title: A Compact Single-Layer Substrate-Integrated Waveguide (SIW) Monopulse Slot Antenna Array

abstract: In this study, a compact single-layer substrate-integrated waveguide (SIW) monopulse slot antenna array is designed. The antenna has a very simple structure and uses SIW-based cavity resonators, instead of a complex feeding network and monopulse comparator, to excite the slot array. The design decreases the size of the feeding network and monopulse comparator by applying cavity resonator principles to the monopulse slotted antenna array, resulting in an obvious improvement of the antenna aperture efficiency and structure complexity. This single-layer scheme, combined with a planar transmission line, is ideal for integration with planar circuits. A 16-element slot array prototype operating at 5.83 GHz is fabricated through a low-cost standard printed circuit board process to validate the design. The simulated and measured results are presented, and good agreement between them is obtained.


Generating a Training Corpus for OCR Post-Correction Using Encoder-Decoder Model


title: ARTIFICIAL INTELLIGENCE IN MEDICAL APPLICATION: AN EXPLORATION

abstract: The advancement in computer technology has encouraged the researchers to develop software for assisting doctors in making decision without consulting the specialists directly. ::: The software development exploits the potential of human intelligence such as reasoning, making decision, learning (by experiencing) and many others. Artificial intelligence is not a new concept, yet it has been accepted as a new technology in computer science. It has been applied in many areas such as education, business, medical and manufacturing. This paper explores the potential of artificial intelligence techniques particularly for web-based medical applications. In addition, a model for web-based medical diagnosis and prediction is proposed.


Inhibitory Effects of Echinacea Angustifolia Essential Oils on the Growth of Five Pathogenic Organisms: Coliform Spp, Pseudomonas Spp,Saccharomyces Cerevisiae, Zygosaccharomyces Bailii and Lactobacillus Plantarum


title: Fidelius Charm: Isolating Unsafe Rust Code

abstract: The Rust programming language has a safe memory model that promises to eliminate critical memory bugs. While the language is strong in doing so, its memory guarantees are lost when any unsafe blocks are used. Unsafe code is often needed to call library functions written in an unsafe language inside a Rust program. We present Fidelius Charm (FC), a system that protects a programmer-specified subset of data in memory from unauthorized access through vulnerable unsafe libraries. FC does this by limiting access to the program's memory while executing unsafe libraries. FC uses standard features of Rust and utilizes the Linux kernel as a trusted base for splitting the address space into a trusted privileged region under the control of functions written in Rust and a region available to unsafe external libraries. This paper presents our design and implementation of FC, presents two case studies for using FC in Rust TLS libraries, and reports on experiments showing its performance overhead is low for typical uses.


title: A System-Aware Cyber Security architecture

abstract: As exemplified in the 2010 Stuxnet attack on an Iranian nuclear facility, attackers have the capabilities to embed infections in equipment that is employed in nuclear power systems. In this paper, a new systems engineering focused approach for mitigating such risks is described. This approach involves the development of a security architectural formulation that integrates a set of reusable security services as an architectural solution that is an embedded component of the system to be protected. The System-Aware architectural approach embeds security components into the system to be protected. The architecture includes services that (1) collect and assess real-time security relevant measurements from the system being protected, (2) perform security analysis on those measurements, and (3) execute system security control actions as required. This architectural formulation results in a defense that is referred to as System-Aware Cyber Security. This includes (1) the integration of a diverse set of dynamically interchangeable redundant subsystems involving hardware and software components provided from multiple vendors to significantly increase the difficulty for adversaries by avoiding a monoculture environment, (2) the development of subsystems that are capable of rapidly changing their attack surface through hardware and software reconfiguration (configuration hopping) in response to perceived threats, (3) data consistency checking services (e.g., intelligent voting mechanisms) for isolating faults and permitting moving surface control actions to avoid operations in a compromised configuration, and (4) forensic analysis techniques for rapid post-attack categorization of whether a given fault is more likely the result of an infected embedded hardware or software component (i.e., cyber attack) or a natural failure. In this paper we present these key elements of the System-Aware Cyber Security architecture and show, including an application example, how they can be integrated to mitigate the risks of insider and supply chain attacks. In addition, this paper outlines an initial vision for a security analysis framework to compare alternative System-Aware security architectures. Finally, we summarize future research that is necessary to facilitate implementation across additional domains critical to the nation's interest. © 2012 Wiley Periodicals, Inc. Syst Eng © 2012 Wiley Periodicals, Inc.


title: Probabilistic estimates of drought impacts on agricultural production: Drought Impacts on Agriculture

abstract: Increases in the severity and frequency of drought in a warming climate may negatively impact agricultural production and food security. Unlike previous studies that have estimated agricultural impacts of climate condition using single-crop yield distributions, we develop a multivariate probabilistic model that uses projected climatic conditions (e.g., precipitation amount or soil moisture) throughout a growing season to estimate the probability distribution of crop yields. We demonstrate the model by an analysis of the historical period 1980–2012, including the Millennium Drought in Australia (2001–2009). We find that precipitation and soil moisture deficit in dry growing seasons reduced the average annual yield of the five largest crops in Australia (wheat, broad beans, canola, lupine, and barley) by 25–45% relative to the wet growing seasons. Our model can thus produce region- and crop-specific agricultural sensitivities to climate conditions and variability. Probabilistic estimates of yield may help decision-makers in government and business to quantitatively assess the vulnerability of agriculture to climate variations. We develop a multivariate probabilistic model that uses precipitation to estimate the probability distribution of crop yields. The proposed model shows how the probability distribution of crop yield changes in response to droughts. During Australia's Millennium Drought precipitation and soil moisture deficit reduced the average annual yield of the five largest crops.


title: Action prediction models for recommender systems based on collaborative filtering and sequence mining hybridization

abstract: Many recommender systems collect online users' activity and infer from it users' preferences. They record user actions of various types (e.g. clicks, views), and predict unknown, possibly future, interactions between users and items, mostly using Collaborative Filtering (CF) or Sequence Mining (SM) techniques. While both techniques have their advantages, in this paper, we show that improved prediction accuracy can be achieved by hybridizing them. The proposed hybrid model uses first an SM model to augment an existing actions' data set and then uses collaborative filtering in the final prediction step. The empirical evaluation, which was conducted on a large real-world dataset, showed that the proposed hybrid model outperforms both stand-alone SM and CF.


title: Synthesis and editing of personalized stylistic human motion

abstract: This paper presents a generative human motion model for synthesis, retargeting, and editing of personalized human motion styles. We first record a human motion database from multiple actors performing a wide variety of motion styles for particular actions. We then apply multilinear analysis techniques to construct a generative motion model of the form x = g(a, e) for particular human actions, where the parameters a and e control "identity" and "style" variations of the motion x respectively. The new modular representation naturally supports motion generalization to new actors and/or styles. We demonstrate the power and flexibility of the multilinear motion models by synthesizing personalized stylistic human motion and transferring the stylistic motions from one actor to another. We also show the effectiveness of our model by editing stylistic motion in style and/or identity space.


title: Differences in the relative influence of product attributes under alternative processing conditions: Attribute importance versus attribute ease of imagability

abstract: We present two experiments that test whether ease of imagability can shift the influence of a product attribute without affecting its assessed importance in the decision. Results of the experiments indicate that more easily imagined attributes may have a disproportionate influence when subjects use imagery in the evaluation, but not when they engage in more analytical processing. Experiments 1 and 2 differ in the manner in which method of evaluation (imagery based vs. nonimagery based) was manipulated. In Experiment 1, use of imagery was manipulated through explicit instructions to either engage in or refrain from using imagery, whereas use of imagery was manipulated in Experiment 2 by varying the overall value of the alternatives. Results of Experiment 2 indicate that subjects who were asked to evaluate alternatives that were described in generally positive terms used imagery in the evaluation, placing greater emphasis on the easily imagined attributes, whereas subjects who were asked to evaluate alternatives that were described in generally negative terms or mixed positive and negative terms did not use imagery, basing their evaluations on attribute importance. Our findings support the hypothesis that consumers may at times evaluate alternatives by using an imagery heuristic , which involves imagining the actual experience with an alternative and assessing the desirability of the alternative according to the affective response to this imagined experience.


title: Power production by a dynamic micro heat engine with an integrated thermal switch

abstract: Mechanical power production by a dynamic micro heat engine with integrated thermal switch is demonstrated. A microengine operated from a constant heat source of 60 °C is shown to produce 350 µW of mechanical power. Employing an active thermal switch to control heat rejection from the microengine enables power to be increased to 2500 µW. Power consumption by the thermal switch is shown to be minimized by operating the cantilever switch at its resonant frequency. Thermal switch power requirements can be reduced to less than 20 µW for operational speeds up to 100 Hz.


title: Review of position-sensorless operation of brushless permanent-magnet machines

abstract: The operation of a brushless permanent-magnet machine requires rotor-position information, which is used to control the frequency and phase angle of the machine's winding currents. Sensorless techniques for estimating rotor position from measurements of voltage and current have been the subject of intensive research. This paper reviews the state of the art in these sensorless techniques, which are broadly classified into three types: motional electromotive force, inductance, and flux linkage.


eview ore than skin deep : Body representation beyond primary omatosensory cortex


Ultra-Low Power Integrated Circuit Design


title: A qualitative investigation on consumer trust in mobile commerce

abstract: Mobile commerce represents a significant development in e-commerce, offering accessibility, ubiquity, mobility, and localisation to users. Despite the potential of mobile commerce, trust is a major obstacle in its adoption and development. Many consumers feel uncomfortable with the idea of conducting commerce over wireless, hand-held devices. The focus of this research is to understand trust in mobile commerce and to identify factors that are important for trust development. The research builds on Siau and Shen's framework which depicts two key factors influencing trust in mobile commerce. This research not only validates and expands on the existing framework, but also provides an expanded conceptual model for future research.


title: Self-Supervised Synonym Extraction from the Web *

abstract: Current synonym extraction methods work in a “closed” way. Given the problem word and set of target words, researchers have to choose words synonymous with the problem word using features such as lexical patterns and distributional similarities. This paper tries to discover synonyms in an “open” way and presents a synonym extraction framework based on self-supervised learning. We first analysis the nature of the open method and argue that a trained pattern-independent model for synonym extraction is feasible. We then model the extraction of synonyms from sentences as a sequential labeling problem and automatically generate labeled training samples by using structured knowledge from online encyclopedias and some generic heuristic rules. Finally, we train some Conditional Random Field (CRF) models and use them to extract synonyms from the web. We successfully extract more than 20 million facts, which contain 826,219 distinct pairs of synonyms.


title: MuVE: Efficient Multi-Objective View Recommendation for Visual Data Exploration

abstract: To support effective data exploration, there is a well-recognized need for solutions that can automatically recommend interesting visualizations, which reveal useful insights into the analyzed data. However, such visualizations come at the expense of high data processing costs, where a large number of views are generated to evaluate their usefulness. Those costs are further escalated in the presence of numerical dimensional attributes, due to the potentially large number of possible binning aggregations, which lead to a drastic increase in the number of possible visualizations. To address that challenge, in this paper we propose the MuVE scheme for Multi-Objective View Recommendation for Visual Data Exploration. MuVE introduces a hybrid multi-objective utility function, which captures the impact of binning on the utility of visualizations. Consequently, novel algorithms are proposed for the efficient recommendation of data visualizations that are based on numerical dimensions. The main idea underlying MuVE is to incrementally and progressively assess the different benefits provided by a visualization, which allows an early pruning of a large number of unnecessary operations. Our extensive experimental results show the significant gains provided by our proposed scheme.


title: Dual-Band Microstrip Bandpass Filter Using Stepped-Impedance Resonators With New Coupling Schemes

abstract: A microstrip bandpass filter using stepped-impedance resonators is designed in low-temperature co-fired ceramic technology for dual-band applications at 2.4 and 5.2 GHz. New coupling schemes are proposed to replace the normal counterparts. It is found that the new coupling scheme for the interstages can enhance the layout compactness of the bandpass filter; while the new coupling scheme at the input and output can improve the performance of the bandpass filter. To validate the design and analysis, a prototype of the bandpass filter was fabricated and measured. It is shown that the measured and simulated performances are in good agreement. The prototype of the bandpass filter achieved insertion loss of 1.25 and 1.87 dB, S11 of -29 and -40 dB, and bandwidth of 21% and 12.7% at 2.4 and 5.2 GHz, respectively. The bandpass filter is further studied for a single-package solution of dual-band radio transceivers. The bandpass filter is, therefore, integrated into a ceramic ball grid array package. The integration is analyzed with an emphasis on the connection of the bandpass filter to the antenna and to the transceiver die


title: Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters

abstract: A large body of work has been devoted to defining and identifying clusters or communities in social and information networks. We explore from a novel perspective several questions related to identifying meaningful communities in large social and information networks, and we come to several striking conclusions. We employ approximation algorithms for the graph partitioning problem to characterize as a function of size the statistical and structural properties of partitions of graphs that could plausibly be interpreted as communities. In particular, we define the network community profile plot, which characterizes the "best" possible community--according to the conductance measure--over a wide range of size scales. We study over 100 large real-world social and information networks. Our results suggest a significantly more refined picture of community structure in large networks than has been appreciated previously. In particular, we observe tight communities that are barely connected to the rest of the network at very small size scales; and communities of larger size scales gradually "blend into" the expander-like core of the network and thus become less "community-like." This behavior is not explained, even at a qualitative level, by any of the commonly-used network generation models. Moreover, it is exactly the opposite of what one would expect based on intuition from expander graphs, low-dimensional or manifold-like graphs, and from small social networks that have served as testbeds of community detection algorithms. We have found that a generative graph model, in which new edges are added via an iterative "forest fire" burning process, is able to produce graphs exhibiting a network community profile plot similar to what we observe in our network datasets.


title: Edge-Host Partitioning of Deep Neural Networks with Feature Space Encoding for Resource-Constrained Internet-of-Things Platforms

abstract: This paper introduces partitioning an inference task of a deep neural network between an edge and a host platform in the IoT environment. We present a DNN as an encoding pipeline, and propose to transmit the output feature space of an intermediate layer to the host. The lossless or lossy encoding of the feature space is proposed to enhance the maximum input rate supported by the edge platform and/or reduce the energy of the edge platform. Simulation results show that partitioning a DNN at the end of convolutional (feature extraction) layers coupled with feature space encoding enables significant improvement in the energy-efficiency and throughput over the baseline configurations that perform the entire inference at the edge or at the host.


title: Studying the Effects of Task Notification Policies on Participation and Outcomes in On-the-Go Crowdsourcing

abstract: Recent years have seen the growth of physical crowdsourcing systems (e.g., Uber; TaskRabbit) that motivate large numbers of people to provide new and improved physical tasking and delivery services on-demand. In these systems, opportunistically relying on people to make convenient contributions may lead to incomplete solutions, while directing people to do inconvenient tasks requires high incentives. To increase people's willingness to participate and reduce the need to incentivize participation, we study on-the-go crowdsourcing as an alternative approach that suggests tasks along people’s existing routes that are conveniently on their way. We explore as a first step in this paper the design of task notification policies that decide when, where, and to whom to suggest tasks. Situating our work in the context of practical problems such as package delivery and lost-and-found searches, we conducted controlled experiments that show how small changes in task notification policy can influence individual participation and actions in significant ways that in turn affect system outcomes. We discuss the implications of our findings on the design of future on-the-go crowdsourcing technologies and applications.


title: Synchronous reluctance motor multi-static MEC model

abstract: High efficiency, low cost and high reliable electrical machines are designed to meet very demanding specifications for a wide range of applications. The SynchRM (Synchronous Reluctance Motor) has very interesting properties and features. One of the main advantages is that the motor can be designed without rare earth materials permanent magnets. In this article, a modelling method of such machines using the MEC (Magnetic Equivalent Circuit) is proposed. The main challenges are to obtain a very accurate parametric model in order to calculate torque variations and to make possible geometric optimizations in future work.


title: A Modified Bow-Tie Antenna for Improved Pulse Radiation

abstract: The analysis, design, and realization of a modified bow-tie antenna optimized for impulse ground penetrating radar (GPR) applications is described. The proposed antenna shows improved properties important for GPR, which include its compact size (in comparison with a conventional bow-tie antenna) and ability to radiate UWB pulses with increased amplitude and very small late-time ringing. A substantial increase in the amplitude of the transmitted pulse is achieved by utilizing radiation from discontinuities introduced by the resistive loading employed in the antenna to suppress late-time ringing. By choosing an optimal distance between the antenna's feed point and the location of the resistive loading, radiations that occur from the antenna's feed point and the mentioned discontinuities at the resistive loading will combine constructively in the boreside direction of the antenna. As a result, one will observe a substantial increase of the amplitude of the transmitted pulse in the boreside direction. Furthermore, an analytical expression describing approximate time-harmonic current distribution is derived to indicate an optimal resistive loading profile for the proposed antenna. Additionally, the traveling-wave current distribution of the antenna is theoretically analyzed to examine the applicability of the obtained time-harmonic expression for pulse excitation. It has been found that when the antenna is resistively loaded both the time-harmonic and traveling-wave currents decay to approach nearly the same value at the end section of the antenna. As the amount of current at the antenna ends corresponds to the level of reflection which occurs there, the derived expression is found to be useful to indicate an optimal loading profile for the proposed antenna. A theoretical model of the proposed antenna has been developed to perform numerical analysis using a modified NEC-2 code. In addition, an experimental verification has been carried out and both the simulation and experiment confirmed the improved properties of the proposed antenna.


title: Segment-based stereo matching using graph cuts

abstract: In this paper we present a new segment-based stereo matching algorithm using graph cuts. In our approach, the reference image is divided into non-overlapping homogeneous segments and the scene structure is represented as a set of planes in the disparity space. The stereo matching problem is formulated as an energy minimization problem in the segment domain instead of the traditional pixel domain. Graph cuts technique is used to fast approximate the optimal solution, which assigns the corresponding disparity plane to each segment. Experiments demonstrate that the performance of our algorithm is comparable to the state-of-the-art stereo algorithms on various data sets. Furthermore, strong performance is achieved in the conventionally difficult areas such as: textureless regions, disparity discontinuous boundaries and occluded portions.


Computational Frameworks for the Fast Fourier Transform (Charles Van Loan)


title: Comparison of Regularization Methods for ImageNet Classification with Deep Convolutional Neural Networks

abstract: Large and Deep Convolutional Neural Networks achieve good results in image classification tasks, but they need methods to prevent overfitting. In this paper we compare performance of different regularization techniques on ImageNet Large Scale Visual Recognition Challenge 2013. We show empirically that Dropout works better than DropConnect on ImageNet dataset.


title: Sparsest solutions of underdetermined linear systems via ` -minimization for 0 < q 1

abstract: Abstract We present a condition on the matrix of an underdetermined linear system which guarantees that the solution of the system with minimal l q -quasinorm is also the sparsest one. This generalizes, and slightly improves, a similar result for the l 1 -norm. We then introduce a simple numerical scheme to compute solutions with minimal l q -quasinorm, and we study its convergence. Finally, we display the results of some experiments which indicate that the l q -method performs better than other available methods.


title: Quantification of residual host cell DNA in adenoviral vectors produced on PER.C6 cells.

abstract: Recombinant adenoviral vectors for gene therapy and vaccination are routinely prepared on cultures of immortalized cells, allowing the production of vector batches of high titer and consistent quality. Quantification of residual DNA from the producing cell line is part of the purity tests for clinical lots. Stringent guidelines stipulate the maximum acceptable level of DNA per dose of vector, and this quantification is therefore a crucial piece of information for researchers and manufacturers alike. In this paper we describe an optimized assay based on real-time polymerase chain reaction (PCR) for the quantification of residual PER.C6 DNA in recombinant adenoviral vectors. In order to reduce the risk of introducing contaminations and to increase the throughput, the assay was designed to require minimum sample handling. Furthermore, DNA extraction from the samples is not necessary, thereby eliminating the need to account for possible sample losses. We also report the results of the assay qualification, demonstrating that the assay is accurate, precise, and sensitive. Finally, we applied the assay successfully to determine the level of host cell DNA in an adenovirus vector produced on PER.C6 cells throughout a standard purification process. Because of its specifications, we anticipate that the assay can have broad applicability to biologics other than adenoviral vectors produced on PER.C6 cells.


Consequences of Causal Ambiguity on Motivation in Lean Production System Adoption: The Role of Moderating Variables


title: The genetic landscape of autism spectrum disorders

abstract: Autism spectrum disorders (ASDs) are a group of heterogeneous neurodevelopmental disorders that show impaired communication and socialization, restricted interests, and stereotypical behavioral patterns. Recent advances in molecular medicine and high throughput screenings, such as array comparative genomic hybridization (CGH) and exome and whole genome sequencing, have revealed both novel insights and new questions about the nature of this spectrum of disorders. What has emerged is a better understanding about the genetic architecture of various genetic subtypes of ASD and correlations of genetic mutations with specific autism subtypes. Based on this new information, we outline a strategy for advancing diagnosis, prognosis, and counseling for patients and families.


title: Flow simulation with locally-refined LBM

abstract: We simulate 3D fluid flow by a locally-refined lattice Boltzmann method (LBM) on graphics hardware. A low resolution LBM simulation running on a coarse grid models global flow behavior of the entire domain with low consumption of computational resources. For regions of interest where small visual details are desired, LBM simulations are performed on fine grids, which are separate grids superposed on the coarse one. The flow properties on boundaries of the fine grids are determined by the global simulation on the coarse grid. Thus, the locally refined fine-grid simulations follow the global fluid behavior, and model the desired small-scale and turbulent flow motion with their denser numerical discretization. A fine grid can be initiated and terminated at any time while the global simulation is running. It can also move inside the domain with a moving object to capture small-scale vortices caused by the object. Besides the performance improvement due to the adaptive simulation, the locally-refined LBM is suitable for acceleration on contemporary graphics hardware (GPU), since it involves only local and linear computations. Therefore, our approach achieves fast and adaptive 3D flow simulation for computer games and other interactive applications.


title: Multimodal transport routing problem considering transshipment and accessibility: The case of the “One Belt One Road” initiative

abstract: The concept of the “One Belt and One Road”(hereinafter referred to as: OBOR) was first initiated by the Chinese government in 2013. This inter-regional initiative has been rapidly and well promoted. Transport system (especially the multimodal transport system) of the OBOR is both the condition and basis for the implementation of the initiative. This paper analyzed the multimodal transport routing problem considering transshipment and accessibility. An integrated time-spatial three-dimensional super-network was constructed for depicting the OBOR multimodal network. Based on that, a mathematical model considering transshipment and accessibility in OBOR was established. Case study of the OBOR was conducted using the proposed model. Problems with different situations of were analyzed.


title: Joint Robust Voicing Detection and Pitch Estimation Based on Residual Harmonics

abstract: This paper focuses on the problem of pitch tracking in noisy conditions. A method using harmonic information in the residual signal is presented. The proposed criterion is used both for pitch estimation, as well as for determining the voicing segments of speech. In the experiments, the method is compared to six state-of-the-art pitch trackers on the Keele and CSTR databases. The proposed technique is shown to be particularly robust to additive noise, leading to a significant improvement in adverse conditions. Index Terms: fundamental frequency, pitch tracking, pitch estimation, voicing decisions


title: Closed Almost Knight’s Tours on 2D and 3D Chessboards

abstract: Let a (generalized) chessboard in two or three dimensions be given. A closed knight’s tour is defined as a Hamiltonian cycle over all cells of the chessboard where all moves are knight’s moves, i. e. have length \(\sqrt{5}\). It is well-characterized for which chessboard sizes it is not possible to construct a closed knight’s tour. On such chessboards a closed almost knight’s tour is defined as a Hamiltonian cycle of minimal length if only moves of length at least \(\sqrt{5}\) are allowed. The problem of determining closed (almost) knight’s tours on 2D and 3D chessboards is equivalent to the so called traveling salesman problem with forbidden neighborhoods on regular 2D and 3D grids if the radius of the forbidden neighborhood is two and hence the minimal feasible distance of two consecutive vertices in the tour equals the length of a knight’s move. In this paper we present explicit construction schemes for closed almost knight’s tours by extending ideas used for the construction of knight’s tours.


title: A probabilistic framework for multi-view feature learning with many-to-many associations via neural networks

abstract: A simple framework Probabilistic Multi-view Graph Embedding (PMvGE) is proposed for multi-view feature learning with many-to-many associations so that it generalizes various existing multi-view methods. PMvGE is a probabilistic model for predicting new associations via graph embedding of the nodes of data vectors with links of their associations. Multi-view data vectors with many-to-many associations are transformed by neural networks to feature vectors in a shared space, and the probability of new association between two data vectors is modeled by the inner product of their feature vectors. While existing multi-view feature learning techniques can treat only either of many-to-many association or non-linear transformation, PMvGE can treat both simultaneously. By combining Mercer's theorem and the universal approximation theorem, we prove that PMvGE learns a wide class of similarity measures across views. Our likelihood-based estimator enables efficient computation of non-linear transformations of data vectors in large-scale datasets by minibatch SGD, and numerical experiments illustrate that PMvGE outperforms existing multi-view methods.


title: Reinforcement Learning in Video Games Using Nearest Neighbor Interpolation and Metric Learning

abstract: Reinforcement learning (RL) has had mixed success when applied to games. Large state spaces and the curse of dimensionality have limited the ability for RL techniques to learn to play complex games in a reasonable length of time. We discuss a modification of Q-learning to use nearest neighbor states to exploit previous experience in the early stages of learning. A weighting on the state features is learned using metric learning techniques, such that neighboring states represent similar game situations. Our method is tested on the arcade game Frogger, and it is shown that some of the effects of the curse of dimensionality can be mitigated.


title: Emma in Action: Declarative Dataflows for Scalable Data Analysis

abstract: Parallel dataflow APIs based on second-order functions were originally seen as a flexible alternative to SQL. Over time, however, their complexity increased due to the number of physical aspects that had to be exposed by the underlying engines in order to facilitate efficient execution. To retain a sufficient level of abstraction and lower the barrier of entry for data scientists, projects like Spark and Flink currently offer domain-specific APIs on top of their parallel collection abstractions. This demonstration highlights the benefits of an alternative design based on deep language embedding. We showcase Emma - a programming language embedded in Scala. Emma promotes parallel collection processing through native constructs like Scala's for-comprehensions - a declarative syntax akin to SQL. In addition, Emma also advocates quasi-quoting the entire data analysis algorithm rather than its individual dataflow expressions. This allows for decomposing the quoted code into (sequential) control flow and (parallel) dataflow fragments, optimizing the dataflows in context, and transparently offloading them to an engine like Spark or Flink. The proposed design promises increased programmer productivity due to avoiding an impedance mismatch, thereby reducing the lag times and cost of data analysis.


title: Embedded System on NTP

abstract: NTP is a protocol designed to synchronize the clocks of computers over a network. This paper will implement the NTP on an embedded system to serve as a server of the NTP Pool Project. The developed embedded system is based on the embedded Linux operating system. The benefits of utilization of the embedded Linux system rely on its lower power consumption, high system stability, the small size of whole system including hardware and software, and no extra cost protected by the GPL license. All these characteristics will provide the embedded Linux system to be a suitable system for the implementation of the NTP that will need the services provided by many non-centralized servers. By setting multiple NTP servers in each network domain and utilizing NTP Pool Project guidelines, it will help to evenly distribute the throughput of each server and enhance the stability of the system. This paper will provide the detailed introduction on the protocol of NTP and the framework of the NTP Pool Project, the development of the NTP on the embedded system, and followed by the suggestions of the implementation of the NTP.


title: Noise Removal from Surface Respiratory EMG Signal

abstract: Abstract — The aim of this study was to remove the two principal noises which disturb the surface electromyography signal (Diaphragm). These signals are the electrocardiogram ECG artefact and the power line interference artefact. The algorithm proposed focuses on a new Lean Mean Square (LMS) Widrow adaptive structure. These structures require a reference signal that is correlated with the noise contaminating the signal. The noise references are then extracted : first with a noise reference mathematically constructed using two different cosine functions; 50Hz (the fundamental) function and 150Hz (the first harmonic) function for the power line interference and second with a matching pursuit technique combined to an LMS structure for the ECG artefact estimation. The two removal procedures are attained without the use of supplementary electrodes. These techniques of filtering are validated on real records of surface diaphragm electromyography signal. The performance of the proposed methods was compared with already conducted research results.


title: The dermatoscopic universe of basal cell carcinoma

abstract: Following the first descriptions of the dermatoscopic pattern of basal cell carcinoma (BCC) that go back to the very early years of dermatoscopy, the list of dermatoscopic criteria associated with BCC has been several times updated and renewed. Up to date, dermatoscopy has been shown to enhance BCC detection, by facilitating its discrimination from other skin tumors and inflammatory skin diseases. Furthermore, upcoming evidence suggests that the method is also useful for the management of the tumor, since it provides valuable information about the histopathologic subtype, the presence of clinically undetectable pigmentation, the expansion of the tumor beyond clinically visible margins and the response to non-ablative treatments. In the current article, we provide a summary of the traditional and latest knowledge on the value of dermatoscopy for the diagnosis and management of BCC.


title: A Petri Net-based Model for Web Service Composition

abstract: The Internet is going through several major changes. It has become a vehicle of Web services rather than just a repository of information. Many organizations are putting their core business competencies on the Internet as a collection of Web services. An important challenge is to integrate them to create new value-added Web services in ways that could never be foreseen forming what is known as Business-to-Business (B2B) services. Therefore, there is a need for modeling techniques and tools for reliable Web service composition. In this paper, we propose a Petri net-based algebra, used to model control flows, as a necessary constituent of reliable Web service composition process. This algebra is expressive enough to capture the semantics of complex Web service combinations.


title: Application of Artificial Neural Networks for Catalysis: A Review

abstract: Machine learning has proven to be a powerful technique during the past decades. Artificial neural network (ANN), as one of the most popular machine learning algorithms, has been widely applied to various areas. However, their applications for catalysis were not well-studied until recent decades. In this review, we aim to summarize the applications of ANNs for catalysis research reported in the literature. We show how this powerful technique helps people address the highly complicated problems and accelerate the progress of the catalysis community. From the perspectives of both experiment and theory, this review shows how ANNs can be effectively applied for catalysis prediction, the design of new catalysts, and the understanding of catalytic structures.


title: A Survey on Optical Interconnects for Data Centers

abstract: Data centers are experiencing an exponential increase in the amount of network traffic that they have to sustain due to cloud computing and several emerging web applications. To face this network load, large data centers are required with thousands of servers interconnected with high bandwidth switches. Current data center networks, based on electronic packet switches, consume excessive power to handle the increased communication bandwidth of emerging applications. Optical interconnects have gained attention recently as a promising solution offering high throughput, low latency and reduced energy consumption compared to current networks based on commodity switches. This paper presents a thorough survey on optical interconnects for next generation data center networks. Furthermore, the paper provides a qualitative categorization and comparison of the proposed schemes based on their main features such as connectivity and scalability. Finally, the paper discusses the cost and the power consumption of these schemes that are of primary importance in the future data center networks.


title: Prediction error estimation: a comparison of resampling methods

abstract: MOTIVATION ::: In genomic studies, thousands of features are collected on relatively few samples. One of the goals of these studies is to build classifiers to predict the outcome of future observations. There are three inherent steps to this process: feature selection, model selection and prediction assessment. With a focus on prediction assessment, we compare several methods for estimating the 'true' prediction error of a prediction model in the presence of feature selection. ::: ::: ::: RESULTS ::: For small studies where features are selected from thousands of candidates, the resubstitution and simple split-sample estimates are seriously biased. In these small samples, leave-one-out cross-validation (LOOCV), 10-fold cross-validation (CV) and the .632+ bootstrap have the smallest bias for diagonal discriminant analysis, nearest neighbor and classification trees. LOOCV and 10-fold CV have the smallest bias for linear discriminant analysis. Additionally, LOOCV, 5- and 10-fold CV, and the .632+ bootstrap have the lowest mean square error. The .632+ bootstrap is quite biased in small sample sizes with strong signal-to-noise ratios. Differences in performance among resampling methods are reduced as the number of specimens available increase. ::: ::: ::: SUPPLEMENTARY INFORMATION ::: A complete compilation of results and R code for simulations and analyses are available in Molinaro et al. (2005) (http://linus.nci.nih.gov/brb/TechReport.htm).


title: Computational Creativity Theory: The FACE and IDEA Descriptive Models

abstract: We introduce computational creativity theory (CCT) as an analogue in computational creativity research to computational learning theory in machine learning. In its current draft, CCT comprises the FACE descriptive model of creative acts as tuples of generative acts, and the IDEA descriptive model of the impact such creative acts may have. To introduce these, we simplify various assumptions about software development, background material given to software, how creative acts are performed by computer, and how audiences consume the results. We use the two descriptive models to perform two comparisons studies, firstly for mathematical discovery software, and secondly for visual art generating programs. We conclude by discussing possible additions, improvements and refinements to CCT.


How well do Computers Solve Math Word Problems? Large-Scale Dataset Construction and Evaluation


title: A survey of the application of gamma processes in maintenance

abstract: This article surveys the application of gamma processes in maintenance. Since the introduction of the gamma process in the area of reliability in 1975, it has been increasingly used to model stochastic deterioration for optimising maintenance. Because gamma processes are well suited for modelling the temporal variability of deterioration, they have proven to be useful in determining optimal inspection and maintenance decisions. An overview is given of the rich theoretical aspects as well as the successful maintenance applications of gamma processes. The statistical properties of the gamma process as a probabilistic stress–strength model are given and put in a historic perspective. Furthermore, methods for estimation, approximation, and simulation of gamma processes are reviewed. Finally, an extensive catalogue of inspection and maintenance models under gamma-process deterioration is presented with the emphasis on engineering applications.


title: Wideband 80 W balanced power amplifier for ISM and LTE-bands

abstract: An 80 W balanced GaN-HEMT power amplifier for 2.45–2.70 GHz using branch-line couplers is presented in this paper. Its performance is compared to a single-transistor solution. The analysis shows that a balanced amplifier is favorable because it simplifies measures to ensure electrical stability and allows better heat sinking.


title: A systematic review and meta-analysis of selected motor learning principles in physiotherapy and medical education

abstract: BackgroundLearning of procedural skills is an essential component in the education of future health professionals. There is little evidence on how procedural skills are best learnt and practiced in education. There is a need for educators to know what specific interventions could be used to increase learning of these skills. However, there is growing evidence from rehabilitation science, sport science and psychology that learning can be promoted with the application of motor learning principles. The aim of this review was to systematically evaluate the evidence for selected motor learning principles in physiotherapy and medical education. The selected principles were: whole or part practice, random or blocked practice, mental or no additional mental practice and terminal or concurrent feedback.MethodsCINAHL, Cochrane Central, Embase, Eric and Medline were systematically searched for eligible studies using pre-defined keywords. Included studies were evaluated on their risk of bias with the Cochrane Collaboration’s risk of bias tool.ResultsThe search resulted in 740 records, following screening for relevance 15 randomised controlled trials including 695 participants were included in this systematic review. Most procedural skills in this review related to surgical procedures. Mental practice significantly improved performance on a post-acquisition test (SMD: 0.43, 95 % CI 0.01 to 0.85). Terminal feedback significantly improved learning on a transfer test (SMD: 0.94, 95 % CI 0.18 to 1.70). There were indications that whole practice had some advantages over part practice and random practice was superior to blocked practice on post-acquisition tests. All studies were evaluated as having a high risk of bias. Next to a possible performance bias in all included studies the method of sequence generation was often poorly reported.ConclusionsThere is some evidence to recommend the use of mental practice for procedural learning in medical education. There is limited evidence to conclude that terminal feedback is more effective than concurrent feedback on a transfer test. For the remaining parameters that were reviewed there was insufficient evidence to make definitive recommendations.


Shortening The Product Development Cycle


title: The Streams of Our Lives: Visualizing Listening Histories in Context

abstract: The choices we take when listening to music are expressions of our personal taste and character. Storing and accessing our listening histories is trivial due to services like Last.fm, but learning from them and understanding them is not. Existing solutions operate at a very abstract level and only produce statistics. By applying techniques from information visualization to this problem, we were able to provide average people with a detailed and powerful tool for accessing their own musical past. LastHistory is an interactive visualization for displaying music listening histories, along with contextual information from personal photos and calendar entries. Its two main user tasks are (1) analysis, with an emphasis on temporal patterns and hypotheses related to musical genre and sequences, and (2) reminiscing, where listening histories and context represent part of one's past. In this design study paper we give an overview of the field of music listening histories and explain their unique characteristics as a type of personal data. We then describe the design rationale, data and view transformations of LastHistory and present the results from both a laband a large-scale online study. We also put listening histories in contrast to other lifelogging data. The resonant and enthusiastic feedback that we received from average users shows a need for making their personal data accessible. We hope to stimulate such developments through this research.


title: Tibial periosteal ganglion cyst: The ganglion in disguise

abstract: Soft tissue ganglions are commonly encountered cystic lesions around the wrist presumed to arise from myxomatous degeneration of periarticular connective tissue. Lesions with similar pathology in subchondral location close to joints, and often simulating a geode, is the less common entity called intraosseous ganglion. Rarer still is a lesion produced by mucoid degeneration and cyst formation of the periostium of long bones, rightly called the periosteal ganglion. They are mostly found in the lower extremities at the region of pes anserinus, typically limited to the periosteum and outer cortex without any intramedullary component. We report the case of a 62 year-old male who presented with a tender swelling on the mid shaft of the left tibia, which radiologically suggested a juxtacortical lesion extending to the soft tissue or a soft tissue neoplasm eroding the bony cortex of tibia. It was later diagnosed definitively as a periosteal ganglion in an atypical location, on further radiologic work-up and histopathological correlation.


title: Automatic Extraction of Function Knowledge From Text

abstract: This paper presents a method to automatically extract function knowledge from natural language text. Our method uses syntactic rules to extract subject-verb-object triplets from parsed text. We then leverage the Functional Basis taxonomy, WordNet, and word2vec to classify the triplets as artifact-function-energy flow knowledge. For evaluation, we compare the function definitions associated with 30 most frequent artifacts compiled in a human-constructed knowledge base, Oregon State University’s Design Repository (DR), to those extracted using our method from 4953 Wikipedia pages classified under the category “Machines”. Our method found function definitions for 66% of the test artifacts. For those artifacts found, our method identified 50% of the function definitions compiled in DR. In addition, 75% of the most frequent function definitions found by our method were also defined in DR. The results demonstrate the promising potential of our method in automatic extraction of function knowledge.Copyright © 2015 by ASME


title: An Attentive Sequence Model for Adverse Drug Event Extraction from Biomedical Text

abstract: Adverse reaction caused by drugs is a potentially dangerous problem which may lead to mortality and morbidity in patients. Adverse Drug Event (ADE) extraction is a significant problem in biomedical research. We model ADE extraction as a Question-Answering problem and take inspiration from Machine Reading Comprehension (MRC) literature, to design our model. Our objective in designing such a model, is to exploit the local linguistic context in clinical text and enable intra-sequence interaction, in order to jointly learn to classify drug and disease entities, and to extract adverse reactions caused by a given drug. Our model makes use of a self-attention mechanism to facilitate intra-sequence interaction in a text sequence. This enables us to visualize and understand how the network makes use of the local and wider context for classification.


title: VSync: Cloud based video streaming service for mobile devices

abstract: Synchronizing videos over file-hosting services on personal cloud such as Dropbox, Box or Onedrive leads to wastage in bandwidth and storage, which can be critical, while using mobile devices. Users can alternatively download the video on-the-go, but that leads to high latency, depending on network bandwidth and video file size. In contrast, adaptive video streaming allows near-real-time viewing by streaming the best possible quality in a given network condition. This feature is achieved by keeping multiple versions of video in cloud, leading to additional costs in cloud storage. Moreover, current solutions can only support a small set of bitrates, leading to abrupt switches in video resolution especially when the network condition is unstable, as often experienced by mobile users. This paper introduces Vsync, a framework for cloud based video synchronization for mobile devices. A video content is streamed using a cloud-based real-time transcoding and transmission framework to provide smooth video quality. Built over prediction models for video transcoding sessions and a QoE based adaptive video streaming protocol, Vsync is able to obtain the improvements of 37 ∼ 80% than other compared schemes. The dataset and evaluation was done on a pool of 220K video clips.


title: Assessment and mitigation of cyber exploits in future aircraft surveillance

abstract: This paper investigates cyber security issues with automatic dependent surveillance broadcast (ADS-B) based air traffic control. Before wide-scale deployment in civil aviation, any airborne or ground-based technology must be ensured to have no adverse impact on safe and profitable system operations, both under normal conditions and failures. With ADS-B, there is a lack of a clear understanding about vulnerabilities, how they can impact airworthiness and what failure conditions they can potentially induce. The proposed work streamlines a threat assessment methodology for security evaluation of ADS-B based surveillance. To the best of our knowledge, this work is the first to identify the need for mechanisms to secure ADS-B based airborne surveillance and propose a security solution. This paper presents preliminary findings and results of the ongoing investigation.12


title: Malaria Infection and Anemia Prevalence in Zambia's Luangwa District: An Area of Near-Universal Insecticide-Treated Mosquito Net Coverage

abstract: We examined the relationship between insecticide-treated mosquito nets (ITNs), malaria parasite infection, and severe anemia prevalence in children in Luangwa District, Zambia, an area with near-universal ITN coverage, at the end of the 2008 and 2010 malaria transmission seasons. Malaria parasite infection prevalence among children < 5 years old was 9.7% (95% confidence interval (CI) = 8.0-11.4%) over both survey years. Prevalence of severe anemia among chil- dren 6-59 months old was 6.9% (95% CI = 5.4-8.5%) over both survey years. Within this context of near-universal ITN coverage, we were unable to detect a significant association between malaria parasite or severe anemia prevalence and ITNs (possession and use). In addition to maintaining universal ITN coverage, it will be essential for the malaria control program to achieve high ITN use and laboratory diagnosis and treatment of all fevers among all age groups to further reduce the malaria burden in this area.


title: Individual Self, Relational Self, Collective Self: Hierarchical Ordering of the Tripartite Self

abstract: The individual self comprise unique attributes, the relational self comprises partner-shared attributes, and the collective self comprises ingroup-shared attributes. All selves are fundamental components of the self-concept, with each being important and meaningful to human experience and with each being associated with health benefits. Are the selves, however, equally important and meaningful? We review a program of research that tested four competing theoretical views suggesting that the motivational hub of human experience is (a) the individual self, (b) the relational self, (b) the collective self, or (c) determined by contextual or cultural factors. The research furnished support to the view that the individual self is the primary form of self-definition. We discuss alternative explanations and implications. We end with the introduction of a theoretical model, the boomerang model, that has the potential to integrate the diverse literature on the topic.


title: Targeting Hypoxia to Improve Non–Small Cell Lung Cancer Outcome

abstract: Oxygen deprivation (hypoxia) in non-small cell lung cancer (NSCLC) is an important factor in treatment resistance and poor survival. Hypoxia is an attractive therapeutic target, particularly in the context of radiotherapy, which is delivered to more than half of NSCLC patients. However, NSCLC hypoxia-targeted therapy trials have not yet translated into patient benefit. Recently, early termination of promising evofosfamide and tarloxotinib bromide studies due to futility highlighted the need for a paradigm shift in our approach to avoid disappointments in future trials. Radiotherapy dose painting strategies based on hypoxia imaging require careful refinement prior to clinical investigation. This review will summarize the role of hypoxia, highlight the potential of hypoxia as a therapeutic target, and outline past and ongoing hypoxia-targeted therapy trials in NSCLC. Evidence supporting radiotherapy dose painting based on hypoxia imaging will be critically appraised. Carefully selected hypoxia biomarkers suitable for integration within future NSCLC hypoxia-targeted therapy trials will be examined. Research gaps will be identified to guide future investigation. Although this review will focus on NSCLC hypoxia, more general discussions (eg, obstacles of hypoxia biomarker research and developing a framework for future hypoxia trials) are applicable to other tumor sites.


title: EmotiGO: Bluetooth-enabled eyewear for unobtrusive physiology-based emotion recognition

abstract: While machines (e.g., computers, tablets, cellphones, robots) have begun to co-exist with humans and cooperatively share various tasks; they are still deficient in interpreting and responding to humans as emotional beings. Wearable biosensors that can be used for computerized emotion recognition have great potential for addressing this issue. This paper presents a novel wearable sensor - EmotiGO - for unobtrusive acquisition of a set of physiological signals, i.e., skin conductivity, photoplethysmography, and skin temperature, which can be used as autonomic readouts of emotions. EmotiGO is unobtrusive and sufficiently lightweight to be worn comfortably without interfering with the users' usual activities. EmotiGO's open software and communication protocol provide an easy way for developers to interact with the device and design custom applications. We present the architecture of the device and results from testing that verify its effectiveness against an FDA-approved system for physiological measurement.


title: The LOCATA Challenge Data Corpus for Acoustic Source Localization and Tracking

abstract: Algorithms for acoustic source localization and tracking are essential for a wide range of applications such as personal assistants, smart homes, tele-conferencing systems, hearing aids, or autonomous systems. Numerous algorithms have been proposed for this purpose which, however, are not evaluated and compared against each other by using a common database so far. The IEEE-AASP Challenge on sound source localization and tracking (LOCATA) provides a novel, comprehensive data corpus for the objective benchmarking of state-of-the-art algorithms on sound source localization and tracking. The data corpus comprises six tasks ranging from the localization of a single static sound source with a static microphone array to the tracking of multiple moving speakers with a moving microphone array. It contains real-world multichannel audio recordings, obtained by hearing aids, microphones integrated in a robot head, a planar and a spherical microphone array in an enclosed acoustic environment, as well as positional information about the involved arrays and sound sources represented by moving human talkers or static loudspeakers.


title: 3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes

abstract: With the introduction of fully convolutional neural networks, deep learning has raised the benchmark for medical image segmentation on both speed and accuracy, and different networks have been proposed for 2D and 3D segmentation with promising results. Nevertheless, most networks only handle relatively small numbers of labels (<10), and there are very limited works on handling highly unbalanced object sizes especially in 3D segmentation. In this paper, we propose a network architecture and the corresponding loss function which improve segmentation of very small structures. By combining skip connections and deep supervision with respect to the computational feasibility of 3D segmentation, we propose a fast converging and computationally efficient network architecture for accurate segmentation. Furthermore, inspired by the concept of focal loss, we propose an exponential logarithmic loss which balances the labels not only by their relative sizes but also by their segmentation difficulties. We achieve an average Dice coefficient of 82% on brain segmentation with 20 labels, with the ratio of the smallest to largest object sizes as 0.14%. Less than 100 epochs are required to reach such accuracy, and segmenting a 128x128x128 volume only takes around 0.4 s.


title: Entity Disambiguation with Web Links

abstract: Entity disambiguation with Wikipedia relies on structured information from redirect pages, article text, inter-article links, and categories. We explore whether web links can replace a curated encyclopaedia, obtaining entity prior, name, context, and coherence models from a corpus of web pages with links to Wikipedia. Experiments compare web link models to Wikipedia models on well-known CoNLL and TAC data sets. Results show that using 34 million web links approaches Wikipedia performance. Combining web link and Wikipedia models produces the best-known disambiguation accuracy of 88.7 on standard newswire test data.


Live Fingerprint Live Fingerprint Spoof Fingerprint Spoof Fingerprint Minutiae-based Local Patches Minutiae-based Local Patches ( a ) ( b )


title: What Makes Interruptions Disruptive?: A Process-Model Account of the Effects of the Problem State Bottleneck on Task Interruption and Resumption

abstract: In this paper we present a computational cognitive model of task interruption and resumption, focusing on the effects of the problem state bottleneck. Previous studies have shown that the disruptiveness of interruptions is for an important part determined by three factors: interruption duration, interrupting-task complexity, and moment of interruption. However, an integrated theory of these effects is still missing. Based on previous research into multitasking, we propose a first step towards such a theory in the form of a process model that attributes these effects to problem state requirements of both the interrupted and the interrupting task. Subsequently, we tested two predictions of this model in two experiments. The experiments confirmed that problem state requirements are an important predictor for the disruptiveness of interruptions. This suggests that interfaces should be designed to a) interrupt users at low-problem state moments and b) maintain the problem state for the user when interrupted.


title: MeasuRouting: a framework for routing assisted traffic monitoring

abstract: Monitoring transit traffic at one or more points in a network is of interest to network operators for reasons of traffic accounting, debugging or troubleshooting, forensics, and traffic engineering. Previous research in the area has focused on deriving a placement of monitors across the network toward the end of maximizing the monitoring utility of the network operator for a given traffic routing. However, both traffic characteristics and measurement objectives can dynamically change over time, rendering a previously optimal placement of monitors suboptimal. It is not feasible to dynamically redeploy/reconfigure measurement infrastructure to cater to such evolving measurement requirements. We address this problem by strategically routing traffic subpopulations over fixed monitors. We refer to this approach as MeasuRouting. The main challenge for MeasuRouting is to work within the constraints of existing intradomain traffic engineering operations that are geared for efficiently utilizing bandwidth resources, or meeting quality-of-service (QoS) constraints, or both. A fundamental feature of intradomain routing, which makes MeasuRouting feasible, is that intradomain routing is often specified for aggregate flows. MeasuRouting can therefore differentially route components of an aggregate flow while ensuring that the aggregate placement is compliant to original traffic engineering objectives. In this paper, we present a theoretical framework for MeasuRouting. Furthermore, as proofs of concept, we present synthetic and practical monitoring applications to showcase the utility enhancement achieved with MeasuRouting.


title: Computational Creativity: A Continuing Journey

abstract: As the final touches are being put to this very special issue of Minds and Machines, which showcases a selection of the finest contributions to the 2008 International Joint Workshop on Computational Creativity, Tony Blair has just unveiled his memoirs to a sharply divided public. As befitting this controversial figure’s polarizing legacy, reactions to his autobiography—entitled A Journey—have ranged from the painfully predictable to the creatively original. The jeering crowds that awaited Mr. Blair outside book signings and other public events were entirely predictable, as were the cries of ‘‘war criminal’’ and other lazy categorizations that have long since lost their creative bite. Slightly less predictable was the hurling of shoes at Mr. Blair, an analogical echo of another time when footwear was used as a cultural missile against George W. Bush. At a Dublin book signing, this minor act of analogical creativity was both enhanced and undermined by the cost-effective and very ironic choice of shoes—flip-flops!—since Mr. Blair is most often criticized for being unwilling to recant (or ‘‘flip-flop’’, in political parlance) for his involvement in the Iraq war. However, the most creative protest to greet the publication of A Journey was also the least noisome. Through a campaign launched via Facebook, protesters were encouraged to quietly and mischievously re-categorize Mr. Blair’s book in whatever bookshop they happened to find it. As reported in the British newspaper The Telegraph, employees in bookstores across the country were puzzled to find copies of the book migrating from its ‘‘official’’ home on the Biography shelves to sections such as Fiction and even True Crime. Euan Booth, the student who originated the Facebook campaign, explained himself thus to The Telegraph:


title: Affect in Human-Robot Interaction

abstract: Abstract : More and more, robots are expected to interact with humans in a social, easily understandable manner, which presupposes effective use of robot affect. This chapter provides a brief overview of research advances into this important aspect of human-robot interaction.


title: Channel assignment in multimedia cellular networks

abstract: This paper deals with multimedia channel assignment in a hexagonal cellular network with two-band buffering. After deriving a lower bound on the minimum bandwidth for real-life situations, we present an algorithm for assigning channels using Genetic Algorithm (GA). We also propose an elegant technique for re-use of the channels, using only eighteen distinct frequency bands on a nine-node subgraph of the network, and then extend it for assignment of the complete network. The proposed algorithm converges very rapidly with required bandwidth close to the derived lower bound.


title: Optimum Design of the Current-Source Flyback Inverter for Decentralized Grid-Connected Photovoltaic Systems

abstract: Two alternative modes of operation for the current-source flyback inverter are investigated in this paper. The discontinuous conduction mode (DCM), where a constant switching frequency (CSF) control method is applied, and the boundary between continuous and DCM (BCM) that is introduced for photovoltaic (PV) applications in this paper (where a variable switching frequency control method is applied). These two control methods are analytically studied and compared in order to establish their advantages as well as their suitability for the development of an inverter for decentralized grid-connected PV applications. An optimum design methodology is developed, aiming for an inverter with the smallest possible volume for the maximum power transfer to the public grid and wide PV energy exploitation. The main advantages of the current-source flyback inverter are very high-power density and high efficiency due to its simple structure, as well as high-power factor regulation. The design and control methodology are validated by personal computer simulation program with integrated circuit emphasis (PSPICE) simulation and experimental results, accomplished on a laboratory prototype.


title: Fabric sensory sleeves for soft robot state estimation

abstract: In this paper, we describe the fabrication and testing of a stretchable fabric sleeve with embedded elastic strain sensors for state reconstruction of a soft robotic joint. The strain sensors are capacitive and composed of graphite-based conductive composite electrodes and a silicone elastomer dielectric. The sensors are screenprinted directly into the fabric sleeve, which contrasts the approach of pre-fabricating sensors and subsequently attaching them to a host. We demonstrate the capabilities of the sensor-embedded fabric sleeve by determining the joint angle and end effector position of a soft pneumatic joint with similar accuracy to a traditional IMU. Furthermore, we show that the sensory sleeve is capable of capturing more complex material states, such as fabric buckling and non-constant curvatures along linkages and joints.


title: Improved 5.9GHz V2V Short Range Path Loss Model

abstract: Modeling large-scale fading effects in Vehicle-to-Vehicle communications (V2V) in the 5.9GHz Dedicated Short Range Communication band has received broad coverage in the literature over the last 15 years. The majority of V2V channel measurement campaigns have focused on describing the expected path loss of the V2V channel through empirical models. The path loss is a channel metric which describes how fast the received signal strength decays with distance. It is well known that the path loss exponent and reference path loss (y-intercept) varies for different environments, but it is not well understood how the channel changes in a given environment relative to lane separation or vehicle orientation. This paper presents an improved path loss model for line-of-sight (LOS) V2V communications at distances less than 100m. The path loss model removes the Gaussian random variable component, typically used to model shadowing in classic power law path loss model, and instead makes the y-intercept and path loss exponent Gaussian random variables. Derived from extensive empirical measurement campaigns in which vehicle orientation, approach direction, and lane separation are considered, the new channel model is compared to experimental data in which the vehicles move at different speeds. The improved path loss model performs a better fit to experimental data than existing path loss models, including two-ray ground reflection, dual-slope piecewise linear, and classic power law.


title: Mining Stack Exchange: Expertise is Evident From Earliest Interactions

abstract: Stack Exchange is a very popular Question & Answer internet community. Users can post questions on a wide variety of topics; other users provide answers, usually within minutes. Participants are not compensated for their services and anyone can freely gain value from the efforts of the users; Stack Exchange is therefore a gift economy. Users, however, do gain reputation points when other users “upvote” their questions and/or answers. Stack Exchange thus functions as a learning community with a strong reputation-seeking element that creates a valuable public good, viz., the QA indeed the data suggests that overall answer scores decrease, and that people’s tenure with the community is unrelated to the quality of their answers. Most interestingly, we show that answering skill, i.e. getting high average answer scores, which is different than reputation, is evident from the start and persists during one’s tenure with the community. Conversely, people providing low rated answers are likely to have done so from the start.


title: Extracting sentiment as a function of discourse structure and topicality

abstract: We present an approach to extracting sentiment from texts that makes use of con- textual information. Using two dierent approaches, we extract the most relevant sentences of a text, and calculate semantic orientation weighing those more heavily. The …rst approach makes use of discourse structure via Rhetorical Structure Theory, and extracts nuclei as the relevant parts; the second approach uses a topic classi…er built using support vector machines, which extracts topic sentences from texts. The use of weights on relevant sentences shows an improvement over word-based methods that consider the entire text equally. In the paper, we also describe an enhancement of our previous word-based methods in the treatment of intensi…ers and negation, and the addition of other parts of speech beyond adjectives.


title: Narrative Visualization: Sharing Insights into Complex Data

abstract: This paper is a reflection on the emerging genre of narrative visualization, a creative response to the need to share complex data engagingly with the public. In it, we explain how narrative visualization offers authors the opportunity to communicate more effectively with their audience by reproducing and sharing an experience of insight similar to their own. To do so, we propose a two part model, derived from previous literature, in which insight is understood as both an experience and also the product of that experience. We then discuss how the design of narrative visualization should be informed by attempts elsewhere to track the provenance of insights and share them in a collaborative setting. Finally, we present a future direction for research that includes using EEG technology to record neurological patterns during episodes of insight experience as the basis for evaluation.


Smart glasses : technology and applications Student report Ubiquitous computing seminar FS 2014


title: Edinburgh Neural Machine Translation Systems for WMT 16

abstract: We participated in the WMT 2016 shared news translation task by building neural translation systems for four language pairs, each trained in both directions: English↔Czech, English↔German, English↔Romanian and English↔Russian. Our systems are based on an attentional encoder-decoder, using BPE subword segmentation for open-vocabulary translation with a fixed vocabulary. We experimented with using automatic back-translations of the monolingual News corpus as additional training data, pervasive dropout, and target-bidirectional models. All reported methods give substantial improvements, and we see improvements of 4.3–11.2 BLEU over our baseline systems. In the human evaluation, our systems were the (tied) best constrained system for 7 out of 8 translation directions in which we participated.12


OVULE ONTOGENY AND MEGASPOROGENESIS IN JACARANDA MIMOSIFOLIA D.DON. (BIGNONIACEAE)


title: Consecutive Support: Better Be Close!

abstract: We propose a new measure of support (the number of occur- rences of a pattern), in which instances are more important if they occur with a certain frequency and close after each other in the stream of trans- actions. We will explain this new consecutive support and discuss how patterns can be found faster by pruning the search space, for instance using so-called parent support recalculation. Both consecutiveness and the notion of hypercliques are incorporated into the Eclat algorithm. Synthetic examples show how interesting phenomena can now be discov- ered in the datasets. The new measure can be applied in many areas, ranging from bio-informatics to trade, supermarkets, and even law en- forcement. E.g., in bio-informatics it is important to find patterns con- tained in many individuals, where patterns close together in one chro- mosome are more significant.


title: Probabilistic Graphical Models in Artificial Intelligence Abstract

abstract: In this paper, we review the role of probabilistic graphical models in artificial intelligence. We start by giving an account of the early years when there was important controversy about the suitability of probability for intelligent systems. We then discuss the main milestones for the foundations of graphical models starting with Pearl's pioneering work. Some of the main techniques for problem solving (abduction, classification, and decision-making) are briefly explained. Finally, we propose some important challenges for future research and highlight relevant applications (forensic reasoning, genomics and the use of graphical models as a general optimization tool).


title: A Multi-Task Framework for Weather Recognition

abstract: Weather recognition is important in practice, while this task has not been thoroughly explored so far. The current trend of dealing with this task is treating it as a single classification problem, i.e., determining whether a given image belongs to a certain weather category or not. However, weather recognition differs significantly from traditional image classification, since several weather features may appear simultaneously. In this case, a simple classification result is insufficient to describe the weather condition. To address this issue, we propose to provide auxiliary weather related information for comprehensive weather description. Specifically, semantic segmentation of weather-cues, such as blue sky and white clouds, is exploited as an auxiliary task in this paper. Moreover, a convolutional neural network (CNN) based multi-task framework is developed which aims to concurrently tackle weather category classification task and weather-cues segmentation task. Due to the intrinsic relationships between these two tasks, exploring auxiliary semantic segmentation of weather-cues can also help to learn discriminative features for the classification task, and thus obtain superior accuracy. To verify the effectiveness of the proposed approach, extra segmentation masks of weather-cues are generated manually on an existing weather image dataset. Experimental results have demonstrated the superior performance of our approach. The enhanced dataset, source codes and pre-trained models are available at https://github.com/wzgwzg/Multitask_Weather.


title: TiOx-Based RRAM Synapse With 64-Levels of Conductance and Symmetric Conductance Change by Adopting a Hybrid Pulse Scheme for Neuromorphic Computing

abstract: We propose TiO x -based resistive switching device for neuromorphic synapse applications. This device is capable of 64-levels conductance states because of their optimized interface between the metal electrode and the TiO x film. To compensate the change in switching power with increasing pulse number, we propose the use of fixed voltage and current pulses in potentiation and depression conditions, respectively. By adopting a hybrid pulse scheme, the symmetry of conductance change under both potentiation and depression conditions is shown to be significantly improved. Both the improved conductance levels and the symmetry of conductance change are directly related with enhanced pattern recognition accuracy, which is confirmed by a neural network simulation.


title: Firefly Algorithms for Multimodal Optimization

abstract: Nature-inspired algorithms are among the most powerful algorithms for optimization. This paper intends to provide a detailed description of a new Firefly Algorithm (FA) for multimodal optimization applications. We will compare the proposed firefly algorithm with other metaheuristic algorithms such as particle swarm optimization (PSO). Simulations and results indicate that the proposed firefly algorithm is superior to existing metaheuristic algorithms. Finally we will discuss its applications and implications for further research.


title: Phylogeny and taxonomy of casebearer moths (Lepidoptera, Coleophoridae) based on morphological and molecular genetic data. 1. Reconstruction of phylogeny of coleophoridae using analysis of COI gene variability

abstract: A phylogenetic study of representatives of the family Coleophoridae was conducted using a comprehensive approach, including methods of morphological and molecular genetic analyses. The existent data on the family system were compared with the results of phylogenetic analysis of the COI mitochondrial gene sequences. Four of the five studied subfamilies (Coleophorinae, Ischnophaninae, Augasminae, and Tolleophorinae) corresponded to their location on the phylogram; representatives of Metriotinae were part of Coleophorinae. According to the aggregate data from molecular phylogeny and morphology, the most numerous subfamily of casebearers, Coleophorinae, is polyphyletic within its current boundaries. The results of our analysis of COI molecular divergence does not refute the monophyly of the tribes Casignetellini, Carpochenini, Klinzigedini, Goniodomini, Casasini, and Atractulini from the subfamily Coleophorinae. The allocation of the tribes Aporipturini and Sistrophoecini within the subfamily does not correspond to the molecular data. Monophyly of the genera Ecebalia, Perygra, and Casignetella was confirmed. These genera are well isolated, which reflects the evolutionary significance of the morphological characters chosen for their taxonomic division. The boundaries of the cluster containing these genera correspond to those of the tribe Casignetellini, justifying the allocation of this tribe within the subfamily. The existence of monophyletic tribes Goniodomini (genus Goniodoma) and Carpochenini (genera Ionescumia, Carpochena, and Falkmisa) was also supported. The exceptions were the genera Kasyfia, Tollsia, and Agapalsa, whose monophyly was not confirmed by our results. The distribution of the sequences of species of these genera indicated a paraphyletic origin of Kasyfia and Tollsia and a polyphyletic origin of Agapalsa.


title: Data Mining Model-Decision Tree for Detecting Emotions Color

abstract: Affective computing is a newly trend the main goal is exploring the human emotion things. The human emotion is leaded into a key position of behavior clue, and hence it should be included within the sensible model when an intelligent system aims to simulate or forecast human responses. This research utilizes decision tree one of data mining model to classify the emotion. This research integrates and manipulates the Thayer's emotion mode and color theory into the decision tree model, C4.5 for an innovative emotion detecting system. This paper uses 320 data in four emotion groups to train and build the decision tree for verifying the accuracy in this system. The result reveals that C4.5 decision tree model can be effective classified the emotion by feedback color from human. For the further research, colors will not the only human behavior clues, even more than all the factors from human interaction.


title: Customer Loyalty Enhancement of Online-to-Offline Marketing in Beauty Industry

abstract: In the competitive and low entrance barrier beauty industry, customer loyalty is a critical factor for business success. Research literature of customer relationship management recommends various factors contributing to customer loyalty in the general setting, however, there are insufficient studies empirically weigh the importance of each critical factor for the beauty industry. This study investigates and ranks empirically the critical factors, which contributes to customer loyalty of Online-to-Offline (O2O) marketing in the beauty industry. Our result shows that customer satisfaction, customer switching costs, customer trust, corporate image and customer value positively influence customer loyalty of O2O marketing and in the order of decreasing importance. Attributes contributing to the five critical factors have also been studied and ranked. Findings of this study can help the beauty industry to develop an effective O2O marketing plan and hence customer loyalty can be enhanced through the process of implementing targeted marketing activities.


title: Optimising sampling rates for accelerometer-based human activity recognition

abstract: Activity recognition with wearable sensing typically uses non-optimal sampling rates.Sampling rates are up to 57% higher than necessary leading to waste of resources.We develop a method for automated, task specific optimisation of sampling rates.Using our method we can maintain recognition performance for the optimal rates.Experimental validation through recognition experiments using classification. Real-world deployments of accelerometer-based human activity recognition systems need to be carefully configured regarding the sampling rate used for measuring acceleration. Whilst a low sampling rate saves considerable energy, as well as transmission bandwidth and storage capacity, it is also prone to omitting relevant signal details that are of interest for contemporary analysis tasks. In this paper we present a pragmatic approach to optimising sampling rates of accelerometers that effectively tailors recognition systems to particular scenarios, thereby only relying on unlabelled sample data from the domain. Employing statistical tests we analyse the properties of accelerometer data and determine optimal sampling rates through similarity analysis. We demonstrate the effectiveness of our method in experiments on 5 benchmark datasets where we determine optimal sampling rates that are each substantially below those originally used whilst maintaining the accuracy of reference recognition systems.


title: RELATIONSHIP BETWEEN SCHOOL EDUCATION AND ECONOMIC GROWTH IN PAKISTAN ARDL Bounds Testing Approach to Cointegration

abstract: The present study is designed to investigate short-run and long-run linkage between school education and economic growth in Pakistan using annual time series data on real GDP, real physical capital, inflation and general school enrollment for the period 1970-71 to 2008-09. Cointegration between school education and economic growth is found in the present study. The results of this study also confirm the existence of two way direct long-run relationship between school education and economic growth in Pakistan. A two way inverse relationship between school education and economic growth is found in short run. Macroeconomic instability due to inflation retards economic growth and school education only in the long-run. A statistical significant and inverse relationship between school education and poverty is observed only in the short-run. On the basis of the findings, this study recommends reduction in macroeconomic instability and poverty. Policy action aiming at increasing school education and accelerating economic growth is desirable.


title: 1 The Open Motion Planning Library

abstract: The open motion planning library (OMPL) is a new library for sampling-based motion planning, which contains implementations of many state-of-the-art planning algorithms. The library is designed in a way that it allows the user to easily solve a variety of complex motion planning problems with minimal input. OMPL facilitates the addition of new motion planning algorithms, and it can be conveniently interfaced with other software components. A simple graphical user interface (GUI) built on top of the library, a number of tutorials, demos, and programming assignments are designed to teach students about sampling-based motion planning. The library is also available for use through Robot Operating System (ROS).


title: Adaptive Relevance Matrices in Learning Vector Quantization

abstract: We propose a new matrix learning scheme to extend relevance learning vector quantization (RLVQ), an efficient prototype-based classification algorithm, toward a general adaptive metric. By introducing a full matrix of relevance factors in the distance measure, correlations between different features and their importance for the classification scheme can be taken into account and automated, and general metric adaptation takes place during training. In comparison to the weighted Euclidean metric used in RLVQ and its variations, a full matrix is more powerful to represent the internal structure of the data appropriately. Large margin generalization bounds can be transferred to this case, leading to bounds that are independent of the input dimensionality. This also holds for local metrics attached to each prototype, which corresponds to piecewise quadratic decision boundaries. The algorithm is tested in comparison to alternative learning vector quantization schemes using an artificial data set, a benchmark multiclass problem from the UCI repository, and a problem from bioinformatics, the recognition of splice sites for C. elegans.


title: Scratch: A Sneak Preview

abstract: Scratch is a networked, media-rich programmingenvironment designed to enhance the development oftechnological fluency at after-school centers ineconomically-disadvantaged communities. Just as theLEGO MindStorms robotics kit added programmabilityto an activity deeply rooted in youth culture (buildingwith LEGO bricks), Scratch adds programmability tothe media-rich and network-based activities that aremost popular among youth at after-school computercenters. Taking advantage of the extraordinaryprocessing power of current computers, Scratchsupports new programming paradigms and activitiesthat were previously infeasible, making it betterpositioned to succeed than previous attempts tointroduce programming to youth.Our working hypothesis is that, as kids work onpersonally meaningful Scratch projects such asanimated stories, games, and interactive art, they willdevelop technological fluency, mathematical andproblem solving skills, and a justifiable self-confidencethat will serve them well in the wider spheres of theirlives.


title: THz 3-D Image Formation Using SAR Techniques: Simulation, Processing and Experimental Results

abstract: An active frequency-modulated continuous wave (FMCW) terahertz (THz) system has been developed to image objects in millimeter-scale resolution in three dimensions. The two-dimensional (2-D) aperture synthesis enables the improvement of cross range resolution, and its high range resolution is achieved through the use of the broadband sweep signal, whose frequency ranges from 514 to 565 GHz. The 3-D data are sufficiently focused by the wavenumber domain approach derived for the dechirped data. We present the THz 3-D imaging strategy using synthetic aperture radar (SAR) techniques, the related theoretical background and the experimental results in this paper.


title: Self-Oscillating Switching Technique for Current Source Parallel Resonant Induction Heating Systems

abstract: This paper presents resonant inverter tuning for current source parallel resonant induction heating systems based on a new self oscillating switching technique. The phase error is suppressed in a wide range of operating frequencies in comparison with Phase Locked Loop (PLL) techniques. The proposed switching method has the capability of tuning under fast changes in the resonant frequency. According to this switching method, a multi-frequency induction heating (IH) system is proposed by using a single inverter. In comparison with multi-level inverter based IH systems, the advantages of this technique are its simple structure, better transients and wide range of operating frequencies. A laboratory prototype was built with an operating frequency of 35 kHz to 55 kHz and 300 W of output power. The performance of the IH system shows the validity of the new switching technique.


title: Deterioration Model Filtering by Gibbs Algorithm and RUL Estimation

abstract: Abstract Nowadays, prognosis of system lifetime is a basic requirement for condition-based maintenance in many application domains where safety, reliability, and availability are considered of first importance. One of the prognosis aims is the prediction of remaining useful life of a component or a system. This latter is modeled based on the different deterioration model, probabilistic or non-probabilistic models. In this paper a probabilistic approach for the remaining useful life estimation is proposed.


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title: L/S Ratio and Cortisol in Amniotic Fluid According to Gestational Age

abstract: Summary: It is well known that corticoids act to induce fetal lung maturation when administered to the fetus or mother. However, their physiologic role has been recently questioned with regard to stimulation of surfactant production.We investigated the process of fetal lung maturation to determine whether or not it is associated with changes in amniotic fluid cortisol.Sixty-two amniotic fluid samples from 53 patients were obtained by transabdominal amniocentesis strictly due to maternal and/or fetal clinical indication, according to the course of pregnancy and the maternal and/or fetal status.The L/S ratio was measured on thin-layer plates by reflectance densitometry and calculated as previously described by Gluck et al.Cortisol levels in amniotic fluid were measured by radioimmunoassay using commercial reagent kits (Gamma coat I125 cortisol; Clinical Assays, Inc.).The level of amniotic fluid cortisol increased with gestational age from wk 30 to 41; a sharp increment was observed at 39 to 41 wk.No significant differences were found when the mean values of cortisol were compared at wk 30 to 32, 33 to 35, and 36 to 38. When the fourth group was included (39 to 41 wk), a statistically significant difference was found (F = 4.63; P < 0.01).There was a progressive increase in L/S ratio during gestation as has already been described, plus a value greater than 2 was found during the 33 to 35 wk period. The statistical analysis performed on L/S ratios at weekly intervals 30 to 32, 33 to 35, and 36 to 38 showed a significant difference in the mean values in the groups studied (F = 6.84; P < 0.005).Therefore, the increment of L/S ratio over 2 was already observed during the 33 to 35 wk period of gestation, whereas the mean cortisol amniotic fluid values remained unchanged between the 30th and the 38th wk. The cortisol peak was observed only during the 39 to 41 wk period.Speculation: It has been demonstrated that glucocorticoids have an important role in accelerating the process of the development of fetal lung maturation. The physiologic fetal lung surfactant production does not necessarily depend on the increase of fetal cortisol levels. It is speculated that cortisol has a permissive role which allows the action of other hormones.


title: Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations

abstract: Collaborative filtering (CF)-based recommender systems represent a promising solution for the rapidly growing mobile music market. However, in the mobile Web environment, a traditional CF system that uses explicit ratings to collect user preferences has a limitation: mobile customers find it difficult to rate their tastes directly because of poor interfaces and high telecommunication costs. Implicit ratings are more desirable for the mobile Web, but commonly used cardinal (interval, ratio) scales for representing preferences are also unsatisfactory because they may increase estimation errors. In this paper, we propose a CF-based recommendation methodology based on both implicit ratings and less ambitious ordinal scales. A mobile Web usage mining (mWUM) technique is suggested as an implicit rating approach, and a specific consensus model typically used in multi-criteria decision-making (MCDM) is employed to generate an ordinal scale-based customer profile. An experiment with the participation of real mobile Web customers shows that the proposed methodology provides better performance than existing CF algorithms in the mobile Web environment.


title: CLARANS: A Method for Clustering Objects for Spatial Data Mining

abstract: Spatial data mining is the discovery of interesting relationships and characteristics that may exist implicitly in spatial databases. To this end, this paper has three main contributions. First, it proposes a new clustering method called CLARANS, whose aim is to identify spatial structures that may be present in the data. Experimental results indicate that, when compared with existing clustering methods, CLARANS is very efficient and effective. Second, the paper investigates how CLARANS can handle not only point objects, but also polygon objects efficiently. One of the methods considered, called the IR-approximation, is very efficient in clustering convex and nonconvex polygon objects. Third, building on top of CLARANS, the paper develops two spatial data mining algorithms that aim to discover relationships between spatial and nonspatial attributes. Both algorithms can discover knowledge that is difficult to find with existing spatial data mining algorithms.