title: A mathematical model for product selection strategies in a recommender system

abstract: Electronic commerce (EC) has become an important support for business. EC is regarded as an efficient platform to bridge the gape between the suppliers and consumers, but how to make use of a huge amount of transaction data and identify potential customers on the internet remains a challenge for an EC company. In particular, to recommend proper products to customers, the preferences of the targeted customers need to be accurately specified and their preferences should be taken into account. This is not only to show the goodwill of the company, but also to retain the customer relation. This study aims to construct a recommender system by focusing on the on-line decision support module with respect to customers' characteristics and supplier's profits. For effective decision support, a mathematical model is developed so that the right product can be recommended to the right person with the best profit for the company. A numerical example is used to illustrate how this model works when both supplier's and consumers' desires are taken into consideration to achieve an optimal Win-Win Strategy for market expansion.


title: A framework for using magic to study the mind

abstract: Over the centuries, magicians have developed extensive knowledge about the manipulation of the human mind—knowledge that has been largely ignored by psychology. It has recently been argued that this knowledge could help improve our understanding of human cognition and consciousness. But to what extent is this possible? What has already been done? And how much could it ultimately contribute to our exploration of the human mind? We propose here a framework outlining how knowledge about magic can be used to help us understand the human mind. Various approaches—both old and new—are surveyed, in terms of four different levels. The first focuses on the methods in magic, using these to suggest new approaches to existing issues in psychology. The second focuses on the effects that magic can produce, such as the sense of wonder induced by seeing an apparently impossible event. Third is the consideration of magic tricks—methods and effects together—as phenomena of scientific interest in their own right. Finally, there is the organization of knowledge about magic into an informative whole, including the possibility of a science centered around the experience of wonder.


A Fast and Robust Feature-Based Scan-Matching Method in 3 D SLAM and the Effect of Sampling Strategies Regular Paper


title: Cognition and Technology

abstract: As the previous chapters emphasized, the human cognition—and the technology necessary to support it—are central to Cyber Situational Awareness. Therefore, this chapter focuses on challenges and approaches to integration of information technology and computational representations of human situation awareness. To illustrate these aspects of CSA, the chapter uses the process of intrusion detection as a key example. We argue that effective development of technologies and processes that produce CAS in a way properly aligned with human cognition calls for cognitive models—dynamic and adaptable computational representations of the cognitive structures and mechanisms involved in developing SA and processing information for decision making. While visualization and machine learning are often seen among the key approaches to enhancing CSA, we point out a number of limitations in their current state of development and applications to CSA. The current knowledge gaps in our understanding of cognitive demands in CSA include the lack of a theoretical model of cyber SA within a cognitive architecture; the decision gap, representing learning, experience and dynamic decision making in the cyberspace; and the semantic gap, addressing the construction of a common language and a set of basic concepts about which the security community can develop a shared understanding.


title: Fully abstract compilation to JavaScript

abstract: Many tools allow programmers to develop applications in high-level languages and deploy them in web browsers via compilation to JavaScript. While practical and widely used, these compilers are ad hoc: no guarantee is provided on their correctness for whole programs, nor their security for programs executed within arbitrary JavaScript contexts. This paper presents a compiler with such guarantees. We compile an ML-like language with higher-order functions and references to JavaScript, while preserving all source program properties. Relying on type-based invariants and applicative bisimilarity, we show full abstraction: two programs are equivalent in all source contexts if and only if their wrapped translations are equivalent in all JavaScript contexts. We evaluate our compiler on sample programs, including a series of secure libraries.


title: Improvement and testing of a robotic manta ray (RoMan-III)

abstract: Manta Ray generates thrust force by flapping two pectoral fins, which inspires the parametric study of the fin that affects the swimming performance. In this paper, the design of a robotic manta ray (RoMan-III) will be presented. A biomimetic flapping fin model will be discussed. The forward flapping motion, turning and gliding motions are considered in the study of Manta Ray's locomotion. Parameters related to thrust generation include the fin flapping amplitude, frequency, fin area, and fin shape. The fin model is derived based on a simplified model by Bernoulli equation. A scheme of motion control is also suggested for the fish locomotion. Experimental results have been obtained to demonstrate the validity of the proposed model.


title: Coreference in Wikipedia: Main Concept Resolution

abstract: Wikipedia is a resource of choice exploited in many NLP applications, yet we are not aware of recent attempts to adapt coreference resolution to this resource. In this work, we revisit a seldom studied task which consists in identifying in a Wikipedia article all the mentions of the main concept being described. We show that by exploiting the Wikipedia markup of a document, as well as links to external knowledge bases such as Freebase, we can acquire useful information on entities that helps to classify mentions as coreferent or not. We designed a classifier which drastically outperforms fair baselines built on top of state-of-the-art coreference resolution systems. We also measure the benefits of this classifier in a full coreference resolution pipeline applied to Wikipedia texts.


Noising and Denoising Natural Language: Diverse Backtranslation for Grammar Correction


title: WIDEBAND MICROWAVE CROSSOVER USING DOU- BLE VERTICAL MICROSTRIP-CPW INTERCONNECT

abstract: The paper presents the design of a novel ultra-wideband microwave crossover for the use in microstrip circuits. The proposed structure includes a double microstrip-coplanar waveguide (CPW) vertical interconnect in single-layer substrate technology which allows an inclusion of a flnite-width coplanar waveguide (CPW) on the top side of the substrate to achieve the required cross-link. The presented design is verifled using the full-wave electromagnetic simulator Ansoft HFSS v.13 and experimental tests. The obtained experimental results show that in the frequency band of 3.2{11GHz, the crossover has an isolation of 20dB accompanied by insertion losses of no more than 1.5dB.


title: Hybrid Scoring and Classification Approaches to Predict Human Pregnane X Receptor Activators

abstract: PURPOSE ::: The human pregnane X receptor (PXR) is a transcriptional regulator of many genes involved in xenobiotic metabolism and excretion. Reliable prediction of high affinity binders with this receptor would be valuable for pharmaceutical drug discovery to predict potential toxicological responses ::: ::: ::: MATERIALS AND METHODS ::: Computational models were developed and validated for a dataset consisting of human PXR (PXR) activators and non-activators. We used support vector machine (SVM) algorithms with molecular descriptors derived from two sources, Shape Signatures and the Molecular Operating Environment (MOE) application software. We also employed the molecular docking program GOLD in which the GoldScore method was supplemented with other scoring functions to improve docking results. ::: ::: ::: RESULTS ::: The overall test set prediction accuracy for PXR activators with SVM was 72% to 81%. This indicates that molecular shape descriptors are useful in classification of compounds binding to this receptor. The best docking prediction accuracy (61%) was obtained using 1D Shape Signature descriptors as a weighting factor to the GoldScore. By pooling the available human PXR data sets we revealed those molecular features that are associated with human PXR activators. ::: ::: ::: CONCLUSIONS ::: These combined computational approaches using molecular shape information may assist scientists to more confidently identify PXR activators.


title: 64 Meaning in Life

abstract: In this chapter, it is argued that meaning in life is an important variable for human well-being. Literature supporting this contention is reviewed, and complexities regarding defining meaning in life are discussed. Definitions of meaning have focused on several components, two of which appear central and unique to meaning in life, suggesting a conceptual framework of meaning in life comprised of two pillars: comprehension and purpose. Comprehension encompasses people’s ability to find patterns, consistency, and significance in the many events and experiences in their lives, and their synthesis and distillation of the mostsalient,important, andmotivatingfactors.Peoplefacethechallenge ofunderstandingtheirselves, the world around them, and their unique niche and interactions within the world, and the notion of comprehension unifies these domains of understanding. Purpose refers to highly motivating, long-term goals about which people are passionate and highly committed. In the framework presented in this chapter, it is suggested that people devote significant resources to the pursuit of their purposes, and that the most effective and rewarding purposes arise from and are congruent with people’s comprehension of their lives. Literature is reviewed regarding where meaning might come from, and other dimensions of meaning are considered (i.e., sources of meaning and search for meaning). Suggestions for future research are proposed.


title: Experimental characterization and analysis of the BITalino platforms against a reference device

abstract: Low-cost hardware platforms for biomedical engineering are becoming increasingly available, which empower the research community in the development of new projects in a wide range of areas related with physiological data acquisition. Building upon previous work by our group, this work compares the quality of the data acquired by means of two different versions of the multimodal physiological computing platform BITalino, with a device that can be considered a reference. We acquired data from 5 sensors, namely Accelerometry (ACC), Electrocardiography (ECG), Electroencephalography (EEG), Electrodermal Activity (EDA) and Electromyography (EMG). Experimental evaluation shows that ACC, ECG and EDA data are highly correlated with the reference in what concerns the raw waveforms. When compared by means of their commonly used features, EEG and EMG data are also quite similar across the different devices.


title: Regularization for Unsupervised Deep Neural Nets

abstract: Unsupervised neural networks, such as restricted Boltzmann machines (RBMs) and deep belief networks (DBNs), are powerful tools for feature selection and pattern recognition tasks. We demonstrate that overfitting occurs in such models just as in deep feedforward neural networks, and discuss possible regularization methods to reduce overfitting. We also propose a "partial" approach to improve the efficiency of Dropout/DropConnect in this scenario, and discuss the theoretical justification of these methods from model convergence and likelihood bounds. Finally, we compare the performance of these methods based on their likelihood and classification error rates for various pattern recognition data sets.


title: A case report of a complete degloving injury of the penile skin

abstract: Introduction ::: Male genital degloving injuries are unusual and rarely caused by animal bite. Usually patients attend health care immediately if bitten in the genital area. Prophylactic antibiotics is routinely used (Gomes et al., 2000). ::: ::: A penile degloving usually begins just proximal of the coronal line and progress down to the base of the shaft. Deep erectile tissue and the spermatic cord are seldom damaged and the endogenous skin of glans usually survives (Brown and Fryer, 1957; Morey et al., 2004; Finical and Arnold, 1999).


title: A Personalized Collaborative Digital Library Environment

abstract: The Web, and consequently the information contained in it, is growing rapidly. Every day a huge amount of newly created information is electronically published in Digital Libraries, whose aim is to satisfy users' information needs.In this paper, we envisage a Digital Library not only as an information resource where users may submit queries to satisfy their daily information need, but also as a collaborative working and meeting space of people sharing common interests. Indeed, we will present a personalized collaborative Digital Library environment, where users may organize the information space according to their own subjective view, may build communities, may become aware of each other, may exchange information and knowledge with other users, and may get recommendations based on preference patterns of users.


title: Machine Translation of Arabic Dialects

abstract: Arabic Dialects present many challenges for machine translation, not least of which is the lack of data resources. We use crowdsourcing to cheaply and quickly build Levantine-English and Egyptian-English parallel corpora, consisting of 1.1M words and 380k words, respectively. The dialectal sentences are selected from a large corpus of Arabic web text, and translated using Amazon's Mechanical Turk. We use this data to build Dialectal Arabic MT systems, and find that small amounts of dialectal data have a dramatic impact on translation quality. When translating Egyptian and Levantine test sets, our Dialectal Arabic MT system performs 6.3 and 7.0 BLEU points higher than a Modern Standard Arabic MT system trained on a 150M-word Arabic-English parallel corpus.


title: 2D and 3D Convolutional Neural Network Fusion for Predicting the Histological Grade of Hepatocellular Carcinoma

abstract: Preoperative Knowledge of the histological grade of hepatocellular carcinoma (HCC) is significant for patient management and prognosis in clinical practice. Recent studies reported that 3D Convolutional Neural Network (CNN) outperformed 2D CNN for lesion characterization. Since 2D and 3D deep feature derived from CNN embed different spatial information of neoplasm, we hypothesize that the performance of lesion characterization might be improved if taking full advantage of both 2D and 3D characterization. In this work, we propose a 2D and 3D CNN fusion architecture to integrate both 2D and 3D spatial information of neoplasm for predicting the histological grade of HCC. Specifically, correlated and individual component analysis (CICA) is performed to fuse the 2D deep features in three orthogonal views and the 3D deep feature in volumetric images of HCC. Experimental results of 46 clinical patients with HCCs demonstrate several encouraging features of the proposed 2D and 3D deep feature fusion framework as follows: (1) Fusion of 2D and 3D deep feature using CICA outperforms 2D or 3D deep feature for predicting the histological grade of HCC. (2) Fusion of 2D deep features derived from three orthogonal views using CICA yields better results than those of 3D deep feature. (3) CICA is better than the conventional concatenation and the correlation learning model for deep feature fusion.


title: Streaming Video over the Internet : Approaches and Directions

abstract: Due to the explosive growth of the Internet and increasing demand for multimedia information on the Web, streaming video over the Internet has received tremendous attention from academia and industry. Transmission of real-time video typically has bandwidth, delay, and loss requirements. However, the current best-effort Internet does not offer any quality of service (QoS) guarantees to streaming video. Furthermore, for video multicast, it is difficult to achieve both efficiency and flexibility. Thus, Internet streaming video poses many challenges. In this article we cover six key areas of streaming video. Specifically, we cover video compression, application-layer QoS control, continuous media distribution services, streaming servers, media synchronization mechanisms, and protocols for streaming media. For each area, we address the particular issues and review major approaches and mechanisms. We also discuss the tradeoffs of the approaches and point out future research directions.


title: A Data-Driven Soft Sensor Modeling Method Based on Deep Learning and its Application

abstract: Soft sensors have been widely used in industrial processes. The core issue of data-driven soft sensors is building soft sensor models with excellent performance and robustness. This paper introduces deep learning to soft sensor modeling and proposes a novel soft sensor modeling method based on a deep learning network that integrates denoising autoencoders with a neural network (DAE-NN). An improved gradient descent is employed to update the model parameters. The proposed modeling method is able to capture the essential information of input data through deep architecture, building soft sensors with excellent performance. The DAE-NN-based soft sensor is applied in practical applications to estimate the oxygen content in flue gasses in 1000-MW ultrasuperficial units. Comparing conventional soft sensor modeling methods, i.e., shallow learning methods, DAE-NN-based soft sensor significantly improves the performance and generalization of data-driven soft sensors. Deep learning provides a very effective and promising method for soft sensor modeling.


title: Preprocessing Reference Sensor Pattern Noise via Spectrum Equalization

abstract: Although sensor pattern noise (SPN) has been proven to be an effective means to uniquely identify digital cameras, some non-unique artifacts, shared amongst cameras undergo the same or similar in-camera processing procedures, often give rise to false identifications. Therefore, it is desirable and necessary to suppress these unwanted artifacts so as to improve the accuracy and reliability. In this work, we propose a novel preprocessing approach for attenuating the influence of the nonunique artifacts on the reference SPN to reduce the false identification rate. Specifically, we equalize the magnitude spectrum of the reference SPN through detecting and suppressing the peaks according to the local characteristics, aiming at removing the interfering periodic artifacts. Combined with 6 SPN extraction or enhancement methods, our proposed Spectrum Equalization Algorithm (SEA) is evaluated on the Dresden image database as well as our own database, and compared with the state-of-the-art preprocessing schemes. Experimental results indicate that the proposed procedure outperforms, or at least performs comparably to, the existing methods in terms of the overall ROC curve and kappa statistic computed from a confusion matrix, and tends to be more resistant to JPEG compression for medium and small image blocks.


title: Robust edge-directed interpolation of magnetic resonance images

abstract: Image interpolation is intrinsically a severely under-determined inverse problem. Traditional non-adaptive methods utilize certain models that fail to respect local image statistics around edges of image structures. In practice, this results in artifacts such as jagged edges and/or blurring. To overcome this short-coming, the concept of edge-directed interpolation has been introduced in different forms. One interesting variant, New Edge Directed Interpolation (NEDI) [9], has successfully exploited the “geometric duality” that linked between the low-resolution (LR) image and its corresponding high-resolution (HR) image. It has been demonstrated that, for scalar images, NEDI is able to produce better results than non-adaptive traditional methods, both visually and quantitatively. In this work, we return to the root of NEDI as a least-squares estimation method of neighborhood patterns, and propose a robust scheme to improve it. The improvement comes two-folds: firstly, a robust least-squares technique is used to improve NEDI's performance to noise; secondly, the NEDI algorithm is extended with the recently proposed Non-Local Mean (NLM) estimation scheme. Moreover, the edge-directed concept is applied to the interpolation of multivalued diffusion weighted images (DWI). The framework is tested on phantom scalar images and real diffusion images, and is shown to be able to achieve better results than the non-adaptive methods, in terms of visual quality as well as quantitative measures.


title: Streaming Algorithms for Extent Problems in High Dimensions

abstract: We develop (single-pass) streaming algorithms for maintaining extent measures of a stream S of n points in Rd. We focus on designing streaming algorithms whose working space is polynomial in d (poly(d)) and sublinear in n. For the problems of computing diameter, width and minimum enclosing ball of S, we obtain lower bounds on the worst-case approximation ratio of any streaming algorithm that uses poly(d) space. On the positive side, we introduce the notion of blurred ball cover and use it for answering approximate farthest-point queries and maintaining approximate minimum enclosing ball and diameter of S. We describe a streaming algorithm for maintaining a blurred ball cover whose working space is linear in d and independent of n.


title: Improving robustness of deep neural networks via spectral masking for automatic speech recognition

abstract: The performance of human listeners degrades rather slowly compared to machines in noisy environments. This has been attributed to the ability of performing auditory scene analysis which separates the speech prior to recognition. In this work, we investigate two mask estimation approaches, namely the state dependent and the deep neural network (DNN) based estimations, to separate speech from noises for improving DNN acoustic models' noise robustness. The second approach has been experimentally shown to outperform the first one. Due to the stereo data based training and ill-defined masks for speech with channel distortions, both methods do not generalize well to unseen conditions and fail to beat the performance of the multi-style trained baseline system. However, the model trained on masked features demonstrates strong complementariness to the baseline model. The simple average of the two system's posteriors yields word error rates of 4.4% on Aurora2 and 12.3% on Aurora4.


title: On Poisson Graphical Models

abstract: Undirected graphical models, such as Gaussian graphical models, Ising, and multinomial/categorical graphical models, are widely used in a variety of applications for modeling distributions over a large number of variables. These standard instances, however, are ill-suited to modeling count data, which are increasingly ubiquitous in big-data settings such as genomic sequencing data, user-ratings data, spatial incidence data, climate studies, and site visits. Existing classes of Poisson graphical models, which arise as the joint distributions that correspond to Poisson distributed node-conditional distributions, have a major drawback: they can only model negative conditional dependencies for reasons of normalizability given its infinite domain. In this paper, our objective is to modify the Poisson graphical model distribution so that it can capture a rich dependence structure between count-valued variables. We begin by discussing two strategies for truncating the Poisson distribution and show that only one of these leads to a valid joint distribution. While this model can accommodate a wider range of conditional dependencies, some limitations still remain. To address this, we investigate two additional novel variants of the Poisson distribution and their corresponding joint graphical model distributions. Our three novel approaches provide classes of Poisson-like graphical models that can capture both positive and negative conditional dependencies between count-valued variables. One can learn the graph structure of our models via penalized neighborhood selection, and we demonstrate the performance of our methods by learning simulated networks as well as a network from microRNA-sequencing data.


title: Rapamycin overcomes multidrug resistance (MDR) mediated by P-glycoprotein (Pgp), Multidrug Resistance Protein-1 (MRP-1), Breast Cancer Resistance Protein (BCRP) and Lung Resistance Protein (LRP) and synergizes with substrate drugs in MDR cells.

abstract: 5410 Mechanisms of drug resistance in acute myeloid leukemia (AML) and other malignancies include decreased cellular uptake and retention of substrate chemotherapy drugs due to overexpression of the ATP-binding cassette (ABC) proteins Pgp, MRP-1 and BCRP and decreased nuclear uptake and retention due to expression of the major vault protein LRP, as well as aberrant activation of cell survival and proliferation pathways, including PI3K/Akt. Rapamycin inhibits mTOR, a downstream effector of PI3K/Akt, has anti-proliferative effects in AML and is also known to block drug transport by Pgp by competitive inhibition. We studied its effects on drug transport and interactions with substrate chemotherapy drugs in cells overexpressing MRP-1, BCRP and LRP, as well as Pgp. Effects on drug uptake were studied by flow cytometry, cell survival in 96-hour cultures by the WST-1 assay and interactions of drug combinations by isobologram analysis. Exposure to rapamycin at 0.25μM, a clinically achievable concentration, for 3 hours increased cellular uptake of substrate chemotherapy drugs in cell lines overexpressing Pgp, MRP-1 and BCRP, and nuclear uptake in cell lines overexpressing LRP, but not in parental cells. Rapamycin dose-response curves were biphasic, with 20 to 40% decrease in cell survival following exposure to nM concentrations, and eradication of all cells following exposure to μM concentrations, suggesting different mechanisms of cell kill by rapamycin at different concentrations. Rapamycin IC 50 ’s were similar in multidrug resistant and parental cells, despite the fact that rapamycin is known to be a substrate for Pgp. Thus the data suggest that, in addition to modulating transport of other substrate drugs, rapamycin also modulates its own transport by Pgp, and likely also by MRP-1, BCRP and LRP. Concurrent treatment with rapamycin and mitoxantrone, a substrate for all four MDR proteins, resulted in a synergistic drug interaction in multidrug resistant cells. Thus rapamycin is a broad-spectrum MDR protein modulator and synergizes with MDR protein substrate drugs in multidrug resistant cells. Effects of rapamycin on drug transport should be considered along with effects on signal transduction in the design of combination regimens.


title: Sparse space-time equalization with L1 norm

abstract: Space-time adaptive equalizers are an important part of multiple-input multiple-output (MIMO) communication systems operating in multi-path environments. Traditional methods of producing adaptive equalizers using short training sequence lengths suffer performance loss, and do not take advantage of known properties of the channel, such as sparsity. We discuss an iterative shrinkage algorithm which utilizes the l 1 norm as a procedure to estimate a sparse equalizer. We present simulation results of this algorithm and make comparisons to traditional methods.


title: A Comprehensive Review of Smart Energy Meters in Intelligent Energy Networks

abstract: The significant increase in energy consumption and the rapid development of renewable energy, such as solar power and wind power, have brought huge challenges to energy security and the environment, which, in the meantime, stimulate the development of energy networks toward a more intelligent direction. Smart meters are the most fundamental components in the intelligent energy networks (IENs). In addition to measuring energy flows, smart energy meters can exchange the information on energy consumption and the status of energy networks between utility companies and consumers. Furthermore, smart energy meters can also be used to monitor and control home appliances and other devices according to the individual consumer’s instruction. This paper systematically reviews the development and deployment of smart energy meters, including smart electricity meters, smart heat meters, and smart gas meters. By examining various functions and applications of smart energy meters, as well as associated benefits and costs, this paper provides insights and guidelines regarding the future development of smart meters.


title: A 25 dBm Digitally Modulated CMOS Power Amplifier for WCDMA/EDGE/OFDM With Adaptive Digital Predistortion and Efficient Power Control

abstract: A digitally modulated power amplifier (DPA) in 1.2 V 0.13 mum SOI CMOS is presented, to be used as a building block in multi-standard, multi-band polar transmitters. It performs direct amplitude modulation of an input RF carrier by digitally controlling an array of 127 unary-weighted and three binary-weighted elementary gain cells. The DPA is based on a novel two-stage topology, which allows seamless operation from 800 MHz through 2 GHz, with a full-power efficiency larger than 40% and a 25.2 dBm maximum envelope power. Adaptive digital predistortion is exploited for DPA linearization. The circuit is thus able to reconstruct 21.7 dBm WCDMA/EDGE signals at 1.9 GHz with 38% efficiency and a higher than 10 dB margin on all spectral specifications. As a result of the digital modulation technique, a higher than 20.1 % efficiency is guaranteed for WCDMA signals with a peak-to-average power ratio as high as 10.8 dB. Furthermore, a 15.3 dBm, 5 MHz WiMAX OFDM signal is successfully reconstructed with a 22% efficiency and 1.53% rms EVM. A high 10-bit nominal resolution enables a wide-range TX power control strategy to be implemented, which greatly minimizes the quiescent consumption down to 10 mW. A 16.4% CDMA average efficiency is thus obtained across a > 70 dB power control range, while complying with all the spectral specifications.


title: GPS and Map matching based vehicle accident detection system

abstract: GPS (Global Positioning System) has become an integral part of a vehicle system which provides speed, time, direction etc besides the navigation data. Speed is one of the primary attributes of vehicle accident. Many lives could have been saved if emergency service could receive accident information timely. This paper proposes to detect an accident from the map matched position of a vehicle by utilizing the GPS speed data and map matching algorithm and send accident location to an Alert Service Center. The GPS provides speed and position in every 0.1 second. The position data will be used in the map matching algorithm to locate the vehicle on the road. The present speed will be compared with the previous speed in every 0.1 second through a Microcontroller Unit. Whenever the speed will be falling below the safe calculated threshold speed, the system will generate an accident situation. It will check the vehicle location from map matching module and generate an accident situation if the vehicle is found outside the road network. This will reduce the false accident detection drastically. The map matched accident location is then sent by utilizing the GSM network. The proposed system will save many accident victims with timely rescue.


title: Column-stores vs. row-stores: how different are they really?

abstract: There has been a significant amount of excitement and recent work on column-oriented database systems ("column-stores"). These database systems have been shown to perform more than an order of magnitude better than traditional row-oriented database systems ("row-stores") on analytical workloads such as those found in data warehouses, decision support, and business intelligence applications. The elevator pitch behind this performance difference is straightforward: column-stores are more I/O efficient for read-only queries since they only have to read from disk (or from memory) those attributes accessed by a query. This simplistic view leads to the assumption that one can obtain the performance benefits of a column-store using a row-store: either by vertically partitioning the schema, or by indexing every column so that columns can be accessed independently. In this paper, we demonstrate that this assumption is false. We compare the performance of a commercial row-store under a variety of different configurations with a column-store and show that the row-store performance is significantly slower on a recently proposed data warehouse benchmark. We then analyze the performance difference and show that there are some important differences between the two systems at the query executor level (in addition to the obvious differences at the storage layer level). Using the column-store, we then tease apart these differences, demonstrating the impact on performance of a variety of column-oriented query execution techniques, including vectorized query processing, compression, and a new join algorithm we introduce in this paper. We conclude that while it is not impossible for a row-store to achieve some of the performance advantages of a column-store, changes must be made to both the storage layer and the query executor to fully obtain the benefits of a column-oriented approach.


title: Combining Technical Analysis with Sentiment Analysis for Stock Price Prediction

abstract: This paper proposes a stock price prediction model, which extracts features from time series data and social networks for prediction of stock prices and evaluates its performance. In this research, we use the features such as numerical dynamics (frequency) of news and comments, overall sentiment analysis of news and comments, as well as technical analysis of historic price and volume. We model the stock price movements as a function of these input features and solve it as a regression problem in a Multiple Kernel Learning regression framework. Experimental results show that our proposed method outperforms other baseline methods in terms of magnitude prediction measures such as RMSE, MAE and MAPE for three famous Japan companies' stocks in US stock market. The results indicate that features other than mining from stock prices themselves improved the performance.


title: Generating Abstractive Summaries of Sports Games from Japanese Tweets

abstract: Microblogs are one of the most important resources for natural language processing. In this paper, we deal with a summarization task of sports events on Twitter. Although many researchers have proposed methods to summarize tweets about sports events, the methods were based on extractive approaches. We focus on an abstractive approach to generate a better summary as compared with the extractive approaches. First, our method detects burst situations in which many users post tweets when a sub-event in a game occurs. Tweets in the burst situations are the inputs of our method. Next, it extracts sub-event elements (SEEs) that contain actions in a game, such as “Player A made a pass to Player B” and “Player B made a shot on goal.” Then, it identifies the optimal order of the extracted SEEs by using a scoring method. Finally, it generates an abstractive summary on the basis of the ordered SEEs, such as “Playler B made a shot on goal from the Player A's pass.” In the experiment, we show the effectiveness of our method as compared with related work based on an extractive approach.


title: C2G2FSnake: automatic tongue image segmentation utilizing prior knowledge

abstract: Extraction of the tongue body from digital images is essential for automated tongue diagnoses in traditional Chinese medicine. This paper presents a fully automated active contour initial method that utilizes prior knowledge of the tongue shape and its location in tongue images. Then colorspace information is introduced to control curve evolution. Combining the geometrical Snake model with the parameterized GVFSnake model, a novel approach for automatic tongue segmentation: C2G2FSnake (color control-geometric & gradient flow Snake) is proposed. This method increases the curve velocity but decreases the complexity. C2G2FSnake greatly extends practical usage to tongue segmentation, at the same time increasing the precision. Compared with other state-of-the-art works using different images of tongue color, C2G2FSnake realizes automatic tongue segmentation with greatly improved accuracy.


title: The MayaArch3D project: A 3D WebGIS for analyzing ancient architecture and landscapes

abstract: There is a need in the humanities for a 3D WebGIS with analytical tools that allow researchers to analyze 3D models linked to spatially referenced data. Geographic Information Systems (GIS) allow for complex spatial analysis of 2.5D data. For example, they offer bird's eye views of landscapes with extruded building footprints, but one cannot 'get on the ground' and interact with true 3D models from a pedestrian perspective. Meanwhile, 3D models and virtual envir- onments visualize data in 3D space, but analytical tools are simple rotation or lighting effects. The MayaArch3D Project is developing a 3D WebGIS—called QueryArch3D—to allow these two distinct approaches to 'talk to each other' for studies of architecture and landscapes—in this case, the eighth-century Maya kingdom of Copan, Honduras. With this tool, researchers can search and query, in real time via a virtual reality (VR) environment, segmented 3D models of multiple resolutions (as well as computer-assisted design and reality- based) that are linked to attribute data stored in a spatial database. Beta tests indicate that this tool can assist researchers in expanding questions and develop- ing new analytical methods in humanities research. This article summarizes the results of a pilot project that started in 2009, with an art historian and an archae- ologist's collaborative research on the ancient Maya kingdom and UNESCO World Heritage site of Copan in Honduras—called MayaArch3D. The project


title: Machine Learning for Personalized Medicine: Predicting Primary Myocardial Infarction from Electronic Health Records

abstract: Electronic health records (EHRs) are an emerging relational domain with large potential to improve clinical outcomes. We apply two statistical relational learning (SRL) algorithms to the task of predicting primary myocardial infarction. We show that one SRL algorithm, relational functional gradient boosting, outperforms propositional learners particularly in the medically-relevant high recall region. We observe that both SRL algorithms predict outcomes better than their propositional analogs and suggest how our methods can augment current epidemiological practices.


title: Assessment of healthcare worker influenza vaccination program in French geriatric wards: a cluster-randomized controlled trial

abstract: Background and aims: The French institute for study of geriatric infection risk (ORIG) has run a multiphase multicenter study (VESTA) to develop and implement active programs promoting healthcare worker (HCW) influenza vaccination. The present article reports results after implementation of the first active program. Method: A cluster-randomized controlled trial was conducted from December 1 to December 15, 2005, and a total of 43 geriatric wards (3646 HCWs) were randomly assigned to two clusters. The program cluster (24 wards; 1918 HCWs) received the active program whereas no action was taken in the control cluster (19 wards; 1728 HCWs). The program was educational; its objective was to convince HCWs to be vaccinated by giving them topdown scientific information and developing a sense of altruism. Data from 1201 HCWs (63%) from the program cluster and 1144 HCWs (66%) from the control cluster were collected. Results: The program failed to increase the HCW influenza vaccination rate (program: 34%; control: 32%; p>0.05), but won the faithfulness of vaccinated HCWs (5% vs 8% HCWs quitted vaccination; p<0.05). Conclusions: Resistance to active influenza vaccination programs was found. Future active programs will have to restore a climate of confidence between sources of knowledge and HCWs and promote “self-protection” in contrast with the protection of elderly people.


title: A Continuous Hand Gestures Recognition Technique for Human-Machine Interaction Using Accelerometer and Gyroscope Sensors

abstract: Recent advances in smart devices have sustained them as a better alternative for the design of human-machine interaction (HMI), because they are equipped with accelerometer sensor, gyroscope sensor, and an advanced operating system. This paper presents a continuous hand gestures recognition technique that is capable of continuous recognition of hand gestures using three-axis accelerometer and gyroscope sensors in a smart device. To reduce the influence of unstableness of a hand making the gesture and compress the data, a gesture coding algorithm is developed. An automatic gesture spotting algorithm is developed to detect the start and end points of meaningful gesture segments. Finally, a gesture is recognized by comparing the gesture code with gesture database using dynamic time warping algorithm. In addition, a prototype system is developed to recognize the continuous hand gestures-based HMI. With the smartphone, the user is able to perform the predefined gestures and control smart appliances using the Samsung AllShare protocol.


title: The Multidimensional Assessment of Interoceptive Awareness (MAIA)

abstract: This paper describes the development of a multidimensional self-report measure of interoceptive body awareness. The systematic mixed-methods process involved reviewing the current literature, specifying a multidimensional conceptual framework, evaluating prior instruments, developing items, and analyzing focus group responses to scale items by instructors and patients of body awareness-enhancing therapies. Following refinement by cognitive testing, items were field-tested in students and instructors of mind-body approaches. Final item selection was achieved by submitting the field test data to an iterative process using multiple validation methods, including exploratory cluster and confirmatory factor analyses, comparison between known groups, and correlations with established measures of related constructs. The resulting 32-item multidimensional instrument assesses eight concepts. The psychometric properties of these final scales suggest that the Multidimensional Assessment of Interoceptive Awareness (MAIA) may serve as a starting point for research and further collaborative refinement.


title: Trust, Risk Barriers and Health Beliefs in Consumer Acceptance of Online Health Services

abstract: The Web is an important health information dissemination channel. Potential benefits cannot materialize unless online health information services are accepted and used by consumers. This study develops a research model by integrating the health belief model (HBM) and extended valence framework to explain user acceptance (behavioral intentions). We collected data from a sample of 703 university students in South Africa. Trust had the strongest effect on acceptance. Perceived risk barriers also had significant impacts on acceptance. Furthermore, we confirmed health belief variables, namely perceived susceptibility and severity, are important to consumer acceptance. Selfefficacy was found to moderate the effect of perceived severity on acceptance. The model explains 47.5% of the variance in intentions to use online health information services. Results have helped us identify the relative salience of HBM and extended valence framework in consumer acceptance of online health information services and have important implications for practice.


title: 3D human face description: landmarks measures and geometrical features

abstract: Distance measures and geometrical features are widely used to describe faces. Generally, they are extracted punctually from landmarks, namely anthropometric reference points. The aims are various, such as face recognition, facial expression recognition, face detection, study of changes in facial morphology due to growth, or dysmorphologies. Most of the time, landmarks were extracted with the help of an algorithm or manually located on the faces. Then, measures are computed or geometrical features are extracted to perform the scope of the study. This paper is intended as a survey collecting and explaining all these features, in order to provide a structured user database of the potential parameters and their characteristics. Firstly, facial soft-tissue landmarks are defined and contextualized; then the various measures are introduced and some results are given; lastly, the most important measures are compared to identify the best one for face recognition applications.


title: Viewpoint-Aware Attentive Multi-view Inference for Vehicle Re-identification

abstract: Vehicle re-identification (re-ID) has the huge potential to contribute to the intelligent video surveillance. However, it suffers from challenges that different vehicle identities with a similar appearance have little inter-instance discrepancy while one vehicle usually has large intra-instance differences under viewpoint and illumination variations. Previous methods address vehicle re-ID by simply using visual features from originally captured views and usually exploit the spatial-temporal information of the vehicles to refine the results. In this paper, we propose a Viewpoint-aware Attentive Multi-view Inference (VAMI) model that only requires visual information to solve the multi-view vehicle reID problem. Given vehicle images of arbitrary viewpoints, the VAMI extracts the single-view feature for each input image and aims to transform the features into a global multiview feature representation so that pairwise distance metric learning can be better optimized in such a viewpointinvariant feature space. The VAMI adopts a viewpoint-aware attention model to select core regions at different viewpoints and implement effective multi-view feature inference by an adversarial training architecture. Extensive experiments validate the effectiveness of each proposed component and illustrate that our approach achieves consistent improvements over state-of-the-art vehicle re-ID methods on two public datasets: VeRi and VehicleID.


Noncentral Distributions A Primer on the Understanding , Use , and Calculation of Confidence Intervals that are Based on Central


Security of Programming Contest Systems


title: Constructive and Unconstructive Repetitive Thought

abstract: The author reviews research showing that repetitive thought (RT) can have constructive or unconstructive consequences. The main unconstructive consequences of RT are (a) depression, (b) anxiety, and (c) difficulties in physical health. The main constructive consequences of RT are (a) recovery from upsetting and traumatic events, (b) adaptive preparation and anticipatory planning, (c) recovery from depression, and (d) uptake of health-promoting behaviors. Several potential principles accounting for these distinct consequences of RT are identified within this review: (a) the valence of thought content, (b) the intrapersonal and situational context in which RT occurs, and (c) the level of construal (abstract vs. concrete processing) adopted during RT. Of the existing models of RT, it is proposed that an elaborated version of the control theory account provides the best theoretical framework to account for its distinct


title: Media segmentation using self-similarity decomposition

abstract: We present a framework for analyzing the structure of digital media streams. Though our methods work for video, text, and audio, we concentrate on detecting the structure of digital music files. In the first step, spectral data is used to construct a similarity matrix calculated from inter-frame spectral similarity.The digital audio can be robustly segmented by correlating a kernel along the diagonal of the similarity matrix. Once segmented, spectral statistics of each segment are computed. In the second step,segments are clustered based on the self-similarity of their statistics. This reveals the structure of the digital music in a set of segment boundaries and labels. Finally, the music is summarized by selecting clusters with repeated segments throughout the piece. The summaries can be customized for various applications based on the structure of the original music.


title: Bacterial gliding motility: multiple mechanisms for cell movement over surfaces.

abstract: The mechanisms responsible for bacterial gliding motility have been a mystery for almost 200 years. Gliding bacteria move actively over surfaces by a process that does not involve flagella. Gliding bacteria are phylogenetically diverse and are abundant in many environments. Recent results indicate that more than one mechanism is needed to explain all forms of bacterial gliding motility. Myxococcus xanthus "social gliding motility" and Synechocystis gliding are similar to bacterial "twitching motility" and rely on type IV pilus extension and retraction for cell movement. In contrast, gliding of filamentous cyanobacteria, mycoplasmas, members of the Cytophaga-Flavobacterium group, and "adventurous gliding" of M. xanthus do not appear to involve pili. The mechanisms of movement employed by these bacteria are still a matter of speculation. Genetic, biochemical, ultrastructural, and behavioral studies are providing insight into the machineries employed by these diverse bacteria that enable them to glide over surfaces.


title: MetaGAN: An Adversarial Approach to Few-Shot Learning

abstract: In this paper, we propose a conceptually simple and general framework called MetaGAN for few-shot learning problems. Most state-of-the-art few-shot classification models can be integrated with MetaGAN in a principled and straightforward way. By introducing an adversarial generator conditioned on tasks, we augment vanilla few-shot classification models with the ability to discriminate between real and fake data. We argue that this GAN-based approach can help few-shot classifiers to learn sharper decision boundary, which could generalize better. We show that with our MetaGAN framework, we can extend supervised few-shot learning models to naturally cope with unsupervised data. Different from previous work in semi-supervised few-shot learning, our algorithms can deal with semi-supervision at both sample-level and task-level. We give theoretical justifications of the strength of MetaGAN, and validate the effectiveness of MetaGAN on challenging few-shot image classification benchmarks.


title: BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation

abstract: Recent leading approaches to semantic segmentation rely on deep convolutional networks trained with human-annotated, pixel-level segmentation masks. Such pixel-accurate supervision demands expensive labeling effort and limits the performance of deep networks that usually benefit from more training data. In this paper, we propose a method that achieves competitive accuracy but only requires easily obtained bounding box annotations. The basic idea is to iterate between automatically generating region proposals and training convolutional networks. These two steps gradually recover segmentation masks for improving the networks, and vise versa. Our method, called "BoxSup", produces competitive results (e.g., 62.0% mAP for validation) supervised by boxes only, on par with strong baselines (e.g., 63.8% mAP) fully supervised by masks under the same setting. By leveraging a large amount of bounding boxes, BoxSup further yields state-of-the-art results on PASCAL VOC 2012 and PASCAL-CONTEXT [26].


title: Rotation invariant neural network-based face detection

abstract: In this paper, we present a neural network-based face detection system. Unlike similar systems which are limited to detecting upright, frontal faces, this system detects faces at any degree of rotation in the image plane. The system employs multiple networks; a "router" network first processes each input window to determine its orientation and then uses this information to prepare the window for one or more "detector" networks. We present the training methods for both types of networks. We also perform sensitivity analysis on the networks, and present empirical results on a large test set. Finally, we present preliminary results for detecting faces rotated out of the image plane, such as profiles and semi-profiles.


title: Smooth human-robot interaction by interruptible gesture planning

abstract: Generating natural body gestures is one of the essential functions in human-robot interaction. Thus various approaches, such as online-planning and the motion database method, have been developed. However, these approaches cannot adapt to user interruption, that usually occurs. Therefore, we propose a novel method for generating motions flexibly and immediately. The proposed method integrates Motion Graph and Probabilistic Roadmap Method. The former helps generate human-like motion, while the latter helps unexpected deviation of the reaction due to human interruption. We implemented the proposed method on the android robot Actroid-SIT as a humanrobot interaction system and conducted evaluation experiments. We confirmed our proposed method can smooth human-robot interaction.


title: Paraphrase identification of malayalam sentences - an experience

abstract: Sentences with different structures may convey the same meaning. Identification of sentences with paraphrases plays an important role in text related research and applications. This work focus on the statistical measures and semantic analysis of malayalam sentences to detect the paraphrases. The statistical similarity measures between sentences, based on symbolic characteristics and structural information, could measure the similarity between sentences without any prior knowledge but only on the statistical information of sentences. The semantic representation of Universal Networking Language(UNL), represents only the inherent meaning in a sentence without any syntactic details. Thus, comparing the UNL graphs of two sentences can give an insight into how semantically similar the two sentences are. Combination of statistical similarity and semantic similarity score results the overall similarity score. This is the first attempt towards paraphrases of malayalam sentences.


title: Fast Semantic Segmentation for Scene Perception

abstract: Semantic segmentation is a challenging problem in computer vision. Many applications, such as autonomous driving and robot navigation with urban road scene, need accurate and efficient segmentation. Most state-of-the-art methods focus on accuracy, rather than efficiency. In this paper, we propose a more efficient neural network architecture, which has fewer parameters, for semantic segmentation in the urban road scene. An asymmetric encoder–decoder structure based on ResNet is used in our model. In the first stage of encoder, we use continuous factorized block to extract low-level features. Continuous dilated block is applied in the second stage, which ensures that the model has a larger view field, while keeping the model small-scale and shallow. The down sampled features from encoder are up sampled with decoder to the same-size output as the input image and the details refined. Our model can achieve end-to-end and pixel-to-pixel training without pretraining from scratch. The parameters of our model are only $0.2M$ , $100 \times$ less than those of others such as SegNet, etc. Experiments are conducted on five public road scene datasets (CamVid, CityScapes, Gatech, KITTI Road Detection, and KITTI Semantic Segmentation), and the results demonstrate that our model can achieve better performance.


Domain Adaptation of SRL Systems for Biological Processes.


title: The development and validation of the cyber dating abuse questionnaire among young couples

abstract: Development and validation of Cyber Dating Abuse Questionnaire.Two factors: direct aggression (greater than 70% prevelence) and control (higher than 10% prevelence).Cyber dating abuse has relationship with offline dating violence and cyberbullying. Cyber dating abuse is a growing phenomenon that has awakened little empirical interest. This study had two objectives: (1) to analyze the psychometric properties of the Cyber Dating Abuse Questionnaire (CDAQ), which is an instrument developed to comprehensively measure this phenomenon; and (2) to conduct an initial analysis of the prevalence and frequency of this type of abuse. The sample consisted of 788 young people between 18 and 30years of age (77.3% women, mean age=22.72years, SD=4.9). First, exploratory and confirmatory factor analyzes revealed a structure composed of two factors for the scales of victimization and for perpetration: direct aggression (an aggressive act with a deliberate intention to hurt the partner/ex-partner, such as insults or threats) and monitoring/control (the use of electronic means to control the partner/ex-partner; for example, the use of personal passwords). Second, the analysis of the relationship between cyber dating abuse and other variables, such as offline physical and psychological violence and cyberbullying, provided additional evidence for the construct validity of the instrument. Third, the reliability analysis (Cronbach's alpha) revealed an adequate internal consistency for the scale. Finally, the prevalence of direct aggression was higher than 10%, and the prevalence of control was greater than 70%, which indicate that both types of cyber abuse seem to be common behaviors among young couples. Finally, the contribution of the present study to previous empirical research and future research is discussed.


RMonto: Ontological Extension to RapidMiner


title: A hybrid biogeography-based optimization and fireworks algorithm

abstract: The paper presents a hybrid biogeography-based optimization (BBO) and fireworks algorithm (FWA) for global optimization. The key idea is to introduce the migration operator of BBO to FWA, in order to enhance information sharing among the population, and thus improve solution diversity and avoid premature convergence. A migration probability is designed to integrate the migration of BBO and the normal explosion operator of FWA, which can not only reduce the computational burden, but also achieve a better balance between solution diversification and intensification. The Gaussian explosion of the enhanced FWA (EFWA) is reserved to keep the high exploration ability of the algorithm. Experimental results on selected benchmark functions show that the hybrid BBO FWA has a significantly performance improvement in comparison with both BBO and EFWA.


title: CNN Is All You Need

abstract: The Convolution Neural Network (CNN) has demonstrated the unique advantage in audio, image and text learning; recently it has also challenged Recurrent Neural Networks (RNNs) with long short-term memory cells (LSTM) in sequence-to-sequence learning, since the computations involved in CNN are easily parallelizable whereas those involved in RNN are mostly sequential, leading to a performance bottleneck. However, unlike RNN, the native CNN lacks the history sensitivity required for sequence transformation; therefore enhancing the sequential order awareness, or position-sensitivity, becomes the key to make CNN the general deep learning model. In this work we introduce an extended CNN model with strengthen position-sensitivity, called PoseNet. A notable feature of PoseNet is the asymmetric treatment of position information in the encoder and the decoder. Experiments shows that PoseNet allows us to improve the accuracy of CNN based sequence-to-sequence learning significantly, achieving around 33-36 BLEU scores on the WMT 2014 English-to-German translation task, and around 44-46 BLEU scores on the English-to-French translation task.


title: Simulation of New Policies for the Baggage Check in the Security Gates of the Airports: The Logiscan Case Study

abstract: In recent years, the trend to mix in the same airports legacy and low-cost companies is heavily affecting the airport operations, stressing the security checks in particular. In fact, low-cost companies force the airports to add additional and strict rules for the hand baggage allowance, with additional checks in the security gates, introducing delays for all the passengers, including the legacy ones. Moreover, recent changes to the rules for hand luggage allowance introduced by legacy airlines are forcing the airport operations management to think of new strategies to automate the entire security check process. One of the most advanced systems presently available to speed up the security operations is Logiscan, developed by Datalogic e Logital in collaboration with the "Aeroporto Guglielmo Marconi di Bologna S.p.A.", the company managing the Bologna Airport. The system automates the operations of size and weight measures and allowance checks and, in conjunction with a redesign of the queuing system, aims to improve the overall system efficiency in terms of accuracy and efficiency. In order to check the new overall system, the entire process has been modeled and analyzed by means of AirSIM, a simulation tool for airport operations based on the well-known OMNeT++ Discrete Event Simulator. AirSIM is an object-oriented simulator able to describe the behavior of different passenger types, as well as to incorporate the logic of different flow management policies. In this paper we will show both how AirSIM can be used to analyze and choose the different parameters of the new security system of an airport, and how the overall system performances can be enhanced


title: The domestic induction heating appliance: An overview of recent research

abstract: In this paper several research topics pertaining to the design and modeling of domestic induction appliances are reviewed. Each topic is summarized, stressing its most significant advances and pointing to its future tendencies. A bibliographic review showing some of the published papers during the last years is included. The emphases and relative contributions of some of them are also discussed.


title: Abstractive Text Summarization by Incorporating Reader Comments

abstract: In neural abstractive summarization field, conventional sequence-to-sequence based models often suffer from summarizing the wrong aspect of the document with respect to the main aspect. To tackle this problem, we propose the task of reader-aware abstractive summary generation, which utilizes the reader comments to help the model produce better summary about the main aspect. Unlike traditional abstractive summarization task, reader-aware summarization confronts two main challenges: (1) Comments are informal and noisy; (2) jointly modeling the news document and the reader comments is challenging. To tackle the above challenges, we design an adversarial learning model named reader-aware summary generator (RASG), which consists of four components: (1) a sequence-to-sequence based summary generator; (2) a reader attention module capturing the reader focused aspects; (3) a supervisor modeling the semantic gap between the generated summary and reader focused aspects; (4) a goal tracker producing the goal for each generation step. The supervisor and the goal tacker are used to guide the training of our framework in an adversarial manner. Extensive experiments are conducted on our large-scale real-world text summarization dataset, and the results show that RASG achieves the stateof-the-art performance in terms of both automatic metrics and human evaluations. The experimental results also demonstrate the effectiveness of each module in our framework. We release our large-scale dataset for further research1.


title: Dynamically Reconfigurable PWM Controller for Three-Phase Voltage-Source Inverters

abstract: This paper presents the design and development of dynamic partially reconfigurable pulsewidth modulation (DPRPWM) controller for three-phase voltage-source inverters (VSI) in a single Xilinx Spartan 3 XCS400PQ208 field programmable gate array (FPGA). The DPRPWM controller is designed such that it switches between the popular PWM techniques like sinusoidal PWM (SPWM) and space vector PWM (SVPWM). The FPGA platform supports the run-time reconfiguration of control functions and algorithms directly in hardware and meets hard real-time performance criteria in terms of timings for PWM generation as well as reconfiguration. The DPRPWM control is simulated and experimentally verified using a low-cost Xilinx Spartan FPGA. The results of SPWM and SVPWM controller are presented and the results prove that the DPRPWM controller for three-phase inverter is highly possible and can be extended to any level/phase PWM controllers.


Future System Challenges in the Design of Renewable Bio-energy Systems and the Synthesis of Sustainable Biorefineries


title: On predicting the time taken to correct bug reports in open source projects

abstract: Existing studies on the maintenance of open source projects focus primarily on the analyses of the overall maintenance of the projects and less on specific categories like the corrective maintenance. This paper presents results from an empirical study of bug reports from an open source project, identifies user participation in the corrective maintenance process through bug reports, and constructs a model to predict the corrective maintenance effort for the project in terms of the time taken to correct faults. Our study focuses on 72482 bug reports from over nine releases of Ubuntu, a popular Linux distribution. We present three main results 1) 95% of the bug reports are corrected by people participating in groups of size ranging from 1 to 8 people, 2) there is a strong linear relationship (about 92%) between the the number of people participating in a bug report and the time taken to correct it, 3) a linear model can be used to predict the time taken to correct bug reports.


title: ADP: automated diagnosis of performance pathologies using hardware events

abstract: Performance characterization of applications' hardware behavior is essential for making the best use of available hardware resources. Modern architectures offer access to many hardware events that are capable of providing information to reveal architectural performance bottlenecks throughout the core and memory hierarchy. These events can provide programmers with unique and powerful insights into the causes of the resource bottlenecks in their applications. However, interpreting these events has been a significant challenge. We present an automated system that uses machine learning to identify an application's performance problems. Our system provides programmers with insights about the performance of their applications while shielding them from the onerous task of digesting hardware events. It uses a decision tree algorithm, random forests on our micro-benchmarks to fingerprint the performance problems. Our system divides a profiled application into functions and automatically classifies each function by the dominant hardware resource bottlenecks. Using the classifications from the hotspot functions, we were able to achieve an average speedup of 1.73 from three applications in the PARSEC benchmark suite. Our system provides programmers with a guideline of where, what, and how to fix the detected performance problems in applications, which would have otherwise required considerable architectural knowledge.


title: Storytelling: The Next Step for Visualization

abstract: Presentation-specifically, its use of elements from storytelling-is the next logical step in visualization research and should be a focus of at least equal importance with exploration and analysis.


title: Deep Continuous Fusion for Multi-sensor 3D Object Detection

abstract: In this paper, we propose a novel 3D object detector that can exploit both LIDAR as well as cameras to perform very accurate localization. Towards this goal, we design an end-to-end learnable architecture that exploits continuous convolutions to fuse image and LIDAR feature maps at different levels of resolution. Our proposed continuous fusion layer encode both discrete-state image features as well as continuous geometric information. This enables us to design a novel, reliable and efficient end-to-end learnable 3D object detector based on multiple sensors. Our experimental evaluation on both KITTI as well as a large scale 3D object detection benchmark shows significant improvements over the state of the art.


title: Security and privacy on internet of things

abstract: There are billions of Internet of things (IoT) devices connecting to the Internet and the number is increasing. As a still ongoing technology, IoT can be used in different fields, such as agriculture, healthcare, manufacturing, energy, retailing and logistics. IoT has been changing our world and the way we live and think. However, IoT has no uniform architecture and there are different kinds of attacks on the different layers of IoT, such as unauthorized access to tags, tag cloning, sybil attack, sinkhole attack, denial of service attack, malicious code injection, and man in middle attack. IoT devices are more vulnerable to attacks because it is simple and some security measures can not be implemented. We analyze the privacy and security challenges in the IoT and survey on the corresponding solutions to enhance the security of IoT architecture and protocol. We should focus more on the security and privacy on IoT and help to promote the development of IoT.


title: An Exploration of Optimization Algorithms for High Performance Tensor Completion

abstract: Tensor completion is a powerful tool used to estimate or recover missing values in multi-way data. It has seen great success in domains such as product recommendation and healthcare. Tensor completion is most often accomplished via low-rank sparse tensor factorization, a computationally expensive non-convex optimization problem which has only recently been studied in the context of parallel computing. In this work, we study three optimization algorithms that have been successfully applied to tensor completion: alternating least squares (ALS), stochastic gradient descent (SGD), and coordinate descent (CCD++). We explore opportunities for parallelism on shared- and distributed-memory systems and address challenges such as memory- and operation-efficiency, load balance, cache locality, and communication. Among our advancements are an SGD algorithm which combines stratification with asynchronous communication, an ALS algorithm rich in level-3 BLAS routines, and a communication-efficient CCD++ algorithm. We evaluate our optimizations on a variety of real datasets using a modern supercomputer and demonstrate speedups through 1024 cores. These improvements effectively reduce time-to-solution from hours to seconds on real-world datasets. We show that after our optimizations, ALS is advantageous on parallel systems of small-to-moderate scale, while both ALS and CCD++ will provide the lowest time-to-solution on large-scale distributed systems.


title: A survey of approaches and trends in person re-identification

abstract: Person re-identification is a fundamental task in automated video surveillance and has been an area of intense research in the past few years. Given an image/video of a person taken from one camera, re-identification is the process of identifying the person from images/videos taken from a different camera. Re-identification is indispensable in establishing consistent labeling across multiple cameras or even within the same camera to re-establish disconnected or lost tracks. Apart from surveillance it has applications in robotics, multimedia and forensics. Person re-identification is a difficult problem because of the visual ambiguity and spatiotemporal uncertainty in a person's appearance across different cameras. These difficulties are often compounded by low resolution images or poor quality video feeds with large amounts of unrelated information in them that does not aid re-identification. The spatial or temporal conditions to constrain the problem are hard to capture. However, the problem has received significant attention from the computer vision research community due to its wide applicability and utility. In this paper, we explore the problem of person re-identification and discuss the current solutions. Open issues and challenges of the problem are highlighted with a discussion on potential directions for further research.


title: Liquid-Tunable Frequency Selective Surfaces

abstract: In this letter, a new technique for designing tunable frequency selective surfaces (FSSs) with the second-order band-pass response is presented and experimentally verified. The technique is applied to an FSS utilizing nonresonant, subwavelength, constituting unit cells. Frequency tuning is achieved by adjusting the location of a liquid metal slug within capillary tubes embedded between the FSS's metallic layers. A wideband flexible frequency tuning covering nearly the entire X band is tested in a waveguide environment and excellent agreement between the measured and simulated results is demonstrated.


title: On the Origin of Thermal Runaway in a Trench Power MOSFET

abstract: In this paper, we investigate the origin of thermal runaway in the trench power MOSFET of a modern smart power IC technology. Experimental data on the temperature rise during power pulses show that the onset of thermal runaway depends on the biasing condition even if the power pulses have equal power dissipation. The beginning of thermal runaway in this work is denoted by the inflection point in the measured temperature data. For the experimental data points, the onset varies from 340 °C to 520 °C. Comparison of these experimental data with an analysis based on the stability factor shows very good agreement. The stability factor analysis demonstrates that, above the temperature compensation point (TCP), the driving force for thermal runaway is the thermally generated leakage current of the parasitic n-p-n bipolar transistor. The decrease of mobility and, hence, the MOS channel current above the TCP stabilizes the power MOSFET. In contrast, below the TCP, both the increase of the MOS channel current and the parasitic n-p-n bipolar transistor leakage current with temperature contribute to the thermal runaway.


title: An HCI View of Configuration Problems

abstract: In recent years, configuration problems have drawn tremendous attention because of their increasing prevalence and their big impact on system availability. We believe that many of these problems are attributable to today's configuration interfaces that have not evolved to accommodate the enormous shift of the system administrator group. Plain text files, as the de facto configuration interfaces, assume administrators' understanding of the system under configuration. They ask administrators to directly edit the corresponding entries with little guidance or assistance. However, this assumption no longer holds for todays administrator group which has expanded greatly to include non- and semi-professional administrators. In this paper, we provide an HCI view of today's configuration problems, and articulate system configuration as a new HCI problem. Moreover, we present the top obstacles to correctly and efficiently configuring software systems, and most importantly their implications on the design and implementation of new-generation configuration interfaces.


title: An Improved Weighted Nuclear Norm Minimization Method for Image Denoising

abstract: Patch-based low rank matrix approximation has shown great potential in image denoising. Among state-of-the-art methods in this topic, the weighted nuclear norm minimization (WNNM) has been attracting significant attention due to its competitive denoising performance. For each local patch in an image, the WNNM method groups nonlocal similar patches by block matching to formulate a low-rank matrix. However, the WNNM often chooses irrelevant patches such that it may lose fine details of the image, resulting in undesirable artifacts in the final reconstruction. In this regards, this paper aims to provide a denoising algorithm which further improves the performance of the WNNM method. For this purpose, we develop a new nonlocal similarity measure by exploiting both pixel intensities and gradients and present a filter that enhances edge information in a patch to improve the performance of low rank approximation. The experimental results on widely used test images demonstrate that the proposed denoising algorithm performs better than other state-of-the-art denoising algorithms in terms of PSNR and SSIM indices as well as visual quality.


title: The physiological response of skin tissues to alternating support pressures in able-bodied subjects

abstract: Abstract Prolonged mechanical loading can lead to breakdown of skin and underlying tissues which can, in turn, develop into a pressure ulcer. The benefits of pressure relief and/or redistribution to minimise risk have been well documented and these strategies can be provided by employing support mattresses in which internal air pressures can be alternated to minimise the risk of pressure ulcers in patients during prolonged periods of bed-rest. The paper describes the performance of a prototype alternating pressure air mattress (APAM), in terms of its ability to maintain skin viability in a group of healthy volunteers lying in a supine position. In particular, the mattress includes a sacral section supported with alternating low pressure (ALP), with values adjusted to subject morphology, using an in-built pressure sensor. The mattress was supported at four different head of bed (HOB) angles ranging from 0 to 60°. Internal mattress pressures and transcutaneous gas ( T c PO 2 / T c PCO 2 ) tensions at the sacrum and a control site, the scapula, were monitored. Interface pressures were also measured. The sensor was found to be sensitive to the BMI values of the 12 healthy volunteers. In the majority of test conditions the internal support produced sacral T c PO 2 values, which either remained similar to those at the scapula or fluctuated at levels providing adequate viability. However in a few cases, associated with a raised HOB angle (≥45°), there was compromise to the skin viability at the sacrum, as reflected in depressed T c PO 2 levels associated with an elevation of T c PCO 2 levels above the normal range. In all cases, interface pressures at the sacrum rarely exceeded 60 mmHg. Although such studies need to be extended to involve bed-bound individuals, the results offer the potential for the development of intelligent APAM systems, whose characteristics can be adjusted to an individual morphology. Such preventive strategies to maintain skin viability at loaded sites will be designed for subjects deemed to be at high risk of developing pressure ulcers.


title: Inhibition of Glycine Transporter-I as a Novel Mechanism for the Treatment of Depression

abstract: Background Antidepressants, aiming at monoaminergic neurotransmission, exhibit delayed onset of action, limited efficacy, and poor compliance. Glutamatergic neurotransmission is involved in depression. However, it is unclear whether enhancement of the N -methyl-D-aspartate (NMDA) subtype glutamate receptor can be a treatment for depression. Methods We studied sarcosine, a glycine transporter-I inhibitor that potentiates NMDA function, in animal models and in depressed patients. We investigated its effects in forced swim test, tail suspension test, elevated plus maze test, novelty-suppressed feeding test, and chronic unpredictable stress test in rats and conducted a 6-week randomized, double-blinded, citalopram-controlled trial in 40 patients with major depressive disorder. Clinical efficacy and side effects were assessed biweekly, with the main outcomes of Hamilton Depression Rating Scale, Global Assessment of Function, and remission rate. The time course of response and dropout rates was also compared. Results Sarcosine decreased immobility in the forced swim test and tail suspension test, reduced the latency to feed in the novelty-suppressed feeding test, and reversed behavioral deficits caused by chronic unpredictable stress test, which are characteristics for an antidepressant. In the clinical study, sarcosine substantially improved scores of Hamilton Depression Rating Scale, Clinical Global Impression, and Global Assessment of Function more than citalopram treatment. Sarcosine-treated patients were much more likely and quicker to remit and less likely to drop out. Sarcosine was well tolerated without significant side effects. Conclusions Our preliminary findings suggest that enhancing NMDA function can improve depression-like behaviors in rodent models and in human depression. Establishment of glycine transporter-I inhibition as a novel treatment for depression waits for confirmation by further proof-of-principle studies.


title: Correlation between actin polymerization and surface receptor segregation in neuroblastoma cells treated with concanavalin A

abstract: In response to concanavalin A (Con A), neuroblastoma cells undergo marked morphological changes which involve the retraction of neurites and the induction of broad and extensive lamellar regions around the cell periphery. From the use of FITC-Con A it was shown that the membrane formed on the induced lamellar regions lacked receptors to Con A from the onset of lamella formation. These receptors were confined to the cell body; they initially showed a uniform distribution and were subsequently collected into patches and finally into aggregates or caps. When the aggregates occurred on the cell periphery their position coincided with areas free of lamellae. Investigations of the lamellar regions in Triton-extracted cell monolayers showed them to consist of a meshwork of actin filaments containing radiating thin filament bundles or microspikes. With increasing time in the presence of Con A there was a progressive increase in the number of radiating microspikes. Previous studies have shown the actin in these lamellar regions to be singly polarized with respect to the cell body. From the segregation of Con A receptors away from areas of actin polymerization in the lamellae it is concluded that actin is involved in some indirect way in surface receptor movement.


title: Audio-visual speech recognition using deep learning

abstract: Audio-visual speech recognition (AVSR) system is thought to be one of the most promising solutions for reliable speech recognition, particularly when the audio is corrupted by noise. However, cautious selection of sensory features is crucial for attaining high recognition performance. In the machine-learning community, deep learning approaches have recently attracted increasing attention because deep neural networks can effectively extract robust latent features that enable various recognition algorithms to demonstrate revolutionary generalization capabilities under diverse application conditions. This study introduces a connectionist-hidden Markov model (HMM) system for noise-robust AVSR. First, a deep denoising autoencoder is utilized for acquiring noise-robust audio features. By preparing the training data for the network with pairs of consecutive multiple steps of deteriorated audio features and the corresponding clean features, the network is trained to output denoised audio features from the corresponding features deteriorated by noise. Second, a convolutional neural network (CNN) is utilized to extract visual features from raw mouth area images. By preparing the training data for the CNN as pairs of raw images and the corresponding phoneme label outputs, the network is trained to predict phoneme labels from the corresponding mouth area input images. Finally, a multi-stream HMM (MSHMM) is applied for integrating the acquired audio and visual HMMs independently trained with the respective features. By comparing the cases when normal and denoised mel-frequency cepstral coefficients (MFCCs) are utilized as audio features to the HMM, our unimodal isolated word recognition results demonstrate that approximately 65 % word recognition rate gain is attained with denoised MFCCs under 10 dB signal-to-noise-ratio (SNR) for the audio signal input. Moreover, our multimodal isolated word recognition results utilizing MSHMM with denoised MFCCs and acquired visual features demonstrate that an additional word recognition rate gain is attained for the SNR conditions below 10 dB.


title: Software-Defined Networking-based Crypto Ransomware Detection Using HTTP Traffic Characteristics

abstract: Abstract Ransomware is currently one of the key threats facing individuals and corporate Internet users. Especially dangerous is crypto ransomware that encrypts important user data, and it is only possible to recover it once a ransom has been paid. Therefore, devising efficient and effective countermeasures is a pressing necessity. In this paper we present a novel Software-Defined Networking (SDN) based detection approach that utilizes the characteristics of the ransomware communication. Based on an observation of network communication between two crypto ransomware families, namely CryptoWall and Locky, we conclude that an analysis of the HTTP message sequences and their respective content sizes is enough to detect such threats. We show the feasibility of our approach by designing and evaluating a proof-of-concept SDN-based detection system. The experimental results confirm that the proposed approach is feasible and efficient.


title: Matching of Remote Sensing Images with Complex Background Variations via Siamese Convolutional Neural Network

abstract: Feature-based matching methods have been widely used in remote sensing image matching given their capability to achieve excellent performance despite image geometric and radiometric distortions. However, most of the feature-based methods are unreliable for complex background variations, because the gradient or other image grayscale information used to construct the feature descriptor is sensitive to image background variations. Recently, deep learning-based methods have been proven suitable for high-level feature representation and comparison in image matching. Inspired by the progresses made in deep learning, a new technical framework for remote sensing image matching based on the Siamese convolutional neural network is presented in this paper. First, a Siamese-type network architecture is designed to simultaneously learn the features and the corresponding similarity metric from labeled training examples of matching and non-matching true-color patch pairs. In the proposed network, two streams of convolutional and pooling layers sharing identical weights are arranged without the manually designed features. The number of convolutional layers is determined based on the factors that affect image matching. The sigmoid function is employed to compute the matching and non-matching probabilities in the output layer. Second, a gridding sub-pixel Harris algorithm is used to obtain the accurate localization of candidate matches. Third, a Gaussian pyramid coupling quadtree is adopted to gradually narrow down the searching space of the candidate matches, and multiscale patches are compared synchronously. Subsequently, a similarity measure based on the output of the sigmoid is adopted to find the initial matches. Finally, the random sample consensus algorithm and the whole-to-local quadratic polynomial constraints are used to remove false matches. In the experiments, different types of satellite datasets, such as ZY3, GF1, IKONOS, and Google Earth images, with complex background variations are used to evaluate the performance of the proposed method. The experimental results demonstrate that the proposed method, which can significantly improve the matching performance of multi-temporal remote sensing images with complex background variations, is better than the state-of-the-art matching methods. In our experiments, the proposed method obtained a large number of evenly distributed matches (at least 10 times more than other methods) and achieved a high accuracy (less than 1 pixel in terms of root mean square error).


title: Nonisolated High Gain DC–DC Converter for DC Microgrids

abstract: DC microgrids are popular due to the integration of renewable energy sources such as solar photovoltaics and fuel cells. Owing to the low output voltage of these dc power generators, high efficient high gain dc–dc converters are in need to connect the dc microgrid. In this paper, a nonisolated high gain dc–dc converter is proposed without using the voltage multiplier cell and/or hybrid switched-capacitor technique. The proposed topology utilizes two nonisolated inductors that are connected in series/parallel during discharging/charging mode. The operation of switches with two different duty ratios is the main advantage of the converter to achieve high voltage gain without using extreme duty ratio. The steady-state analysis of the proposed converter using two different duty ratios is discussed in detail. In addition, a 100 W, 20/200 V prototype circuit of the high gain dc–dc converter is developed, and the performance is validated using experimental results.


title: Performance analysis of 802.11 CSMA/CA for infrastructure networks under finite load conditions

abstract: In this paper we investigate the performance of 802.11 carrier sense multiple access with collision avoidance algorithm for infrastructure networks under finite load conditions. An analytical model based on discrete time Markov chain is proposed to compute essential performance characteristics. The closed form solutions for the chain's stationary distribution and system throughput are derived. We model each wireless station as an M/G/1/ queue. The model accounts for arbitrary packet arrival rate, wireless channel access delay, transmission retry limits and arbitrary packet size distribution. Analytical results are verified through extensive simulation


title: Collision prediction for polyhedra under screw motions

abstract: The prediction of collisions amongst N rigid objects may be reduced to a series of computations of the time to first contact for all pairs of objects. Simple enclosing bounds and hierarchical partitions of the space-time domain are often used to avoid testing object-pairs that clearly will not collide. When the remaining pairs involve only polyhedra under straight-line translation, the exact computation of the collision time and of the contacts requires only solving for intersections between linear geometries. When a pair is subject to a more general relative motion, such a direct collision prediction calculation may be intractable. The popular brute force collision detection strategy of executing the motion for a series of small time steps and of checking for static interferences after each step is often computationally prohibitive. We propose instead a less expensive collision prediction strategy, where we approximate the relative motion between pairs of objects by a sequence of screw motion segments, each defined by the relative position and orientation of the two objects at the beginning and at the end of the segment. We reduce the computation of the exact collision time and of the corresponding face/vertex and edge/edge collision points to the numeric extraction of the roots of simple univariate analytic functions. Furthermore, we propose a series of simple rejection tests, which exploit the particularity of the screw motion to immediately decide that some objects do not collide or to speed-up the prediction of collisions by about 30%, avoiding on average 3/4 of the root-finding queries even when the object actually collide.


title: Localized Supervised Metric Learning on Temporal Physiological Data

abstract: Effective patient similarity assessment is important for clinical decision support. It enables the capture of past experience as manifested in the collective longitudinal medical records of patients to help clinicians assess the likely outcomes resulting from their decisions and actions. However, it is challenging to devise a patient similarity metric that is clinically relevant and semantically sound. Patient similarity is highly context sensitive: it depends on factors such as the disease, the particular stage of the disease, and co-morbidities. One way to discern the semantics in a particular context is to take advantage of physicians’ expert knowledge as reflected in labels assigned to some patients. In this paper we present a method that leverages localized supervised metric learning to effectively incorporate such expert knowledge to arrive at semantically sound patient similarity measures. Experiments using data obtained from the MIMIC II database demonstrate the effectiveness of this approach.


title: Semi-supervised Elastic net for pedestrian counting

abstract: Pedestrian counting plays an important role in public safety and intelligent transportation. Most pedestrian counting algorithms based on supervised learning require much labeling work and rarely exploit the topological information of unlabelled data in a video. In this paper, we propose a Semi-Supervised Elastic Net (SSEN) regression method by utilizing sequential information between unlabelled samples and their temporally neighboring samples as a regularization term. Compared with a state-of-the-art algorithm, extensive experiments indicate that our algorithm can not only select sparse representative features from the original feature space without losing their interpretability, but also attain superior prediction performance with only very few labelled frames.


title: Control Methods of Inverter-Interfaced Distributed Generators in a Microgrid System

abstract: Microgrids are a new concept for future energy distribution systems that enable renewable energy integration and improved energy management capability. Microgrids consist of multiple distributed generators (DGs) that are usually integrated via power electronic inverters. In order to enhance power quality and power distribution reliability, microgrids need to operate in both grid-connected and island modes. Consequently, microgrids can suffer performance degradation as the operating conditions vary due to abrupt mode changes and variations in bus voltages and system frequency. This paper presents controller design and optimization methods to stably coordinate multiple inverter-interfaced DGs and to robustly control individual interface inverters against voltage and frequency disturbances. Droop-control concepts are used as system-level multiple DG coordination controllers, and control theory is applied to device-level inverter controllers. Optimal control parameters are obtained by particle-swarm-optimization algorithms, and the control performance is verified via simulation studies.


title: Chip scale package with low cost substrate evaluation and characterization

abstract: In recent years, low cost solution was demanded to develop for Chip scale package series package to achieve the same performance or better than original. Regarding this requirement, the low cost substrate (coreless substrate) was developed to replace traditional substrate to achieve this target easily such as Molded Interconnection System BGA (MISBGA), Embedded Trace Substrate (ETS) and Single Layer Substrate (SLP) to effectively shrink package thickness, provide equivalent or more I/O interconnections and low cost solution. This paper will aim to investigate CSP package characteristics with MISBGA, ETS and SLP substrate by using Finite Element Method (FEM) to discover the strong and weak point in those coreless substrates. According to previous experience, the warpage performance of coreless substrates would become more sensitive than traditional substrate since no rigid core material supporting package. Therefore, the higher accuracy of predictive warpage from simulation would become more important in FEM model. Based on that, this study will input TMA&DMA material property measuring in house instead of traditional data sheet material property and also consider the structure tolerance and material property tolerance into the FEM model to show maximum, mean and minimum predictive warpage which method is more similar with real moire measurement data to check the warpage to meet criteria or not. According to lots simulation work, it is successful to apply low cost solution of coreless substrate into novel CSP series and also can achieve shrinking package thickness, equivalent or more I/O interconnections and better thermal capability advantages from new warpage simulation methodology and thermal prediction.


title: Automatic Forest-Fire Measuring Using Ground Stations and Unmanned Aerial Systems

abstract: This paper presents a novel system for automatic forest-fire measurement using cameras distributed at ground stations and mounted on Unmanned Aerial Systems (UAS). It can obtain geometrical measurements of forest fires in real-time such as the location and shape of the fire front, flame height and rate of spread, among others. Measurement of forest fires is a challenging problem that is affected by numerous potential sources of error. The proposed system addresses them by exploiting the complementarities between infrared and visual cameras located at different ground locations together with others onboard Unmanned Aerial Systems (UAS). The system applies image processing and geo-location techniques to obtain forest-fire measurements individually from each camera and then integrates the results from all the cameras using statistical data fusion techniques. The proposed system has been extensively tested and validated in close-to-operational conditions in field fire experiments with controlled safety conditions carried out in Portugal and Spain from 2001 to 2006.