title: Clinical value of whole-body FDG-PET for recurrent gastric cancer: a multicenter study.

abstract: OBJECTIVE ::: The purpose of this multicenter study was to evaluate the clinical usefulness of positron emission tomography (PET) using (18)F-fluorodeoxyglucose (FDG) for suspected recurrent gastric cancer. ::: ::: ::: METHODS ::: We performed a retrospective review of 92 consecutive patients who underwent PET [either integrated PET/computed tomography (CT) or manual fusion of dedicated PET and CT] scans for post-treatment surveillance of gastric cancer between June 2006 and December 2007. Of these patients, 46 patients were suspected of recurrence by other imaging modalities (Group A), 19 patients were suspected of recurrence by tumor markers without definite findings (Group B) and the remaining 27 patients underwent a PET scan without evidence of recurrence (Group C). The diagnostic performance and prevalence of the clinical impact of FDG-PET were analyzed. ::: ::: ::: RESULTS ::: Recurrence of gastric cancer was confirmed in 31 patients (67%) in Group A, in 11 patients (58%) in Group B and in 2 patients (7%) in Group C. In addition, colon cancer (n = 3), lung cancer (n = 1) and pulmonary carcinoid (n = 1) were identified in five patients (5%). In patient-basis, the sensitivity, specificity and diagnostic accuracy of PET for recurrence were 81%, 87% and 83%, respectively, in Group A, 73%, 88% and 79%, respectively, in Group B and 50%, 88% and 85%, respectively, in Group C. Therapeutic management was influenced by PET results in 22 patients (48%) in Group A, in 8 patients (42%) in Group B and in 2 patients (7%) in Group C, including cases in which PET was helpful for detecting second primary cancer. ::: ::: ::: CONCLUSIONS ::: PET with FDG yielded useful information in patients with suspected recurrent gastric cancer, especially when recurrence was suspected in the clinical setting.


title: New dynamic model for a Ballbot system

abstract: A Ballbot is a self-balanced mobile robot designed for omnidirectional mobility. The structure self-balanced on a ball giving to the system only one contact point with the ground. In this paper the dynamical model of a Ballbot system is investigated in order to find a linearized model which is able to describe the three-dimensional dynamics of the mechatronic system by a simpler set of equations. Due to the system's complexity, the equations of motion are often obtained by the energy method of Lagrange, they consist of a vast nonlinear ordinary differential equations (ODE), which are often numerically linearized for small perturbations. The present paper proposes to model the whole 3D dynamics of the Ballbot with the Newton-Euler formalism and Tait-Bryan angles in order to describe the model in terms of the system's physical parameters without resorting to numeric solution. This physical modelling is introduced to allow the simplification of the dynamic motion control of the ballbot.


title: FF-Replan: A baseline for probabilistic planning

abstract: FF-Replan was the winner of the 2004 International Probabilistic Planning Competition (IPPC-04) (Younes & Littman 2004a) and was also the top performer on IPPC-06 domains, though it was not an official entry. This success was quite surprising, due to the simplicity of the approach. In particular, FF-Replan calls FF on a carefully constructed deterministic variant of the planning problem and selects actions according to the plan until observing an unexpected effect, upon which it replans. Despite the obvious shortcomings of the approach and its strawman nature, it is the state-of-the-art in probabilistic planning as measured on recent competition benchmarks. This paper gives the first technical description of FF-Replan and provides an analysis of its results on all of the recent IPPC-04 and IPPC-06 domains. We hope that this will inspire extensions and insight into the approach and planning domains themselves that will soon lead to the dethroning of FF-Replan.


title: Managing political differences in social media

abstract: Most people associate with people like themselves, a process called homophily. Exposure to diversity, however, makes us more informed as individuals and as a society. In this paper, we investigate political disagreements on Facebook to explore the conditions under which diverse opinions can coexist online. Via a mixed methods approach comprising 103 survey responses and 13 interviews with politically engaged American social media users, we found that participants who perceived more differences with their friends engaged less on Facebook than those who perceived more homogeneity. Weak ties were particularly brittle to political disagreements, despite being the ties most likely to offer diversity. Finally, based on our findings we suggest potential design opportunities to bridge across ideological difference: 1) support exposure to weak ties; and 2) make common ground visible while friends converse.


title: The validity of animal models of depression

abstract: Eighteen animal, models of depression are reviewed in relation to three sets of validating criteria. Of the 18 models, five could only be assessed for predictive, validity, seven could be assessed for predictive and face validity, and six could potentially have predictive, face and construct validity. Some traditional models (reserpine reversal, amphetamine potentiation) are rejected as invalid; the models with the highest overall validity are the intracranial self-stimulation, chronic stress and learned helplessness models in rats, and the primate separation model.


title: [Anemia in women of reproductive age. The results of a national probability survey].

abstract: Iron deficiency is one of the most prevalent forms of malnutrition which is clinically known as anemia. Functional consequences of anemia include impairment of cardiovascular performance, limitation in productivity, higher incidence of low birth weight and premature delivery, and increased maternal mortality. This paper presents a descriptive analysis of anemia in women of reproductive age in Mexico. Data were collected by the Ministry of Health through a National Nutrition Survey in 1988, which draw a representative sample from four regions: North, Center, South and Federal District. Anemia was more prevalent in pregnant (18.17%) than in non-pregnant women (15.38%). Those women living in predominantly indigenous communities had higher prevalence of anemia (24.02%) than non-indigenous women (14.67%). Anemia was more prevalent in urban areas (15.54%) than in rural (13.56%). Mean +/- standard deviation values for hemoglobin were lower in pregnant women (12.5 +/- 1.6 g/dL) than in non-pregnant ones (13.7 +/- 1.6 g/dL). Consistently, the Northern and Southern regions were worse off than the Center and the Federal District. These data indicate that anemia is a public health problem in Mexico. The functional consequences of this deficiency justify interventions to treat and prevent it.


title: On the use of maximum entropy/autoregressive spectrum in harmonic analysis of time series

abstract: The maximum entropy (ME) spectrum, or its equivalent form of the autoregressive (AR) spectrum, has been used as a tool for harmonic analysis of time series in geophysics. This paper critically examines its usage in estimating the amplitude and the exponential decay rate of a harmonic function. The argument is based upon Prony's relation, which relates a complex-conjugate pair of poles for the AR model of the time series on one hand, to the complex frequency of one harmonic component in the time series on the other. It is found that: (i) the ME spectrum can be used as an estimator for the decay rate in a way similar to the Fourier spectral analysis; (ii) the ME spectrum contains no information whatsoever about the amplitude, contrary to what has been claimed and practiced in geophysical applications.


title: Occlusion Patterns for Object Class Detection

abstract: Despite the success of recent object class recognition systems, the long-standing problem of partial occlusion remains a major challenge, and a principled solution is yet to be found. In this paper we leave the beaten path of methods that treat occlusion as just another source of noise - instead, we include the occluder itself into the modelling, by mining distinctive, reoccurring occlusion patterns from annotated training data. These patterns are then used as training data for dedicated detectors of varying sophistication. In particular, we evaluate and compare models that range from standard object class detectors to hierarchical, part-based representations of occluder/occludee pairs. In an extensive evaluation we derive insights that can aid further developments in tackling the occlusion challenge.


title: Back to the Future - Part 1. The medico-legal autopsy from ancient civilization to the post-genomic era

abstract: Part 1 of the review "Back to the Future" examines the historical evolution of the medico-legal autopsy and microscopy techniques, from Ancient Civilization to the Post-Genomic Era. In the section focusing on "The Past", the study of historical sources concerning the origins and development of the medico-legal autopsy, from the Bronze Age until the Middle Ages, shows how, as early as 2000 BC, the performance of autopsies for medico-legal purposes was a known and widespread practice in some ancient civilizations in Egypt, the Far East and later in Europe. In the section focusing on "The Present", the improvement of autopsy techniques by Friedrich Albert Zenker and Rudolf Virchow and the contemporary development of optical microscopy techniques for forensic purposes during the 19th and 20th centuries are reported, emphasizing, the regulation of medico-legal autopsies in diverse nations around the world and the publication of international guidelines or best practices elaborated by International Scientific Societies. Finally, in "The Future" section, innovative robotized and advanced microscopy systems and techniques, including their possible use in the bio-medicolegal field, are reported, which should lead to the improvement and standardization of the autopsy methodology, thereby achieving a more precise identification of natural and traumatic pathologies.


title: Programming with Personalized PageRank: A Locally Groundable First-Order Probabilistic Logic

abstract: Many information-management tasks (including classification, retrieval, information extraction, and information integration) can be formalized as inference in an appropriate probabilistic first-order logic. However, most probabilistic first-order logics are not efficient enough for realistically-sized instances of these tasks. One key problem is that queries are typically answered by "grounding" the query---i.e., mapping it to a propositional representation, and then performing propositional inference---and with a large database of facts, groundings can be very large, making inference and learning computationally expensive. Here we present a first-order probabilistic language which is well-suited to approximate "local" grounding: in particular, every query $Q$ can be approximately grounded with a small graph. The language is an extension of stochastic logic programs where inference is performed by a variant of personalized PageRank. Experimentally, we show that the approach performs well on an entity resolution task, a classification task, and a joint inference task; that the cost of inference is independent of database size; and that speedup in learning is possible by multi-threading.


title: Implicit Regularization in Deep Learning

abstract: In an attempt to better understand generalization in deep learning, we study several possible explanations. We show that implicit regularization induced by the optimization method is playing a key role in generalization and success of deep learning models. Motivated by this view, we study how different complexity measures can ensure generalization and explain how optimization algorithms can implicitly regularize complexity measures. We empirically investigate the ability of these measures to explain different observed phenomena in deep learning. We further study the invariances in neural networks, suggest complexity measures and optimization algorithms that have similar invariances to those in neural networks and evaluate them on a number of learning tasks.


title: SAMSA2: a standalone metatranscriptome analysis pipeline

abstract: BackgroundComplex microbial communities are an area of growing interest in biology. Metatranscriptomics allows researchers to quantify microbial gene expression in an environmental sample via high-throughput sequencing. Metatranscriptomic experiments are computationally intensive because the experiments generate a large volume of sequence data and each sequence must be compared with reference sequences from thousands of organisms.ResultsSAMSA2 is an upgrade to the original Simple Annotation of Metatranscriptomes by Sequence Analysis (SAMSA) pipeline that has been redesigned for standalone use on a supercomputing cluster. SAMSA2 is faster due to the use of the DIAMOND aligner, and more flexible and reproducible because it uses local databases. SAMSA2 is available with detailed documentation, and example input and output files along with examples of master scripts for full pipeline execution.ConclusionsSAMSA2 is a rapid and efficient metatranscriptome pipeline for analyzing large RNA-seq datasets in a supercomputing cluster environment. SAMSA2 provides simplified output that can be examined directly or used for further analyses, and its reference databases may be upgraded, altered or customized to fit the needs of any experiment.


title: Real-to-Virtual Domain Unification for End-to-End Autonomous Driving

abstract: In the spectrum of vision-based autonomous driving, vanilla end-to-end models are not interpretable and suboptimal in performance, while mediated perception models require additional intermediate representations such as segmentation masks or detection bounding boxes, whose annotation can be prohibitively expensive as we move to a larger scale. More critically, all prior works fail to deal with the notorious domain shift if we were to merge data collected from different sources, which greatly hinders the model generalization ability. In this work, we address the above limitations by taking advantage of virtual data collected from driving simulators, and present DU-drive, an unsupervised real-to-virtual domain unification framework for end-to-end autonomous driving. It first transforms real driving data to its less complex counterpart in the virtual domain and then predicts vehicle control commands from the generated virtual image. Our framework has three unique advantages: 1) it maps driving data collected from a variety of source distributions into a unified domain, effectively eliminating domain shift; 2) the learned virtual representation is simpler than the input real image and closer in form to the "minimum sufficient statistic" for the prediction task, which relieves the burden of the compression phase while optimizing the information bottleneck tradeoff and leads to superior prediction performance; 3) it takes advantage of annotated virtual data which is unlimited and free to obtain. Extensive experiments on two public driving datasets and two driving simulators demonstrate the performance superiority and interpretive capability of DU-drive.


Volumetric convnets with mixed residual connections for automated prostate segmentation from 3d MR images


title: Traffic sign detection and recognition based on random forests

abstract: Graphical abstractDisplay Omitted In this paper we present a new traffic sign detection and recognition (TSDR) method, which is achieved in three main steps. The first step segments the image based on thresholding of HSI color space components. The second step detects traffic signs by processing the blobs extracted by the first step. The last one performs the recognition of the detected traffic signs. The main contributions of the paper are as follows. First, we propose, in the second step, to use invariant geometric moments to classify shapes instead of machine learning algorithms. Second, inspired by the existing features, new ones have been proposed for the recognition. The histogram of oriented gradients (HOG) features has been extended to the HSI color space and combined with the local self-similarity (LSS) features to get the descriptor we use in our algorithm. As a classifier, random forest and support vector machine (SVM) classifiers have been tested together with the new descriptor. The proposed method has been tested on both the German Traffic Sign Detection and Recognition Benchmark and the Swedish Traffic Signs Data sets. The results obtained are satisfactory when compared to the state-of-the-art methods.


title: SMART LIVING USING BLUETOOTH- BASED ANDROID SMARTPHONE

abstract: With the development of modern technology and Android Smartphone, Smart Living is gradually changing people’s life. Bluetooth technology, which aims to exchange data wirelessly in a short distance using short-wavelength radio transmissions, is providing a necessary technology to create convenience, intelligence and controllability. In this paper, a new Smart Living system called home lighting control system using Bluetooth-based Android Smartphone is proposed and prototyped. First Smartphone, Smart Living and Bluetooth technology are reviewed. Second the system architecture, communication protocol and hardware design aredescribed. Then the design of a Bluetooth-based Smartphone application and the prototype are presented. It is shown that Android Smartphone can provide a platform to implement Bluetooth-based application for Smart Living.


Survey on Malicious Web Pages Detection Techniques


title: LabVIEW based control software for finger force sensor instrumentation design

abstract: This report describes National Instruments (NI) LabVIEW software for the control of finger force instrumentation for the quantification of Repetitive Stress Injury (RSI), Carpal Tunnel Syndrome (CTS), or other applications requiring automated data acquisition of applied finger force using resistive type force sensors. The quantification of finger force requires the precise data collection from resistive force sensors for all five fingers and keyboard key switches. This abstract describes how a LabVIEW based software application allows for the precise control over data force collection for all fingers and key switches, while acquiring human subject's study information, isometric & datalogging finger force, and providing the user with visual force measurements feedback. The implementation of this software provides a fast prototyping for evaluating finger force instrumentation. While no other LabVIEW based software application is available to date for this finger force data collection, our system proved rugged and flexible enough to accommodate fine tuning changes on hardware and software, with a minimum of dead time during usage. The software is available for download at www.joseagraz.com.


title: Permissions snapshots: Assessing users' adaptation to the Android runtime permission model

abstract: The Android operating system changed its security- and privacy-related permission model recently, offering its users the ability to control resources that applications are allowed to access on their devices. This major change to the traditional coarse-grained permission system was anticipated for a long time by privacy-aware users. This paper presents the first study that analyzes Android users' adaptation to the fine-grained runtime permission model, regarding their security and privacy controls. We gathered anonymous data from 50 participants who downloaded our application and answered questions related to the new permission model. The results indicate that the majority of users prefer the new model. We also collected data that demonstrate users' security controls at the given time. Our analysis shows that individuals make consistent choices regarding the resources they allow to various applications to access.


title: Multiple Pedestrian Tracking From Monocular Videos in an Interacting Multiple Model Framework

abstract: We present a multiple pedestrian tracking method for monocular videos captured by a fixed camera in an interacting multiple model (IMM) framework. Our tracking method involves multiple IMM trackers running in parallel, which are tied together by a robust data association component. We investigate two data association strategies which take into account both the target appearance and motion errors. We use a 4D color histogram as the appearance model for each pedestrian returned by a people detector that is based on the histogram of oriented gradients features. Short-term occlusion problems and false negative errors from the detector are dealt with using a sliding window of video frames, where tracking persists in the absence of observations. Our method has been evaluated, and compared both qualitatively and quantitatively with four state-of-the-art visual tracking methods using benchmark video databases. The experiments demonstrate that, on average, our tracking method outperforms these four methods.


title: A study of elderly people's emotional understanding of prompts given by virtual humans

abstract: This paper presents a study conducted to understand how facial expressions in audio-visual prompts given by virtual humans are understood by elderly people on an emotional level with an aim to design emotionally aligned prompts for persons with cognitive disabilities who often need assistance from a caregiver to complete daily living activities such as washing hands, making food, or getting dressed. Artificially intelligent systems have been developed that can assist in such situations. Our long term aim is to enhance such systems by delivering automated prompts that are emotionally aligned with individuals in order to have better human-system interaction in helping with the tasks. This paper presents a set of prompts of male and female virtual humans with a focus on their facial expressions. A user study with elderly persons was conducted with respect to three basic and important dimensions of emotional experience: Evaluation, Potency, and Activity (EPA). Results show that there is significant consensus on E and P dimensions, and some consensus on the A dimension.


title: Automatic Scaling of Fish Images

abstract: In aquaculture breeding programs where large numbers of fish need to be rapidly phenotyped, the absolute physical dimensions of fish (in millimeters or inches) are often required to be extracted from electronic images in order to measure the size of the fish. While it is possible to infer the length of the fish in pixels, the absolute scale of the image (in pixels-per-millimeter or dots-per-inch) is largely unknown without a reference grid, or requires additional hardware, data collection and/or record-keeping management overheads. One cost and time effective solution is to capture the absolute scale by including a measuring ruler in the photographed scene and from which a computer program can automatically identify the scale of the photo and calculate fish morphometric measurements. To assist such workflow, this study developed an algorithm that automatically detects a ruler in a given image, and automatically extracts its scale as distance (in fractional number of pixels) between the ruler's graduation marks. The algorithm was applied to 445 publicly available images of barramundi or Asian seabass (Lates calcarifer), where a millimeter-graded ruler was included in each image. Convolutional Neural Network (CNN) was trained to segment the images into ruler, background, fish and label sections. Then the distance-extraction algorithm was applied to the ruler section of the images. The false-negative rate was less than 2%, where the ruler graduation distances could not be extracted in only 2-6 (out of 445) images even when the test images were rotated up to 90 degrees. The mean absolute relative error (MARE) of the inferred distances was 1-2%.


title: iDedup: latency-aware, inline data deduplication for primary storage

abstract: Deduplication technologies are increasingly being deployed to reduce cost and increase space-efficiency in corporate data centers. However, prior research has not applied deduplication techniques inline to the request path for latency sensitive, primary workloads. This is primarily due to the extra latency these techniques introduce. Inherently, deduplicating data on disk causes fragmentation that increases seeks for subsequent sequential reads of the same data, thus, increasing latency. In addition, deduplicating data requires extra disk IOs to access on-disk deduplication metadata. In this paper, we propose an inline deduplication solution, iDedup, for primary workloads, while minimizing extra IOs and seeks. ::: ::: Our algorithm is based on two key insights from real-world workloads: i) spatial locality exists in duplicated primary data; and ii) temporal locality exists in the access patterns of duplicated data. Using the first insight, we selectively deduplicate only sequences of disk blocks. This reduces fragmentation and amortizes the seeks caused by deduplication. The second insight allows us to replace the expensive, on-disk, deduplication metadata with a smaller, in-memory cache. These techniques enable us to tradeoff capacity savings for performance, as demonstrated in our evaluation with real-world workloads. Our evaluation shows that iDedup achieves 60-70% of the maximum deduplication with less than a 5% CPU overhead and a 2-4% latency impact.


title: Estimating the γ‐ray contribution to the signal of fission chambers with Monte Carlo simulations

abstract: Abstract The fission chambers that provide in-core measurements of the neutron flux in nuclear reactors are also sensitive to the γ ‐ ray flux. According to experimental data, this contribution can be dominant in current mode. We present a calculation route, relying on a description of the physical phenomena, that compute the γ ‐ ray signal in a fission chamber, coming directly from the γ ‐ ray flux, and indirectly from the neutron flux and activated structures. We use it in a working example for which we show that the main contribution of the γ ‐ ray flux to the signal comes from the interaction of the γ ‐ rays with the electrodes. The relative contribution of gammas created by neutron interactions in the detector is small. The comparison of this example with experimental results yields a good qualitative agreement: the γ ‐ ray signal may dominate in current mode, but is effectively suppressed in fluctuation mode. This calculation route will be useful for the design of fission chambers to be operated under high neutron and γ ‐ ray flux.


title: The RIPASA score is sensitive and specific for the diagnosis of acute appendicitis in a western population

abstract: Aim ::: The definitive diagnosis of acute appendicitis (AA) requires histopathological examination. Various clinical diagnostic scoring systems attempt to reduce negative appendectomy rates. The most commonly used in Western Europe and the USA is the Alvarado score. The Raja Isteri Pengiran Anak Saleha appendicitis (RIPASA) score achieves better sensitivity and specificity in Asian and Middle Eastern populations. We aimed to determine the diagnostic accuracy of the RIPASA score in Irish patients with AA.


title: Imputations of Missing Values in Practice: Results from Imputations of Serum Cholesterol in 28 Cohort Studies

abstract: Missing values, common in epidemiologic studies, are a major issue in obtaining valid estimates. Simulation studies have suggested that multiple imputation is an attractive method for imputing missing values, but it is relatively complex and requires specialized software. For each of 28 studies in the Asia Pacific Cohort Studies Collaboration, a comparison of eight imputation procedures (unconditional and conditional mean, multiple hot deck, expectation maximization, and four different approaches to multiple imputation) and the naive, complete participant analysis are presented in this paper. Criteria used for comparison were the mean and standard deviation of total cholesterol and the estimated coronary mortality hazard ratio for a one-unit increase in cholesterol. Further sensitivity analyses allowed for systematic over- or underestimation of cholesterol. For 22 studies for which less than 10% of the values for cholesterol were missing, and for the pooled Asia Pacific Cohort Studies Collaboration, all methods gave similar results. For studies with roughly 10-60% missing values, clear differences existed between the methods, in which case past research suggests that multiple imputation is the method of choice. For two studies with over 60% missing values, no imputation method seemed to be satisfactory.


title: Features of graded category structure: Generalizing the family resemblance and polymorphous concept models

abstract: Many real-world categories contain graded structure: certain category members are rated as more typical or representative of the category than others. Research has shown that this graded structure can be well predicted by the degree of commonality across the feature sets of category members. We demonstrate that two prominent feature-based models of graded structure, the family resemblance (Rosch & Mervis, 1975) and polymorphous concept models (Hampton, 1979), can be generalized via the contrast model (Tversky, 1977) to include both common and distinctive feature information, and apply the models to the prediction of typicality in 11 semantic categories. The results indicate that both types of feature information play a role in the prediction of typicality, with common features weighted more heavily for within-category predictions, and distinctive features weighted more heavily for contrast-category predictions. The same pattern of results was found in additional analyses employing rated goodness and exemplar generation frequency. It is suggested that these findings provide insight into the processes underlying category formation and representation.


Survey of Different Types of CAPTCHA


title: A compact UWB rectangular slotted monopole antenna

abstract: A compact rectangular slotted monopole antenna for ultra wideband (UWB) application is presented. The designed antenna has a simple structure and compact size of 25 × 26 mm ::: 2 ::: . This antenna consist of radiating patch with two steps and one slot introduced on it for bandwidth enhancement and a ground plane. Antenna is feed with 50Ω microstrip line. IE3D method of moments based simulation software is used for design and FR4 substrate of dielectric constant value 4.4 with loss tangent 0.02.


title: Interactive maps for visual data exploration

abstract: Descartes (formerly called IRIS) is a software system designed to support visual exploration of spatially referenced data, e.g. demographic, economical, or cultural information about geographical objects or locations such as countries, districts, or cities. Descartes offers two integrated services: automated presentation of data on maps, and facilities to interactively manipulate these maps. Automated mapping is enabled by incorporating generic knowledge on map design into the system. Descartes selects suitable presentation methods according to characteristics of the variables to be analysed and relationships among those variables if more than one were selected simultaneously. The cartographic knowledge of Descartes allows non-cartographers to receive proper presentations of their data, and the automation of map construction helps the users to save valuable time that can better be used for data analysis and problem-solving. Exploratory data analysis requires highly interactive, dynamic data displays. We s...


title: Integrating National Culture into IS Research: The Need for Current Individual Level Measures

abstract: Cross-cultural IS research is beginning to mature; however, much is left to do. This article reviews the most popular conceptualization of National Culture and offers suggestions for improvements in measurement. While Hofstedefs culture dimensions uncertainty avoidance, power distance, masculinity/femininity, and individualism/collectivism are still widely used in many disciplines; it is not guaranteed that the measures still hold after over 30 years. Empirical evidence is presented from two studies that indicate that shifts might have occurred. Because the usual national culture constructs are measured at the national level, they also should not be used in individual models of behavior or technology acceptance.


title: Deep Representation for Abnormal Event Detection in Crowded Scenes

abstract: Abnormal event detection is extremely important, especially for video surveillance. Nowadays, many detectors have been proposed based on hand-crafted features. However, it remains challenging to effectively distinguish abnormal events from normal ones. This paper proposes a deep representation based algorithm which extracts features in an unsupervised fashion. Specially, appearance, texture, and short-term motion features are automatically learned and fused with stacked denoising autoencoders. Subsequently, long-term temporal clues are modeled with a long short-term memory (LSTM) recurrent network, in order to discover meaningful regularities of video events. The abnormal events are identified as samples which disobey these regularities. Moreover, this paper proposes a spatial anomaly detection strategy via manifold ranking, aiming at excluding false alarms. Experiments and comparisons on real world datasets show that the proposed algorithm outperforms state of the arts for the abnormal event detection problem in crowded scenes.


title: A distributed media server management scheme

abstract: In a distributed multimedia system, the distribution of digitized video, audio or other type of streamed data presents problems in both data communications and management of media servers. The system should be able to serve users in a wide geographical area and to adapt itself to a varying number of users. We propose an Internet-based management scheme for a media server system, assembled by a variable number of media servers geographically dispersed, where user requests are directed dynamically to an appropriate server according to their location and served in a unicast way. The scalability of the system is based on multicast type of negotiation between servers and shifting the management of content and load from centralized gateway to the servers.


title: Harvard Aging Brain Study: Dataset and accessibility

abstract: Abstract The Harvard Aging Brain Study is sharing its data with the global research community. The longitudinal dataset consists of a 284-subject cohort with the following modalities acquired: demographics, clinical assessment, comprehensive neuropsychological testing, clinical biomarkers, and neuroimaging. To promote more extensive analyses, imaging data was designed to be compatible with other publicly available datasets. A cloud-based system enables access to interested researchers with blinded data available contingent upon completion of a data usage agreement and administrative approval. Data collection is ongoing and currently in its fifth year.


title: CHI ’ 95 MOSAIC OF CREATIVITY ■ May 7-11 1995 ~ Usability Inspection Methods

abstract: Usability inspection is the generic name for a set of costeffective ways of evaluating user interfaces to find usability problems. They are fairly informal methods and easy to use.


title: REDUCING THE TIME DEPENDENT COMPLEXITY IN ORGANIZATIONAL SYSTEMS USING THE CONCEPT OF FUNCTIONAL PERIODICITY

abstract: Economic growth and sustainable value creation are a company’s most important long-term targets. However, practical experience shows that large structures notoriously suffer from efficiency losses due to increasing organizational complexity and bureaucracy. To realize a profitable growth, companies have to be good in handling their internal diseconomies caused by the increased complexity of large organizations [Matt, 2007/b]. This hypothesis is confirmed by a study of the St. Gallen Centre of Organizational Excellence (CORE) in which 300 large European enterprises were analyzed within a period of 10 years [Gomez et al., 2007]. The results of the research show: the central challenge for profitable growth consists in a company’s ability to continuously improve its organization in efficiency and flexibility. Thus, one of the major challenges is to select an organizational system configuration that promotes a sustainable business growth and is easy to operate and manage. In this paper a concept for reducing the complexity of an organizational system is discussed in order to maintain a company’s high system efficiency as a success factor for sustainable growth. Starting from the Axiomatic Design based complexity theory a procedure is presented that helps system designers and operations managers not only to design organizational systems with low or zero time-independent complexity, but also to re-initialize the once designed organizational system before time-dependent combinatorial complexity drives it to fail, i.e. to lose its competitiveness due to heavy losses in efficiency. A central aspect is the identification of a functional periodicity. With the help of a practical example the concept of the organizational periodicity is explained, a generally applicable cycle with four phases that is adapted to the individual company’s situation by compressing or stretching the sinus curve over the timeline.


title: A Trolling Hierarchy in Social Media and A Conditional Random Field For Trolling Detection

abstract: An-ever increasing number of social media websites, electronic newspapers and Internet forums allow visitors to leave comments for others to read and interact. This exchange is not free from participants with malicious intentions, which do not contribute with the written conversation. Among different communities users adopt strategies to handle such users. In this paper we present a comprehensive categorization of the trolling phenomena resource, inspired by politeness research and propose a model that jointly predicts four crucial aspects of trolling: intention, interpretation, intention disclosure and response strategy. Finally, we present a new annotated dataset containing excerpts of conversations involving trolls and the interactions with other users that we hope will be a useful resource for the research community.


Real-Time Fashion-Guided Clothing Semantic Parsing: A Lightweight Multi-Scale Inception Neural Network and Benchmark.


title: Background Modeling by Stability of Adaptive Features in Complex Scenes

abstract: The single-feature-based background model often fails in complex scenes, since a pixel is better described by several features, which highlight different characteristics of it. Therefore, the multi-feature-based background model has drawn much attention recently. In this paper, we propose a novel multi-feature-based background model, named stability of adaptive feature (SoAF) model, which utilizes the stabilities of different features in a pixel to adaptively weigh the contributions of these features for foreground detection. We do this mainly due to the fact that the features of pixels in the background are often more stable. In SoAF, a pixel is described by several features and each of these features is depicted by a unimodal model that offers an initial label of the target pixel. Then, we measure the stability of each feature by its histogram statistics over a time sequence and use them as weights to assemble the aforementioned unimodal models to yield the final label. The experiments on some standard benchmarks, which contain the complex scenes, demonstrate that the proposed approach achieves promising performance in comparison with some state-of-the-art approaches.


title: Multi-criteria design of 6-DoF fully-constrained cable driven redundant parallel manipulator

abstract: The purpose of this paper is to optimize dimensions of KNTU 6-DoF cable driven redundant parallel manipulators. This manipulator is under investigation for possible high-speed application such as object manipulator. Usage of cable parallel manipulator depends on kinematic characteristic and control algorithm. This optimization is based on different performance indices including the singularity measure, the kinematic sensitivity, the stiffness and free collision workspace. Stiffness of the cable is also taken into consideration because of its effect on kinematic stiffness of moving platform manipulators performance. Points with cable collision, singular or actuator wrench infeasible configuration characteristic are eliminating from entire workspace.


title: Constrained K-means Clustering with Background Knowledge

abstract: Clustering is traditionally viewed as an unsupervised method for data analysis. However, in some cases information about the problem domain is available in addition to the data instances themselves. In this paper, we demonstrate how the popular k-means clustering algorithm can be protably modied to make use of this information. In experiments with articial constraints on six data sets, we observe improvements in clustering accuracy. We also apply this method to the real-world problem of automatically detecting road lanes from GPS data and observe dramatic increases in performance.


title: Calibration for high-definition camera rigs with marker chessboard

abstract: The geometrical calibration of a high-definition camera rig is an important step for 3D film making and computer vision applications. Due to the large amount of image data in high-definition, maintaining execution speeds appropriate for on-set, on-line adjustment procedures is one of the biggest challenges for machine vision based calibration methods. Our aims are to provide a low-cost, fast and accurate system to calibrate both the intrinsic and extrinsic parameters of a stereo camera rig. We first propose a novel calibration target that we call marker chessboard to speed up the corner detection. Then we develop an automatic key frame selection algorithm to optimize frames used in calibration. We also propose a bundle adjustment method to overcome the geometrical inaccuracy of the chessboard. Finally we introduce an online stereo camera calibration system based on the above improvements.


title: Teaching Senti-Lexicon for Automated Sentiment Polarity Definition in Teaching Evaluation

abstract: This research significantly achieved the construction of a teaching evaluation sentiment lexicon and an automated sentiment orientation polarity definition in teaching evaluation. The Teaching Senti-lexicon will compute the weights of terms and phrases obtained from student opinions, which are stored in teaching evaluation suggestions in the form of open-ended questions. This Teaching Senti-lexicon consists of three main attributes, including: teaching corpus, category and sentiment weight score. The sentiment orientation polarity was computed with its mean function being sentiment class definitions. A number of 175 instances were randomised using teaching feedback responses which were posted by students studying at Loei Raja hat University. The contributions of this paper propose an effective teaching sentiment analysis method, especially for teaching evaluation. In this paper, the experimented model employed SVM, ID3 and Naive Bayes algorithms, which were implemented in order to analyse sentiment classifications with a 97% highest accuracy of SVM. This model is also applied to improve upon their teaching as well.


Bootstrapped Text-level Named Entity Recognition for Literature


title: Using Simulated Annealing and Ant-Colony Optimization Algorithms to Solve the Scheduling Problem

abstract: The scheduling problem is one of the most challenging problems faced in many different areas of everyday life. This problem can be formulated as a combinatorial optimization problem, and it has been solved with various methods using meta-heuristics and intelligent algorithms. We present in this paper a solution to the scheduling problem using two different heuristics namely Simulated Annealing and Ant Colony Optimization. A study comparing the performances of both solutions is described and the results are analyzed.


title: Social media and political communication: a social media analytics framework

abstract: In recent years, social media are said to have an impact on the public discourse and communication in the society. In particular, social media are increasingly used in political context. More recently, microblogging services (e.g., Twitter) and social network sites (e.g., Facebook) are believed to have the potential for increasing political participation. While Twitter is an ideal platform for users to spread not only information in general but also political opinions publicly through their networks, political institutions (e.g., politicians, political parties, political foundations, etc.) have also begun to use Facebook pages or groups for the purpose of entering into direct dialogs with citizens and encouraging more political discussions. Previous studies have shown that from the perspective of political institutions, there is an emerging need to continuously collect, monitor, analyze, summarize, and visualize politically relevant information from social media. These activities, which are subsumed under “social media analytics,” are considered difficult tasks due to a large numbers of different social media platforms as well as the large amount and complexity of information and data. Systematic tracking and analysis approaches along with appropriate scientific methods and techniques in political domain are still lacking. In this paper, we propose a methodological framework for social media analytics in political context. More specifically, our framework summarizes most important politically relevant issues from the perspective of political institutions and corresponding methodologies from different scientific disciplines.


title: Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator

abstract: A new algorithm called Mersenne Twister (MT) is proposed for generating uniform pseudorandom numbers. For a particular choice of parameters, the algorithm provides a super astronomical period of 2 19937 −1 and 623-dimensional equidistribution up to 32-bit accuracy, while using a working area of only 624 words. This is a new variant of the previously proposed generators, TGFSR, modified so as to admit a Mersenne-prime period. The characteristic polynomial has many terms. The distribution up to v bits accuracy for 1 ≤ v ≤ 32 is also shown to be good. An algorithm is also given that checks the primitivity of the characteristic polynomial of MT with computational complexity O(p 2 ) where p is the degree of the polynomial. We implemented this generator in portable C-code. It passed several stringent statistical tests, including diehard. Its speed is comparable to other modern generators. Its merits are due to the efficient algorithms that are unique to polynomial calculations over the two-element field.


title: Visualization evaluation for cyber security: trends and future directions

abstract: The Visualization for Cyber Security research community (VizSec) addresses longstanding challenges in cyber security by adapting and evaluating information visualization techniques with application to the cyber security domain. This research effort has created many tools and techniques that could be applied to improve cyber security, yet the community has not yet established unified standards for evaluating these approaches to predict their operational validity. In this paper, we survey and categorize the evaluation metrics, components, and techniques that have been utilized in the past decade of VizSec research literature. We also discuss existing methodological gaps in evaluating visualization in cyber security, and suggest potential avenues for future research in order to help establish an agenda for advancing the state-of-the-art in evaluating cyber security visualizations.


title: Liver biopsy assessment in chronic viral hepatitis: a personal, practical approach

abstract: The terminology for assessment of chronic viral hepatitis in liver biopsy specimens has become confusing with the proliferation of grading and staging schemes that have paralleled the rise of the hepatitis C epidemic and the importance of mixed viral infections. This review represents a personal approach to the interpretation of these biopsy specimens, aiming at clarifying and simplifying the important points for the general pathologist confronted by these diagnostic dilemmas. The most commonly used schemes-Ishak modification of the Knodell 'hepatic activity index', Scheuer, Metavir, Batts-Ludwig classifications-are presented with evaluation of their pros and cons. Which scheme is selected is less important than the consistent use of a single scheme and the clear naming of that scheme in pathology reports. The importance and clinical implications of identifying severe necroinflammatory activity in the form of 'confluent necrosis' is discussed. Pathologists must also be clear about assessing concomitant diseases, in particular, alcoholic or non-alcoholic fatty liver disease, and be aware that grading/staging schemes for chronic hepatitis do not apply to mixed disease conditions. Other important features to be evaluated in all chronic hepatitis biopsy specimens include iron (which may represent hereditary hemochromatosis or secondary uptake) and neoplasia-associated changes, namely large cell change and small cell change; these findings and their clinical import are updated and reviewed. Sample approaches to composing useful diagnostic reports are also presented.


title: Users Aren't (Necessarily) Lazy: Using NeuroIS to Explain Habituation to Security Warnings

abstract: Warning messages are one of the last lines of defense in information security, and are fundamental to users’ security interactions with technology. Unfortunately, research shows that users routinely ignore security warnings. A key contributor to this disregard is habituation, the diminishing of attention through frequent exposure. However, previous research has examined habituation indirectly by observing its influence on security behavior, rather than measuring habituation itself. We contribute by using functional magnetic resonance imaging (fMRI) to directly observe habituation as it occurs in the brain. Our results show that with repeated exposure to warnings, neural activity in the visual processing centers sharply decreases. We also show that this process occurs for images of both security warnings and general software applications, although habituation is more severe for security warnings. Our findings suggest that habituation is not due to users’ laziness or carelessness, but is a natural consequence of how the brain works.


title: Using Three Machine Learning Techniques for Predicting Breast Cancer Recurrence

abstract: Objective: The number and size of medical databases are increasing rapidly but most of these data are not analyzed ::: for finding the valuable and hidden knowledge. Advanced data mining techniques can be used to discover hidden ::: patterns and relationships. Models developed from these techniques are useful for medical practitioners to make right ::: decisions. The present research studied the application of data mining techniques to develop predictive models for ::: breast cancer recurrence in patients who were followed-up for two years. ::: Method: The patients were registered in the Iranian Center for Breast Cancer (ICBC) program from 1997 to 2008. ::: The dataset contained 1189 records, 22 predictor variables, and one outcome variable. We implemented machine ::: learning techniques, i.e., Decision Tree (C4.5), Support Vector Machine (SVM), and Artificial Neural Network (ANN) to ::: develop the predictive models. The main goal of this paper is to compare the performance of these three well-known ::: algorithms on our data through sensitivity, specificity, and accuracy. ::: Results and Conclusion: Our analysis shows that accuracy of DT, ANN and SVM are 0.936, 0.947 and 0.957 ::: respectively. The SVM classification model predicts breast cancer recurrence with least error rate and highest accuracy. ::: The predicted accuracy of the DT model is the lowest of all. The results are achieved using 10-fold cross-validation for ::: measuring the unbiased prediction accuracy of each model.


title: Visual servoing based on an analytical homography decomposition

abstract: This paper presents a new vision-based control method for positioning a camera with respect to an unknown planar object. Standard methods use non-linear state observers based on homography decomposition. In the general case, there are two possible solutions to the decomposition problem. Thus, some additional "a priori" information must be used. In this paper, we propose to use an analytical decomposition of the homography matrix in order to define a new control objective that allows to discard the false solution without any "a priori" information. The stability of the control law has been proved.


title: A Novel Performance Evaluation Methodology for Single-Target Trackers

abstract: This paper addresses the problem of single-target tracker performance evaluation. We consider the performance measures, the dataset and the evaluation system to be the most important components of tracker evaluation and propose requirements for each of them. The requirements are the basis of a new evaluation methodology that aims at a simple and easily interpretable tracker comparison. The ranking-based methodology addresses tracker equivalence in terms of statistical significance and practical differences. A fully-annotated dataset with per-frame annotations with several visual attributes is introduced. The diversity of its visual properties is maximized in a novel way by clustering a large number of videos according to their visual attributes. This makes it the most sophistically constructed and annotated dataset to date. A multi-platform evaluation system allowing easy integration of third-party trackers is presented as well. The proposed evaluation methodology was tested on the VOT2014 challenge on the new dataset and 38 trackers, making it the largest benchmark to date. Most of the tested trackers are indeed state-of-the-art since they outperform the standard baselines, resulting in a highly-challenging benchmark. An exhaustive analysis of the dataset from the perspective of tracking difficulty is carried out. To facilitate tracker comparison a new performance visualization technique is proposed.


title: Hierarchical Event Selection for Video Storyboards with a Case Study on Snooker Video Visualization

abstract: Video storyboard, which is a form of video visualization, summarizes the major events in a video using illustrative visualization. There are three main technical challenges in creating a video storyboard, (a) event classification, (b) event selection and (c) event illustration. Among these challenges, (a) is highly application-dependent and requires a significant amount of application specific semantics to be encoded in a system or manually specified by users. This paper focuses on challenges (b) and (c). In particular, we present a framework for hierarchical event representation, and an importance-based selection algorithm for supporting the creation of a video storyboard from a video. We consider the storyboard to be an event summarization for the whole video, whilst each individual illustration on the board is also an event summarization but for a smaller time window. We utilized a 3D visualization template for depicting and annotating events in illustrations. To demonstrate the concepts and algorithms developed, we use Snooker video visualization as a case study, because it has a concrete and agreeable set of semantic definitions for events and can make use of existing techniques of event detection and 3D reconstruction in a reliable manner. Nevertheless, most of our concepts and algorithms developed for challenges (b) and (c) can be applied to other application areas.


title: An Ontology-based Context Model in Intelligent Environments

abstract: Computing becomes increasingly mobile and pervasive today; these changes imply that applications and services must be aware of and adapt to their changing contexts in highly dynamic environments. Today, building context-aware systems is a complex task due to lack of an appropriate infrastructure support in intelligent environments. A context-aware infrastructure requires an appropriate context model to represent, manipulate and access context information. In this paper, we propose a formal context model based on ontology using OWL to address issues including semantic context representation, context reasoning and knowledge sharing, context classification, context dependency and quality of context. The main benefit of this model is the ability to reason about various contexts. Based on our context model, we also present a Service-Oriented Context-Aware Middleware (SOCAM) architecture for building of context-aware services.


title: Leveraging semantic web technologies for standardized knowledge modeling and retrieval from the Holy Qur'an and religious texts

abstract: In recent years, the desire to automate religious learning using the emerging technologies has led to a number of applications being introduced to ease the retrieval of knowledge from religious literature, especially the holy books. In this paper we investigate the need for developing knowledge-based, efficient and collaborative frameworks for retrieving knowledge from distributed knowledge sources, primarily related to the Holy Qur'an and associated scholarly texts (e.g. Books of Ahadith or Qur'anic Exegeses) to make the process of learning easy, comprehensive and effective. We propose a vision for leveraging the emergent role of Semantic Web technologies for providing efficient and flexible means of knowledge modeling, storage, publishing, reasoning and retrieval from distributed Qur'anic knowledge sources.


title: Decision theory, reinforcement learning, and the brain.

abstract: Decision making is a core competence for animals and humans acting and surviving in environments they only partially comprehend, gaining rewards and punishments for their troubles. Decision-theoretic concepts permeate experiments and computational models in ethology, psychology, and neuroscience. Here, we review a well-known, coherent Bayesian approach to decision making, showing how it unifies issues in Markovian decision problems, signal detection psychophysics, sequential sampling, and optimal exploration and discuss paradigmatic psychological and neural examples of each problem. We discuss computational issues concerning what subjects know about their task and how ambitious they are in seeking optimal solutions; we address algorithmic topics concerning model-based and model-free methods for making choices; and we highlight key aspects of the neural implementation of decision making.


title: Radiographic identification of loose bodies in the traumatized hip joint.

abstract: Acrylic spacers and cubes of cortical bone of known dimensions were placed in predetermined locations in cadaver hip joints, which were then studied with plain radiography and linear, hypocycloidal, and computed tomography (CT). Joint space widening was not measurable on plain radiographs of the pelvis when 2-mm spacers were placed anywhere within the hip joint. When 4-mm spacers were used, widening measured 2 mm in the axis of measurement corresponding to the location of the spacer. Linear tomography did not permit identification of the 2-mm cubes; however, hypocycloidal tomography and CT consistently showed them anywhere within the hip joint. Radiation dose and clinical recommendations are discussed.


title: Overview of wideband Butler matrix in microstrip-slot technology for beamforming antenna applications

abstract: This paper describes the development of 4×4 wideband compact Butler matrix operating between 1.6 GHz and 2.4 GHz. In order to attain wideband characteristics, the matrix uses microstrip-slot technology, which involves two microstrip dielectric layers with a shared ground plane and a coupling slot. The multilayer beamforming network design is compact in size and avoids the use of crossovers, which makes it more advantageous than the conventional Butler matrix. To verify the performances of the beamformer, a four-antenna array is connected to the proposed 4×4 Butler matrix to shape a switched-beam system. Simulation results are presented and discussed.


title: Radioimmunoelectrophoretic identification of poliovirus inhibitors and their characteristic mode of action

abstract: Poliovirus inhibitors present in normal equine and bovine sera were studied by means of radioimmunoelectrophoresis and column chromatography. The inhibitors in individual equine sera were identified as either one of the three immunoglobulins, γM, γA(T) and γG, or mixtures of them, while those in bovine sera were found to be either one of γM and γG globulins, or the mixture of the two.


title: Active Multi-View Object Recognition and Online Feature Selection

abstract: Adaptive action selection is crucial for object recognition on robots with limited operating capabilities. It offers much desired flexibility in trading off between the costs of acquiring information and making robust and reliable inference under uncertainty. In this paper, we describe an information-theoretic framework that combines and unifies two common techniques: view planning for resolving ambiguities and occlusions and online feature selection for reducing computational costs. Concretely, our algorithm adaptively chooses two strategies: utilize simple-to-compute features that are the most informative for the recognition task or move to new viewpoints that optimally reduce the expected uncertainties on the identity of the object. Extensive empirical studies have validated the effectiveness of the proposed framework. On a large RGB-D dataset, dynamic feature selection alone reduces the computation time at runtime by five folds, and when combining it with viewpoint selection, we significantly increase the recognition accuracy on average by 8–15% absolute, compared to systems that do not use these two strategies. Lastly, we have also demonstrated successfully the effectiveness of the framework on a quadcopter platform with limited operating time.


title: Life history of Acanthocollaritrema umbilicatum Travassos, Freitas and Bührnheim, 1965 (Digenea: Cryptogonimidae)

abstract: The trematode Acanthocollaritrema umbilicatum Travassos, Freitas and Buhrnheim 1965 is redescribed and data on its life cycle are provided for the first time. Adults were obtained from the common snook, Centropomus undecimalis (Bloch 1792), and both rediae and cercariae from the snail Heleobia australis (d'Orbigny 1835), a new intermediate host. Metacercariae were found encysted among the scales, fins, and musculature and in the buccal cavity of naturally infected fishes, Poecilia vivipara Bloch and Schneider, 1801, Jenynsia multidentata (Jenyns 1842), and Phalloptychus januarius (Hensel 1868), all new intermediate hosts. The examination of the type and freshly obtained adults of A. umbilicatum has shown that they possess 54-64 circumoral spines, in a double row. Experimental infections were achieved in the intermediate hosts H. australis and P. vivipara.


title: Hyperpoints and Fine Vocabularies for Large-Scale Location Recognition

abstract: Structure-based localization is the task of finding the absolute pose of a given query image w.r.t. a pre-computed 3D model. While this is almost trivial at small scale, special care must be taken as the size of the 3D model grows, because straight-forward descriptor matching becomes ineffective due to the large memory footprint of the model, as well as the strictness of the ratio test in 3D. Recently, several authors have tried to overcome these problems, either by a smart compression of the 3D model or by clever sampling strategies for geometric verification. Here we explore an orthogonal strategy, which uses all the 3D points and standard sampling, but performs feature matching implicitly, by quantization into a fine vocabulary. We show that although this matching is ambiguous and gives rise to 3D hyperpoints when matching each 2D query feature in isolation, a simple voting strategy, which enforces the fact that the selected 3D points shall be co-visible, can reliably find a locally unique 2D-3D point assignment. Experiments on two large-scale datasets demonstrate that our method achieves state-of-the-art performance, while the memory footprint is greatly reduced, since only visual word labels but no 3D point descriptors need to be stored.


title: Using Multi-Instance Enrollment to Improve Performance of 3D Face Recognition

abstract: This paper explores the use of multi-instance enrollment as a means to improve the performance of 3D face recognition. Experiments are performed using the ND-2006 3D face data set which contains 13,450 scans of 888 subjects. This is the largest 3D face data set currently available and contains a substantial amount of varied facial expression. Results indicate that the multi-instance enrollment approach outperforms a state-of-the-art component-based recognition approach, in which the face to be recognized is considered as an independent set of regions.


title: DeepSketch: Deep convolutional neural networks for sketch recognition and similarity search

abstract: In this paper, we present a system for sketch classification and similarity search. We used deep convolution neural networks (ConvNets), state of the art in the field of image recognition. They enable both classification and medium/highlevel features extraction. We make use of ConvNets features as a basis for similarity search using k-Nearest Neighbors (kNN). Evaluation are performed on the TU-Berlin benchmark. Our main contributions are threefold: first, we use ConvNets in contrast to most previous approaches based essentially on hand crafted features. Secondly, we propose a ConvNet that is both more accurate and lighter/faster than the two only previous attempts at making use of ConvNets for handsketch recognition. We reached an accuracy of 75.42%. Third, we shown that similarly to their application on natural images, ConvNets allow the extraction of medium-level and high-level features (depending on the depth) which can be used for similarity search.1


title: Compressed Sensing of EEG for Wireless Telemonitoring with Low Energy Consumption and Inexpensive Hardware

abstract: Telemonitoring of electroencephalogram (EEG) through wireless body-area networks is an evolving direction in personalized medicine. Among various constraints in designing such a system, three important constraints are energy consumption, data compression, and device cost. Conventional data compression methodologies, although effective in data compression, consumes significant energy and cannot reduce device cost. Compressed sensing (CS), as an emerging data compression methodology, is promising in catering to these constraints. However, EEG is nonsparse in the time domain and also nonsparse in transformed domains (such as the wavelet domain). Therefore, it is extremely difficult for current CS algorithms to recover EEG with the quality that satisfies the requirements of clinical diagnosis and engineering applications. Recently, block sparse Bayesian learning (BSBL) was proposed as a new method to the CS problem. This study introduces the technique to the telemonitoring of EEG. Experimental results show that its recovery quality is better than state-of-the-art CS algorithms, and sufficient for practical use. These results suggest that BSBL is very promising for telemonitoring of EEG and other nonsparse physiological signals.


title: An analysis of the supplier selection process

abstract: Customers select suppliers based on the relative importance of different attributes such as quality, price, flexibility, and delivery performance. This study examines the difference between managers' rating of the perceived importance of different supplier attributes and their actual choice of suppliers in an experimental setting. We use two methods: a Likert scale set of questions, to determine the importance of supplier attributes; and a discrete choice analysis (DCA) experiment, to examine the choice of suppliers. The results indicate that although managers say that quality is the most important attribute for a supplier, they actually choose suppliers based largely on cost and delivery performance.


title: SeSPHR: A Methodology for Secure Sharing of Personal Health Records in the Cloud

abstract: The widespread acceptance of cloud based services in healthcare sector has resulted in cost effective and convenient exchange of Personal Health Records (PHRs) among several participating entities of the e-Health systems. Nevertheless, storing confidential health information to cloud servers is susceptible to revelation or theft and calls for the development of methodologies that ensure the privacy of the PHRs. Therefore, we propose a methodology called SeSPHR for secure sharing of the PHRs in the cloud. The SeSPHR scheme ensures patient-centric control on the PHRs and preserves the confidentiality of the PHRs. The patients store the encrypted PHRs on the un-trusted cloud servers and selectively grant access to different types of users on different portions of the PHRs. A semi-trusted proxy called Setup and Re-encryption Server (SRS) is introduced to set up the public/private key pairs and to produce the re-encryption keys. Moreover, the methodology is secure against insider threats and also enforces a forward and backward access control. Furthermore, we formally analyze and verify the working of SeSPHR methodology through High Level Petri Nets (HLPN). Performance evaluation with regard to time consumption indicates that the SeSPHR methodology has potential to be employed for securely sharing the PHRs in the cloud.


title: Stochastic Variance Reduction for Nonconvex Optimization

abstract: We study nonconvex finite-sum problems and analyze stochastic variance reduced gradient (SVRG) methods for them. SVRG and related methods have recently surged into prominence for convex optimization given their edge over stochastic gradient descent (SGD); but their theoretical analysis almost exclusively assumes convexity. In contrast, we prove non-asymptotic rates of convergence (to stationary points) of SVRG for nonconvex optimization, and show that it is provably faster than SGD and gradient descent. We also analyze a subclass of nonconvex problems on which SVRG attains linear convergence to the global optimum. We extend our analysis to mini-batch variants of SVRG, showing (theoretical) linear speedup due to mini-batching in parallel settings.


title: Øzone: Efficient execution with zero timing leakage for modern microarchitectures

abstract: Time variation during program execution can leak sensitive information. Time variations due to program control flow and hardware resource contention have been used to steal encryption keys in cipher implementations such as AES and RSA. A number of approaches to mitigate timing-based side-channel attacks have been proposed including cache partitioning, control-flow obfuscation and injecting timing noise into the outputs of code. While these techniques make timing-based side-channel attacks more difficult, they do not eliminate the risks. Prior techniques are either too specific or too expensive, and all leave remnants of the original timing side channel for later attackers to attempt to exploit. In this work, we show that the state-of-the-art techniques in timing side-channel protection, which limit timing leakage but do not eliminate it, still have significant vulnerabilities to timing-based side-channel attacks. To provide a means for total protection from timing-based side-channel attacks, we develop Ozone, the first zero timing leakage execution resource for a modern microarchitecture. Code in Ozone execute under a special hardware thread that gains exclusive access to a single core's resources for a fixed (and limited) number of cycles during which it cannot be interrupted. Memory access under Ozone thread execution is limited to a fixed size uncached scratchpad memory, and all Ozone threads begin execution with a known fixed microarchitectural state. We evaluate Ozone using a number of security sensitive kernels that have previously been targets of timing side-channel attacks, and show that Ozone eliminates timing leakage with minimal performance overhead.


title: Analysis of TV audience flow based on the lingered channel matrix

abstract: At present, the analysis of television ratings gives priority to ordinary sectional analysis which is represented by audience ratings, market share and other indices, and has little analysis of the audience flowing. Additionally, traditional research method of audience flowing has some problems such as high time cost, large storage space, weak reusability and no analysis about overall flow trend or audience clustering. This paper proposes a new scheme on the analysis of audience flowing. It is based on the established lingered channel matrix produced by raw viewing data. From the angle of channels (or programs) and users, this paper adopts the conventional mathematical statistics method and clustering algorithm to reflect audience flowing between different channels and their own viewing behavior in detail. The lingered channel matrix established by the scheme clearly shows the channel that audience linger at every moment, leaving the original viewing data away and avoiding the problem of high cost of time and storage space by repeatedly reading and processing raw data. Moreover, the matrix provides the data base for the subsequent extension research, in order to realize the analysis of TV audience flowing in a deeply vertical level.


title: Investigations on optical, structural and thermal properties of phosphate glasses containing terbium ions

abstract: Aluminophosphate glasses belonging to the Li2O–BaO–Al2O3– La2O3–P2O5 system doped with Tb3+ were prepared and investigated. Methods as Induced Coupled Plasma-Mass Spectrometry (ICP-MS), Induced Coupled Plasma-Atomic Emission Spectroscopy (ICP-AES) and X-ray diffraction (XRD) have been used to establish the elemental composition of these vitreous materials. The influence of the Tb3+ ions on the optical properties of the phosphate glasses has been investigated in relation with the structural characteristics of the vitreous matrix. The optical behavior has been studied by ultra-violet-visible (UV–Vis) spectroscopy, revealing electronic transitions specific for terbium ions. Fluorescence spectroscopy measurements have been performed by excitation in the UV and visible domains (377 nm and 488 nm) which resulted in the most significant fluorescence peaks in the Vis domain (540 and 547 nm). Structural information via vibration modes were provided by Fourier Transform Infrared (FTIR) absorption spectra in the 400–4000 cm−1 range. Absorption peaks specific for the vitreous phosphate matrix were put in evidence as P-O-P symmetrical and asymmetrical stretching vibration modes, P-O-P bend, PO2- symmetrical and asymmetrical stretching vibration modes, P=O stretching vibration mode as well as P-O-H water absorbance. Raman spectra acquired in the 100–4000 cm−1 range by 488, 514 and 633 nm laser excitation sources disclosed peaks also specific for the phosphate matrix, proving the role of phosphorous oxide as a vitreous network former. Differential Scanning Calorimetry and Thermogravimetric Analysis (DSC-TGA) provided information regarding the phase transformations that took place during the heating process and the associated thermal effects.


title: String Stable Heterogeneous Vehicle Platoon Using Cooperative Adaptive Cruise Control

abstract: In this paper, a heterogeneous vehicle platoon equipped with Cooperative Adaptive Cruise Control (CACC) systems is studied. First, various causes of heterogeneity are reviewed. A selection of parameters is made, for which string stability is analyzed. The influence of controller parameters and headway time on string stability is studied. Numerical simulation results provide guidelines on how to choose controller parameters and headway time for different vehicles using CACC.


title: Graph Fourier transform based on directed Laplacian

abstract: In this paper, we redefine the Graph Fourier Transform (GFT) under the DSP$_\mathrm{G}$ framework. We consider the Jordan eigenvectors of the directed Laplacian as graph harmonics and the corresponding eigenvalues as the graph frequencies. For this purpose, we propose a shift operator based on the directed Laplacian of a graph. Based on our shift operator, we then define total variation of graph signals, which is used in frequency ordering. We achieve natural frequency ordering and interpretation via the proposed definition of GFT. Moreover, we show that our proposed shift operator makes the LSI filters under DSP$_\mathrm{G}$ to become polynomial in the directed Laplacian.


title: Multi-lingual Concept Extraction with Linked Data and Human-in-the-Loop

abstract: Ontologies are dynamic artifacts that evolve both in structure and content. Keeping them up-to-date is a very expensive and critical operation for any application relying on semantic Web technologies. In this paper we focus on evolving the content of an ontology by extracting relevant instances of ontological concepts from text. We propose a novel technique which is (i) completely language independent, (ii) combines statistical methods with human-in-the-loop and (iii) exploits Linked Data as bootstrapping source. Our experiments on a publicly available medical corpus and on a Twitter dataset show that the proposed solution achieves comparable performances regardless of language, domain and style of text. Given that the method relies on a human-in-the-loop, our results can be safely fed directly back into Linked Data resources.


title: Ground truth evaluation of large urban 6D SLAM

abstract: In the past many solutions for simultaneous localization and mapping (SLAM) have been presented. Recently these solutions have been extended to map large environments with six degrees of freedom (DoF) poses. To demonstrate the capabilities of these SLAM algorithms it is common practice to present the generated maps and successful loop closing. Unfortunately there is often no objective performance metric that allows to compare different approaches. This fact is attributed to the lack of ground truth data. For this reason we present a novel method that is able to generate this ground truth data based on reference maps. Further on, the resulting reference path is used to measure the absolute performance of different 6D SLAM algorithms building a large urban outdoor map.


A Survey of Smartphone-based Sensing in Vehicles for ITS Applications


title: Novel Coplanar Line-Points Invariants for Robust Line Matching Across Views

abstract: Robust line matching across wide-baseline views is a challenging task in computer vision. Most of the existing methods highly depend on the positional relationships between lines and the associated textures. These cues are sensitive to various image transformations especially perspective deformations, and likely to fail in the scenarios where few texture present. In this paper, we construct a new coplanar line-points invariant upon a newly developed projective invariant, named characteristic number, and propose a line matching algorithm using the invariant. The construction of this invariant uses intersections of coplanar lines instead of endpoints, rendering more robust matching across views. Additionally, a series of line-points invariant values generate the similarity metric for matching that is less affected by mismatched interest points than traditional approaches. Accurate homography recovered from the invariant allows all lines, even those without interest points around them, a chance to be matched. Extensive comparisons with the state-of-the-art validate the matching accuracy and robustness of the proposed method to projective transformations. The method also performs well for image pairs with few textures and similar textures.


title: Digital Holographic Microscopy: A Quantitative Label-Free Microscopy Technique for Phenotypic Screening

abstract: Digital Holographic Microscopy (DHM) is a label-free imaging technique allowing visualization of transparent cells with classical imaging cell culture plates. The quantitative DHM phase contrast image provided is related both to the intracellular refractive index and to cell thickness. DHM is able to distinguish cellular morphological changes on two representative cell lines (HeLa and H9c2) when treated with doxorubicin and chloroquine, two cytotoxic compounds yielding distinct phenotypes. We analyzed parameters linked to cell morphology and to the intracellular content in endpoint measurements and further investigated them with timelapse recording. The results obtained by DHM were compared with other optical label-free microscopy techniques, namely Phase Contrast, Differential Interference Contrast and Transport of Intensity Equation (reconstructed from three bright-field images). For comparative purposes, images were acquired in a common 96-well plate format on the different motorized microscopes. In contrast to the other microscopies assayed, images generated with DHM can be easily quantified using a simple automatized on-the-fly analysis method for discriminating the different phenotypes generated in each cell line. The DHM technology is suitable for the development of robust and unbiased image-based assays.


title: Flux pinning centres correlated along the c-axis in PLD-YBCO films

abstract: Flux pinning centres having correlation along the c-axis in epitaxial YBCO films were investigated by measuring the magnetic-field angle ? dependence of the critical current density JC and by observing the microstructure. First, c-axis oriented YBCO films were prepared on four different substrates at three different target-to-substrate distances D by using a pulsed-laser-deposition method. Films deposited on LaAlO3 (100) and CeO2/Al2O3 with large D (112 or 142 mm) showed narrow peaks in the JC versus ? curves when the magnetic field was applied parallel to the c-axis (B//c). In these films, a high density (up to 5.5 ?m?1) of planar defects, most probably stacking faults parallel to the c-axis, were evident. The ratio of JC (B//c) to JC (B//a?b) significantly increased as the density of the planar defects increased. In contrast, films deposited on SrTiO3 (100), LaAlO3 (100) and NdGaO3 (110) with small D (50?60 mm) showed high, broad JC peaks around B//c, and had a high density (up to 430 ?m?2) of nanometre-sized precipitates that were elongated along the c-axis. The ratio of JC (B//c) to JC (B//a?b) increased as the density of these small elongated precipitates increased. In conclusion, planar defects such as stacking faults parallel to the c-axis, and the small elongated precipitates are effective as c-axis correlated flux pinning centres.


title: Firefly algorithm with neighborhood attraction

abstract: Abstract Firefly algorithm (FA) is a new optimization technique based on swarm intelligence. It simulates the social behavior of fireflies. The search pattern of FA is determined by the attractions among fireflies, whereby a less bright firefly moves toward a brighter firefly. In FA, each firefly can be attracted by all other brighter fireflies in the population. However, too many attractions may result in oscillations during the search process and high computational time complexity. To overcome these problems, we propose a new FA variant called FA with neighborhood attraction (NaFA). In NaFA, each firefly is attracted by other brighter fireflies selected from a predefined neighborhood rather than those from the entire population. Experiments are conducted using several well-known benchmark functions. The results show that the proposed strategy can efficiently improve the accuracy of solutions and reduce the computational time complexity.


title: Indoor Relocalization in Challenging Environments With Dual-Stream Convolutional Neural Networks

abstract: This paper presents an indoor relocalization system using a dual-stream convolutional neural network (CNN) with both color images and depth images as the network inputs. Aiming at the pose regression problem, a deep neural network architecture for RGB-D images is introduced, a training method by stages for the dual-stream CNN is presented, different depth image encoding methods are discussed, and a novel encoding method is proposed. By introducing the range information into the network through a dual-stream architecture, we not only improved the relocalization accuracy by about 20% compared with the state-of-the-art deep learning method for pose regression, but also greatly enhanced the system robustness in challenging scenes such as large-scale, dynamic, fast movement, and night-time environments. To the best of our knowledge, this is the first work to solve the indoor relocalization problems based on deep CNNs with RGB-D camera. The method is first evaluated on the Microsoft 7-Scenes data set to show its advantage in accuracy compared with other CNNs. Large-scale indoor relocalization is further presented using our method. The experimental results show that 0.3 m in position and 4° in orientation accuracy could be obtained. Finally, this method is evaluated on challenging indoor data sets collected from motion capture system. The results show that the relocalization performance is hardly affected by dynamic objects, motion blur, or night-time environments. Note to Practitioners —This paper was motivated by the limitations of the existing indoor relocalization technology that is significant for mobile robot navigation. Using this technology, robots can infer where they are in a previously visited place. Previous visual localization methods can hardly be put into wide application for the reason that they have strict requirements for the environments. When faced with challenging scenes such as large-scale environments, dynamic objects, motion blur caused by fast movement, night-time environments, or other appearance changed scenes, most existing methods tend to fail. This paper introduces deep learning into the indoor relocalization problem and uses dual-stream CNN (depth stream and color stream) to realize 6-DOF pose regression in an end-to-end manner. The localization error is about 0.3 m and 4° in a large-scale indoor environments. And what is more important, the proposed system does not lose efficiency in some challenging scenes. The proposed encoding method of depth images can also be adopted in other deep neural networks with RGB-D cameras as the sensor.


title: BioCaster: detecting public health rumors with a Web-based text mining system

abstract: Summary: BioCaster is an ontology-based text mining system for detecting and tracking the distribution of infectious disease outbreaks from linguistic signals on the Web. The system continuously analyzes documents reported from over 1700 RSS feeds, classifies them for topical relevance and plots them onto a Google map using geocoded information. The background knowledge for bridging the gap between Layman's terms and formal-coding systems is contained in the freely available BioCaster ontology which includes information in eight languages focused on the epidemiological role of pathogens as well as geographical locations with their latitudes/longitudes. The system consists of four main stages: topic classification, named entity recognition (NER), disease/location detection and event recognition. Higher order event analysis is used to detect more precisely specified warning signals that can then be notified to registered users via email alerts. Evaluation of the system for topic recognition and entity identification is conducted on a gold standard corpus of annotated news articles. ::: ::: Availability: The BioCaster map and ontology are freely available via a web portal at http://www.biocaster.org. ::: ::: Contact: pj.ca.iin@reilloc


title: A Stability Based Method for Discovering Structure in Clustered Data

abstract: We present a method for visually and quantitatively assessing the presence of structure in clustered data. The method exploits measurements of the stability of clustering solutions obtained by perturbing the data set. Stability is characterized by the distribution of pairwise similarities between clusterings obtained from sub samples of the data. High pairwise similarities indicate a stable clustering pattern. The method can be used with any clustering algorithm; it provides a means of rationally defining an optimum number of clusters, and can also detect the lack of structure in data. We show results on artificial and microarray data using a hierarchical clustering algorithm.


title: Fog Vehicular Computing: Augmentation of Fog Computing Using Vehicular Cloud Computing

abstract: Fog computing has emerged as a promising solution for accommodating the surge of mobile traffic and reducing latency, both known to be inherent problems of cloud computing. Fog services, including computation, storage, and networking, are hosted in the vicinity of end users (edge of the network), and, as a result, reliable access is provisioned to delay-sensitive mobile applications. However, in some cases, the fog computing capacity is overwhelmed by the growing number of demands from patrons, particularly during peak hours, and this can subsequently result in acute performance degradation. In this article, we address this problem by proposing a new concept called fog vehicular computing (FVC) to augment the computation and storage power of fog computing. We also design a comprehensive architecture for FVC and present a number of salient applications. The result of implementation clearly shows the effectiveness of the proposed architecture. Finally, some open issues and envisioned directions are discussed for future research in the context of FVC.


Crime Blindness: The Impact of Inattentional Blindness on Eyewitness Description Accuracy


title: Structural transformation for best-possible obfuscation of sequential circuits

abstract: Obfuscation is a technique that makes comprehending a design difficult and hides the secrets in the design. An obfuscation is called best-possible if the obfuscated design leaks no more information than any other design of the same function. In this paper, we prove that any best-possible obfuscation of a sequential circuit can be accomplished by a sequence of four operations: retiming, resynthesis, sweep, and conditional stuttering. Based on this fundamental result, we also develop a key-based obfuscation scheme to protect design Intellectual Properties (IPs) against piracy. The novel obfuscation method embeds a secret key in the power-up state of IC, which is only known by the IP rights owner. Without the key, the IC still functions but its efficiency will be much degraded. Unlike existing IC metering techniques, the secret key in our approach is implicit thus it can also be used as a hidden watermark. Potential attacks and the countermeasures are thoroughly examined, and experimental results demonstrate the effectiveness of the method.


title: SPACE AND ATTENTION IN PARIETAL CORTEX

abstract: The space around us is represented not once but many times in parietal cortex. These multiple representations encode locations and objects of interest in several egocentric reference frames. Stimulus representations are transformed from the coordinates of receptor surfaces, such as the retina or the cochlea, into the coordinates of effectors, such as the eye, head, or hand. The transformation is accomplished by dynamic updating of spatial representations in conjunction with voluntary movements. This direct sensory-to-motor coordinate transformation obviates the need for a single representation of space in environmental coordinates. In addition to representing object locations in motoric coordinates, parietal neurons exhibit strong modulation by attention. Both topdown and bottom-up mechanisms of attention contribute to the enhancement of visual responses. The saliance of a stimulus is the primary factor in determining the neural response to it. Although parietal neurons represent objects in motor coordinates, visual responses are independent of the intention to perform specific motor acts.


title: A Secure Data Deduplication Scheme Based on Differential Privacy

abstract: In cloud computing environment, especially in big data era, adversary may use data deduplication service supported by the cloud service provider as a side channel to eavesdrop users' privacy or sensitive information. In order to tackle this serious issue, in this paper, we propose a secure data deduplication scheme based on differential privacy. The highlights of the proposed scheme lie in constructing a hybrid cloud framework, using convergent encryption algorithm to encrypt original files, and introducing differential privacy mechanism to resist against the side channel attack. Performance evaluation shows that our scheme is able to effectively save network bandwidth and disk storage space during the processes of data deduplication. Meanwhile, security analysis indicates that our scheme can resist against the side channel attack and related files attack, and prevent the disclosure of privacy information.


title: Applications of Blockchain Technology beyond Cryptocurrency

abstract: Blockchain (BC), the technology behind the Bitcoin crypto-currency system, is considered to be both alluring and critical for ensuring enhanced security and (in some implementations, non-traceable) privacy for diverse applications in many other domains including in the Internet of Things (IoT) eco-system. Intensive research is currently being conducted in both academia and industry applying the Blockchain technology in multifarious applications. Proof-of-Work (PoW), a cryptographic puzzle, plays a vital role in ensuring BC security by maintaining a digital ledger of transactions, which is considered to be incorruptible. Furthermore, BC uses a changeable Public Key (PK) to record the users' identity, which provides an extra layer of privacy. Not only in cryptocurrency has the successful adoption of BC been implemented but also in multifaceted non-monetary systems such as in: distributed storage systems, proof-of-location, healthcare, decentralized voting and so forth. Recent research articles and projects/applications were surveyed to assess the implementation of BC for enhanced security, to identify associated challenges and to propose solutions for BC enabled enhanced security systems.


title: Tiled convolutional neural networks

abstract: Convolutional neural networks (CNNs) have been successfully applied to many tasks such as digit and object recognition. Using convolutional (tied) weights significantly reduces the number of parameters that have to be learned, and also allows translational invariance to be hard-coded into the architecture. In this paper, we consider the problem of learning invariances, rather than relying on hard-coding. We propose tiled convolution neural networks (Tiled CNNs), which use a regular "tiled" pattern of tied weights that does not require that adjacent hidden units share identical weights, but instead requires only that hidden units k steps away from each other to have tied weights. By pooling over neighboring units, this architecture is able to learn complex invariances (such as scale and rotational invariance) beyond translational invariance. Further, it also enjoys much of CNNs' advantage of having a relatively small number of learned parameters (such as ease of learning and greater scalability). We provide an efficient learning algorithm for Tiled CNNs based on Topographic ICA, and show that learning complex invariant features allows us to achieve highly competitive results for both the NORB and CIFAR-10 datasets.


title: Personalized Semantic Word Vectors

abstract: Distributed word representations are able to capture syntactic and semantic regularities in text. In this paper, we present a word representation scheme that incorporates authorship information. While maintaining similarity among related words in the induced distributed space, our word vectors can be effectively used for some text classification tasks too. We build on a log-bilinear document model (lbDm), which extracts document features, and word vectors based on word co-occurrence counts. First, we propose a log-bilinear author model (lbAm), which contains an additional author matrix. We show that by directly learning author feature vectors, as opposed to document vectors, we can learn better word representations for the authorship attribution task. Furthermore, authorship information has been found to be useful for sentiment classification. We enrich the author model with a sentiment tensor, and demonstrate the effectiveness of this hybrid model (lbHm) through our experiments on a movie review-classification dataset.


title: Kernelization of Tensor-Based Models for Multiway Data Analysis: Processing of Multidimensional Structured Data

abstract: Tensors (also called multiway arrays) are a generalization of vectors and matrices to higher dimensions based on multilinear algebra. The development of theory and algorithms for tensor decompositions (factorizations) has been an active area of study within the past decade, e.g., [1] and [2]. These methods have been successfully applied to many problems in unsupervised learning and exploratory data analysis. Multiway analysis enables one to effectively capture the multilinear structure of the data, which is usually available as a priori information about the data. Hence, it might provide advantages over matrix factorizations by enabling one to more effectively use the underlying structure of the data. Besides unsupervised tensor decompositions, supervised tensor subspace regression and classification formulations have been also successfully applied to a variety of fields including chemometrics, signal processing, computer vision, and neuroscience.