title: Phylogeography of the intertidal copepod Tigriopus californicus reveals substantially reduced population differentiation at northern latitudes

abstract: Previous studies of the intertidal copepod Tigriopus californicus revealed one of the highest levels of mitochondrial DNA differentiation ever reported among conspecific populations. The present study extends the geographical sampling northward, adding populations from northern California to south-east Alaska. The mitochondrial phylogeny for the entire species range, based on cytochrome oxidase I sequences for a total of 49 individuals from 27 populations, again shows extreme differentiation among populations (up to 23%). However, populations from Oregon northwards appear to be derived and have interpopulation divergences five times lower than those between southern populations. Furthermore, although few individuals were sequenced from each locality, populations from Puget Sound northward had significantly reduced levels of within-population variation. These patterns are hypothesized to result from the contraction and expansion of populations driven by recent ice ages.


title: A game theoretic model of computer network exploitation campaigns

abstract: Increasingly, cyberspace is the battlefield of choice for twenty first century criminal activity and foreign conflict. This suggests that traditional modeling and simulation approaches have stalled in the information security domain. We propose a game theoretic model based on a multistage model of computer network exploitation (CNE) campaigns comprising reconnaissance, tooling, implant, lateral movement, exfiltration and cleanup stages. In each round of the game, the attacker chooses whether to proceed with the next stage of the campaign, nature decides whether the defender is cognizant of the campaign's progression, and the defender chooses to respond in an active or passive fashion. We propose a dynamic, asymmetric, complete-information, general-sum game to model CNE campaigns and techniques to estimate this game's parameters. Researchers can extend this work to other threat models, and practitioners can use this work for decision support.


title: Adversarial Dropout Regularization

abstract: We present a method for transferring neural representations from label-rich source domains to unlabeled target domains. Recent adversarial methods proposed for this task learn to align features across domains by fooling a special domain critic network. However, a drawback of this approach is that the critic simply labels the generated features as in-domain or not, without considering the boundaries between classes. This can lead to ambiguous features being generated near class boundaries, reducing target classification accuracy. We propose a novel approach, Adversarial Dropout Regularization (ADR), to encourage the generator to output more discriminative features for the target domain. Our key idea is to replace the critic with one that detects non-discriminative features, using dropout on the classifier network. The generator then learns to avoid these areas of the feature space and thus creates better features. We apply our ADR approach to the problem of unsupervised domain adaptation for image classification and semantic segmentation tasks, and demonstrate significant improvement over the state of the art. We also show that our approach can be used to train Generative Adversarial Networks for semi-supervised learning.


title: Machine Learning in Medicine

abstract: Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular variant of poker, learned the laws of physics from experimental data, and become experts in video games − tasks that would have been deemed impossible not too long ago. In parallel, the number of companies centered on applying complex data analysis to varying industries has exploded, and it is thus unsurprising that some analytic companies are turning attention to problems in health care. The purpose of this review is to explore what problems in medicine might benefit from such learning approaches and use examples from the literature to introduce basic concepts in machine learning. It is important to note that seemingly large enough medical data sets and adequate learning algorithms have been available for many decades, and yet, although there are thousands of papers applying machine learning algorithms to medical data, very few have contributed meaningfully to clinical care. This lack of impact stands in stark contrast to the enormous relevance of machine learning to many other industries. Thus, part of my effort will be to identify what obstacles there may be to changing the practice of medicine through statistical learning approaches, and discuss how these might be overcome.


title: A method of 3D CAD model retrieval based on spatial bag of words

abstract: In order to improve both the discriminative power for models' local parts and the searching efficiency in 3D CAD model retrieval, a novel hierarchical feature descriptor for retrieval based on spatial bag of words is proposed in this paper. By extracting the essential information from Boundary Representation (B-Rep), 3D CAD models are transformed to Labelled Attribute Adjacency Graphs (LAAGs). Next, the models in training dataset are segmented into different regions according to their corresponding LAAG with an improved segmentation method. All collections of these local regions are described as local feature vectors with graph spectrum, and the codebook is created by clustering all these vectors. Each library model is then decomposed with the same methods mentioned above and globally represented as a spatial histogram of word pairs along with the adjacent relations of its regions, called Spatial Bags-of-Words (SBoWs),and then, the hierarchical feature descriptor(HFD) of each library model composed of global SBoWs and local graph spectrum is constructed. Finally, according to HFD, a two-level searching framework is presented for CAD model retrieval: the candidates are acquired by comparing the query with each target model based on their SBoWs vectors, and the remaining candidates are verified using optimal matching algorithm according to their local features. Experimental results show that the proposed methods promote both retrieval quality and efficiency significantly, so they can support the effective reuse of CAD models for enterprises.


title: Video Annotation and Tracking with Active Learning

abstract: We introduce a novel active learning framework for video annotation. By judiciously choosing which frames a user should annotate, we can obtain highly accurate tracks with minimal user effort. We cast this problem as one of active learning, and show that we can obtain excellent performance by querying frames that, if annotated, would produce a large expected change in the estimated object track. We implement a constrained tracker and compute the expected change for putative annotations with efficient dynamic programming algorithms. We demonstrate our framework on four datasets, including two benchmark datasets constructed with key frame annotations obtained by Amazon Mechanical Turk. Our results indicate that we could obtain equivalent labels for a small fraction of the original cost.


title: Benchmarking steganographic and steganalysis techniques

abstract: There have been a number of steganography embedding techniques proposed over the past few years. In turn the development of these ::: techniques have led to an increased interest in steganalysis techniques. More specifically Universal steganalysis techniques have become more attractive since they work independently of the embedding technique. In this work, our goal is to compare a number of universal steganalysis techniques proposed in the literature which include techniques based on binary similarity measures, wavelet coefficients' statistics, and DCT based image features. These universal steganalysis techniques are tested against a number of well know embedding techniques, including Outguess, F5, Model based, and perturbed quantization. Our experiments are done using a large dataset of JPEG images, obtained by randomly crawling a ::: set of publicly available websites. The image dataset is categorized with respect to the size and quality. We benchmark embedding rate versus detectability performances of several widely used embedding as well as universal steganalysis techniques. Furthermore, we provide a framework for benchmarking future techniques.


title: A 2-kW Single-Phase Seven-Level Flying Capacitor Multilevel Inverter With an Active Energy Buffer

abstract: High-efficiency and compact single-phase inverters are desirable in many applications such as solar energy harvesting and electric vehicle chargers. This paper presents a 2-kW, 60-Hz, 450-V $ _{\text{DC}}$ -to-240-V $_{\text{AC}}$ power inverter, designed and tested subject to the specifications of the Google/IEEE Little Box Challenge. The inverter features a seven-level flying capacitor multilevel converter, with low-voltage GaN switches operating at 120 kHz. The inverter also includes an active buffer for twice-line-frequency power pulsation decoupling, which reduces the required capacitance by a factor of 8 compared to conventional passive decoupling capacitors, while maintaining an efficiency above 99%. The inverter prototype is a self-contained box that achieves a high power density of 216 W/in $^3$ and a peak overall efficiency of 97.6%, while meeting the constraints including input current ripple, load transient, thermal, and FCC Class B EMC specifications.


title: Exploiting generalization in the subspaces for faster model-based learning

abstract: Due to the lack of enough generalization in the state-space, common methods in Reinforcement Learning (RL) suffer from slow learning speed especially in the early learning trials. This paper introduces a model-based method in discrete state-spaces for increasing learning speed in terms of required experience (but not required computational time) by exploiting generalization in the experiences of the subspaces. A subspace is formed by choosing a subset of features in the original state representation (full-space). Generalization and faster learning in a subspace are due to many-to-one mapping of experiences from the full-space to each state in the subspace. Nevertheless, due to inherent perceptual aliasing in the subspaces, the policy suggested by each subspace does not generally converge to the optimal policy. Our approach, called Model Based Learning with Subspaces (MoBLeS), calculates confidence intervals of the estimated Q-values in the full-space and in the subspaces. These confidence intervals are used in the decision making, such that the agent benefits the most from the possible generalization while avoiding from detriment of the perceptual aliasing in the subspaces. Convergence of MoBLeS to the optimal policy is theoretically investigated. Additionally, we show through several experiments that MoBLeS improves the learning speed in the early trials.


title: Modeling Student Interest in Science, Technology, Engineering and Mathematics

abstract: This paper presents preliminary results of a Raytheon project that uses systems engineering techniques to understand the intricacies of the U.S. educational system and to assist in the evaluation of proposed system changes with a goal of doubling the numbers of science, technology, engineering and mathematics (STEM) college graduates by 2015. Specifically, a system dynamics model has been developed, the initial version of which targets increasing the number of students both capable and interested in pursuing careers in STEM disciplines. To examine student interest and capabilities in STEM, the flow of students through the education system and the workforce are modeled. Separate flows delineate students that are interested and uninterested in STEM. Of those interested, the model further separates the students into those interested in teaching STEM, and those who are interested in STEM careers in industry. A few scenarios have been analyzed that examine changes for improving student capabilities. These scenarios investigate how teacher competency affects student interest and capability in STEM. Variations in teachers' capabilities caused by changes in teacher salary, class size, and tenure and retention policies are examined. The structure of the model allows it to be easily adapted to test new scenarios. For example, it is possible to separate out the career paths of specific groups, such as engineers, rather than looking at all STEM disciplines at once. Initial results provide insight into the value and viability of a few proposed changes and indicate that with continued research, model development, and analysis it will be possible to further assess proposed improvements in the U.S education system. Once effective improvements are identified, a roadmap can be created to provide policy makers with direction for future action. The ultimate goal of this project is to make the model available as an open source for use by a broad community of researchers. While much remains to be done, the systems engineering techniques and system dynamics model presented here provide a starting point for understanding the U.S. education system.


title: Combined-Phase-Shift Waveguide Polarizer

abstract: A waveguide polarizer exploiting two different phase shift phenomena is presented in this letter. Iris-type discontinuities are in fact introduced in a waveguide structure having different propagation constants for the two principal polarizations. In this way, the required 90 differential phase shift is obtained combining the iris phase shift with the waveguide one. Several operative conditions arise from the combination of the two contributions. This approach has been used to design a broadband waveguide polarizer for the C-band antenna feed system of the Sardinia radio telescope with -40 dB reflection coefficients and a -35 dB cross polarization level in a 30% bandwidth.


title: No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysis

abstract: In this paper we develop a new framework that captures the common landscape underlying the common non-convex low-rank matrix problems including matrix sensing, matrix completion and robust PCA. In particular, we show for all above problems (including asymmetric cases): 1) all local minima are also globally optimal; 2) no high-order saddle points exists. These results explain why simple algorithms such as stochastic gradient descent have global converge, and efficiently optimize these non-convex objective functions in practice. Our framework connects and simplifies the existing analyses on optimization landscapes for matrix sensing and symmetric matrix completion. The framework naturally leads to new results for asymmetric matrix completion and robust PCA.


title: J Youth Adolescence DOI 10.1007/s10964-009-9432-x EMPIRICAL RESEARCH Financial Socialization of First-year College Students: The Roles of Parents, Work, and Education

abstract: This cross-sectional study tests a conceptual financial socialization process model, specifying four-levels that connect anticipatory socialization during adolescence to young adults’ current financial learning, to their financial attitudes, and to their financial behavior. A total of 2,098 first-year college students (61.9% females) participated in the survey, representing a diverse ethnic group (32.6% minority participation: Hispanic 14.9%, Asian/Asian American 9%, Black 3.4%, Native American 1.8% and other 3.5%). Structural equation modeling indicated that parents, work, and high school financial education during adolescence predicted young adults’ current financial learning, attitude and behavior, with the role played by parents substantially greater than the role played by work experience and high school financial education combined. Data also supported the proposed hierarchical financial socialization four-level model, indicating that early financial socialization is related to financial learning, which in turn is related to financial attitudes and subsequently to financial behavior. The study presents a discussion of how the theories of consumer socialization and planned behavior were combined effectively to depict the financial development of young adults. Several practical implications are also provided for parents, educators and students.


title: Design of Microstrip Parallel Coupled Band Pass Filter for Global Positioning System

abstract: Radio frequency (RF) filters operating in the microwave frequency range are needed for applications including wireless and satellite communications as well as military applications. Most communication system contains an RF front end which performs signal processing with RF filters. Planar or printed circuit board (PCB) based filters are popular and relatively practical to design. Band pass filters play a significant role in wireless communication systems. Transmitted and received signals have to be filtered at a certain center frequency with a specific bandwidth. This paper describes the design of low cost L-band Parallel coupled microstrip Band pass filter(BPF) for GPS system by using micro strip layout at center frequency 1.575 GHz for permittivity 4.4 value with a substrate thickness 1.6mm for order n=3. The design and simulation are performed using 3D full wave electromagnetic simulator IE3D.We have also develop program code in MATLAB to plot the insertion loss (S21 parameter) and return loss(S11) of the Band pass filter.


title: High dynamic range imaging for stereoscopic scene representation

abstract: This paper presents a method for generating high dynamic range and disparity images by simultaneously capturing the high and low exposure images using a pair of cameras. The proposed stereoscopic high dynamic range imaging technique is able to record multiple exposures without any time delay, and thus suitable for high dynamic range video synthesis. We have demonstrated that it is possible to construct the camera response function using a pair of images with different amount of exposure. The intensities of the stereo images can then be normalized for correspondence matching. Experiments using the Middlebury stereo datasets are presented.


title: Dynamic Reconfiguration in Camera Networks: A Short Survey

abstract: There is a clear trend in camera networks toward enhanced functionality and flexibility, and a fixed static deployment is typically not sufficient to fulfill these increased requirements. Dynamic network reconfiguration helps to optimize the network performance to the currently required specific tasks while considering the available resources. Although several reconfiguration methods have been recently proposed, e.g., for maximizing the global scene coverage or maximizing the image quality of specific targets, there is a lack of a general framework highlighting the key components shared by all these systems. In this paper, we propose a reference framework for network reconfiguration and present a short survey of some of the most relevant state-of-the-art works in this field, showing how they can be reformulated in our framework. Finally, we discuss the main open research challenges in camera network reconfiguration.


title: Concreteness and Subjectivity as Dimensions of Lexical Meaning

abstract: We quantify the lexical subjectivity of adjectives using a corpus-based method, and show for the first time that it correlates with noun concreteness in large corpora. These cognitive dimensions together influence how word meanings combine, and we exploit this fact to achieve performance improvements on the semantic classification of adjective-noun pairs.


title: Antenna in package design for WLAN using MCM-D manufacturing technology

abstract: This paper introduces an antenna design based on MCM-D manufacturing technology to realize an antenna-integrated package for IEEE 802.11b/g application. Co-design guidelines are employed to include the parasitic effects caused by the integration of the antenna and the RF module. The loop antenna is located on the second layer of the MCM-D substrate. The antenna incorporates the capacitively feed strip which is fed by the coplanar waveguide (CPW). By the coupling feed technique, the size of the proposed antenna is only 3.8 mm × 4.7 mm over the WLAN band (2.4–2.484 GHz). Furthermore, the resonant frequency can be adjusted by tuning the length of the coupling strip. The results show that the coupling-fed loop antenna achieved a gain of 1.6 dBi and radiation efficiency of 85 % at 2.45 GHz in a very compact size (0.03 λ 0 × 0.04 λ 0 ). In addition, the occupied area of the antenna is very small (4.4%) compared to the overall area of the package; therefore, the proposed method is very useful for package antenna design. The detailed parameters studies are presented, which demonstrate the feasibility of the proposed method.


title: Designing deep networks for surface normal estimation

abstract: In the past few years, convolutional neural nets (CNN) have shown incredible promise for learning visual representations. In this paper, we use CNNs for the task of predicting surface normals from a single image. But what is the right architecture? We propose to build upon the decades of hard work in 3D scene understanding to design a new CNN architecture for the task of surface normal estimation. We show that incorporating several constraints (man-made, Manhattan world) and meaningful intermediate representations (room layout, edge labels) in the architecture leads to state of the art performance on surface normal estimation. We also show that our network is quite robust and show state of the art results on other datasets as well without any fine-tuning.


title: Giant voltage-induced deformation in dielectric elastomers near the verge of snap-through instability

abstract: Dielectric elastomers are capable of large voltage-induced deformation, but achieving such large deformation in practice has been a major challenge due to electromechanical instability and electric breakdown. The complex nonlinear behavior suggests an important opportunity: electromechanical instability can be harnessed to achieve giant voltage-induced deformation. We introduce the following principle of operation: place a dielectric elastomer near the verge of snap-through instability, trigger the instability with voltage, and bend the snap-through path to avert electric breakdown. We demonstrate this principle of operation with a commonly used experimental setup—a dielectric membrane mounted on a chamber of air. The behavior of the membrane can be changed dramatically by varying parameters such as the initial pressure in the chamber, the volume of the chamber, and the prestretch of the membrane. We use a computational model to analyze inhomogeneous deformation and map out bifurcation diagrams to guide the experiment. With suitable values of the parameters, we obtain giant voltage-induced expansion of area by 1692%, far beyond the largest value reported in the literature.


title: Characterizing Honeypot-Captured Cyber Attacks: Statistical Framework and Case Study

abstract: Rigorously characterizing the statistical properties of cyber attacks is an important problem. In this paper, we propose the first statistical framework for rigorously analyzing honeypot-captured cyber attack data. The framework is built on the novel concept of stochastic cyber attack process, a new kind of mathematical objects for describing cyber attacks. To demonstrate use of the framework, we apply it to analyze a low-interaction honeypot dataset, while noting that the framework can be equally applied to analyze high-interaction honeypot data that contains richer information about the attacks. The case study finds, for the first time, that long-range dependence (LRD) is exhibited by honeypot-captured cyber attacks. The case study confirms that by exploiting the statistical properties (LRD in this case), it is feasible to predict cyber attacks (at least in terms of attack rate) with good accuracy. This kind of prediction capability would provide sufficient early-warning time for defenders to adjust their defense configurations or resource allocations. The idea of “gray-box” (rather than “black-box”) prediction is central to the utility of the statistical framework, and represents a significant step towards ultimately understanding (the degree of) the predictability of cyber attacks.


title: Context in Artificial Intelligence: II. Key elements of contexts

abstract: Context is the challenge for the coming years in artificial intelligence. In the companion paper [6], we present a view of how context is considered through the literature in various domains. In this paper, we present the main results of discussions at some workshops and the first conference focusing on the notion of context. We point out the opposition between two viewpoints on context, namely the engineering and the cognitive ones. We show that this opposition is only superficial because they do not consider context at the same level, one is at the level of the knowledge representation, the other at the level of the interaction between two agents.


title: Reduction of Nitric Oxide by Coal Char at Temperatures of 1250–1750 K

abstract: INTRODUCTION An important factor to be considered in developing NO control strategies for pulverized coal combustors is the potential for the reduction of NO by char. Such reaction has been shown to reduce appreciably the emissions of NO from fluidized bed coal combustors (Pereira et al., 1975). A number of experimental studies were carried out of the NO/char reactions using fixed and fluidized bed reactors (Furusawa et al., 1977; Chan, 1977; Sprouse, 1977; Beer et al., 1977) in which the products of the reaction were identified and kinetic parameters of the reaction were determined. The present paper is concerned with the destruction of NO by char at temperatures of interest in pulverized coal flames.


title: Design of over-current protection circuit for power transistors

abstract: The paper presents an over-current protection circuit designed for high voltage power switches. The proposed protection mechanism employs a current limiter to monitor the current flowing through the power switch. The current limiter protects the power switch from potential damage by automatically switching off the device once the current exceeds the preset rating value. Simulated results exhibit that the capability of the designed current limiter can work up to 1 MHz where the maximum current is above 1A using 0.25 μm Bipolar-CMOS-DMOS (BCD) technology.


title: ByteFreq: Malware clustering using byte frequency

abstract: Increased number of malware samples have created many challenges for Antivirus companies. One of these challenges is clustering the large number of malware samples they receive daily. Malware authors use malware generation kits to create different instances of the same malware. So most of these malicious samples are polymorphic instances of previously known malware family only. Clustering these large number of samples rapidly and accurately without spending much time on processing the sample have become a critical requirement. In this paper we proposed, implemented and evaluated a method, called ByteFreq that can cluster large number of samples using byte frequency. Byte frequency is represented as time series and SAX (Symbolic Aggregation approXimation)[1] is used to convert the time series in symbolic representation. We evaluated proposed system on real world malware samples and achieved 0.92 precision and 0.96 recall accuracy.


title: Ontology-Based Data Access to Big Data

abstract: Recent approaches to ontology-based data access (OBDA) have extended the focus from relational database systems to other types of backends such as cluster frameworks in order to cope with the four Vs associated with big data: volume, veracity, variety and velocity (stream processing). The abstraction that an ontology provides is a benefit from the enduser point of view, but it represents a challenge for developers because high-level queries must be transformed into queries executable on the backend level. In this paper, we discuss and evaluate an OBDA system that uses STARQL (Streaming and Temporal ontology Access with a Reasoning-based Query Language), as a high-level query language to access data stored in a SPARK cluster framework. The development of the STARQL-SPARK engine show that there is a need to provide a homogeneous interface to access both static and temporal as well as streaming data because cluster frameworks usually lack such an interface. The experimental evaluation shows that building a scalable OBDA system that runs with SPARK is more than plug-and-play as one needs to know quite well the data formats and the data organisation in the cluster framework.


title: Clique Trip: feeling related in different cars

abstract: Contemporary car design must not only focus on technology supporting the driver and the driving task: it needs to create positive experiences for drivers and passengers alike. This case study, the Clique Trip, is an example of designing a positive social (i.e. relatedness) experience in the automotive context, addressing the analysis, the design, and the evaluation of the experience. The Clique Trip experience creates a feeling of closeness and relatedness among friends when being in a "motorcade". It is derived from experience reports, implemented in the car and evaluated on the road. Qualitative and quantitative results revealed its capability to create the targeted social experience.


title: A Novel Earth Mover's Distance Methodology for Image Matching with Gaussian Mixture Models

abstract: The similarity or distance measure between Gaussian mixture models (GMMs) plays a crucial role in content-based image matching. Though the Earth Mover's Distance (EMD) has shown its advantages in matching histogram features, its potentials in matching GMMs remain unclear and are not fully explored. To address this problem, we propose a novel EMD methodology for GMM matching. We first present a sparse representation based EMD called SR-EMD by exploiting the sparse property of the underlying problem. SR-EMD is more efficient and robust than the conventional EMD. Second, we present two novel ground distances between component Gaussians based on the information geometry. The perspective from the Riemannian geometry distinguishes the proposed ground distances from the classical entropy-or divergence-based ones. Furthermore, motivated by the success of distance metric learning of vector data, we make the first attempt to learn the EMD distance metrics between GMMs by using a simple yet effective supervised pair-wise based method. It can adapt the distance metrics between GMMs to specific classification tasks. The proposed method is evaluated on both simulated data and benchmark real databases and achieves very promising performance.


title: Forecasting of global and direct solar irradiance using stochastic learning methods, ground experiments and the NWS database

abstract: We develop and validate a medium-term solar irradiance forecasting model by adopting predicted meteorological variables from the US National Weather Service’s (NWS) forecasting database as inputs to an Artificial Neural Network (ANN) model. Since the inputs involved are the same as the ones available from a recently validated forecasting model, we include mean bias error (MBE), root mean square error (RMSE), and correlation coefficient (R2) comparisons between the more established forecasting model and the proposed ones. An important component of our study is the development of a set of criteria for selecting relevant inputs. The input variables are selected using a version of the Gamma test combined with a genetic algorithm. The solar geotemporal variables are found to be critically important, while the most relevant meteorological variables include sky cover, probability of precipitation, and maximum and minimum temperatures. Using the relevant input sets identified by the Gamma test, the developed forecasting models improve RMSEs for GHI by 10–15% over the reference model. Prediction intervals based on regression of the squared residuals on the input variables are also derived.


title: A dimensional analysis of creativity and mental illness: Do anxiety and depression symptoms predict creative cognition, creative accomplishments, and creative self-concepts?

abstract: The link, if any, between creativity and mental illness is one of the most controversial topics in modern creativity research. The present research assessed the relationships between anxiety and depression symptom dimensions and several facets of creativity: divergent thinking, creative self-concepts, everyday creative behaviors, and creative accomplishments. Latent variable models estimated effect sizes and their confidence intervals. Overall, measures of anxiety, depression, and social anxiety predicted little variance in creativity. Few models explained more than 3% of the variance, and the effect sizes were small and inconsistent in direction.


title: Classification of dental diseases using CNN and transfer learning

abstract: Automated medical assistance system is in high demand with the advances in research in the machine learning area. In many such applications, availability of labeled medical dataset is a primary challenge and dataset of dental diseases is not an exception. An attempt towards accurate classification of dental diseases is addressed in this paper. Labeled dataset consisting of 251 Radio Visiography (RVG) x-ray images of 3 different classes is used for classification. Convolutional neural network (CNN) has become a most effective tool in machine learning which enables solving the problems like image recognition, segmentation, classification, etc., with high order of accuracy. It is found from literature that CNN performs well in natural image classification problems where large dataset is available. In this paper we experimented on the performance of CNN for diagnosis of small labeled dental dataset. In addition, transfer learning is used to improve the accuracy. Experimental results are presented for three different architectures of CNN. Overall accuracy achieved is very encouraging.


title: Memory-centric video processing

abstract: This work presents a domain-specific memory subsystem based on a two-level memory hierarchy. It targets the application domain of video post-processing applications including video enhancement and format conversion. These applications are based on motion compensation and/or broad class of content adaptive filtering to provide the highest quality of pictures. Our approach meets the required performance and has sufficient flexibility for the application domain. It especially aims at the implementation-wise most challenging applications: compute-intensive and bandwidth-demanding applications that provide the highest quality at high picture resolutions. The lowest level of the memory hierarchy, closest to the processing element, the L0 scratchpad, is organized specifically to enable fast retrieval of an arbitrarily positioned 2-D block of pixels to the processing element. To guarantee the performance, most of its addressing logic is hardwired, leaving a user a set of API for initialization and storing/loading the data to/from the L0 scratchpad. The next level of the memory hierarchy, the L1 scratchpad, minimizes the off-chip memory bandwidth requirements. The L1 scratchpad is organized specifically to enable efficient aligned block-based accesses. With lower data rates compared to the L0 scratchpad and aligned block access, software-based addressing is used to enable full flexibility. The two-level memory hierarchy exploits prefetching to further improve the performance.


title: Partition Search Revisited

abstract: Partition search is a form of game search, proposed by Matthew L. Ginsberg in 1996, who wrote that the method “incorporates dependency analysis, allowing substantial reductions in the portion of the tree that needs to be expanded.” In this paper, some improvements of the partition search algorithm are proposed. The effectiveness of the most important extension we contribute, which we call local partition search , has been verified experimentally. The results obtained (which we present in the paper) show that using this extension, leads, in the case of bridge, to a significant reduction (almost by half) of the search tree size and calculation time. Another extension we proposed allows for more effective usage of the transposition table (using it to narrow the search window or by cutting more than one entry). Additionally, we contribute a formal proof of the correctness of all presented partition search variants. We draw conclusions from it about a possible generalization of partition search by making the definition of a partition system less restrictive. We also provide a formal definition of a partition system for the double dummy bridge.


title: Support vector machines: theory and applications

abstract: This chapter presents a summary of the issues discussed during the one day workshop on “Support Vector Machines (SVM) Theory and Applications” organized as part of the Advanced Course on Artificial Intelligence (ACAI ’99) in Chania, Greece [19]. The goal of the chapter is twofold: to present an overview of the background theory and current understanding of SVM, and to discuss the papers presented as well as the issues that arose during the workshop.


title: Single View Point Omnidirectional Camera Calibration from Planar Grids

abstract: This paper presents a flexible approach for calibrating omnidirectional single viewpoint sensors from planar grids. These sensors are increasingly used in robotics where accurate calibration is often a prerequisite. Current approaches in the field are either based on theoretical properties and do not take into account important factors such as misalignment or camera-lens distortion or over-parametrised which leads to minimisation problems that are difficult to solve. Recent techniques based on polynomial approximations lead to impractical calibration methods. Our model is based on an exact theoretical projection function to which we add well identified parameters to model real-world errors. This leads to a full methodology from the initialisation of the intrinsic parameters to the general calibration. We also discuss the validity of the approach for fish-eye and spherical models. An implementation of the method is available as OpenSource software on the author's Web page. We validate the approach with the calibration of parabolic, hyperbolic, folded mirror, wide-angle and spherical sensors.


title: A review of microvalves

abstract: This review gives a brief overview of microvalves, and focuses on the actuation mechanisms and their applications. One of the stumbling blocks for successful miniaturization and commercialization of fully integrated microfluidic systems was the development of reliable microvalves. Applications of the microvalves include flow regulation, on/off switching and sealing of liquids, gases or vacuums. Microvalves have been developed in the form of active or passive microvalves employing mechanical, non-mechanical and external systems. Even though great progress has been made during the last 20 years, there is plenty of room for further improving the performance of existing microvalves.


title: Averaging covariance matrices for EEG signal classification based on the CSP: An empirical study

abstract: This paper presents an empirical comparison of covariance matrix averaging methods for EEG signal classification. Indeed, averaging EEG signal covariance matrices is a key step in designing brain-computer interfaces (BCI) based on the popular common spatial pattern (CSP) algorithm. BCI paradigms are typically structured into trials and we argue that this structure should be taken into account. Moreover, the non-Euclidean structure of covariance matrices should be taken into consideration as well. We review several approaches from the literature for averaging covariance matrices in CSP and compare them empirically on three publicly available datasets. Our results show that using Riemannian geometry for averaging covariance matrices improves performances for small dimensional problems, but also the limits of this approach when the dimensionality increases.


title: Hydrological modelling using artificial neural networks

abstract: This review considers the application of artificial neural networks (ANNs) to rainfall-runoff modelling and flood forecasting. This is an emerging field of research, character- ized by a wide variety of techniques, a diversity of geographical contexts, a general absence of intermodel comparisons, and inconsistent reporting of model skill. This article begins by outlining the basic principles of ANN modelling, common network architectures and training algorithms. The discussion then addresses related themes of the division and preprocessing of data for model calibration/validation; data standardization techniques; and methods of evaluating ANN model performance. A literature survey underlines the need for clear guidance in current modelling practice, as well as the comparison of ANN methods with more conven- tional statistical models. Accordingly, a template is proposed in order to assist the construction of future ANN rainfall-runoff models. Finally, it is suggested that research might focus on the extraction of hydrological 'rules' from ANN weights, and on the development of standard performance measures that penalize unnecessary model complexity.


title: Introducing Concept And Syntax Transition Networks for Image Captioning

abstract: The area of image captioning i.e. the automatic generation of short textual descriptions of images has experienced much progress recently. However, image captioning approaches often only focus on describing the content of the image without any emotional or sentimental dimension which is common in human captions. This paper presents an approach for image captioning designed specifically to incorporate emotions and feelings into the caption generation process. The presented approach consists of a Deep Convolutional Neural Network (CNN) for detecting Adjective Noun Pairs in the image and a novel graphical network architecture called "Concept And Syntax Transition (CAST)" network for generating sentences from these detected concepts.


title: Video-Based Human Walking Estimation Using Joint Gait and Pose Manifolds

abstract: We study two fundamental issues about video-based human walking estimation, where the goal is to estimate 3D gait kinematics (i.e., joint positions) from 2D gait appearances (i.e., silhouettes). One is how to model the gait kinematics from different walking styles, and the other is how to represent the gait appearances captured under different views and from individuals of distinct walking styles and body shapes. Our research is conducted in three steps. First, we propose the idea of joint gait-pose manifold (JGPM), which represents gait kinematics by coupling two nonlinear variables, pose (a specific walking stage) and gait (a particular walking style) in a unified latent space. We extend the Gaussian process latent variable model (GPLVM) for JGPM learning, where two heuristic topological priors, a torus and a cylinder, are considered and several JGPMs of different degrees of freedom (DoFs) are introduced for comparative analysis. Second, we develop a validation technique and a series of benchmark tests to evaluate multiple JGPMs and recent GPLVMs in terms of their performance for gait motion modeling. It is shown that the toroidal prior is slightly better than the cylindrical one, and the JGPM of 4 DoFs that balances the toroidal prior with the intrinsic data structure achieves the best performance. Third, a JGPM-based visual gait generative model (JGPM-VGGM) is developed, where JGPM plays a central role to bridge the gap between the gait appearances and the gait kinematics. Our proposed JGPM-VGGM is learned from Carnegie Mellon University MoCap data and tested on the HumanEva-I and HumanEva-II data sets. Our experimental results demonstrate the effectiveness and competitiveness of our algorithms compared with existing algorithms.


title: Effects of Culture and Education on Neuropsychological Testing: A Preliminary Study With Indigenous and Nonindigenous Population

abstract: We analyzed the influence of education and of culture on the neuropsychological profile of an indigenous and a nonindigenous population. The sample included 27 individuals divided into four groups: (a) seven illiterate Maya indigenous participants, (b) six illiterate Pame indigenous participants, (c) seven nonindigenous participants with no education, and (d) seven Maya indigenous participants with 1 to 4 years of education . A brief neuropsychological test battery developed and standardized in Mexico was individually administered. Results demonstrated differential effects for both variables. Both groups of indigenous participants (Maya and Pame) obtained higher scores in visuospatial tasks, and the level of education had significant effects on working and verbal memory. Our data suggested that culture dictates what it is important for survival and that education could be considered as a type of subculture that facilitates the development of certain skills.


title: Linear discriminant analysis: A detailed tutorial

abstract: Linear Discriminant Analysis (LDA) is a very common ::: technique for dimensionality reduction problems as a preprocessing ::: step for machine learning and pattern classification ::: applications. At the same time, it is usually used as a ::: black box, but (sometimes) not well understood. The aim of ::: this paper is to build a solid intuition for what is LDA, and ::: how LDA works, thus enabling readers of all levels be able ::: to get a better understanding of the LDA and to know how to ::: apply this technique in different applications. The paper first ::: gave the basic definitions and steps of how LDA technique ::: works supported with visual explanations of these steps. ::: Moreover, the two methods of computing the LDA space, i.e. ::: class-dependent and class-independent methods, were explained ::: in details. Then, in a step-by-step approach, two numerical ::: examples are demonstrated to show how the LDA ::: space can be calculated in case of the class-dependent and ::: class-independent methods. Furthermore, two of the most ::: common LDA problems (i.e. Small Sample Size (SSS) and ::: non-linearity problems) were highlighted and illustrated, and ::: state-of-the-art solutions to these problems were investigated and explained. Finally, a number of experiments was conducted ::: with different datasets to (1) investigate the effect of ::: the eigenvectors that used in the LDA space on the robustness ::: of the extracted feature for the classification accuracy, ::: and (2) to show when the SSS problem occurs and how it can ::: be addressed.


title: Induction motor tests using MATLAB/Simulink and their integration into undergraduate electric machinery courses

abstract: This work describes MATLAB/Simulink implementation of three induction motor tests, namely dc, no-load, and blocked-rotor tests performed to identify equivalent circuit parameters. These simulation models are developed to support and enhance electric machinery education at the undergraduate level. The proposed tests have been successfully integrated into electric machinery courses at Drexel University, Philadelphia, PA, and Nigde University, Nigde, Turkey.


title: Voice Loops as Coordination Aids in Space Shuttle Mission Control

abstract: Voice loops, an auditory groupware technology, are essential coordination support tools for experienced practitioners in domains such as air traffic management, aircraft carrier operations and space shuttle mission control. They support synchronous communication on multiple channels among groups of people who are spatially distributed. In this paper, we suggest reasons for why the voice loop system is a successful medium for supporting coordination in space shuttle mission control based on over 130 hours of direct observation. Voice loops allow practitioners to listen in on relevant communications without disrupting their own activities or the activities of others. In addition, the voice loop system is structured around the mission control organization, and therefore directly supports the demands of the domain. By understanding how voice loops meet the particular demands of the mission control environment, insight can be gained for the design of groupware tools to support cooperative activity in other event-driven domains.


title: Literature Fingerprinting: A New Method for Visual Literary Analysis

abstract: In computer-based literary analysis different types of features are used to characterize a text. Usually, only a single feature value or vector is calculated for the whole text. In this paper, we combine automatic literature analysis methods with an effective visualization technique to analyze the behavior of the feature values across the text. For an interactive visual analysis, we calculate a sequence of feature values per text and present them to the user as a characteristic fingerprint. The feature values may be calculated on different hierarchy levels, allowing the analysis to be done on different resolution levels. A case study shows several successful applications of our new method to known literature problems and demonstrates the advantage of our new visual literature fingerprinting.


title: A User Simulator for Task-Completion Dialogues

abstract: Despite widespread interests in reinforcement-learning for task-oriented dialogue systems, several obstacles can frustrate research and development progress. First, reinforcement learners typically require interaction with the environment, so conventional dialogue corpora cannot be used directly. Second, each task presents specific challenges, requiring separate corpus of task-specific annotated data. Third, collecting and annotating human-machine or human-human conversations for task-oriented dialogues requires extensive domain knowledge. Because building an appropriate dataset can be both financially costly and time-consuming, one popular approach is to build a user simulator based upon a corpus of example dialogues. Then, one can train reinforcement learning agents in an online fashion as they interact with the simulator. Dialogue agents trained on these simulators can serve as an effective starting point. Once agents master the simulator, they may be deployed in a real environment to interact with humans, and continue to be trained online. To ease empirical algorithmic comparisons in dialogues, this paper introduces a new, publicly available simulation framework, where our simulator, designed for the movie-booking domain, leverages both rules and collected data. The simulator supports two tasks: movie ticket booking and movie seeking. Finally, we demonstrate several agents and detail the procedure to add and test your own agent in the proposed framework.


title: Intrinsically motivated neuroevolution for vision-based reinforcement learning

abstract: Neuroevolution, the artificial evolution of neural networks, has shown great promise on continuous reinforcement learning tasks that require memory. However, it is not yet directly applicable to realistic embedded agents using high-dimensional (e.g. raw video images) inputs, requiring very large networks. In this paper, neuroevolution is combined with an unsupervised sensory pre-processor or compressor that is trained on images generated from the environment by the population of evolving recurrent neural network controllers. The compressor not only reduces the input cardinality of the controllers, but also biases the search toward novel controllers by rewarding those controllers that discover images that it reconstructs poorly. The method is successfully demonstrated on a vision-based version of the well-known mountain car benchmark, where controllers receive only single high-dimensional visual images of the environment, from a third-person perspective, instead of the standard two-dimensional state vector which includes information about velocity.


title: DeepSpark: Spark-Based Deep Learning Supporting Asynchronous Updates and Caffe Compatibility

abstract: The increasing complexity of deep neural networks (DNNs) has made it challenging to exploit existing large-scale data process pipelines for handling massive data and parameters involved in DNN training. Distributed computing platforms and GPGPU-based acceleration provide a mainstream solution to this computational challenge. In this paper, we propose DeepSpark, a distributed and parallel deep learning framework that simultaneously exploits Apache Spark for large-scale distributed data management and Caffe for GPU-based acceleration. DeepSpark directly accepts Caffe input specifications, providing seamless compatibility with existing designs and network structures. To support parallel operations, DeepSpark automatically distributes workloads and parameters to Caffe-running nodes using Spark and iteratively aggregates training results by a novel lock-free asynchronous variant of the popular elastic averaging stochastic gradient descent (SGD) update scheme, effectively complementing the synchronized processing capabilities of Spark. DeepSpark is an on-going project, and the current release is available at this http URL


title: A differential pattern of neural response toward sad versus happy facial expressions in major depressive disorder

abstract: BACKGROUND: ::: Accurate recognition of facial expressions is crucial for social functioning. In depressed individuals, implicit and explicit attentional biases away from happy and toward sad stimuli have been demonstrated. These may be associated with the negative cognitions in these individuals. ::: ::: METHODS: ::: Using event-related functional magnetic resonance imaging (fMRI), neural responses to happy and sad facial expressions were measured in 14 healthy individuals and 16 individuals with major depressive disorder. ::: ::: RESULTS: ::: Healthy but not depressed individuals demonstrated linear increases in response in bilateral fusiform gyri and right putamen to expressions of increasing happiness, while depressed individuals demonstrated linear increases in response in left putamen, left parahippocampal gyrus/amygdala, and right fusiform gyrus to expressions of increasing sadness. There was a negative correlation in depressed individuals between depression severity and magnitude of neural response within right fusiform gyrus to happy expressions. ::: ::: CONCLUSIONS: ::: Our findings indicate preferential increases in neural response to sad but not happy facial expressions in neural regions involved in the processing of emotional stimuli in depressed individuals. These findings may be associated with the above pattern of implicit and explicit attentional biases in these individuals and suggest a potential neural basis for the negative cognitions and social dysfunction in major depression.


title: Dual-fisheye omnidirectional stereo

abstract: We propose a novel omnidirectional stereo camera setup that is formed by two ultra-wide field-of-view (FOV) fisheye cameras. The proposed configuration is formed by two 245-degree FOV fisheye cameras, facing opposite directions, that are rigidly mounted on two sides of a rod. The overlapping view in the two fisheye images forms a ring-shaped spatial stereo setup. Our system provides stereo observations with full 360-degree FOV in horizontal directions and 65-degree FOV in the vertical direction. In addition, the two fisheye cameras altogether also provide full spherical monocular coverage of the surrounding environment. We address challenges in camera modeling, fisheye intrinsic calibration, stereo self-calibration, and depth estimation. We develop a lens-specific fisheye camera calibration method that uses manufacturer data for the optical lens to assist with the intrinsic calibration and develop an online self-calibration approach for estimating stereo extrinsic parameters. The overlapping camera views are rectified into stereo image pairs, from which a spatial stereo matching pipeline is developed for depth estimation in all horizontal directions. We show both qualitative and quantitative analysis to validate our approach.


title: UHF RFID tag antenna design for on-body applications

abstract: This paper addresses the antennas design for passive UHF rfid applications involving the human body as the object to be tagged. Novel antenna geometry is proposed for conjugate impedance matching to the complex impedance of the rfid transponder. The proposed tag is capable of achieving high read range directly attached to a human thorax or in free space.


title: 60-GHz LTCC Wideband Vertical Off-Center Dipole Antenna and Arrays

abstract: Wideband 60-GHz vertical off-center dipole antenna and its arrays on low-temperature cofired ceramic substrate are presented in this paper. The dipole antenna is designed using the off-center-fed technology to cover all the four channels defined in 60-GHz standards from 57 to 66 GHz. A 4 × 4 planar array is optimized to achieve a maximum gain of 15.6 dBi at 60 GHz and above 10.2 dBi in the passband, while a beam-steering array is optimized to give a 5-dB measured beamwidth wider than 80° at 60 GHz. Measured results indicate our designs meet the above requirements well and satisfy 60-GHz applications.


title: What's wrong? Understanding beginners' problems with Prolog

abstract: This paper reviews psychological research on programming and applies it to the problems of learning and teaching Prolog. We present a psychological model that explains how a certain class of errors in programs comes about. The model fits quite well with the results of a small sample of students and problems. The problems that underlie these and other errors seem to be (a) the complexity of the Prolog primitives (unification and backtracking) and (b) the misfit between students' naive solutions to a problem and the constructs that are available in Prolog (e.g. iterative solutions do not map easily to recursive programs). This suggests that learning Prolog could be helped by (1) coherent and detailed instruction about how Prolog works, (2) emphasis on finding recursive solutions that do not rely on primitives such as assignment and (3) instruction in programming techniques that allow students to implement procedural solutions.


title: Mindfulness and self-compassion as predictors of psychological wellbeing in long-term meditators and matched nonmeditators

abstract: Mindfulness training has well-documented effects on psychological health. Recent findings suggest that increases in both mindfulness and self-compassion may mediate these outcomes; however, their separate and combined effects are rarely examined in the same participants. This study investigated cross-sectional relationships between self-reported mindfulness, self-compassion, meditation experience, and psychological wellbeing in 77 experienced meditators and 75 demographically matched nonmeditators. Most mindfulness and self-compassion scores were significantly correlated with meditation experience and psychological wellbeing. Mindfulness and self-compassion accounted for significant independent variance in wellbeing. A significant relationship between meditation experience and wellbeing was completely accounted for by a combination of mindfulness and self-compassion scores. Findings suggest that both mindfulness and self-compassion skills may play important roles in the improved wellbeing associated with ...


title: Learning to Generate Pseudo-Code from Source Code Using Statistical Machine Translation (T)

abstract: Pseudo-code written in natural language can aid the comprehension of source code in unfamiliar programming languages. However, the great majority of source code has no corresponding pseudo-code, because pseudo-code is redundant and laborious to create. If pseudo-code could be generated automatically and instantly from given source code, we could allow for on-demand production of pseudo-code without human effort. In this paper, we propose a method to automatically generate pseudo-code from source code, specifically adopting the statistical machine translation (SMT) framework. SMT, which was originally designed to translate between two natural languages, allows us to automatically learn the relationship between source code/pseudo-code pairs, making it possible to create a pseudo-code generator with less human effort. In experiments, we generated English or Japanese pseudo-code from Python statements using SMT, and find that the generated pseudo-code is largely accurate, and aids code understanding.


title: Performance Guaranteed Network Acceleration via High-Order Residual Quantization

abstract: Input binarization has shown to be an effective way for network acceleration. However, previous binarization scheme could be regarded as simple pixel-wise thresholding operations (i.e., order-one approximation) and suffers a big accuracy loss. In this paper, we propose a highorder binarization scheme, which achieves more accurate approximation while still possesses the advantage of binary operation. In particular, the proposed scheme recursively performs residual quantization and yields a series of binary input images with decreasing magnitude scales. Accordingly, we propose high-order binary filtering and gradient propagation operations for both forward and backward computations. Theoretical analysis shows approximation error guarantee property of proposed method. Extensive experimental results demonstrate that the proposed scheme yields great recognition accuracy while being accelerated.


title: Bowler’s thumb: ultrasound diagnosis of a neuroma of the ulnar digital nerve of the thumb

abstract: Bowler’s thumb is a rare traumatic neuropathy of the ulnar digital nerve of the thumb. We present a case of bowler’s thumb in a 21-year-old male recreational bowler who presented with a painful mass on the ulnar side of the right thumb. Magnetic resonance (MR) imaging of the hand was inconclusive. However, subsequent ultrasound (US) showed asymmetric enlargement of the ulnar digital nerve of the thumb with marked epineural thickening corresponding to the palpable mass, confirming the clinical diagnosis of bowler’s thumb. Although this condition is typically diagnosed clinically, imaging can help to clarify clinical findings. In our experience, MR imaging is a clinician’s preferred choice when further evaluating physical findings of digital nerve pathology despite the lack of evidence to support MR as a primary imaging modality for these patients. This case illustrates the role that US can play as an initial imaging modality for the evaluation of small peripheral nerves.


title: Flexible learning on the sphere via adaptive needlet shrinkage and selection

abstract: This paper introduces an approach for exible, robust Bayesian modeling of structure in spherical data sets. Our method is based upon a recent construction called the needlet, which is a particular form of spherical wavelet with many favorable statistical and computational properties. We perform shrinkage and selection of needlet coecients, focusing on two main alternatives: empirical-Bayes thresholding for selection, and the horseshoe prior for shrinkage. We study the performance of the proposed methodology both on simulated data and on a real data set involving the cosmic microwave background radiation. Horseshoe shrinkage of needlet coecients is shown to yield the best overall performance against some common benchmarks.


title: Studying toxic behavior influence and player chat in an online video game

abstract: Many online collaborative games, e-sports in particular, heavily rely on teamwork. However, players can act in an antisocial way during the match, creating dissent into the match. This kind of behavior is referred to as toxic. We aim to discover the influence brought by toxic behavior in a popular e-sport, League of Legends, through the study of communication patterns of players during the match. We discovered that different communication patterns exist, and that they are directly related to player performance and level of toxic behavior. We also propose metrics to analyze players' performance and the toxic contamination level, which measures the negative impacts of the toxic behavior. Our analysis contributes to shed light on how players behave in an online game, and opens ways to provide a better ambience on the online video game community.


A Tutorial on Reinforcement Learning Techniques


title: An Eye-tracking Study of User Interactions with Query Auto Completion

abstract: Query Auto Completion (QAC) suggests possible queries to web search users from the moment they start entering a query. This popular feature of web search engines is thought to reduce physical and cognitive effort when formulating a query. Perhaps surprisingly, despite QAC being widely used, users' interactions with it are poorly understood. This paper begins to address this gap. We present the results of an in-depth user study of user interactions with QAC in web search. While study participants completed web search tasks, we recorded their interactions using eye-tracking and client-side logging. This allows us to provide a first look at how users interact with QAC. We specifically focus on the effects of QAC ranking, by controlling the quality of the ranking in a within-subject design. We identify a strong position bias that is consistent across ranking conditions. Due to this strong position bias, ranking quality affects QAC usage. We also find an effect on task completion, in particular on the number of result pages visited. We show how these effects can be explained by a combination of searchers' behavior patterns, namely monitoring or ignoring QAC, and searching for spelling support or complete queries to express a search intent. We conclude the paper with a discussion of the important implications of our findings for QAC evaluation.


title: A Comparison Study of Ellipsoid Fitting for Pose Normalization of Hippocampal Shapes

abstract: Pose normalization is an important step to establish shape correspondence for group comparison of anatomical structures. The most basic and widely used way is ellipsoid fitting, which provides three principal axes for shape alignment, and is often solved by least square fitting. In this paper, it is recognized that the deformation caused by neuro-degenerative diseases is usually locally irregular, behaving like the outliers to the majority of the anatomical surfaces. Therefore we hypothesize that the distance function in L1-norm may perform better than that in L2-norm for hippocampal surface fitting, and thus conduct a study to compare the influence of different distance functions. In particular, we show how to perform ellipsoid fitting via L1-norm based algebraic and geometric distances, and experimentally compare their performance together with the conventional L2-norm based distance functions. Our study demonstrates that L1-norm approach fits the majority of the surface, while L2-norm approach tends to fit the irregularity.


title: Architecture Harmonization Between Cloud Radio Access Networks and Fog Networks

abstract: To guarantee the ubiquitous and fully autonomous Internet connections in our daily life, the new technical challenges of mobile communications lie on the efficient utilization of resource and social information. To facilitate the innovation of the fifth generation (5G) networks, the cloud radio access network (RAN) and fog network have been proposed to respond newly emerging traffic demands. The cloud RAN functions more toward centralized resource management to achieve optimal transmissions. The fog network takes advantage of social information and edge computing to efficiently alleviate the end-to-end latency. In this paper, we conduct a comprehensive survey of these two network structures, and then investigate possible harmonization to integrate both for the diverse needs of 5G mobile communications. We analytically study the harmonization of cloud RAN and fog network from various points of view, including the cache of Internet contents, mobility management, and radio access control. The performance of transition between the cloud RAN and the fog network has been presented and the subsequent switching strategy has been proposed to ensure engineering flexibility and success.


title: Credit Default Mining Using Combined Machine Learning and Heuristic Approach

abstract: Predicting potential credit default accounts in advance is challenging. Traditional statistical techniques typically cannot handle large amounts of data and the dynamic nature of fraud and humans. To tackle this problem, recent research has focused on artificial and computational intelligence based approaches. In this work, we present and validate a heuristic approach to mine potential default accounts in advance where a risk probability is precomputed from all previous data and the risk probability for recent transactions are computed as soon they happen. Beside our heuristic approach, we also apply a recently proposed machine learning approach that has not been applied previously on our targeted dataset [15]. As a result, we find that these applied approaches outperform existing state-of-the-art approaches.


title: Neural Random Projections for Language Modelling

abstract: Neural network-based language models deal with data sparsity problems by mapping the large discrete space of words into a smaller continuous space of real-valued vectors. By learning distributed vector representations for words, each training sample informs the neural network model about a combinatorial number of other patterns. In this paper, we exploit the sparsity in natural language even further by encoding each unique input word using a fixed sparse random representation. These sparse codes are then projected onto a smaller embedding space which allows for the encoding of word occurrences from a possibly unknown vocabulary, along with the creation of more compact language models using a reduced number of parameters. We investigate the properties of our encoding mechanism empirically, by evaluating its performance on the widely used Penn Treebank corpus. We show that guaranteeing approximately equidistant (nearly orthogonal) vector representations for unique discrete inputs is enough to provide the neural network model with enough information to learn --and make use-- of distributed representations for these inputs.


title: Pharmaceutical cold chain management: Platform based on a distributed ledger

abstract: In the process of distributing pharmaceutical products multiple stages are involved until products are delivered to patient. A reliable way for validating and verifying that products had been maintained within a licensed range is required. The paper presents a solution for pharmaceutical cold chain management using distributed ledger technologies. An application framework is proposed for shipment tracking which will deliver information to all stakeholders during the distribution phase of pharmaceutical products. The solution is based on Hyperledger Sawtooth distributed ledger framework, which has been extended with a custom transactions family and a sensors gateway for automatically collecting data from temperature tracking devices. The focus of the paper is to describe the data model and how entities of the system communicate.


title: Altered Kv2.1 functioning promotes increased excitability in hippocampal neurons of an Alzheimer’s disease mouse model

abstract: Altered neuronal excitability is emerging as an important feature in Alzheimer’s disease (AD). Kv2.1 potassium channels are important modulators of neuronal excitability and synaptic activity. We investigated Kv2.1 currents and its relation to the intrinsic synaptic activity of hippocampal neurons from 3xTg-AD (triple transgenic mouse model of Alzheimer's disease) mice, a widely employed preclinical AD model. Synaptic activity was also investigated by analyzing spontaneous [Ca2+]i spikes. Compared with wild-type (Non-Tg (non-transgenic mouse model)) cultures, 3xTg-AD neurons showed enhanced spike frequency and decreased intensity. Compared with Non-Tg cultures, 3xTg-AD hippocampal neurons revealed reduced Kv2.1-dependent Ik current densities as well as normalized conductances. 3xTg-AD cultures also exhibited an overall decrease in the number of functional Kv2.1 channels. Immunofluorescence assay revealed an increase in Kv2.1 channel oligomerization, a condition associated with blockade of channel function. In Non-Tg neurons, pharmacological blockade of Kv2.1 channels reproduced the altered pattern found in the 3xTg-AD cultures. Moreover, compared with untreated sister cultures, pharmacological inhibition of Kv2.1 in 3xTg-AD neurons did not produce any significant modification in Ik current densities. Reactive oxygen species (ROS) promote Kv2.1 oligomerization, thereby acting as negative modulator of the channel activity. Glutamate receptor activation produced higher ROS levels in hippocampal 3xTg-AD cultures compared with Non-Tg neurons. Antioxidant treatment with N-Acetyl-Cysteine was found to rescue Kv2.1-dependent currents and decreased spontaneous hyperexcitability in 3xTg-AD neurons. Analogous results regarding spontaneous synaptic activity were observed in neuronal cultures treated with the antioxidant 6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid (Trolox). Our study indicates that AD-related mutations may promote enhanced ROS generation, oxidative-dependent oligomerization, and loss of function of Kv2.1 channels. These processes can be part on the increased neuronal excitability of these neurons. These steps may set a deleterious vicious circle that eventually helps to promote excitotoxic damage found in the AD brain.


title: Overconfidence in IT Investment Decisions: Why Knowledge can be Boon and Bane at the same Time

abstract: Despite their strategic relevance in organizations, information technology (IT) investments still result in alarmingly high failure rates. As IT investments are such a delicate high%risk/high%reward matter, it is crucia l for organizations to avoid flawed IT investment decision%making. Previous research in co nsumer and organizational decision%making shows that a decision’s accuracy is often influenced by decision% makers’ overconfidence and that the magnitude of overconfidence strongly depends on decision%makers’ certainty of their knowledge. Draw ing on these strands of research, our findings from a field survey (N=166) show that IT managers’ decisions in IT outsourcing are indeed affected by overconfidence. However, an in%depth investigation of three types of knowledge, namely experienced, objective and subjective knowledge, reveals that different types of knowledge can have contrasting effects on overconfidence and thus on the quality of IT outsourcing decisions. Knowledge can be a boon and bane at the same time. Implications for research and practice are discussed.


title: Women also Snowboard: Overcoming Bias in Captioning Models (Extended Abstract)

abstract: Most machine learning methods are known to capture and exploit biases of the training data. While some biases are beneficial for learning, others are harmful. Specifically, image captioning models tend to exaggerate biases present in training data. This can lead to incorrect captions in domains where unbiased captions are desired, or required, due to over reliance on the learned prior and image context. We investigate generation of gender specific caption words (e.g. man, woman) based on the person's appearance or the image context. We introduce a new Equalizer model that ensures equal gender probability when gender evidence is occluded in a scene and confident predictions when gender evidence is present. The resulting model is forced to look at a person rather than use contextual cues to make a gender specific prediction. The losses that comprise our model, the Appearance Confusion Loss and the Confident Loss, are general, and can be added to any description model in order to mitigate impacts of unwanted bias in a description dataset. Our proposed model has lower error than prior work when describing images with people and mentioning their gender and more closely matches the ground truth ratio of sentences including women to sentences including men.


title: Towards an intelligent framework for multimodal affective data analysis

abstract: An increasingly large amount of multimodal content is posted on social media websites such as YouTube and Facebook everyday. In order to cope with the growth of such so much multimodal data, there is an urgent need to develop an intelligent multi-modal analysis framework that can effectively extract information from multiple modalities. In this paper, we propose a novel multimodal information extraction agent, which infers and aggregates the semantic and affective information associated with user-generated multimodal data in contexts such as e-learning, e-health, automatic video content tagging and human-computer interaction. In particular, the developed intelligent agent adopts an ensemble feature extraction approach by exploiting the joint use of tri-modal (text, audio and video) features to enhance the multimodal information extraction process. In preliminary experiments using the eNTERFACE dataset, our proposed multi-modal system is shown to achieve an accuracy of 87.95%, outperforming the best state-of-the-art system by more than 10%, or in relative terms, a 56% reduction in error rate.


title: Knowledge will propel machine understanding of content: extrapolating from current examples

abstract: Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.


title: Think-aloud protocols: a comparison of three think-aloud protocols for use in testing data-dissemination web sites for usability

abstract: We describe an empirical, between-subjects study on the use of think-aloud protocols in usability testing of a federal data-dissemination Web site. This double-blind study used three different types of think-aloud protocols: a traditional protocol, a speech-communication protocol, and a coaching protocol. A silent condition served as the control. Eighty participants were recruited and randomly pre-assigned to one of four conditions. Accuracy and efficiency measures were collected, and participants rated their subjective satisfaction with the site. Results show that accuracy is significantly higher in the coaching condition than in the other conditions. The traditional protocol and the speech-communication protocol are not statistically different from each other with regard to accuracy. Participants in the coaching condition are more satisfied with the Web site than participants in the traditional or speech-communication condition. In addition, there are no significant differences with respect to efficiency (time-on-task). This paper concludes with recommendations for usability practitioners.


Ex-Post Egalitarianism ∗


title: Active Antenna Tracking System with Directional Antennas for Enhancing Wireless Communication Capabilities of a Networked Robotic System

abstract: This paper presents a networked robotic system design capable of enhancing wireless communication capabilities communication range and bandwidth. The core of the system is active antenna tracking with directional antennas. The proposed system is decentralized and consists mainly of a mobile robot system and a command center system. Each system is equipped with off-the-shelf network devices such as antennas, access points AP, and network switches. For directional antennas to be beneficial to our system, we propose a weighted centroid algorithm WCA to provide active antenna tracking and direction-of-arrival DOA estimation. This system can be used in GPS-denied environments as our system does not require the aid of additional sensors to provide location information. Through extensive field experiments in different environments, including a fire training center and with various antenna selections, such as omni-to-omni, omni-to-directional, and directional-to-directional antennas, we demonstrate the effectiveness of our proposed system. We expect that our system can be applied in a variety of rescue, surveillance, and emergency scenarios where high bandwidth and long-distance communications are needed.


title: Immersive authoring of tangible augmented reality applications

abstract: In this paper, we suggest a new approach for authoring tangible augmented reality applications, called 'immersive authoring.' The approach allows the user to carry out the authoring tasks within the AR application being built, so that the development and testing of the application can be done concurrently throughout the development process. We describe the functionalities and the interaction design for the proposed authoring system that are specifically targeted for intuitive specification of scenes and various object behaviors. Several cases of applications developed using the authoring system are presented. A small pilot user study was conducted to compare the proposed method to a non-immersive approach, and the results have shown that the users generally found it easier and faster to carry out authoring tasks in the immersive environment.


title: Extracting Scientific Figures with Distantly Supervised Neural Networks

abstract: Non-textual components such as charts, diagrams and tables provide key information in many scientific documents, but the lack of large labeled datasets has impeded the development of data-driven methods for scientific figure extraction. In this paper, we induce high-quality training labels for the task of figure extraction in a large number of scientific documents, with no human intervention. To accomplish this we leverage the auxiliary data provided in two large web collections of scientific documents (arXiv and PubMed) to locate figures and their associated captions in the rasterized PDF. We share the resulting dataset of over 5.5 million induced labels---4,000 times larger than the previous largest figure extraction dataset---with an average precision of 96.8%, to enable the development of modern data-driven methods for this task. We use this dataset to train a deep neural network for end-to-end figure detection, yielding a model that can be more easily extended to new domains compared to previous work. The model was successfully deployed in Semantic Scholar, a large-scale academic search engine, and used to extract figures in 13 million scientific documents.


title: An Efficient Privacy-Preserving Algorithm Based on Randomized Response in IoT-Based Smart Grid

abstract: Among existing privacy-preserving approaches, Differential Privacy (DP) is a powerful tool that can provide privacy-preserving noisy query answers over statistical databases and has been widely adopted in many practical fields. In particular, as a privacy machine of DP, Randomized Aggregable Privacy-Preserving Ordinal Response (RAPPOR) enables strong privacy, efficient, and high-utility guarantees for each client string in data crowdsourcing. However, as for Internet of Things(IoT), such as smart gird, data are often processed in batches. Therefore, developing a new random response algorithm that can support batch-processing tend to make it more efficient and suitable for IoT applications than existing random response algorithms. In this paper, we propose a new randomized response algorithm that can achieve differential-privacy and utility guar-antees for consumer's behaviors, and process a batch of data at each time. Firstly, by applying sparse coding in this algorithm, a behavior signature dictionary is created from the aggregated energy consumption data in fog. Then, we add noise into the behavior signature dictionary by classical randomized response techniques and achieve the differential privacy after data re-aggregation. Through the security analysis with the principle of differential privacy and experimental results verification, we find that our Algorithm can preserve consumer's privacy with-out comprising utility.


title: Serial ATA Commands Logger for Security Monitoring on FPGA Devices

abstract: Enforcing the computer systems security policy is highly reliant upon the use of security monitors. The monitoring system should provide a complete view of the monitored system in a stealth mode. The Serial ATA Commands Logger system described in this paper intercepts the ATA commands sent by a host bus adapter to an attached storage device, by monitoring the exchanged traffic, in a transparent manner. Transparency is obtained by integrating the FPGA based system on the physical connection between the two communicating devices. The intercepted commands and parameters are sent to a serial console. The system can interface with commodity hardware devices by supporting Serial ATA 6.0 Gbps data transfer rate and advanced features like Native Command Queuing. Experimental results demonstrate a negligible impact on throughput of data transfers between the host bus adapters and the storage devices. The Serial ATA Commands Logger establishes the basis for a security framework deemed to enable further research and development of storage security focused applications: integrity checkers, intrusion detection systems, data recovery techniques.


title: Radial guided 4-way unequal power divider using substrate integrated waveguide with center-fed structure

abstract: Radially-guided low-profile equal and unequal power division functions are accomplished by using Substrate Integrated Waveguide (SIW) technology with center feeding structures and additional power/phase-control vias. The controlled positions of guiding posts provides the unequal division ratios and phase variations at each port. At this time, the width of the SIW has been kept the same in spite of the shifted position of guiding posts. The electrical performances including return loss, transmission characteristics and phase variations are discussed through simulation and measured data.


title: Cyber Security and Power System Communication—Essential Parts of a Smart Grid Infrastructure

abstract: The introduction of “smart grid” solutions imposes that cyber security and power system communication systems must be dealt with extensively. These parts together are essential for proper electricity transmission, where the information infrastructure is critical. The development of communication capabilities, moving power control systems from “islands of automation” to totally integrated computer environments, have opened up new possibilities and vulnerabilities. Since several power control systems have been procured with “openness” requirements, cyber security threats become evident. For refurbishment of a SCADA/EMS system, a separation of the operational and administrative computer systems must be obtained. The paper treats cyber security issues, and it highlights access points in a substation. Also, information security domain modeling is treated. Cyber security issues are important for “smart grid” solutions. Broadband communications open up for smart meters, and the increasing use of wind power requires a “smart grid system”.


title: Development of planar antennas in multi-layer packages for RF-system-on-a-package applications

abstract: The integration of an antenna with an RF module is an attractive solution for realizing a small size and low cost wireless transceiver. Multi-layer planar antennas for system-on-a-package (SOP) application are presented. Two different types of antennas for the low temperature co-fired ceramic (LTCC) package and the multi-layer organic (MLO) package are developed. For the LTCC package, the cavity-backed patch antenna has been developed, in order to increase bandwidth and to reduce the size of the ground plane. A via feed is used for direct connection with other RF blocks. For the cavity backed patch antenna, bandwidth is increased by 20% and real estate including ground plane is reduced by 50%, compared to the conventional patch antenna. For the MLO package, a lifted slot antenna has been developed. In order to reduce loss at feeding point and to minimize pattern distortion, via feed has been used. The antenna has been designed at 5.8 GHz and has gain of 3.7 dBi and bandwidth of 14%.


title: A 77-GHz FMCW Radar System Using On-Chip Waveguide Feeders in 65-nm CMOS

abstract: This paper presents a 77-GHz radar system using a transmitter (Tx) and receiver (Rx) integrated with on-chip waveguide feeders in 65-nm CMOS. The newly proposed on-chip waveguide feeder shows the insertion loss of about 2 dB and more than 30% bandwidth. Additionally, both the Tx and Rx are integrated with internal $\times$ 10 frequency multipliers. Therefore, a radar system can be easily implemented without sensitive millimeter-wave packaging technology by mounting the Tx and Rx chips on the waveguide aperture. The interconnections for the low-frequency reference and baseband signals can be realized on the low-cost FR-4 printed circuit board. The radar system shows 9-dBm output power and 13-dB down-conversion gain from the waveguide port.


title: Improvement Schemes for Indoor Mobile Location Estimation: A Survey

abstract: Location estimation is significant in mobile and ubiquitous computing systems. The complexity and smaller scale of the indoor environment impose a great impact on location estimation. The key of location estimation lies in the representation and fusion of uncertain information from multiple sources. The improvement of location estimation is a complicated and comprehensive issue. A lot of research has been done to address this issue. However, existing research typically focuses on certain aspects of the problem and specific methods. This paper reviews mainstream schemes on improving indoor location estimation from multiple levels and perspectives by combining existing works and our own working experiences. Initially, we analyze the error sources of common indoor localization techniques and provide a multilayered conceptual framework of improvement schemes for location estimation. This is followed by a discussion of probabilistic methods for location estimation, including Bayes filters, Kalman filters, extended Kalman filters, sigma-point Kalman filters, particle filters, and hidden Markov models. Then, we investigate the hybrid localization methods, including multimodal fingerprinting, triangulation fusing multiple measurements, combination of wireless positioning with pedestrian dead reckoning (PDR), and cooperative localization. Next, we focus on the location determination approaches that fuse spatial contexts, namely, map matching, landmark fusion, and spatial model-aided methods. Finally, we present the directions for future research.


title: Motor control exercise for symptomatic lumbar disc herniation: protocol for a systematic review and meta-analysis

abstract: Introduction Lumbar disc herniation (LDH) is a common condition in adults and can impose a heavy burden on both the individual and society. It is defined as displacement of disc components beyond the intervertebral disc space. Various conservative treatments have been recommended for the treatment of LDH and physical therapy plays a major role in the management of patients. Therapeutic exercise is effective for relieving pain and improving function in individuals with symptomatic LDH. The aim of this systematic review is to evaluate the effectiveness of motor control exercise (MCE) for symptomatic LDH. Methods and analysis We will include all clinical trial studies with a concurrent control group which evaluated the effect of MCEs in patients with symptomatic LDH. We will search PubMed, SCOPUS, PEDro, SPORTDiscus, CINAHL, CENTRAL and EMBASE with no restriction of language. Primary outcomes of this systematic review are pain intensity and functional disability and secondary outcomes are functional tests, muscle thickness, quality of life, return to work, muscle endurance and adverse events. Study selection and data extraction will be performed by two independent reviewers. The assessment of risk of bias will be implemented using the PEDro scale. Publication bias will be assessed by funnel plots, Begg9s and Egger9s tests. Heterogeneity will be evaluated using the I 2 statistic and the χ 2 test. In addition, subgroup analyses will be conducted for population and the secondary outcomes. All meta-analyses will be performed using Stata V.12 software. Ethics and dissemination No ethical concerns are predicted. The systematic review findings will be published in a peer-reviewed journal and will also be presented at national/international academic and clinical conferences. Trial registration number CRD42016038166.


title: A facial recognition application based on incremental supervised learning

abstract: Facial recognition applications present a great interest in the area of computer vision, with various methods and approaches that provide impressive performance. However, not all studies investigate the possibilities of using proper feature extraction methods with efficient classifiers, for applications that facial expression is not required for detection. In this sense, we propose another facial recognition application based on Local Binary Patterns or the fusion of Local Binary Patterns and Discrete Cosines Transform for feature extraction, with a classifier based on a Simplified Fuzzy Adaptive Resonance Theory Map neural network. Experiments results on two open source face databases (AT&T, Extended Yale B) show that the new approach achieves promising results.


title: From Point Clouds to Mesh Using Regression

abstract: Surface reconstruction from a point cloud is a standard subproblem in many algorithms for dense 3D reconstruction from RGB images or depth maps. Methods, performing only local operations in the vicinity of individual points, are very fast, but reconstructed models typically contain lots of holes. On the other hand, regularized volumetric approaches, formulated as a global optimization, are typically too slow for real-time interactive applications. We propose to use a regression forest based method, which predicts the projection of a grid point to the surface, depending on the spatial configuration of point density in the grid point neighborhood. We designed a suitable feature vector and efficient oct-tree based GPU evaluation, capable of predicting surface of high resolution 3D models in milliseconds. Our method learns and predicts surfaces from an observed point cloud sparser than the evaluation grid, and therefore effectively acts as a regularizer.


title: On the Relationship Between the OpenAI Evolution Strategy and Stochastic Gradient Descent

abstract: Because stochastic gradient descent (SGD) has shown promise optimizing neural networks with millions of parameters and few if any alternatives are known to exist, it has moved to the heart of leading approaches to reinforcement learning (RL). For that reason, the recent result from OpenAI showing that a particular kind of evolution strategy (ES) can rival the performance of SGD-based deep RL methods with large neural networks provoked surprise. This result is difficult to interpret in part because of the lingering ambiguity on how ES actually relates to SGD. The aim of this paper is to significantly reduce this ambiguity through a series of MNIST-based experiments designed to uncover their relationship. As a simple supervised problem without domain noise (unlike in most RL), MNIST makes it possible (1) to measure the correlation between gradients computed by ES and SGD and (2) then to develop an SGD-based proxy that accurately predicts the performance of different ES population sizes. These innovations give a new level of insight into the real capabilities of ES, and lead also to some unconventional means for applying ES to supervised problems that shed further light on its differences from SGD. Incorporating these lessons, the paper concludes by demonstrating that ES can achieve 99% accuracy on MNIST, a number higher than any previously published result for any evolutionary method. While not by any means suggesting that ES should substitute for SGD in supervised learning, the suite of experiments herein enables more informed decisions on the application of ES within RL and other paradigms.


In-band Network Telemetry via Programmable Dataplanes


title: Hierarchical Learning in Stochastic Domains: Preliminary Results

abstract: This paper presents the HDG learning algorithm, which uses a hierarchical decomposition of the state space to make learning to achieve goals more efficient with a small penalty in path quality. Special care must be taken when performing hierarchical planning and learning in stochastic domains, because macro-operators cannot be executed ballistically. The HDG algorithm, which is a descendent of Watkins' Q-learning algorithm, is described here and preliminary empirical results are presented.