title: Virtual View Generation with A Hybrid Camera Array

abstract: Virtual view synthesis from an array of cameras has been an essential element of three-dimensional video broadcasting/conferencing. In this paper, we propose a scheme based on a hybrid camera array consisting of four regular video cameras and one time-of-flight depth camera. During rendering, we use the depth image from the depth camera as initialization, and compute a view-dependent scene geometry using constrained plane sweeping from the regular cameras. View-dependent texture mapping is then deployed to render the scene at the desired virtual viewpoint. Experimental results show that the addition of the time-of-flight depth camera greatly improves the rendering quality compared with an array of regular cameras with similar sparsity. In the application of 3D video boardcasting/conferencing, our hybrid camera system demonstrates great potential in reducing the amount of data for compression/streaming while maintaining high rendering quality.


Image Classification using Convolutional Neural Network


title: A Deeper Understanding of Sequence in Narrative Visualization

abstract: Conveying a narrative with visualizations often requires choosing an order in which to present visualizations. While evidence exists that narrative sequencing in traditional stories can affect comprehension and memory, little is known about how sequencing choices affect narrative visualization. We consider the forms and reactions to sequencing in narrative visualization presentations to provide a deeper understanding with a focus on linear, 'slideshow-style' presentations. We conduct a qualitative analysis of 42 professional narrative visualizations to gain empirical knowledge on the forms that structure and sequence take. Based on the results of this study we propose a graph-driven approach for automatically identifying effective sequences in a set of visualizations to be presented linearly. Our approach identifies possible transitions in a visualization set and prioritizes local (visualization-to-visualization) transitions based on an objective function that minimizes the cost of transitions from the audience perspective. We conduct two studies to validate this function. We also expand the approach with additional knowledge of user preferences for different types of local transitions and the effects of global sequencing strategies on memory, preference, and comprehension. Our results include a relative ranking of types of visualization transitions by the audience perspective and support for memory and subjective rating benefits of visualization sequences that use parallelism as a structural device. We discuss how these insights can guide the design of narrative visualization and systems that support optimization of visualization sequence.


title: Adaptive Decontamination of the Training Set: A Unified Formulation for Discriminative Visual Tracking

abstract: Tracking-by-detection methods have demonstrated competitive performance in recent years. In these approaches, the tracking model heavily relies on the quality of the training set. Due to the limited amount of labeled training data, additional samples need to be extracted and labeled by the tracker itself. This often leads to the inclusion of corrupted training samples, due to occlusions, misalignments and other perturbations. Existing tracking-by-detection methods either ignore this problem, or employ a separate component for managing the training set. We propose a novel generic approach for alleviating the problem of corrupted training samples in tracking-bydetection frameworks. Our approach dynamically manages the training set by estimating the quality of the samples. Contrary to existing approaches, we propose a unified formulation by minimizing a single loss over both the target appearance model and the sample quality weights. The joint formulation enables corrupted samples to be downweighted while increasing the impact of correct ones. Experiments are performed on three benchmarks: OTB-2015 with 100 videos, VOT-2015 with 60 videos, and Temple-Color with 128 videos. On the OTB-2015, our unified formulation significantly improves the baseline, with a gain of 3:8% in mean overlap precision. Finally, our method achieves state-of-the-art results on all three datasets.


Detached Walk-in-Place


title: Tracing female gamer identity. An empirical study into gender and stereotype threat perceptions

abstract: Women often deem to be outside game culture resulting in a low gamer identity profile. A nuanced and detailed examination of how gender identity and threatening experiences tap into their play practices has hitherto been lacking however. The present study fills this gap by examining how female players express a gamer identity and how this relates to perceptions of threat and stigmatization. Based on a large-scale survey directed at female players, a statistical model is specified taking into account how respondents attribute a gamer label to their self-concept. Results suggest that the cognitive, evaluative, and affective dimensions of female identity predict gamer identification in distinct ways. Moreover, rather surprising, perceptions of stereotype threat and stigma consciousness are positively related to women’s gamer identification. An opposite relation is however observed for women’s awareness of being stigmatized by male players.


title: Conditional Identity-Based Broadcast Proxy Re-Encryption and Its Application to Cloud Email

abstract: Recently, a number of extended Proxy Re-Encryptions (PRE), e.g. Conditional (CPRE), identity-based PRE (IPRE) and broadcast PRE (BPRE), have been proposed for flexible applications. By incorporating CPRE, IPRE and BPRE, this paper proposes a versatile primitive referred to as conditional identity-based broadcast PRE (CIBPRE) and formalizes its semantic security. CIBPRE allows a sender to encrypt a message to multiple receivers by specifying these receivers’ identities, and the sender can delegate a re-encryption key to a proxy so that he can convert the initial ciphertext into a new one to a new set of intended receivers. Moreover, the re-encryption key can be associated with a condition such that only the matching ciphertexts can be re-encrypted, which allows the original sender to enforce access control over his remote ciphertexts in a fine-grained manner. We propose an efficient CIBPRE scheme with provable security. In the instantiated scheme, the initial ciphertext, the re-encrypted ciphertext and the re-encryption key are all in constant size, and the parameters to generate a re-encryption key are independent of the original receivers of any initial ciphertext. Finally, we show an application of our CIBPRE to secure cloud email system advantageous over existing secure email systems based on Pretty Good Privacy protocol or identity-based encryption.


title: Empirical Big Data Research: A Systematic Literature Mapping

abstract: Background: Big Data is a relatively new eld of research and technology, and literature reports a wide variety of concepts labeled with Big Data. The maturity of a research eld can be measured in the number of publications containing empirical results. In this paper we present the current status of empirical research in Big Data. Method: We employed a systematic mapping method with which we mapped the collected research according to the labels Variety, Volume and Velocity. In addition, we addressed the application areas of Big Data. Results: We found that 151 of the assessed 1778 contributions contain a form of empirical result and can be mapped to one or more of the 3 V’s and 59 address an application area. Conclusions: The share of publications containing empirical results is well below the average compared to computer science research as a whole. In order to mature the research on Big Data, we recommend applying empirical methods to strengthen the condence in the reported results. Based on our trend analysis we consider Variety to be the most promising uncharted area in Big Data.


title: Toward terabyte pattern mining: an architecture-conscious solution

abstract: We present a strategy for mining frequent item sets from terabyte-scale data sets on cluster systems. The algorithm embraces the holistic notion of architecture-conscious datamining, taking into account the capabilities of the processor, the memory hierarchy and the available network interconnects. Optimizations have been designed for lowering communication costs using compressed data structures and a succinct encoding. Optimizations for improving cache, memory and I/O utilization using pruningand tiling techniques, and smart data placement strategies are also employed. We leverage the extended memory spaceand computational resources of a distributed message-passing clusterto design a scalable solution, where each node can extend its metastructures beyond main memory by leveraging 64-bit architecture support. Our solution strategy is presented in the context of FPGrowth, a well-studied and rather efficient frequent pattern mining algorithm. Results demonstrate that the proposed strategy result in near-linearscaleup on up to 48 nodes.


title: A Performance Model for GPUs with Caches

abstract: To exploit the abundant computational power of the world's fastest supercomputers, an even workload distribution to the typically heterogeneous compute devices is necessary. While relatively accurate performance models exist for conventional CPUs, accurate performance estimation models for modern GPUs do not exist. This paper presents two accurate models for modern GPUs: a sampling-based linear model, and a model based on machine-learning (ML) techniques which improves the accuracy of the linear model and is applicable to modern GPUs with and without caches. We first construct the sampling-based linear model to predict the runtime of an arbitrary OpenCL kernel. Based on an analysis of NVIDIA GPUs' scheduling policies we determine the earliest sampling points that allow an accurate estimation. The linear model cannot capture well the significant effects that memory coalescing or caching as implemented in modern GPUs have on performance. We therefore propose a model based on ML techniques that takes several compiler-generated statistics about the kernel as well as the GPU's hardware performance counters as additional inputs to obtain a more accurate runtime performance estimation for modern GPUs. We demonstrate the effectiveness and broad applicability of the model by applying it to three different NVIDIA GPU architectures and one AMD GPU architecture. On an extensive set of OpenCL benchmarks, on average, the proposed model estimates the runtime performance with less than 7 percent error for a second-generation GTX 280 with no on-chip caches and less than 5 percent for the Fermi-based GTX 580 with hardware caches. On the Kepler-based GTX 680, the linear model has an error of less than 10 percent. On an AMD GPU architecture, Radeon HD 6970, the model estimates with 8 percent of error rates. The proposed technique outperforms existing models by a factor of 5 to 6 in terms of accuracy.


A Melody Composer for both Tonal and Non-Tonal Languages


title: Generalized Tensor Models for Recurrent Neural Networks

abstract: Recurrent Neural Networks (RNNs) are very successful at solving challenging problems with sequential data. However, this observed efficiency is not yet entirely explained by theory. It is known that a certain class of multiplicative RNNs enjoys the property of depth efficiency --- a shallow network of exponentially large width is necessary to realize the same score function as computed by such an RNN. Such networks, however, are not very often applied to real life tasks. In this work, we attempt to reduce the gap between theory and practice by extending the theoretical analysis to RNNs which employ various nonlinearities, such as Rectified Linear Unit (ReLU), and show that they also benefit from properties of universality and depth efficiency. Our theoretical results are verified by a series of extensive computational experiments.


title: Dynamic modeling of trust in human-machine interactions

abstract: In an increasingly automated world, trust between humans and autonomous systems is critical for successful integration of these systems into our daily lives. In particular, for autonomous systems to work cooperatively with humans, they must be able to sense and respond to the trust of the human. This inherently requires a control-oriented model of dynamic human trust behavior. In this paper, we describe a gray-box modeling approach for a linear third-order model that captures the dynamic variations of human trust in an obstacle detection sensor. The model is parameterized based on data collected from 581 human subjects, and the goodness of fit is approximately 80% for a general population. We also discuss the effect of demographics, such as national culture and gender, on trust behavior by re-parameterizing our model for subpopulations of data. These demographic-based models can be used to help autonomous systems further predict variations in human trust dynamics.


title: Reconfigurable Physical Unclonable Functions - Enabling technology for tamper-resistant storage

abstract: A PUF or Physical Unclonable Function is a function that is embodied in a physical structure that consists of many random uncontrollable components which originate from process variations during manufacturing. Due to this random structure a physical stimulus or challenge generates unpredictable responses. Because of their physical properties PUFs are unclonable and very promising primitives for the purpose of authentication and storage of cryptographic keys. Previous work on PUFs considers mainly static challenge-response PUFs. In many applications, however, a dynamic PUF would be desirable, e.g., in order to allow the key derived from the PUF to be updated. We define a new primitive, the reconfigurable PUF (rPUF) which is a PUF with a mechanism to transform it into a new PUF with a new unpredictable and uncontrollable challenge-response behavior, even if the challengeresponse behavior of the original PUF is already known. We present two practical instantiations of a reconfigurable PUF. One is a new variant of the optical PUF, and the other is based on phase change memory. We also illustrate how an rPUF can be used to protect non-volatile storage against invasive physical attacks.


title: Evaluating the Impact of Social Selfishness on the Epidemic Routing in Delay Tolerant Networks

abstract: To cope with the uncertainty of transmission opportunities between mobile nodes, Delay Tolerant Networks (DTN) routing exploits opportunistic forwarding mechanism. This mechanism requires nodes to forward messages in a cooperative and altruistic way. However, in the real world, most of the nodes exhibit selfish behaviors such as individual and social selfishness. In this paper, we investigate the problem of how social selfishness influences the performance of epidemic routing in DTN. First, we model the message delivery process with social selfishness as a two dimensional continuous time Markov chain. Then, we obtain the system performance of message delivery delay and delivery cost by explicit expressions. Numerical results show that DTN is quite robust to social selfishness, which increases the message delivery delay, but there is more reducing of delivery cost.


title: End-to-end speech recognition and keyword search on low-resource languages

abstract: In recent years, so-called, “end-to-end” speech recognition systems have emerged as viable alternatives to traditional ASR frameworks. Keyword search, localizing an orthographic query in a speech corpus, is typically performed by using automatic speech recognition (ASR) to generate an index. Previous work has evaluated the use of end-to-end systems for ASR on well known corpora (WSJ, Switchboard, TIMIT, etc.) in high-resource languages like English and Mandarin. In this work, we investigate the use of Connectionist Temporal Classification (CTC) networks, recurrent encoder-decoders with attention, two end-to-end ASR systems for keyword search and speech recognition on low resource languages. We find end-to-end systems can generate high quality 1-best transcripts on low-resource languages, but, because they generate very sharp posteriors, their utility is limited for KWS. We explore a number of ways to address this limitation with modest success. Experimental results reported are based on the IARPA BABEL OP3 languages and evaluation framework. This paper represents the first results using “end-to-end” techniques for speech recognition and keyword search on low-resource languages.


title: Example-directed synthesis: a type-theoretic interpretation

abstract: Input-output examples have emerged as a practical and user-friendly specification mechanism for program synthesis in many environments. While example-driven tools have demonstrated tangible impact that has inspired adoption in industry, their underlying semantics are less well-understood: what are "examples" and how do they relate to other kinds of specifications? This paper demonstrates that examples can, in general, be interpreted as refinement types. Seen in this light, program synthesis is the task of finding an inhabitant of such a type. This insight provides an immediate semantic interpretation for examples. Moreover, it enables us to exploit decades of research in type theory as well as its correspondence with intuitionistic logic rather than designing ad hoc theoretical frameworks for synthesis from scratch. We put this observation into practice by formalizing synthesis as proof search in a sequent calculus with intersection and union refinements that we prove to be sound with respect to a conventional type system. In addition, we show how to handle negative examples, which arise from user feedback or counterexample-guided loops. This theory serves as the basis for a prototype implementation that extends our core language to support ML-style algebraic data types and structurally inductive functions. Users can also specify synthesis goals using polymorphic refinements and import monomorphic libraries. The prototype serves as a vehicle for empirically evaluating a number of different strategies for resolving the nondeterminism of the sequent calculus---bottom-up theorem-proving, term enumeration with refinement type checking, and combinations of both---the results of which classify, explain, and validate the design choices of existing synthesis systems. It also provides a platform for measuring the practical value of a specification language that combines "examples" with the more general expressiveness of refinements.


title: DeepMimic: example-guided deep reinforcement learning of physics-based character skills

abstract: A longstanding goal in character animation is to combine data-driven specification of behavior with a system that can execute a similar behavior in a physical simulation, thus enabling realistic responses to perturbations and environmental variation. We show that well-known reinforcement learning (RL) methods can be adapted to learn robust control policies capable of imitating a broad range of example motion clips, while also learning complex recoveries, adapting to changes in morphology, and accomplishing user-specified goals. Our method handles keyframed motions, highly-dynamic actions such as motion-captured flips and spins, and retargeted motions. By combining a motion-imitation objective with a task objective, we can train characters that react intelligently in interactive settings, e.g., by walking in a desired direction or throwing a ball at a user-specified target. This approach thus combines the convenience and motion quality of using motion clips to define the desired style and appearance, with the flexibility and generality afforded by RL methods and physics-based animation. We further explore a number of methods for integrating multiple clips into the learning process to develop multi-skilled agents capable of performing a rich repertoire of diverse skills. We demonstrate results using multiple characters (human, Atlas robot, bipedal dinosaur, dragon) and a large variety of skills, including locomotion, acrobatics, and martial arts.


title: Techniques for requirements elicitation

abstract: The authors survey and evaluate techniques for eliciting requirements of computer-based systems, paying particular attention to dealing with social issues. The methods surveyed include introspection, interviews, questionnaires, and protocol, conversation, interaction, and discourse analyses. The last three techniques grew out of ethnomethodology and sociolinguistics. They can elicit tacit knowledge by observing actual interactions in the workplace, and can also be applied to the system development process itself. >


title: Automatic transcription of Turkish microtonal music

abstract: Automatic music transcription, a central topic in music signal analysis, is typically limited to equal-tempered music and evaluated on a quartertone tolerance level. A system is proposed to automatically transcribe microtonal and heterophonic music as applied to the makam music of Turkey. Specific traits of this music that deviate from properties targeted by current transcription tools are discussed, and a collection of instrumental and vocal recordings is compiled, along with aligned microtonal reference pitch annotations. An existing multi-pitch detection algorithm is adapted for transcribing music with 20 cent resolution, and a method for converting a multi-pitch heterophonic output into a single melodic line is proposed. Evaluation metrics for transcribing microtonal music are applied, which use various levels of tolerance for inaccuracies with respect to frequency and time. Results show that the system is able to transcribe microtonal instrumental music at 20 cent resolution with an F-measure of 56.7%, outperforming state-of-the-art methods for the same task. Case studies on transcribed recordings are provided, to demonstrate the shortcomings and the strengths of the proposed method.


title: Robot Calligraphy using Pseudospectral Optimal Control in Conjunction with a Simulated Brush Model

abstract: Chinese calligraphy is a unique form of art that has great artistic value but is difficult to master. In this paper, we make robots write calligraphy. Learning methods could teach robots to write, but may not be able to generalize to new characters. As such, we formulate the calligraphy writing problem as a trajectory optimization problem, and propose a new virtual brush model for simulating the real dynamic writing process. Our optimization approach is taken from pseudospectral optimal control, where the proposed dynamic virtual brush model plays a key role in formulating the objective function to be optimized. We also propose a stroke-level optimization to achieve better performance compared to the character-level optimization proposed in previous work. Our methodology shows good performance in drawing aesthetically pleasing characters.


title: GSM mobile phone localization using time difference of arrival and angle of arrival estimation

abstract: The localization methods time difference of arrival (TDOA) and angle of arrival (AOA) are investigated in their capability of high-resolution detection of GSM signals. Because GSM is of narrowband nature, a bandwidth expansion technique that significantly increases the resolution of TDOA is introduced. The algorithms maximum likelihood estimator (for TDOA), root multiple signal classification and estimation of signal parameters via rotational invariance techniques (for AOA) are proposed as a reference for each method. Simulations conducted with noisy signals and multipath propagation demonstrate the potential of TDOA for GSM localization, especially for search and rescue applications. AOA on the other hand is very sensitive to multipath, and is therefore only recommended for open space scenarios.


title: Slip Casting of Titanium and Titanium Hydride Stabilized Emulsions for the Production of Porous Bulk Titanium

abstract: In recent years, porous titanium and titanium alloy structures received increasing attention from the biomaterials community. These applications require not only custom porous properties depending on the area of application, but also high purity material. In order to achieve this, green structures were produced by slip casting of emulsions stabilized with titanium and titanium hydride particles. After removing the emulsion template by drying in atmospheric conditions, the green structures were consolidated by sintering in inert atmosphere at 950°C. The obtained structures were examined using scanning electron microscopy, density and compressive strength measurements. The results show that it is possible to obtain either homogeneous porosity with a high compressive strength or materials with a graded porosity by controlling the emulsion parameters.


title: Character Segmentation for Automatic CAPTCHA Solving

abstract: Many websites utilise CAPTCHA (Completely Automatic Public Turing tests to tell Computers and Humans Apart) schemes as human interaction proofs to grant access to their services only to people rather than spam bots. In this paper, we examine the security of six widely used types of CAPTCHA and present novel attacks against all of them, achieving success rates of up to 88%. We made improvements to three previously published attacks against the Hotmail, Wikipedia, and Slashdot challenges and devised novel and successful attacks against BotDetect's Wavy chess, reCAPTCHA, and a new variant of the Wikipedia scheme. Furthermore, we implemented a library that includes customisable segmentation algorithms and character recognisers. This library can serve as a tool for further investigating CAPTCHA security. Even though the difficulty and time needed to develop our CAPTCHA solver algorithms varied significantly between different schemes, none of these CAPTCHAS proved to be resistant to the attacks we devised. Based on our findings, we make recommendations for strengthening CAPTCHA methods to make them more resistant to automated attacks such as ours.


title: Deep Steering: Learning End-to-End Driving Model from Spatial and Temporal Visual Cues

abstract: In recent years, autonomous driving algorithms using low-cost vehicle-mounted cameras have attracted increasing endeavors from both academia and industry. There are multiple fronts to these endeavors, including object detection on roads, 3-D reconstruction etc., but in this work we focus on a vision-based model that directly maps raw input images to steering angles using deep networks. This represents a nascent research topic in computer vision. The technical contributions of this work are three-fold. First, the model is learned and evaluated on real human driving videos that are time-synchronized with other vehicle sensors. This differs from many prior models trained from synthetic data in racing games. Second, state-of-the-art models, such as PilotNet, mostly predict the wheel angles independently on each video frame, which contradicts common understanding of driving as a stateful process. Instead, our proposed model strikes a combination of spatial and temporal cues, jointly investigating instantaneous monocular camera observations and vehicle's historical states. This is in practice accomplished by inserting carefully-designed recurrent units (e.g., LSTM and Conv-LSTM) at proper network layers. Third, to facilitate the interpretability of the learned model, we utilize a visual back-propagation scheme for discovering and visualizing image regions crucially influencing the final steering prediction. Our experimental study is based on about 6 hours of human driving data provided by Udacity. Comprehensive quantitative evaluations demonstrate the effectiveness and robustness of our model, even under scenarios like drastic lighting changes and abrupt turning. The comparison with other state-of-the-art models clearly reveals its superior performance in predicting the due wheel angle for a self-driving car.


title: Optimization of the internal grid of an offshore wind farm using Genetic algorithm

abstract: Offshore wind energy is a promising solution thanks to its best performance of production. However its development leads to many technical and especially economic challenges among them the electrical grid topology attracts a large investment. In this paper, our objective is to minimize a part of this total investment which represents the initial cost of the middle voltage cables in the internal network with different cross sections. An approach based on Genetic Algorithm is developed to find the best topology to connect all wind turbines and substations. The proposed model initially accepts all possible configurations: radial, star, ring, and tree. The results prove that the optimization model can be used for designing the electrical architecture of the internal network of an offshore wind farm.


title: Robust Semisupervised Graph Classifier Learning With Negative Edge Weights

abstract: In a semi-supervised learning scenario, (possibly noisy) partially observed labels are used as input to train a classifier, in order to assign labels to unclassified samples. In this paper, we study this classifier learning problem from a graph signal processing (GSP) perspective. Specifically, by viewing a binary classifier as a piecewise constant graph-signal in a high-dimensional feature space, we cast classifier learning as a signal restoration problem via a classical maximum a posteriori (MAP) formulation. Unlike previous graph-signal restoration works, we consider in addition edges with negative weights that signify anti-correlation between samples. One unfortunate consequence is that the graph Laplacian matrix $\mathbf{L}$ can be indefinite, and previously proposed graph-signal smoothness prior $\mathbf{x}^T \mathbf{L} \mathbf{x}$ for candidate signal $\mathbf{x}$ can lead to pathological solutions. In response, we derive an optimal perturbation matrix $\boldsymbol{\Delta}$ - based on a fast lower-bound computation of the minimum eigenvalue of $\mathbf{L}$ via a novel application of the Haynsworth inertia additivity formula---so that $\mathbf{L} + \boldsymbol{\Delta}$ is positive semi-definite, resulting in a stable signal prior. Further, instead of forcing a hard binary decision for each sample, we define the notion of generalized smoothness on graph that promotes ambiguity in the classifier signal. Finally, we propose an algorithm based on iterative reweighted least squares (IRLS) that solves the posed MAP problem efficiently. Extensive simulation results show that our proposed algorithm outperforms both SVM variants and graph-based classifiers using positive-edge graphs noticeably.


title: Low-Profile Conformal UHF RFID Tag Antenna for Integration With Water Bottles

abstract: Development of a low-profile and conformal RFID tag antenna with an omnidirectional radiation pattern when mounted on a water bottle is presented. The antenna design and design parameters uncertainty are quantified using numerical simulations. The simulation-based antenna design is verified experimentally for single and multiple tagged bottles configurations.


title: Antenna-on-package on low-cost organic substrate for 60 GHz wireless communication applications

abstract: This work proposes a Yagi-Uda array with fan-like driver for millimeter-wave application. The proposed antenna array is built on a low-cost and low-loss multilayer organic substrate for flip chip ball grid array (FCBGA) package. The package size is 16 × 8 × 0.44 mm3. The proposed four-element antenna array results in a peak gain of 12 – 14.5 dBi at operating frequency band. The proposed antenna achieves both broadband and high gain characteristics for millimeter-wave application. The proposed antenna array is also used to develop a low-cost 60-GHz switched-beam antenna array with butler matrix network. This work provides a cost-effective approach to implement a switched-beam antenna array for fifth-generation mobile communications (5G), IEEE 802.11ad, such as smart phone, laptops, and wireless router.


title: Major Depressive Disorder Is Associated With Broad Impairments on Neuropsychological Measures of Executive Function: A Meta-Analysis and Review

abstract: Major depressive disorder (MDD) is one of the most common mental illnesses (with an estimated lifetime prevalence of 16.6%), and is associated with significant impairments in social, occupational, and educational functioning (Kessler, Berglund, et al., 2005). Cognitive impairments are now widely acknowledged as an important aspect of MDD. Indeed, the Diagnostic and Statistical Manual of Mental Disorders IV (DSM-IV) criteria for MDD include “diminished ability to think or concentrate, or indecisiveness” (American Psychiatric Association, 2000). Like this criterion, many theories have posited non-specific impairments in cognitive function associated with MDD, for example low motivation leading to difficulty with effortful tasks (e.g., Weingartner, Cohen, Murphy, Martello, & Gerdt, 1981), depleted cognitive resources in general (e.g., Mathews & MacLeod, 1994), difficulty initiating efficient cognitive strategies (e.g., Hertel & Gerstle, 2003), or slowed processing speed (e.g., Den Hartog, Derix, van Bemmel, Kremer, & Jolles, 2003; Nebes et al., 2000). ::: ::: More recently, it has been proposed that executive function (EF) may be particularly impaired in individuals with MDD, and that problems in other domains, such as memory, attention, and problem-solving, may arise because these abilities rely heavily on aspects of EF and prefrontal function (Levin, Heller, Mohanty, Herrington, & Miller, 2007; Nitschke, Heller, Imig, McDonald, & Miller, 2004). While EF has been defined in different ways, these definitions all share the idea that EFs are higher-level cognitive processes, which control and regulate lower-level processes (e.g., perception, motor responses) to effortfully guide behavior towards a goal, especially in non-routine situations (e.g., Alvarez & Emory, 2006; Banich, 2009). Thus, EFs are distinct from more automatic cognitive processes that have been over-learned by repetition (e.g., motor, reading, and language skills, semantic memory, object recognition; Shallice & Burgess, 1996). EFs allow us to respond flexibly to the environment: to break out of habits, make decisions and evaluate risks, plan for the future, prioritize and sequence actions, and cope with novel situations, among many other things. In other words, EFs are essential for successfully navigating nearly all of our daily activities. Impairments in EF thus have serious consequences, which may be as important to quality of life and functional outcomes as affective symptoms. ::: ::: EF appears to be especially vulnerable to disruption, with evidence for EF impairments associated with disorders including schizophrenia (e.g., Fioravanti, Carlone, Vitale, Cinti, & Clare, 2005), attention-deficit/hyperactivity disorder (e.g., Willcutt, Doyle, Nigg, Faraone, & Pennington, 2005), and obsessive-compulsive disorder (e.g., Olley, Malhi, & Sachdev, 2007), among others. Thus, it has been proposed that EF deficits may be transdiagnostic intermediate phenotypes or risk factors for emotional, behavioral, and psychotic disorders (Nolen-Hoeksema & Watkins, 2011). However, the existence and nature of EF impairments associated with MDD remain strongly debated, with some arguing that patients with MDD have no appreciable impairments in cognition (e.g., Grant, Thase, & Sweeney, 2001) and others that they have pronounced neuropsychological impairments (e.g., Porter, Gallagher, Thompson, & Young, 2003). Addressing this debate will be critical not only for understanding cognitive symptoms associated with MDD, but also for clarifying the degree to which EF deficits span disorders.


title: Design of Ultra-Wideband Substrate Integrated Waveguide (SIW) Filters in Zigzag Topology

abstract: In this letter we propose a modified substrate integrated waveguide (SIW) zigzag filter topology, which allows the flexible introduction of extra cross-couplings between non adjacent resonators. This novel topology permits to control the location of the transmission zeros, thus improving the resulting filter selectivity. Therefore, it has been used to design filters in compliance with the restrictive ultra-wideband European mask. In order to validate the new proposed topology, two filter prototypes have been designed, manufactured and measured, showing very good agreement with simulation data.


title: Unsupervised feature learning from time series

abstract: In this paper we study the problem of learning discriminative features (segments), often referred to as shapelets [Ye and Keogh, 2009] of time series, from unlabeled time series data. Discovering shapelets for time series classification has been widely studied, where many search-based algorithms are proposed to efficiently scan and select segments from a pool of candidates. However, such types of search-based algorithms may incur high time cost when the segment candidate pool is large. Alternatively, a recent work [Grabocka et al., 2014] uses regression learning to directly learn, instead of searching for, shapelets from time series. Motivated by the above observations, we propose a new Unsupervised Shapelet Learning Model (USLM) to efficiently learn shapelets from unlabeled time series data. The corresponding learning function integrates the strengths of pseudo-class label, spectral analysis, shapelets regularization term and regularized least-squares to auto-learn shapelets, pseudo-class labels and classification boundaries simultaneously. A coordinate descent algorithm is used to iteratively solve the learning function. Experiments show that USLM outperforms search-based algorithms on real-world time series data.


title: A bug Mining tool to identify and analyze security bugs using Naive Bayes and TF-IDF

abstract: Bug report contains a vital role during software development, However bug reports belongs to different categories such as performance, usability, security etc. This paper focuses on security bug and presents a bug mining system for the identification of security and non-security bugs using the term frequency-inverse document frequency (TF-IDF) weights and naive bayes. We performed experiments on bug report repositories of bug tracking systems such as bugzilla and debugger. In the proposed approach we apply text mining methodology and TF-IDF on the existing historic bug report database based on the bug s description to predict the nature of the bug and to train a statistical model for manually mislabeled bug reports present in the database. The tool helps in deciding the priorities of the incoming bugs depending on the category of the bugs i.e. whether it is a security bug report or a non-security bug report, using naive bayes. Our evaluation shows that our tool using TF-IDF is giving better results than the naive bayes method.


title: High step down ratio (400 V to 1 V) phase shift full bridge DC/DC converter for data center power supplies with GaN FETs

abstract: Energy efficiency of typical data centers is less than 50% where more than half of the power is consumed during power conversion, distribution, cooling, etc. In this paper, a single power stage architecture that converts 400 V directly to 1 V is discussed. Input series and output parallel structure (ISOP) is selected due to the high input voltage and large output current operation condition. Phase shift full bridge (PSFB) DC/DC converter with high step down ratio (66:1) is built with Gallium Nitride (GaN) FETs targeting at high efficiency. This paper mainly focuses on the study of a single 66 V to 1 V converter. Prototypes of the PSFB converter is designed, built, and tested. Preliminary experimental results are provided to verify the design.


title: A Modified fingerprint image thinning algorithm

abstract: Most fingerprint recognition applications rely heavily on efficient and fast image enhancement algorithms. Image thinning is a very important stage of image enhancement. A good thinning algorithm preserves the structure of the original fingerprint image, reduces the amount of data needed to process and helps improve the feature extraction accuracy and efficiency. In this paper we describe and compare some of the most used fingerprint thinning algorithms. Results show that faster algorithms have difficulty preserving connectivity. Zhang and Suen's algorithm gives the least processing time, while Guo and Hall's algorithm produces the best skeleton quality. A modified Zhang and Suen's algorithm is proposed, that is efficient and fast, and better preserves structure and connectivity.


title: Self-organizing map in Matlab: the SOM toolbox

abstract: The Self-Organizing Map (SOM) is a vector quantization method which places the prototype vectors on a regular low-dimensional grid in an ordered fashion. This makes the SOM a powerful visualization tool. The SOM Toolbox is an implementation of the SOM and its visualization in the Matlab 5 computing environment. In this article, the SOM Toolbox and its usage are shortly presented. Also its performance in terms of computational load is evaluated and compared to a corresponding Cprogram.


title: Evidence for perceptual deficits in associative visual (prosop)agnosia: a single-case study

abstract: Associative visual agnosia is classically defined as normal visual perception stripped of its meaning [Archiv fur Psychiatrie und Nervenkrankheiten 21 (1890) 22/English translation: Cognitive Neuropsychol. 5 (1988) 155]: these patients cannot access to their stored visual memories to categorize the objects nonetheless perceived correctly. However, according to an influential theory of visual agnosia [Farah, Visual Agnosia: Disorders of Object Recognition and What They Tell Us about Normal Vision, MIT Press, Cambridge, MA, 1990], visual associative agnosics necessarily present perceptual deficits that are the cause of their impairment at object recognition Here we report a detailed investigation of a patient with bilateral occipito-temporal lesions strongly impaired at object and face recognition. NS presents normal drawing copy, and normal performance at object and face matching tasks as used in classical neuropsychological tests. However, when tested with several computer tasks using carefully controlled visual stimuli and taking both his accuracy rate and response times into account, NS was found to have abnormal performances at high-level visual processing of objects and faces. Albeit presenting a different pattern of deficits than previously described in integrative agnosic patients such as HJA and LH, his deficits were characterized by an inability to integrate individual parts into a whole percept, as suggested by his failure at processing structurally impossible three-dimensional (3D) objects, an absence of face inversion effects and an advantage at detecting and matching single parts. Taken together, these observations question the idea of separate visual representations for object/face perception and object/face knowledge derived from investigations of visual associative (prosop)agnosia, and they raise some methodological issues in the analysis of single-case studies of (prosop)agnosic patients.


title: Lifting the veil: improving accountability and social transparency in Wikipedia with wikidashboard

abstract: Wikis are collaborative systems in which virtually anyone can edit anything. Although wikis have become highly popular in many domains, their mutable nature often leads them to be distrusted as a reliable source of information. Here we describe a social dynamic analysis tool called WikiDashboard which aims to improve social transparency and accountability on Wikipedia articles. Early reactions from users suggest that the increased transparency afforded by the tool can improve the interpretation, communication, and trustworthiness of Wikipedia articles.


title: PLASMA: a plan-based layered architecture for software model-driven adaptation

abstract: Modern software-intensive systems are expected to adapt, often while the system is executing, to changing requirements, failures, and new operational contexts. This paper describes an approach to dynamic system adaptation that utilizes plan-based and architecture-based mechanisms. Our approach utilizes an architecture description language (ADL) and a planning-as-model-checking technology to enable dynamic replanning. The ability to automatically generate adaptation plans based solely on ADL models and an application problem description simplifies the specification and use of adaptation mechanisms for system architects. The approach uses a three-layer architecture that, while similar to previous work, provides several significant improvements. We apply our approach within the context of a mobile robotics case study.


title: A service-oriented middleware for building context-aware

abstract: The advancement of wireless networks and mobile computing necessitates more advanced applications and services to be built with context-awareness enabled and adaptability to their changing contexts. Today, building context-aware services is a complex task due to the lack of an adequate infrastructure support in pervasive computing environments. In this article, we propose a Service-Oriented Context-Aware Middleware (SOCAM) architecture for the building and rapid prototyping of context-aware services. It provides efficient support for acquiring, discovering, interpreting and accessing various contexts to build context-aware services. We also propose a formal context model based on ontology using Web Ontology Language to address issues including semantic representation, context reasoning, context classification and dependency. We describe our context model and the middleware architecture, and present a performance study for our prototype in a smart home environment.


title: A CMOS Chopper Amplifier

abstract: A highly sensitive CMOS chopper amplifier for low-frequency applications is described. It is realized with a second-order low-pass selective amplifier using a continuous-time filtering technique. The circuit has been integrated in a 3-/spl mu/m p-well CMOS technology. The chopper amplifier DC grain is 38 dB with a 200-Hz bandwidth. The equivalent input noise is 63 nV//spl radic/Hz and free from 1/f noise. The input offset is below 5 /spl mu/V for a tuning error less than 1%. The amplifier consumes only 34 /spl mu/W.


title: Finding your next core business.

abstract: How do you know when your core needs to change? And how do you determine what should replace it? From an in-depth study of 25 companies, the author, a strategy consultant, has discovered that it's possible to measure the vitality of a business's core. If it needs reinvention, he says, the best course is to mine hidden assets. Some of the 25 companies were in deep crisis when they began the process of redefining themselves. But, says Zook, management teams can learn to recognize early signs of erosion. He offers five diagnostic questions with which to evaluate the customers, key sources of differentiation, profit pools, capabilities, and organizational culture of your core business. The next step is strategic regeneration. In four-fifths of the companies Zook examined, a hidden asset was the centerpiece of the new strategy. He provides a map for identifying the hidden assets in your midst, which tend to fall into three categories: undervalued business platforms, untapped insights into customers, and underexploited capabilities. The Swedish company Dometic, for example, was manufacturing small absorption refrigerators for boats and RVs when it discovered a hidden asset: its understanding of, and access to, customers in the RV market. The company took advantage of a boom in that market to refocus on complete systems for live-in vehicles. The Danish company Novozymes, which produced relatively low-tech commodity enzymes such as those used in detergents, realized that its underutilized biochemical capability in genetic and protein engineering was a hidden asset and successfully refocused on creating bioengineered specialty enzymes. Your next core business is not likely to announce itself with fanfare. Use the author's tools to conduct an internal audit of possibilities and pinpoint your new focus.


title: Performance Enhancement of Novel InAs/Si Hetero Double-Gate Tunnel FET Using Gaussian Doping

abstract: In this paper, for the first time, an InAs/Si heterojunction double-gate tunnel FET (H-DGTFET) has been analyzed for low-power high-frequency applications. For this purpose, the suitability of the device for low-power applications is investigated by extracting the threshold voltage of the device using a transconductance change method and a constant current method. Furthermore, the effects of uniform and Gaussian drain doping profile on dc characteristics and analog/RF performances are investigated for different channel lengths. A highly doped layer is placed in the channel near the source-channel junction, and this decreases the width of the depletion region, which improves the ON-current (ION) and the RF performance. Fur- thermore, the circuit-level performance assessment is done by implementing a common source amplifier using the H-DGTFET; a 3-dB roll-off frequency of 230.11 GHz and a unity-gain frequency of 5.4 THz were achieved.


title: Wideband Circular Polarization Reconfigurable Antenna

abstract: This communication introduces a polarization reconfigurable wideband circularly polarized (CP) antenna, which consists of four radiating arms connected to a reconfigurable feeding network. The four radiating arms excited by the feeding network are able to generate wideband CP waves with bidirectional radiation patterns in free space. In order to increase the gain and obtain a broadside radiation pattern, the proposed antenna is placed above a metallic reflector with the distance of quarter wavelength at the center frequency. In addition, polarization reconfigurability is realized by utilizing PIN diodes in the feeding transmission lines such that left-handed circular polarization (LHCP) and right-handed circular polarization (RHCP) modes can be selectively excited by controlling the on / off states of the PIN diodes. The proposed antenna exhibits a wide impedance bandwidth of 80% and an overlapped axial ratio (AR) bandwidth of 23.5% for both modes. The antenna gain is stable across the operating bandwidth with the peak gain of 4.8 dBic. The antenna has a wide AR beamwidth of 90°. The presented work is suitable for GPS, CNSS, or RFID applications.


title: On the Waveform for 5G

abstract: In this article, an overview and an in-depth analysis of the most discussed 5G waveform candidates are presented. In addition to general requirements, the nature of each waveform is revealed including the motivation, the underlying methodology, and the associated advantages and disadvantages. Furthermore, these waveform candidates are categorized and compared both qualitatively and quantitatively. By doing all these, the study in this work offers not only design guidelines but also operational suggestions for the 5G waveform.


title: Image thresholding by minimizing the measures of fuzzines

abstract: Abstract This paper introduces a new image thresholding method based on minimizing the measures of fuzziness of an input image. The membership function in the thresholding method is used to denote the characteristic relationship between a pixel and its belonging region (the object or the background). In addition, based on the measure of fuzziness, a fuzzy range is defined to find the adequate threshold value within this range. The principle of the method is easy to understand and it can be directly extended to multilevel thresholding. The effectiveness of the new method is illustrated by using the test images of having various types of histograms. The experimental results indicate that the proposed method has demonstrated good performance in bilevel and trilevel thresholding.


title: Firmaster: Analysis Tool for Home Router Firmware

abstract: As the Internet has changed the way people communicate with each other in everyday life, the number of home Wi-Fi access routers has grown up significantly over the past few years. However, the security of routers used in every household is still in the low level. The common vulnerabilities in routers can be easily exploited by an attacker in order to obtain user’s sensitive information or even compromise the devices to be a part of the botnet network. Therefore, we developed one-stop service firmware analysis tool that can perform both static and dynamic analysis for the router firmware called “Firmaster”. textbfThe program is operated under graphical user interface (GUI) of Qt creator running on the Ubuntu Linux machine. textbfVulnerabilities of firmware analyzed by Firmaster program are based on OWASP’s Top 10 IoT Vulnerabilities 2014. Firmaster contains seven main functions: password cracking, SSL scanning, web static analysis, firmware update analysis, web dynamic analysis, port scanning and the summary report.


An Improved Relative Self-Attention Mechanism for Transformer with Application to Music Generation.


Incorporating Topic Sentence on Neural News Headline Generation


title: An Intrinsic Information Content Metric for Semantic Similarity in WordNet

abstract: Information Content (IC) is an important dimension of word knowledge when assessing the similarity of two terms or word senses. The conventional way of measuring the IC of word senses is to combine knowledge of their hierarchical structure from an ontology like WordNet with statistics on their actual usage in text as derived from a large corpus. In this paper we present a wholly intrinsic measure of IC that relies on hierarchical structure alone. We report that this measure is consequently easier to calculate, yet when used as the basis of a similarity mechanism it yields judgments that correlate more closely with human assessments than other, extrinsic measures of IC that additionally employ corpus analysis.


title: Hybrid sampling Bayesian Occupancy Filter

abstract: Modeling and monitoring dynamic environments is a complex task but is crucial in the field of intelligent vehicle. A traditional way of addressing these issues is the modeling of moving objects, through Detection And Tracking of Moving Objects (DATMO) methods. An alternative to a classic object model framework is the occupancy grid filtering domain. Instead of segmenting the scene into objects and track them, the environment is represented as a regular grid of occupancy, in which each cell is tracked at a sub-object level. The Bayesian Occupancy Filter is a generic occupancy grid framework which predicts the spread of spatial occupancy by estimating cell velocity distributions. However its velocity model, corresponding to a transition histogram per cell, leads to huge data management which in practice makes it hardly compatible to severe computational and hardware constraints, like in many embedded systems. In this paper, we present a new representation for the BOF, describing the environment through a mix of static and dynamic occupancy. This differentiation enables the use of a model adapted to the considered nature: static occupancy is described in a classic occupancy grid, while dynamic occupancy is modeled by a set of moving particles. Both static and dynamic parts are jointly generated and evaluated, their distribution over the cells being adjusted. This approach leads to a more compact model and to drastically improve the accuracy of the results, in particular in term of velocities. Experimental results show that the number of values required to model the velocities have been reduced from a typical 900 per cell (for a 30x30 neighborhood) to less than 2 per cell in average. The massive data compression allows to plan dedicated embedded devices.


title: Dancing-to-Music Character Animation

abstract: In computer graphics, considerable research has been conducted o n realistic human motion synthesis. However, most research does not consider human emotional aspects, which ofte n strongly affect human motion. This paper presents a new approach for synthesizing dance performance matched to input music, based on the emotional aspects of dance performance. Our method consists of a motion analysis , a music analysis, and a motion synthesis based on the extracted features. In the analysis steps, motion and music fea ture vectors are acquired. Motion vectors are derived from motion rhythm and intensity, while music vectors are de rived from musical rhythm, structure, and intensity. For synthesizing dance performance, we first find candid ate motion segments whose rhythm features are matched to those of each music segment, and then we find the motion seg ment set whose intensity is similar to that of music segments. Additionally, our system supports having animato rs control the synthesis process by assigning desired motion segments to the specified music segments. The ex perimental results indicate that our method actually creates dance performance as if a character was listening and expressively dancing to the music.


GPU Kernels for Block-Sparse Weights


title: Development of a bipedal humanoid robot-control method of whole body cooperative dynamic biped walking

abstract: The authors have focused on the bipedal humanoid robot expected to play an active role in human living space, through studies on an anthropomorphic biped walking robot. As the first stage of developing a bipedal humanoid robot, the authors developed the human-size 35 active DOF bipedal humanoid robot "WABIAN" and the human-size 41 active DOF bipedal humanoid robot "WABIAN-R". The authors also proposed a basic control method of whole body cooperative dynamic biped walking that uses trunk or trunk-waist cooperative motion to compensate for three-axis (pitch, roll and yaw-axis) moment generated not only by the motion of the lower-limbs planned arbitrarily but by the time trajectory of the hands planned arbitrarily. Using these systems and the control method, normal biped walking (forward and backward), dynamic dance, waving arms and hip, dynamic carrying of a load using its arms, and trunk-waist cooperative dynamic walking are achieved.


title: Slow Feature Analysis: Unsupervised Learning of Invariances

abstract: Invariant features of temporally varying signals are useful for analysis and classification. Slow feature analysis (SFA) is a new method for learning invariant or slowly varying features from a vectorial input signal. It is based on a nonlinear expansion of the input signal and application of principal component analysis to this expanded signal and its time derivative. It is guaranteed to find the optimal solution within a family of functions directly and can learn to extract a large number of decorrelated features, which are ordered by their degree of invariance. SFA can be applied hierarchically to process high-dimensional input signals and extract complex features. SFA is applied first to complex cell tuning properties based on simple cell output, including disparity and motion. Then more complicated input-output functions are learned by repeated application of SFA. Finally, a hierarchical network of SFA modules is presented as a simple model of the visual system. The same unstructured network can learn translation, size, rotation, contrast, or, to a lesser degree, illumination invariance for one-dimensional objects, depending on only the training stimulus. Surprisingly, only a few training objects suffice to achieve good generalization to new objects. The generated representation is suitable for object recognition. Performance degrades if the network is trained to learn multiple invariances simultaneously.


title: What you always wanted to know about Datalog (and never dared to ask

abstract: Datalog, a database query language based on the logic programming paradigm, is described. The syntax and semantics of Datalog and its use for querying a relational database are presented. Optimization methods for achieving efficient evaluations of Datalog queries are classified, and the most relevant methods are presented. Various improvements of Datalog currently under study are discussed, and what is still needed in order to extend Datalog's applicability to the solution of real-life problems is indicated. >


title: Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation

abstract: Feature subset selection has become an important challenge in areas of pattern recognition, machine learning and data mining. As different semantics are hidden in numerical and categorical features, there are two strategies for selecting hybrid attributes: discretizing numerical variables or numericalize categorical features. In this paper, we introduce a simple and efficient hybrid attribute reduction algorithm based on a generalized fuzzy-rough model. A theoretic framework of fuzzy-rough model based on fuzzy relations is presented, which underlies a foundation for algorithm construction. We derive several attribute significance measures based on the proposed fuzzy-rough model and construct a forward greedy algorithm for hybrid attribute reduction. The experiments show that the technique of variable precision fuzzy inclusion in computing decision positive region can get the optimal classification performance. Number of the selected features is the least but accuracy is the best.


Enclothed cognition


title: Power flow control of a triple active bridge DC-DC converter using GaN power devices for a low-voltage DC power distribution system

abstract: This paper describes the power flow control of a triple active bridge (TAB) DC-DC converter for a DC power distribution system. The proposed phase shift control utilized in power distribution is based on varying the phase shift angle among three ports of the converter. First the simulation system is a prototype 200-V TAB converter with double loads for low-voltage DC power distribution systems using the closed-loop phase shift control. In the experiment, an open-loop TAB converter rated at 500 W power was constructed and tested with double loads, using a three-winding transformer and Gallium Nitride (GaN) power devices. The smooth output waveforms indicate the good controllability of the power flow as well as functional performance in the low-voltage DC power distribution system.


title: Recombinant and native TviCATL from Trypanosoma vivax: Enzymatic characterisation and evaluation as a diagnostic target for animal African trypanosomosis

abstract: Abstract African animal trypanosomosis ( nagana ) is caused by tsetse-transmitted protozoan parasites. Their cysteine proteases are potential chemotherapeutic and diagnostic targets. The N -glycosylated catalytic domain of Trypanosoma vivax cathepsin L-like cysteine protease, r Tvi CATL cat , was recombinantly expressed and purified from culture supernatants while native Tvi CATL was purified from T. vivax Y486 parasite lysates. Typical of Clan CA, family C 1 proteases, Tvi CATL activity is sensitive to E-64 and cystatin and substrate specificity is defined by the S 2 pocket. Leucine was preferred in P 2 and basic and non-bulky, hydrophobic residues accepted in P 1 and P 3 respectively. Reversible aldehyde inhibitors, antipain, chymostatin and leupeptin, with Arg in P 1 and irreversible peptidyl chloromethylketone inhibitors with hydrophobic residues in P 2 inhibited Tvi CATL activity. Tvi CATL digested host proteins: bovine haemoglobin, serum albumin, fibrinogen and denatured collagen (gelatine) over a wide pH range, including neutral to slightly acidic pH. The recombinant catalytic domain of Tvi CATL showed promise as a diagnostic target for detecting T. vivax infection in cattle in an indirect antibody detection ELISA.


title: Cell type discovery and representation in the era of high-content single cell phenotyping

abstract: BACKGROUND ::: A fundamental characteristic of multicellular organisms is the specialization of functional cell types through the process of differentiation. These specialized cell types not only characterize the normal functioning of different organs and tissues, they can also be used as cellular biomarkers of a variety of different disease states and therapeutic/vaccine responses. In order to serve as a reference for cell type representation, the Cell Ontology has been developed to provide a standard nomenclature of defined cell types for comparative analysis and biomarker discovery. Historically, these cell types have been defined based on unique cellular shapes and structures, anatomic locations, and marker protein expression. However, we are now experiencing a revolution in cellular characterization resulting from the application of new high-throughput, high-content cytometry and sequencing technologies. The resulting explosion in the number of distinct cell types being identified is challenging the current paradigm for cell type definition in the Cell Ontology. ::: ::: ::: RESULTS ::: In this paper, we provide examples of state-of-the-art cellular biomarker characterization using high-content cytometry and single cell RNA sequencing, and present strategies for standardized cell type representations based on the data outputs from these cutting-edge technologies, including "context annotations" in the form of standardized experiment metadata about the specimen source analyzed and marker genes that serve as the most useful features in machine learning-based cell type classification models. We also propose a statistical strategy for comparing new experiment data to these standardized cell type representations. ::: ::: ::: CONCLUSION ::: The advent of high-throughput/high-content single cell technologies is leading to an explosion in the number of distinct cell types being identified. It will be critical for the bioinformatics community to develop and adopt data standard conventions that will be compatible with these new technologies and support the data representation needs of the research community. The proposals enumerated here will serve as a useful starting point to address these challenges.


title: Visualizing and Comparing Convolutional Neural Networks

abstract: Convolutional Neural Networks (CNNs) have achieved comparable error rates to well-trained human on ILSVRC2014 image classification task. To achieve better performance, the complexity of CNNs is continually increasing with deeper and bigger architectures. Though CNNs achieved promising external classification behavior, understanding of their internal work mechanism is still limited. In this work, we attempt to understand the internal work mechanism of CNNs by probing the internal representations in two comprehensive aspects, i.e., visualizing patches in the representation spaces constructed by different layers, and visualizing visual information kept in each layer. We further compare CNNs with different depths and show the advantages brought by deeper architecture.


title: Persistent target tracking using likelihood fusion in wide-area and full motion video sequences

abstract: Vehicle tracking using airborne wide-area motion imagery (WAMI) for monitoring urban environments is very challenging for current state-of-the-art tracking algorithms, compared to object tracking in full motion video (FMV). Characteristics that constrain performance in WAMI to relatively short tracks range from the limitations of the camera sensor array including low frame rate and georegistration inaccuracies, to small target support size, presence of numerous shadows and occlusions from buildings, continuously changing vantage point of the platform, presence of distractors and clutter among other confounding factors. We describe our Likelihood of Features Tracking (LoFT) system that is based on fusing multiple sources of information about the target and its environment akin to a track-before-detect approach. LoFT uses image-based feature likelihood maps derived from a template-based target model, object and motion saliency, track prediction and management, combined with a novel adaptive appearance target update model. Quantitative measures of performance are presented using a set of manually marked objects in both WAMI, namely Columbus Large Image Format (CLIF), and several standard FMV sequences. Comparison with a number of single object tracking systems shows that LoFT outperforms other visual trackers, including state-of-the-art sparse representation and learning based methods, by a significant amount on the CLIF sequences and is competitive on FMV sequences.


title: Substrate-integrated waveguide (SIW) out-of-phase T-junction

abstract: A planar substrate-integrated waveguide (SIW) T-junction, with transmission properties similar to an E-plane waveguide T-junction, is described. It comprises an SIW H-plane T-junction, and phase-inverting and phase-non-inverting sections inserted into the arms of the T-junction. The inverting section comprises a coplanar-waveguide (CPW) vertical interconnect that couples a pair of antipodal back-to-back SIW-to-CPW transitions. This arrangement swaps current from the top and bottom walls of the SIW to achieve phase-inversion. The non-inverting section comprises a pair of back-to-back SIW-to-CPW transitions, and is the same physical length as the inverting section and has a similar transmission apart from the no phase-inversion. The junction can be constructed using a 2-layer planar technology such as a double-sided printed circuit board. Simulations of a junction operating from 4 to 6 GHz demonstrate the feasibility of the approach.


title: Using the SLEUTH urban growth model to simulate the impacts of future policy scenarios on urban land use in the Tehran metropolitan area in Iran

abstract: The SLEUTH model, based on the Cellular Automata (CA), can be applied to city development simulation in metropolitan areas. In this study the SLEUTH model was used to model the urban expansion and predict the future possible behavior of the urban growth in Tehran. The fundamental data were five Landsat TM and ETM images of 1988, 1992, 1998, 2001 and 2010. Three scenarios were designed to simulate the spatial pattern. The first scenario assumed historical urbanization mode would persist and the only limitations for development were height and slope. The second one was a compact scenario which makes the growth mostly internal and limited the expansion of suburban areas. The last scenario proposed a polycentric urban structure which let the little patches grow without any limitation and would not consider the areas beyond the specific buffer zone from the larger patches for development. Results showed that the urban growth rate was greater in the first scenario in comparison with the other two scenarios. Also it was shown that the third scenario was more suitable for Tehran since it could avoid undesirable effects such as congestion and pollution and was more in accordance with the conditions of Tehran city.


title: Cauchy’s Theorem and Edge Lengths of Convex Polyhedra

abstract: In this paper we explore, from an algorithmic point of view, the extent to which the facial angles and combinatorial structure of a convex polyhedron determine the polyhedron--in particular the edge lengths and dihedral angles of the polyhedron. Cauchy's rigidity theorem of 1813 states that the dihedral angles are uniquely determined. Finding them is a significant algorithmic problem which we express as a spherical graph drawing problem. Our main result is that the edge lengths, although not uniquely determined, can be found via linear programming. We make use of significant mathematics on convex polyhedra by Stoker, Van Heijenoort, Gale, and Shepherd.


A Reverse Approach to Named Entity Extraction and Linking in Microposts


title: Fuzzy logic in KNIME - modules for approximate reasoning

abstract: In this paper we describe the open source data analytics platform KNIME, focusing particularly on extensions and modules supporting fuzzy sets and fuzzy learning algorithms such as fuzzy clustering algorithms, rule induction methods, and interactive clustering tools. In addition we outline a number of experimental extensions, which are not yet part of the open source release and present two illustrative examples from real world applications to demonstrate the power of the KNIME extensions.


title: Performance study of hybrid Wireless Mesh Protocol (HWMP) for IEEE 802.11s WLAN mesh networks

abstract: Wireless Mesh Network (WMN) have been envisioned as an important solution to the next generation wireless networking which can be used in wireless community networks, wireless enterprise networks, transportation systems, home networking and last-mile wireless internet access. Many proprietary mesh solutions were developed by individual vendor but in order to interoperability; IEEE forms a task group called IEEE 802.11s to develop an integrated mesh networking solution. Hybrid Wireless Mesh protocol (HWMP) and airtime metrics as default routing protocol and routing metrics set by the task group. There is few test bed and many simulation studies have been done to evaluate the performance of the HWMP protocol with the assumption of unique type of flow with fixed packet size and packet rate. However, real networks carry a diverse application (video, voice, FTP, Email etc) with different characteristics (packet size, data rate). In this paper, we are investigated and analyzed the performance of HWMP protocol under such heterogeneous application characteristics.


THE IMPACT OF SYMBOLIC INTERACTIONISM ON RESEARCH STUDIES ABOUT COMMUNICATION SCIENCE


title: A Literature Review on Cloud Computing Adoption Issues in Enterprises

abstract: Cloud computing has received increasing interest from enterprises since its inception. With its innovative information technology (IT) services delivery model, cloud computing could add technical and strategic business value to enterprises. However, cloud computing poses highly concerning internal (e.g., Top management and experience) and external issues (e.g., regulations and standards). This paper presents a systematic literature review to explore the current key issues related to cloud computing adoption. This is achieved by reviewing 51 articles published about cloud computing adoption. Using the grounded theory approach, articles are classified into eight main categories: internal, external, evaluation, proof of concept, adoption decision, implementation and integration, IT governance, and confirmation. Then, the eight categories are divided into two abstract categories: cloud computing adoption factors and processes, where the former affects the latter. The results of this review indicate that enterprises face serious issues before they decide to adopt cloud computing. Based on the findings, the paper provides a future information systems (IS) research agenda to explore the previously under-investigated areas regarding cloud computing adoption factors and processes. This paper calls for further theoretical, methodological, and empirical contributions to the research area of cloud computing adoption by enterprises.


title: Network Computing

abstract: VNC is an ultra thin client system based on a simple display protocol that is platform independent. It achieves mobile computing without requiring the user to carry any hardware. VNC provides access to home computing environments from anywhere in the world, on whatever computing infrastructure happens to be available-including, for example, public Web browsing terminals in airports. In addition, VNC allows a single desktop to be accessed from several places simultaneously, thus supporting application sharing in the style of computer supported cooperative work (CSCW). The technology underlying VNC is a simple remote display protocol. It is the simplicity of this protocol that makes VNC so powerful. Unlike other remote display protocols such as the X Window System and Citrix's ICA, the VNC protocol is totally independent of operating system, windowing system, and applications. The VNC system is freely available for download from the ORL Web site at http://www.orl.co.uk/vnc/. We begin the article by summarizing the evolution of VNC from our work on thin client architectures. We then describe the structure of the VNC protocol, and conclude by discussing the ways we use VNC technology now and how it may evolve further as new clients and servers are developed.


title: Communicating Robot Motion Intent with Augmented Reality

abstract: Humans coordinate teamwork by conveying intent through social cues, such as gestures and gaze behaviors. However, these methods may not be possible for appearance-constrained robots that lack anthropomorphic or zoomorphic features, such as aerial robots. We explore a new design space for communicating robot motion intent by investigating how augmented reality (AR) might mediate human-robot interactions. We develop a series of explicit and implicit designs for visually signaling robot motion intent using AR, which we evaluate in a user study. We found that several of our AR designs significantly improved objective task efficiency over a baseline in which users only received physically-embodied orientation cues. In addition, our designs offer several trade-offs in terms of intent clarity and user perceptions of the robot as a teammate.


title: A 5/4-approximation algorithm for minimum 2-edge-connectivity

abstract: A 5/4-approximation algorithm is presented for the minimum cardinality 2-edge-connected spanning subgraph problem in undirected graphs. This improves the previous best approximation ratio of 4/3. It is shown that our ratio is tight with respect to current lower bounds, and any further improvement is possible only if new lower bounds are discovered.


Continuous Backbone “ Continuum ” Robot Manipulators : A Review


title: Swipe and Tell: Using Implicit Feedback to Predict User Engagement on Tablets

abstract: When content consumers explicitly judge content positively, we consider them to be engaged. Unfortunately, explicit user evaluations are difficult to collect, as they require user effort. Therefore, we propose to use device interactions as implicit feedback to detect engagement. ::: ::: We assess the usefulness of swipe interactions on tablets for predicting engagement and make the comparison with using traditional features based on time spent. ::: ::: We gathered two unique datasets of more than 250,000 swipes, 100,000 unique article visits, and over 35,000 explicitly judged news articles by modifying two commonly used tablet apps of two newspapers. We tracked all device interactions of 407 experiment participants during one month of habitual news reading. ::: ::: We employed a behavioral metric as a proxy for engagement, because our analysis needed to be scalable to many users, and scanning behavior required us to allow users to indicate engagement quickly. ::: ::: We point out the importance of taking into account content ordering, report the most predictive features, zoom in on briefly read content and on the most frequently read articles. ::: ::: Our findings demonstrate that fine-grained tablet interactions are useful indicators of engagement for newsreaders on tablets. The best features successfully combine both time-based aspects and swipe interactions.


title: On the singular values of Weber modular functions

abstract: The minimal polynomials of the singular values of the classical Weber modular functions give far simpler defining polynomials for the class fields of imaginary quadratic fields than the minimal polynomials of singular moduli of level 1. We describe computations of these polynomials and give conjectural formulas describing the prime decomposition of their resultants and discriminants, extending the formulas of Gross-Zagier for the level 1 case.


title: LegUp: high-level synthesis for FPGA-based processor/accelerator systems

abstract: In this paper, we introduce a new open source high-level synthesis tool called LegUp that allows software techniques to be used for hardware design. LegUp accepts a standard C program as input and automatically compiles the program to a hybrid architecture containing an FPGA-based MIPS soft processor and custom hardware accelerators that communicate through a standard bus interface. Results show that the tool produces hardware solutions of comparable quality to a commercial high-level synthesis tool.


title: Semi-Supervised Sequential Labeling and Segmentation Using Giga-Word Scale Unlabeled Data

abstract: This paper provides evidence that the use of more unlabeled data in semi-supervised learning can improve the performance of Natural Language Processing (NLP) tasks, such as part-of-speech tagging, syntactic chunking, and named entity recognition. We first propose a simple yet powerful semi-supervised discriminative model appropriate for handling large scale unlabeled data. Then, we describe experiments performed on widely used test collections, namely, PTB III data, CoNLL’00 and ’03 shared task data for the above three NLP tasks, respectively. We incorporate up to 1G-words (one billion tokens) of unlabeled data, which is the largest amount of unlabeled data ever used for these tasks, to investigate the performance improvement. In addition, our results are superior to the best reported results for all of the above test collections.


title: Baiting Inside Attackers Using Decoy Documents

abstract: The insider threat remains one of the most vexing problems in computer security. A number of approaches have been proposed to detect nefarious insider actions including user modeling and profiling techniques, policy and access enforcement techniques, and misuse detection. In this work we propose trap-based defense mechanisms and a deployment platform for addressing the problem of insiders attempting to exfiltrate and use sensitive information. The goal is to confuse and confound an adversary requiring more effort to identify real information from bogus information and provide a means of detecting when an attempt to exploit sensitive information has occurred. “Decoy Documents” are automatically generated and stored on a file system by the D3 System with the aim of enticing a malicious user. We introduce and formalize a number of properties of decoys as a guide to design trap-based defenses to increase the likelihood of detecting an insider attack. The decoy documents contain several different types of bogus credentials that when used, trigger an alert. We also embed “stealthy beacons” inside the documents that cause a signal to be emitted to a server indicating when and where the particular decoy was opened. We evaluate decoy documents on honeypots penetrated by attackers demonstrating the feasibility of the method.


title: Stacked Generalization

abstract: This paper introduces stacked generalization, a scheme for minimizing the generalization error rate of one or more generalizers. Stacked generalization works by deducing the biases of the generalizer(s) with respect to a provided learning set. This deduction proceeds by generalizing in a second space whose inputs are (for example) the guesses of the original generalizers when taught with part of the learning set and trying to guess the rest of it, and whose output is (for example) the correct guess. When used with multiple generalizers, stacked generalization can be seen as a more sophisticated version of cross-validation, exploiting a strategy more sophisticated than cross-validation's crude winner-takes-all for combining the individual generalizers. When used with a single generalizer, stacked generalization is a scheme for estimating (and then correcting for) the error of a generalizer which has been trained on a particular learning set and then asked a particular question. After introducing stacked generalization and justifying its use, this paper presents two numerical experiments. The first demonstrates how stacked generalization improves upon a set of separate generalizers for the NETtalk task of translating text to phonemes. The second demonstrates how stacked generalization improves the performance of a single surface-fitter. With the other experimental evidence in the literature, the usual arguments supporting cross-validation, and the abstract justifications presented in this paper, the conclusion is that for almost any real-world generalization problem one should use some version of stacked generalization to minimize the generalization error rate. This paper ends by discussing some of the variations of stacked generalization, and how it touches on other fields like chaos theory.


title: Flowers or a Robot Army ? Encouraging Awareness & Activity with Personal , Mobile Displays

abstract: Personal, mobile displays, such as those on mobile phones, are ubiquitous, yet for the most part, underutilized. We present results from a field experiment that investigated the effectiveness of these displays as a means for improving awareness of daily life (in our case, self-monitoring of physical activity). Twenty-eight participants in three experimental conditions used our UbiFit system for a period of three months in their day-to-day lives over the winter holiday season. Our results show, for example, that participants who had an awareness display were able to maintain their physical activity level (even during the holidays), while the level of physical activity for participants who did not have an awareness display dropped significantly. We discuss our results and their general implications for the use of everyday mobile devices as awareness displays.


title: Efficient large scale distributed matrix computation with spark

abstract: Matrix computation is the core of many massive data-intensive analytical applications such mining social networks, recommendation systems and nature language processing. Due to the importance of matrix computation, it has been widely studied for many years. In the Big Data ear, as the scale of the matrix grows, traditional single-node matrix computation systems can hardly cope with such large data and computation. Existing distributed matrix computation solutions are still not efficient enough, or have poor fault tolerance and usability. In this paper, we propose Marlin, an efficient distributed matrix computation library which is built on top of Spark. Marlin contains several distributed matrix operation algorithms and provides high-level matrix computation primitives for users. In Marlin, we proposed three distributed matrix multiplication algorithms for different situations. Based on this, we designed an adaptive model to choose the best approach for different problems. Moreover, to improve the computation performance, instead of naively using Spark, we put forward some optimizations including taking advantage of the native linear algebra library, reducing shuffle communication and increasing parallelism. Experimental results show that Marlin is over an order of magnitude faster than R (a widely-used statistical computing system) and the existing distributed matrix operation algorithms based on MapReduce. Moreover, Marlin achieves comparable performance to the specialized MPI-based matrix multiplication algorithm SUMMA but uses a general dataflow engine and gains common dataflow features such as scalability and fault tolerance.


title: Gunshot detection and localization using sensor networks

abstract: Acoustic sensors or sensor networks can be efficiently used to spot and observe targets of interest and have the ability to conclude the exact location of an object or event within the sensor network's coverage range. For the detection and spot estimation of shooter, the schemes of gunshot localization require one or more sensing technologies. In current situation the importance of counter sniper systems cannot be denied. An efficient counter sniper system that can provide immediate shooter location will provide many benefits to our security personnel. In this paper, the Time Difference Of Arrival (TDOA) of Shock Wave and Muzzle Blast is used to estimate the shooter location. The proposed design and algorithm was validated and shooter origin was resolute that was very close to theoretical values. The result shows that the scheme worked very fine and is capable of locating sniper fires very efficiently, competently and effectively. The designed algorithm worked very well and results were found to be very accurately.


title: Applications of Markov Decision Processes in Communication Networks: a Survey

abstract: We present in this chapter a survey on applications of MDPs to communication networks. We survey both the different application areas in communication networks as well as the theoretical tools that have been developed to model and to solve the resulting control problems.


title: Question Retrieval with High Quality Answers in Community Question Answering

abstract: This paper studies the problem of question retrieval in community question answering (CQA). To bridge lexical gaps in questions, which is regarded as the biggest challenge in retrieval, state-of-the-art methods learn translation models using answers under an assumption that they are parallel texts. In practice, however, questions and answers are far from "parallel". Indeed, they are heterogeneous for both the literal level and user behaviors. There are a particularly large number of low quality answers, to which the performance of translation models is vulnerable. To address these problems, we propose a supervised question-answer topic modeling approach. The approach assumes that questions and answers share some common latent topics and are generated in a "question language" and "answer language" respectively following the topics. The topics also determine an answer quality signal. Compared with translation models, our approach not only comprehensively models user behaviors on CQA portals, but also highlights the instinctive heterogeneity of questions and answers. More importantly, it takes answer quality into account and performs robustly against noise in answers. With the topic modeling approach, we propose a topic-based language model, which matches questions not only on a term level but also on a topic level. We conducted experiments on large scale data from Yahoo! Answers and Baidu Knows. Experimental results show that the proposed model can significantly outperform state-of-the-art retrieval models in CQA.


title: Antennas for Global Navigation Satellite System (GNSS)

abstract: Global Navigation Satellite System (GNSS) will in effect be fully deployed and operational in a few years, even with the delays in Galileo as a consequence of European Union's financial difficulties. The vastly broadened GNSS spectra, spread densely across 1146-1616 MHz, versus the narrow Global Positioning System (GPS) L1 and L2 bands, together with a constellation of over 100 Medium Earth Orbit (MEO) and Geostationary Earth Orbit (GEO) satellites versus GPS' 24 MEO satellites, are revolutionizing the design of GNSS receive antennas. For example, a higher elevation cutoff angle will be preferred. As a result, fundamental changes in antenna design, new features and applications, as well as cost structures are ongoing. Existing GNSS receive antenna technologies are reviewed and design challenges are discussed.


title: Extending NegEx with Kernel Methods for Negation Detection in Clinical Text

abstract: NegEx is a popular rule-based system used to identify negated concepts in clinical notes. This system has been reported to perform very well by numerous studies in the past. In this paper, we demonstrate the use of kernel methods to extend the performance of NegEx. A kernel leveraging the rules of NegEx and its output as features, performs as well as the rule-based system. An improvement in performance is achieved if this kernel is coupled with a bag of words kernel. Our experiments show that kernel methods outperform the rule-based system, when evaluated within and across two different open datasets. We also present the results of a semi-supervised approach to the problem, which improves performance on the data.


title: Action recognition with joints-pooled 3D deep convolutional descriptors

abstract: Torso joints can be considered as the landmarks of human body. An action consists of a series of body poses which are determined by the positions of the joints. With the rapid development of RGB-D camera technique and pose estimation research, the acquisition of the body joints has become much easier than before. Thus, we propose to incorporate joint positions with currently popular deep-learned features for action recognition. In this paper, we present a simple, yet effective method to aggregate convolutional activations of a 3D deep convolutional neural network (3D CNN) into discriminative descriptors based on joint positions. Two pooling schemes for mapping body joints into convolutional feature maps are discussed. The joints-pooled 3D deep convolutional descriptors (JDDs) are more effective and robust than the original 3D CNN features and other competing features. We evaluate the proposed descriptors on recognizing both short actions and complex activities. Experimental results on real-world datasets show that our method generates promising results, outperforming state-of-the-art results significantly.


title: Elimination of Spurious Ambiguity in Transition-Based Dependency Parsing

abstract: We present a novel technique to remove spurious ambiguity from transition systems for dependency parsing. Our technique chooses a canonical sequence of transition operations (computation) for a given dependency tree. Our technique can be applied to a large class of bottom-up transition systems, including for instance Nivre [2004] and Attardi [2006].


title: Omnivore: An Optimizer for Multi-device Deep Learning on CPUs and GPUs

abstract: We study the factors affecting training time in multi-device deep learning systems. Given a specification of a convolutional neural network, our goal is to minimize the time to train this model on a cluster of commodity CPUs and GPUs. We first focus on the single-node setting and show that by using standard batching and data-parallel techniques, throughput can be improved by at least 5.5x over state-of-the-art systems on CPUs. This ensures an end-to-end training speed directly proportional to the throughput of a device regardless of its underlying hardware, allowing each node in the cluster to be treated as a black box. Our second contribution is a theoretical and empirical study of the tradeoffs affecting end-to-end training time in a multiple-device setting. We identify the degree of asynchronous parallelization as a key factor affecting both hardware and statistical efficiency. We see that asynchrony can be viewed as introducing a momentum term. Our results imply that tuning momentum is critical in asynchronous parallel configurations, and suggest that published results that have not been fully tuned might report suboptimal performance for some configurations. For our third contribution, we use our novel understanding of the interaction between system and optimization dynamics to provide an efficient hyperparameter optimizer. Our optimizer involves a predictive model for the total time to convergence and selects an allocation of resources to minimize that time. We demonstrate that the most popular distributed deep learning systems fall within our tradeoff space, but do not optimize within the space. By doing this optimization, our prototype runs 1.9x to 12x faster than the fastest state-of-the-art systems.


title: Global cancer statistics, 2012

abstract: Cancer constitutes an enormous burden on society in more and less economically developed countries alike. The occurrence of cancer is increasing because of the growth and aging of the population, as well as an increasing prevalence of established risk factors such as smoking, overweight, physical inactivity, and changing reproductive patterns associated with urbanization and economic development. Based on GLOBOCAN estimates, about 14.1 million new cancer cases and 8.2 million deaths occurred in 2012 worldwide. Over the years, the burden has shifted to less developed countries, which currently account for about 57% of cases and 65% of cancer deaths worldwide. Lung cancer is the leading cause of cancer death among males in both more and less developed countries, and has surpassed breast cancer as the leading cause of cancer death among females in more developed countries; breast cancer remains the leading cause of cancer death among females in less developed countries. Other leading causes of cancer death in more developed countries include colorectal cancer among males and females and prostate cancer among males. In less developed countries, liver and stomach cancer among males and cervical cancer among females are also leading causes of cancer death. Although incidence rates for all cancers combined are nearly twice as high in more developed than in less developed countries in both males and females, mortality rates are only 8% to 15% higher in more developed countries. This disparity reflects regional differences in the mix of cancers, which is affected by risk factors and detection practices, and/or the availability of treatment. Risk factors associated with the leading causes of cancer death include tobacco use (lung, colorectal, stomach, and liver cancer), overweight/obesity and physical inactivity (breast and colorectal cancer), and infection (liver, stomach, and cervical cancer). A substantial portion of cancer cases and deaths could be prevented by broadly applying effective prevention measures, such as tobacco control, vaccination, and the use of early detection tests.


title: A Data Augmentation Methodology to Improve Age Estimation Using Convolutional Neural Networks

abstract: Recent advances in deep learning methodologies are enabling the construction of more accurate classifiers. However, existing labeled face datasets are limited in size, which prevents CNN models from reaching their full generalization capabilities. A variety of techniques to generate new training samples based on data augmentation have been proposed, but the great majority is limited to very simple transformations. The approach proposed in this paper takes into account intrinsic information about human faces in order to generate an augmented dataset that is used to train a CNN, by creating photo-realistic smooth face variations based on Active Appearance Models optimized for human faces. An experimental evaluation taking CNN models trained with original and augmented versions of the MORPH face dataset allowed an increase of 10% in the F-Score and yielded Receiver Operating Characteristic curves that outperformed state-of-the-art work in the literature.