one of the most widely used architectures, for generating alternative representations of speech signal. Speech is a highly redundant signal and hence efficient representation of speech signal that exploits this redundancy is an important issue in synthesis, transmission and recognition of the human voice. It has been observed that MLP forms suitable internal representations in terms of the activation of its units to establish an association as specified by a given input-output relation. The authors explore the nature of this internal representation formed by an MLP while establishing an autoassociation of spectral patterns of speech signal. >


title: Adolescent Brain Development: A Period of Vulnerabilities and Opportunities. Keynote Address

abstract: Abstract: This article introduces and summarizes the goals of the symposium. It also provides an overview of a conceptual framework for understanding adolescence, which emphasizes how the very nature of this developmental transition requires an interdisciplinary approach—one that focuses on brain/behavior/social-context interactions during this important maturational period. More specifically it describes a set of neurobehavioral changes that appear to be linked to pubertal development, which appear to have a significant effect on motivation and emotion, and considers these puberty-specific changes in affect in relation to a much larger set of developmental changes in adolescence. This framework is used to argue for the need for a transdisciplinary dialogue that brings together work in several areas of neuroscience (including animal models) and normal development with clinical and social policy research aimed at early intervention and prevention strategies.


title: Measuring usability: are effectiveness, efficiency, and satisfaction really correlated?

abstract: Usability comprises the aspects effectiveness, efficiency, and satisfaction. The correlations between these aspects are not well understood for complex tasks. We present data from an experiment where 87 subjects solved 20 information retrieval tasks concerning programming problems. The correlation between efficiency, as indicated by task completion time, and effectiveness, as indicated by quality of solution, was negligible. Generally, the correlations among the usability aspects depend in a complex way on the application domain, the user's experience, and the use context. Going through three years of CHI Proceedings, we find that 11 out of 19 experimental studies involving complex tasks account for only one or two aspects of usability. When these studies make claims concerning overall usability, they rely on risky assumptions about correlations between usability aspects. Unless domain specific studies suggest otherwise, effectiveness, efficiency, and satisfaction should be considered independent aspect of usability and all be included in usability testing.


Learning Classification with Unlabeled Data


title: SPROUT: Lazy vs. Eager Query Plans for Tuple-Independent Probabilistic Databases

abstract: A paramount challenge in probabilistic databases is the scalable computation of confidences of tuples in query results. This paper introduces an efficient secondary-storage operator for exact computation of queries on tuple-independent probabilistic databases. We consider the conjunctive queries without self-joins that are known to be tractable on any tuple-independent database, and queries that are not tractable in general but become tractable on probabilistic databases restricted by functional dependencies. Our operator is semantically equivalent to a sequence of aggregations and can be naturally integrated into existing relational query plans. As a proof of concept, we developed an extension of the PostgreSQL 8.3.3 query engine called SPROUT. We study optimizations that push or pull our operator or parts thereof past joins. The operator employs static information, such as the query structure and functional dependencies, to decide which constituent aggregations can be evaluated together in one scan and how many scans are needed for the overall confidence computation task. A case study on the TPC-H benchmark reveals that most TPC-H queries obtained by removing aggregations can be evaluated efficiently using our operator. Experimental evaluation on probabilistic TPC-H data shows substantial efficiency improvements when compared to the state of the art.


title: Example-Tracing Tutors: Intelligent Tutor Development for Non-programmers

abstract: In 2009, we reported on a new Intelligent Tutoring Systems (ITS) technology, example-tracing tutors, that can be built without programming using the Cognitive Tutor Authoring Tools (CTAT). Creating example-tracing tutors was shown to be 4–8 times as cost-effective as estimates for ITS development from the literature. Since 2009, CTAT and its associated learning management system, the Tutorshop, have been extended and have been used for both research and real-world instruction. As evidence that example-tracing tutors are an effective and mature ITS paradigm, CTAT-built tutors have been used by approximately 44,000 students and account for 40 % of the data sets in DataShop, a large open repository for educational technology data sets. We review 18 example-tracing tutors built since 2009, which have been shown to be effective in helping students learn in real educational settings, often with large pre/post effect sizes. These tutors support a variety of pedagogical approaches, beyond step-based problem solving, including collaborative learning, educational games, and guided invention activities. CTAT and other ITS authoring tools illustrate that non-programmer approaches to building ITS are viable and useful and will likely play a key role in making ITS widespread.


title: A comparative study of adaptive, automatic recognition of disordered speech.

abstract: Speech-driven assistive technology can be an attractive alternative to conventional interfaces for people with physical disabilities. However, often the lack of motor-control of the speech articulators results in disordered speech, as condition known as dysarthria. Dysarthric speakers can generally not obtain satisfactory performances with off-the-shelf automatic speech recognition (ASR) products and disordered speech ASR is an increasingly active research area. Sparseness of suitable data is a big challenge. The experiments described here use UAspeech, one of the largest dysarthric databases available, which is still easily an order of magnitude smaller than typical speech databases. This study investigates how far fundamental training and adaptation techniques developed in the LVCSR community can take us. A variety of ASR systems using maximum likelihood and MAP adaptation strategies are established with all speakers obtaining significant improvements compared to the baseline system regardless of the severity of their condition. The best systems show on average 34% relative improvement on known published results. An analysis of the correlation between intelligibility of the speaker and the type of system which would represent an optimal operating point in terms of performance shows that for severely dysarthric speakers, the exact choice of system configuration is more critical than for speakers with less disordered speech.


title: Performance Analysis of IEC 61850 Sampled Value Process Bus Networks

abstract: Process bus networks are the next stage in the evolution of substation design, bringing digital technology to the high voltage switchyard. Benefits of process buses include facilitating the use of Non-Conventional Instrument Transformers, improved disturbance recording and phasor measurement and the removal of costly, and potentially hazardous, copper cabling from substation switchyards and control rooms. This paper examines the role a process bus plays in an IEC 61850 based Substation Automation System. Measurements taken from a process bus substation are used to develop an understanding of the network characteristics of "whole of substation" process buses. The concept of "coherent transmission" is presented and the impact of this on Ethernet switches is examined. Experiments based on substation observations are used to investigate in detail the behavior of Ethernet switches with sampled value traffic. Test methods that can be used to assess the adequacy of a network are proposed, and examples of the application and interpretation of these tests are provided. Once sampled value frames are queued by an Ethernet switch the additional delay incurred by subsequent switches is minimal, and this allows their use in switchyards to further reduce communications cabling, without significantly impacting operation. The performance and reliability of a process bus network operating with close to the theoretical maximum number of digital sampling units (merging units or electronic instrument transformers) was investigated with networking equipment from several vendors, and has been demonstrated to be acceptable.


title: Is a picture worth a thousand words? A Deep Multi-Modal Fusion Architecture for Product Classification in e-commerce

abstract: Classifying products into categories precisely and efficiently is a major challenge in modern e-commerce. The high traffic of new products uploaded daily and the dynamic nature of the categories raise the need for machine learning models that can reduce the cost and time of human editors. In this paper, we propose a decision level fusion approach for multi-modal product classification using text and image inputs. We train input specific state-of-the-art deep neural networks for each input source, show the potential of forging them together into a multi-modal architecture and train a novel policy network that learns to choose between them. Finally, we demonstrate that our multi-modal network improves the top-1 accuracy $\%$ over both networks on a real-world large-scale product classification dataset that we collected from Walmart.com. While we focus on image-text fusion that characterizes e-commerce domains, our algorithms can be easily applied to other modalities such as audio, video, physical sensors, etc.


title: An Improved Binary Whale Optimization Algorithm for Feature Selection of Network Intrusion Detection

abstract: As for network intrusion detection, the time and space consumption of the detection algorithm increases with the feature dimension. The irrelevant or redundant features may cause the detection accuracy to decrease. In order to improve the timeliness and reliability of intrusion detection, feature selection is one of the most effective methods proposed currently. Considering that Whale Optimization Algorithm (WOA) has good classification performance and dimensionality reduction in reducing feature redundancy, it can be applied to feature selection of network intrusion detection. The WOA converges slowly in the search process and easily falls into a local optimum in the updating mechanism, which restricts the classification performance and dimensionality reduction of the algorithm. Therefore, this paper adopts the nonlinear convergence factor strategy in the search process and introduces the Particle Swarm Optimization (PSO) strategy in the updating mechanism. An Improved Binary Whale Optimization Algorithm (IBWOA) is then proposed in order to achieve a better dimension reduction on the premise of ensuring accuracy for feature selection of intrusion detection. In the experimental stage, multiple data sets in the UCI database for machine learning were used for verification by comparing with other feature selection methods. Finally, the KDD CUP 99 data set was used for verification of feature selection in intrusion detection. The experimental results show that, compared with Genetic Algorithm (GA), PSO and WOA, the IBWOA improves the classification accuracy and dimensionality reduction in feature selection of intrusion detection.


title: Embodied Question Answering

abstract: We present a new AI task -- Embodied Question Answering (EmbodiedQA) -- where an agent is spawned at a random location in a 3D environment and asked a natural language question ("What color is the car?"). In order to answer, the agent must first intelligently navigate to explore the environment, gather information through first-person (egocentric) vision, and then answer the question ("orange"). ::: This challenging task requires a range of AI skills -- active perception, language understanding, goal-driven navigation, commonsense reasoning, and grounding of language into actions. In this work, we develop the environments, end-to-end-trained reinforcement learning agents, and evaluation protocols for EmbodiedQA.


title: A modified gardner detector for multilevel PAM/QAM system

abstract: In multilevel pulse amplitude modulation (PAM) and quadrature amplitude modulation (QAM) systems, there is a strong demand for a reliable and precise timing phase provided by the timing recovery circuit. However, reducing timing phase jitter just by narrowing the bandwidth of the timing recovery loop will decrease the capability of tracking the time variant channel. To avoid this drawback, the only way to improve pattern-dependent timing jitter is to utilize some signal processing means. In this paper, we propose a modified Gardner detection scheme for symbol timing recovery of multilevel PAM/QAM signals, which offers a theoretically jitter-free phase detector even in the case of non-coherent demodulation. Also in practical applications, such detector with reduced complexity can provide a remarkable amount of jitter reduction. Simulation results show that the proposed modification leads to significant performance improvement for multilevel PAM/QAM signals.


title: Towards a Better Metric for Evaluating Question Generation Systems

abstract: There has always been criticism for using $n$-gram based similarity metrics, such as BLEU, NIST, etc, for evaluating the performance of NLG systems. However, these metrics continue to remain popular and are recently being used for evaluating the performance of systems which automatically generate questions from documents, knowledge graphs, images, etc. Given the rising interest in such automatic question generation (AQG) systems, it is important to objectively examine whether these metrics are suitable for this task. In particular, it is important to verify whether such metrics used for evaluating AQG systems focus on answerability of the generated question by preferring questions which contain all relevant information such as question type (Wh-types), entities, relations, etc. In this work, we show that current automatic evaluation metrics based on $n$-gram similarity do not always correlate well with human judgments about answerability of a question. To alleviate this problem and as a first step towards better evaluation metrics for AQG, we introduce a scoring function to capture answerability and show that when this scoring function is integrated with existing metrics, they correlate significantly better with human judgments. The scripts and data developed as a part of this work are made publicly available at https://github.com/PrekshaNema25/Answerability-Metric


title: How Recommender Systems

abstract: This chapter aims to provide an overview of the class of multi-criteria recommender systems, i.e., the category of recommender systems that use multi-criteria preference ratings. Traditionally, the vast majority of recommender systems literature has focused on providing recommendations by modelling a user’s utility (or preference) for an item as a single preference rating. However, where possible, capturing richer user preferences along several dimensions—for example, capturing not only the user’s overall preference for a given movie but also her preferences for specific movie aspects (such as acting, story, or visual effects)—can provide opportunities for further improvements in recommendation quality. As a result, a number of recommendation techniques that attempt to take advantage of such multi-criteria preference information have been developed in recent years. A review of current algorithms that use multi-criteria ratings for calculating predictions and generating recommendations is provided. The chapter concludes with a discussion on open issues and future challenges for the class of multi-criteria rating recommenders.


ChairIO – The Chair-Based Interface


title: Towards a type system for analyzing javascript programs

abstract: JavaScript is a popular language for client-side web scripting. It has a dubious reputation among programmers for two reasons. First, many JavaScript programs are written against a rapidly evolving API whose implementations are sometimes contradictory and idiosyncratic. Second, the language is only weakly typed and comes virtually without development tools. ::: ::: The present work is a first attempt to address the second point. It does so by defining a type system that tracks the possible traits of an object and flags suspicious type conversions. Because JavaScript is a classless, object-based language with first-class functions, the type system must include singleton types, subtyping, and first class record labels. The type system covers a representative subset of the language and there is a type soundness proof with respect to an operational semantics.


title: Deep Facial Action Unit Recognition from Partially Labeled Data

abstract: Current work on facial action unit (AU) recognition requires AU-labeled facial images. Although large amounts of facial images are readily available, AU annotation is expensive and time consuming. To address this, we propose a deep facial action unit recognition approach learning from partially AU-labeled data. The proposed approach makes full use of both partly available ground-truth AU labels and the readily available large scale facial images without annotation. Specifically, we propose to learn label distribution from the ground-truth AU labels, and then train the AU classifiers from the large-scale facial images by maximizing the log likelihood of the mapping functions of AUs with regard to the learnt label distribution for all training data and minimizing the error between predicted AUs and ground-truth AUs for labeled data simultaneously. A restricted Boltzmann machine is adopted to model AU label distribution, a deep neural network is used to learn facial representation from facial images, and the support vector machine is employed as the classifier. Experiments on two benchmark databases demonstrate the effectiveness of the proposed approach.


title: Exploring Factors Related to Completion of an Online Undergraduate-Level Introductory Statistics Course

abstract: Data were collected from 353 online undergraduate introductory statistics students at the beginning of a semester using the Goals and Outcomes Associated with Learning Statistics (GOALS) instrument, an abbreviated form the Statistics Anxiety Rating Scale (STARS), a survey of expected grade and expected time commitment, and the first lesson quiz. At the end of the semester, whether or not each student successfully completed the course with a grade of D or higher was recorded. It was hypothesized that students who successfully completed the course would have had favorable ratings on each of these variables. While there were no significant differences between students who did and did not successfully complete the course in terms of anxiety, attitudes, or expected time commitment, students who completed the course had higher scores on the GOALS, higher expected grades, and higher scores on the first quiz of the semester. Stepwise logistic regression found that students’ attitudes towards statistics teachers and scores on the first quiz of the semester could be used to predict whether or not students would successfully complete the course. Based on these findings, suggestions for online instructors are given.


title: On the use of autocorrelation analysis for pitch detection

abstract: One of the most time honored methods of detecting pitch is to use some type of autocorrelation analysis on speech which has been appropriately preprocessed. The goal of the speech preprocessing in most systems is to whiten, or spectrally flatten, the signal so as to eliminate the effects of the vocal tract spectrum on the detailed shape of the resulting autocorrelation function. The purpose of this paper is to present some results on several types of (nonlinear) preprocessing which can be used to effectively spectrally flatten the speech signal The types of nonlinearities which are considered are classified by a non-linear input-output quantizer characteristic. By appropriate adjustment of the quantizer threshold levels, both the ordinary (linear) autocorrelation analysis, and the center clipping-peak clipping autocorrelation of Dubnowski et al. [1] can be obtained. Results are presented to demonstrate the degree of spectrum flattening obtained using these methods. Each of the proposed methods was tested on several of the utterances used in a recent pitch detector comparison study by Rabiner et al. [2] Results of this comparison are included in this paper. One final topic which is discussed in this paper is an algorithm for adaptively choosing a frame size for an autocorrelation pitch analysis.


title: PDFFigures 2.0: Mining figures from research papers

abstract: Figures and tables are key sources of information in many scholarly documents. However, current academic search engines do not make use of figures and tables when semantically parsing documents or presenting document summaries to users. To facilitate these applications we develop an algorithm that extracts figures, tables, and captions from documents called "PDFFigures 2.0." Our proposed approach analyzes the structure of individual pages by detecting captions, graphical elements, and chunks of body text, and then locates figures and tables by reasoning about the empty regions within that text. To evaluate our work, we introduce a new dataset of computer science papers, along with ground truth labels for the locations of the figures, tables, and captions within them. Our algorithm achieves impressive results (94% precision at 90% recall) on this dataset surpassing previous state of the art. Further, we show how our framework was used to extract figures from a corpus of over one million papers, and how the resulting extractions were integrated into the user interface of a smart academic search engine, Semantic Scholar (www.semanticscholar.org). Finally, we present results of exploratory data analysis completed on the extracted figures as well as an extension of our method for the task of section title extraction. We release our dataset and code on our project webpage for enabling future research (http://pdffigures2.allenai.org).


title: Organizational cybernetics for waste management authorities: a case study

abstract: This paper is concerned with the organization and the operational viability of Solid Waste Management Authorities (SWMA). The frequently changing performance requirements, the continuous search for alternative technological, financial and institutional options, the unstable market of recyclables, the variations in waste characteristics, and the need for public-private partnerships are only some of the factors that shape and drastically affect the operating environment of a SWMA. Consequently, for any such Authority (be it a small municipal department or a huge organization serving a megapolis), there is an emerging need for: • adaptation to such variations • organizational cohesion. Both adaptability and cohesion are prerequisites for the organization’s effectiveness and viability (ability to maintain distinct existence in the long term).