title: A Language-Independent Ontology Construction Method Using Tagged Images in Folksonomy

abstract: This paper presents a language-independent ontology (LION) construction method that uses tagged images in an image folksonomy. Existing multilingual frameworks that construct an ontology deal with concepts translated on the basis of parallel corpora, which are not always available; however, the proposed method enables LION construction without parallel corpora by using visual features extracted from tagged images as the alternative. In the proposed method, visual similarities in tagged images are leveraged to aggregate synonymous concepts across languages. The aggregated concepts take on intrinsic semantics of themselves, while they also hold distinct characteristics in different languages. Then relationships between concepts are extracted on the basis of visual and textual features. The proposed method constructs a LION whose nodes and edges correspond to the aggregated concepts and relationships between them, respectively. The LION enables successful image retrieval across languages since each of the aggregated concepts can be referred to in different languages. Consequently, the proposed method removes the language barriers by providing an easy way to access a broader range of tagged images for users in the folksonomy, regardless of the language they use.


title: Tactile sensing in dexterous robot hands - Review

abstract: Tactile sensing is an essential element of autonomous dexterous robot hand manipulation. It provides information about forces of interaction and surface properties at points of contact between the robot fingers and the objects. Recent advancements in robot tactile sensing led to development of many computational techniques that exploit this important sensory channel. This paper reviews current state-of-the-art of manipulation and grasping applications that involve artificial sense of touch and discusses pros and cons of each technique. The main issues of artificial tactile sensing are addressed. General requirements of a tactile sensor are briefly discussed and the main transduction technologies are analyzed. Twenty eight various tactile sensors, each integrated into a robot hand, are classified in accordance with their transduction types and applications. Previously issued reviews are focused on hardware part of tactile sensors, whereas we present an overview of algorithms and tactile feedback-based control systems that exploit signals from the sensors. The applications of these algorithms include grasp stability estimation, tactile object recognition, tactile servoing and force control. Drawing from advancements in tactile sensing technology and taking into consideration its drawbacks, this paper outlines possible new directions of research in dexterous manipulation. We present a review of tactile sensing applications in dexterous robot hand manipulation.This problem is key to dexterous manipulation, and no update reviews are available.The main types of tactile sensors and their integration with robot hands are discussed.An overview of tactile data processing techniques and its applications is presented.


title: Microbial influences in inflammatory bowel diseases.

abstract: The predominantly anaerobic microbiota of the distal ileum and colon contain an extraordinarily complex variety of metabolically active bacteria and fungi that intimately interact with the host's epithelial cells and mucosal immune system. Crohn's disease, ulcerative colitis, and pouchitis are the result of continuous microbial antigenic stimulation of pathogenic immune responses as a consequence of host genetic defects in mucosal barrier function, innate bacterial killing, or immunoregulation. Altered microbial composition and function in inflammatory bowel diseases result in increased immune stimulation, epithelial dysfunction, or enhanced mucosal permeability. Although traditional pathogens probably are not responsible for these disorders, increased virulence of commensal bacterial species, particularly Escherichia coli, enhance their mucosal attachment, invasion, and intracellular persistence, thereby stimulating pathogenic immune responses. Host genetic polymorphisms most likely interact with functional bacterial changes to stimulate aggressive immune responses that lead to chronic tissue injury. Identification of these host and microbial alterations in individual patients should lead to selective targeted interventions that correct underlying abnormalities and induce sustained and predictable therapeutic responses.


Self-balancing two-wheeled robot


title: Average current mode control of switching power supplies

abstract: Current mode control as usually implemented in switching power supplies actually senses and controls peak inductor current. This gives rise to many serious problems, including poor noise immunity, a need for slope compensation, and peak-to-average current errors which the inherently low current loop gain cannot correct. Average current mode control eliminates these problems and may be used effectively to control currents other than inductor current, allowing a much broader range of topological application.


title: ULDBs: Databases with Uncertainty and Lineage

abstract: This paper introduces ULDBs, an extension of relational databases with simple yet expressive constructs for representing and manipulating both lineage and uncertainty. Uncertain data and data lineage are two important areas of data management that have been considered extensively in isolation, however many applications require the features in tandem. Fundamentally, lineage enables simple and consistent representation of uncertain data, it correlates uncertainty in query results with uncertainty in the input data, and query processing with lineage and uncertainty together presents computational benefits over treating them separately.We show that the ULDB representation is complete, and that it permits straightforward implementation of many relational operations. We define two notions of ULDB minimality--data-minimal and lineage-minimal--and study minimization of ULDB representations under both notions. With lineage, derived relations are no longer self-contained: their uncertainty depends on uncertainty in the base data. We provide an algorithm for the new operation of extracting a database subset in the presence of interconnected uncertainty. Finally, we show how ULDBs enable a new approach to query processing in probabilistic databases.ULDBs form the basis of the Trio system under development at Stanford.


title: Stationary Frame Induction Motor Feed Forward Current Controller With Back EMF Compensation

abstract: In this paper, a novel induction motor (IM) stator current controller is proposed based on the stationary frame feed forward controlling structure. The proposed regulator represents an improved variant of the existing stationary and rotational frame IM stator current regulators based on the back EMF compensation. The paper novelty consists of the stationary frame controlling structure based on two feed forward actions that enable asymptotic reference tracking. Namely, the novel stationary frame regulator includes a proportional integral (PI) regulation term combined with a stator resistance voltage drop and back EMF feed forward compensations, with the back EMF compensation based on the stator flux value estimated by means of a programmable low-pass filter. The feedback term is based on a PI controller, with the corresponding parameter tuning procedure outlined in the paper. The simulation runs are included in the paper in order to examine the regulator performance in relation to the factors critical to the IM stator current regulation: variable IM model parameter values, transport delay introduced in the control loop, variable stator frequency, and rotor speed values. Also, the experimental tests are presented and carried out on an experimental setup for different operating conditions typical for an IM drive.


title: Defect Detection in Textures through the Use of Entropy as a Means for Automatically Selecting the Wavelet Decomposition Level

abstract: This paper presents a robust method for defect detection in textures, entropy-based automatic selection of the wavelet decomposition level (EADL), based on a wavelet reconstruction scheme, for detecting defects in a wide variety of structural and statistical textures. Two main features are presented. One of the new features is an original use of the normalized absolute function value (NABS) calculated from the wavelet coefficients derived at various different decomposition levels in order to identify textures where the defect can be isolated by eliminating the texture pattern in the first decomposition level. The second is the use of Shannon’s entropy, calculated over detail subimages, for automatic selection of the band for image reconstruction, which, unlike other techniques, such as those based on the co-occurrence matrix or on energy calculation, provides a lower decomposition level, thus avoiding excessive degradation of the image, allowing a more accurate defect segmentation. A metric analysis of the results of the proposed method with nine different thresholding algorithms determined that selecting the appropriate thresholding method is important to achieve optimum performance in defect detection. As a consequence, several different thresholding algorithms depending on the type of texture are proposed.


title: Underwater image enhancement: using wavelength compensation and image dehazing (WCID)

abstract: Underwater environments often cause color scatter and color cast during photography. Color scatter is caused by haze effects occurring when light reflected from objects is absorbed or scattered multiple times by particles in the water. This in turn lowers the visibility and contrast of the image. Color cast is caused by the varying attenuation of light in different wavelengths, rendering underwater environments bluish. To address distortion from color scatter and color cast, this study proposes an algorithm to restore underwater images that combines a dehazing algorithm with wavelength compensation (WCID). Once the distance between the objects and the camera was estimated using dark channel prior, the haze effects from color scatter were removed by the dehazing algorithm. Next, estimation of the photography scene depth from the residual energy ratios of each wavelength in the background light of the image was performed. According to the amount of attenuation of each wavelength, reverse compensation was conducted to restore the distortion from color cast. An underwater video downloaded from the Youtube website was processed using WCID, Histogram equalization, and a traditional dehazing algorithm. Comparison of the results revealed that WCID simultaneously resolved the issues of color scatter and color cast as well as enhanced image contrast and calibrated color cast, producing high quality underwater images and videos.


title: A Method to Evaluate CFG Comparison Algorithms

abstract: Control-Flow Graph (CFG) similarity is a core technique in many areas, including malware detection and software plagiarism detection. While many algorithms have been proposed in the literature, their relative strengths and weaknesses have not been previously studied. Moreover, it is not even clear how to perform such an evaluation. In this paper we therefore propose the first methodology for evaluating CFG similarity algorithms with respect to accuracy and efficiency. At the heart of our methodology is a technique to automatically generate benchmark graphs, CFGs of known edit distances. We show the result of applying our methodology to four popular algorithms. Our results show that an algorithm proposed by Hu et al. is most efficient both in terms of running time and accuracy.


title: Fast-FMI: Non-reference image fusion metric

abstract: In this paper, we present a non-reference image fusion metric based on the mutual information of image features. Whereas a recent metric proposed by the author called FMI achieves such a goal, the algorithm is complex and has high memory requirements for its calculations. This paper shows how to modify the model of FMI, and proposes a faster algorithm to achieve similar results. The new algorithm achieves a significant complexity reduction in comparison to the previous model. Various experiments prove the efficiency of the algorithm in consistency with the subjective criteria. Matlab source code for this metric is provided at http://www.mathworks.com/matlabcentral/fileexchange/45926.


title: Air-Filled Parallel-Plate Cylindrical Modified Luneberg Lens Antenna for Multiple-Beam Scanning at Millimeter-Wave Frequencies

abstract: This paper presents a novel design of cylindrical modified Luneberg lens antenna at millimeter-wave (mm-wave) frequencies in which no dielectric is needed as lens material. The cylindrical modified Luneberg lens consists of two air-filled, almost-parallel plates whose spacing continuously varies with the radius to simulate the general Luneberg's Law. A planar antipodal linearly-tapered slot antenna (ALTSA) is placed between the parallel plates at the focal position of the lens as a feed antenna. A combined ray-optics/diffraction method and CST-MWS are used to analyze and design this lens antenna. Measured results of a fabricated cylindrical modified Luneberg lens with a diameter of 100 mm show good agreement with theoretical predictions. At the design frequency of 30 GHz, the measured 3-dB E- and H-plane beamwidths are 8.6° and 68°, respectively. The first sidelobe level in the E-plane is -20 dB, and the cross-polarization is -28 dB below peak. The measured aperture efficiency is 68% at 30 GHz, and varies between 50% and 71% over the tested frequency band of 29-32 GHz. Due to its rotational symmetry, this lens can be used to launch multiple beams by implementing an arc array of planar ALTSA elements at the periphery of the lens. A 21-element antenna array with a -3-D dB beam crossover and a scan angle of 180° is demonstrated. The measured overall scan coverage is up to ±80° with gain drop less than -3 dB.


title: An Efficient Fusion-Based Defogging

abstract: Degradation in visibility is often introduced to images captured in poor weather conditions, such as fog or haze. To overcome this problem, conventional approaches focus mainly on the enhancement of the overall image contrast. However, because of the unspecified light-source distribution or unsuitable mathematical constraints of the cost functions, it is often difficult to achieve quality results. In this paper, a fusion-based transmission estimation method is introduced to adaptively combine two different transmission models. Specifically, the new fusion weighting scheme and the atmospheric light computed from the Gaussian-based dark channel method improve the estimation of the locations of the light sources. To reduce the flickering effect introduced during the process of frame-based dehazing, a flicker-free module is formulated to alleviate the impacts. The systematic assessments show that this approach is capable of achieving superior defogging and dehazing performance, compared with superior defogging and dehazing performance, compared with the state-of-the-art methods, both quantitatively and qualitatively.


title: Cycling down under: a comparative analysis of bicycling trends and policies in Sydney and Melbourne

abstract: The purpose of this paper is to document and explain differences in cycling between Australia’s two largest cities. Our comparative case study analysis is based on a wide range of statistical datasets, secondary reports, and interviews with a panel of 22 bicycling policy and planning experts. The main finding is that cycling levels in Melbourne are roughly twice as high as in Sydney and have been growing three times as fast in recent years. The difference is due to Melbourne’s more favorable topography, climate, and road network as well as more supportive public policies. In particular, Melbourne has more and better integrated cycling infrastructure as well as more extensive cycling programs, advocacy, and promotional events. Melbourne also benefits from safer cycling than Sydney, which suffers from a lack of traffic-protected cycling facilities and aggressive motorist behavior toward cyclists on the road. While cycling has been increasing in Australia, it remains at very low levels relative to northern Europe, where both land use and transport policies are far more supportive of bicycling while discouraging car use through numerous restrictions and financial disincentives.


title: MoodScope: building a mood sensor from smartphone usage patterns

abstract: We report a first-of-its-kind smartphone software system, MoodScope, which infers the mood of its user based on how the smartphone is used. Compared to smartphone sensors that measure acceleration, light, and other physical properties, MoodScope is a "sensor" that measures the mental state of the user and provides mood as an important input to context-aware computing. We run a formative statistical mood study with smartphone-logged data collected from 32 participants over two months. Through the study, we find that by analyzing communication history and application usage patterns, we can statistically infer a user's daily mood average with an initial accuracy of 66%, which gradu-ally improves to an accuracy of 93% after a two-month personal-ized training period. Motivated by these results, we build a service, MoodScope, which analyzes usage history to act as a sensor of the user's mood. We provide a MoodScope API for developers to use our system to create mood-enabled applications. We further create and deploy a mood-sharing social application.


title: Controlled Flight of a Biologically Inspired, Insect-Scale Robot

abstract: Flies are among the most agile flying creatures on Earth. To mimic this aerial prowess in a similarly sized robot requires tiny, high-efficiency mechanical components that pose miniaturization challenges governed by force-scaling laws, suggesting unconventional solutions for propulsion, actuation, and manufacturing. To this end, we developed high-power-density piezoelectric flight muscles and a manufacturing methodology capable of rapidly prototyping articulated, flexure-based sub-millimeter mechanisms. We built an 80-milligram, insect-scale, flapping-wing robot modeled loosely on the morphology of flies. Using a modular approach to flight control that relies on limited information about the robot’s dynamics, we demonstrated tethered but unconstrained stable hovering and basic controlled flight maneuvers. The result validates a sufficient suite of innovations for achieving artificial, insect-like flight.


title: The acceptability of assistive technology to older people

abstract: Assistive technology (AT) is defined in this paper as ‘any device or system that allows an individual to perform a task that they would otherwise be unable to do, or increases the ease and safety with which the task can be performed’ (Cowan and Turner-Smith 1999). Its importance in contributing to older people's independence and autonomy is increasingly recognised, but there has been little research into the viability of extensive installations of AT. This paper focuses on the acceptability of AT to older people, and reports one component of a multi-disciplinary research project that examined the feasibility, acceptability, costs and outcomes of introducing AT into their homes. Sixty-seven people aged 70 or more years were interviewed in-depth during 2001 to find out about their use and experience of a wide range of assistive technologies. The findings suggest a complex model of acceptability, in which a ‘felt need’ for assistance combines with ‘product quality’. The paper concludes by considering the tensions that may arise in the delivery of acceptable assistive technology.


title: Improved Design of High-Performance Parallel Decimal Multipliers

abstract: The new generation of high-performance decimal floating-point units (DFUs) is demanding efficient implementations of parallel decimal multipliers. In this paper, we describe the architectures of two parallel decimal multipliers. The parallel generation of partial products is performed using signed-digit radix-10 or radix-5 recodings of the multiplier and a simplified set of multiplicand multiples. The reduction of partial products is implemented in a tree structure based on a decimal multioperand carry-save addition algorithm that uses unconventional (non BCD) decimal-coded number systems. We further detail these techniques and present the new improvements to reduce the latency of the previous designs, which include: optimized digit recoders for the generation of 2n-tuples (and 5-tuples), decimal carry-save adders (CSAs) combining different decimal-coded operands, and carry-free adders implemented by special designed bit counters. Moreover, we detail a design methodology that combines all these techniques to obtain efficient reduction trees with different area and delay trade-offs for any number of partial products generated. Evaluation results for 16-digit operands show that the proposed architectures have interesting area-delay figures compared to conventional Booth radix-4 and radix--8 parallel binary multipliers and outperform the figures of previous alternatives for decimal multiplication.


title: A methodology for effective implementation of lean strategies and its performance evaluation in manufacturing organizations

abstract: Purpose – The purpose of this paper is to develop an effective methodology for implementing lean manufacturing strategies and a leanness evaluation metric using continuous performance measurement (CPM).Design/methodology/approach – Based on five lean principles, a systematic lean implementation methodology for manufacturing organizations has been proposed. A simplified leanness evaluation metric consisting of both efficiency and effectiveness attributes of manufacturing performance has been developed for continuous evaluation of lean implementation. A case study to validate the proposed methodology has been conducted and proposed CPM metric has been used to assess the manufacturing leanness.Findings – Proposed methodology is able to systematically identify manufacturing wastes, select appropriate lean tools, identify relevant performance indicators, achieve significant performance improvement and establish lean culture in the organization. Continuous performance measurement matrices in terms of efficiency...


title: Nuclear PARP-1 protein overexpression is associated with poor overall survival in early breast cancer.

abstract: BACKGROUND ::: Poly(ADP-ribose)polymerase-1 (PARP-1) is a highly promising novel target in breast cancer. However, the expression of PARP-1 protein in breast cancer and its associations with outcome are yet poorly characterized. ::: ::: ::: PATIENTS AND METHODS ::: Quantitative expression of PARP-1 protein was assayed by a specific immunohistochemical signal intensity scanning assay in a range of normal to malignant breast lesions, including a series of patients (N = 330) with operable breast cancer to correlate with clinicopathological factors and long-term outcome. ::: ::: ::: RESULTS ::: PARP-1 was overexpressed in about a third of ductal carcinoma in situ and infiltrating breast carcinomas. PARP-1 protein overexpression was associated to higher tumor grade (P = 0.01), estrogen-negative tumors (P < 0.001) and triple-negative phenotype (P < 0.001). The hazard ratio (HR) for death in patients with PARP-1 overexpressing tumors was 7.24 (95% CI; 3.56-14.75). In a multivariate analysis, PARP-1 overexpression was an independent prognostic factor for both disease-free (HR 10.05; 95% CI 5.42-10.66) and overall survival (HR 1.82; 95% CI 1.32-2.52). ::: ::: ::: CONCLUSIONS ::: Nuclear PARP-1 is overexpressed during the malignant transformation of the breast, particularly in triple-negative tumors, and independently predicts poor prognosis in operable invasive breast cancer.


Knowledge-Driven Event Embedding for Stock Prediction


title: Genetic Algorithms for Tracking Changing Environments

abstract: Genetic algorithms perform an adaptive search by maintaining a population of candidate solutions that are allocated dynamically to promising regions of the search space. The distributed nature of the genetic search provides a natural source of power for searching in changing environments. As long as sufficient diversity remains in the population the genetic algorithm can respond to a changing response surface by reallocating future trials. However, the tendency of genetic algorithms to converge rapidly reduces their ability to identify regions of the search space that might suddenly become more attractive as the environment changes. This paper presents a modification of the standard generational genetic algorithm that is designed to maintain the diversity required to track a changing response surface. An experimental study shows some promise for the new technique.


title: A novel adaptive unscented Kalman filter for nonlinear estimation

abstract: The normal unscented Kalman filter (UKF) suffers from performance degradation and even divergence while mismatch between the noise distribution assumed to be known as a priori by UKF and the true ones in a real system. In this paper, a novel adaptive UKF (AUKF) is developed and applied to nonlinear joint estimation of both time-varying states and parameters. A cost function is built based on the error between the covariance matrices of innovation and their corresponding estimations. An adaptive algorithm is then designed to online update the covariance of the process noise by minimizing the cost function. The updated covariance is further fed back into the normal UKF. As a result of using such an adaptive mechanism, the robustness of conventional UKF is substantially improved with respect to the uncertain and time-varying noise covariance in the real system. To illustrate this mechanism, simulations are conducted on the dynamics of an unmanned helicopter by jointly estimating both the states and model errors. The improvements of the proposed AUKF are demonstrated by comparing the results with and without the adaptive mechanism.


title: Pareto-optimal search-based software engineering (POSBSE): A literature survey

abstract: The Search-Based Software Engineering (SBSE) community is increasingly recognizing the inherit “multiobjectiveness” in Software Engineering problems. The old ways of aggregating all objectives into one may very well be behind us. We perform a well-deserved literature survey of SBSE papers that used multiobjective search to find Pareto-optimal solutions, and we pay special attention to the chosen algorithms, tools, and quality indicators, if any. We conclude that the SBSE field has seen a trend of adopting the Multiobjective Evolutionary Optimization Algorithms (MEOAs) that are widely used in other fields (such as NSGA-II and SPEA2) without much scrutiny into the reason why one algorithm should be preferred over the others. We also find that the majority of published work only tackled two-objective problems (or formulations of problems), leaving much to be desired in terms of exploiting the power of MEOAs to discover solutions to intractable problems characterized by many trade-offs and complex constraints.


title: Combination of Language Models for Word Prediction: An Exponential Approach

abstract: This paper proposes an exponential interpolation to merge a part-of-speech-based language model and a word-based $n$ -gram language model to accomplish word prediction tasks. In order to find a set of mathematical equations to properly describe the language modeling, a model based on partial differential equations is proposed. With the appropriate initial conditions, it was found an interpolation model similar to the traditional maximum entropy language model. Improvements in keystroke saved and perplexity over the word-based $n$ -gram language model and two other traditional interpolation models is obtained, considering three different languages. The proposed interpolation model also provides additional improvement in hit rate parameter.


title: Deep Hierarchical Reinforcement Learning Algorithm in Partially Observable Markov Decision Processes

abstract: In recent years, reinforcement learning has achieved many remarkable successes due to the growing adoption of deep learning techniques and the rapid growth in computing power. Nevertheless, it is well-known that flat reinforcement learning algorithms are often not able to learn well and data-efficient in tasks having hierarchical structures, e.g. consisting of multiple subtasks. Hierarchical reinforcement learning is a principled approach that is able to tackle these challenging tasks. On the other hand, many real-world tasks usually have only partial observability in which state measurements are often imperfect and partially observable. The problems of RL in such settings can be formulated as a partially observable Markov decision process (POMDP). In this paper, we study hierarchical RL in POMDP in which the tasks have only partial observability and possess hierarchical properties. We propose a hierarchical deep reinforcement learning approach for learning in hierarchical POMDP. The deep hierarchical RL algorithm is proposed to apply to both MDP and POMDP learning. We evaluate the proposed algorithm on various challenging hierarchical POMDP.


title: Morality and competence in person- and self-perception

abstract: Morality and competence are posited to constitute two basic kinds of content in person- and self-perception. Moral content dominates person-perception because it typically has a direct and unconditional bearing on the well-being of other people surrounding the person who is described by the trait (including the perceiver). Competence dominates self-perception because it has a direct bearing on the well-being of the perceiver. A comprehensive research programme is reviewed showing that morality of others matters to the perceiver to a much higher degree than his/her competence. When forming global evaluations of others, the perceiver is more interested in their moral than competence qualities, construes their behaviour in moral terms, and his or her impressions and emotional responses are more strongly based on morality than competence considerations. Just the opposite is true for self-perception and self-attitudes. Own behaviours are construed more readily in competence than moral terms, and own competence...


title: Collaborative Pharmacy Student Learning Outline for Mobile Atmosphere

abstract: the idea of this research is for the concern of Collaborative learning based mobile factors by applying via pharmacy students of the college. We focus on three features, computer mutual learning, learning process module, and student learning mode. In this paper, student-focused instruct module, student edge section, teacher interface section, learner section, solution problem section, curriculum section, control section, and diagnose section are planned. This system permits students to be sustained with a real time approach, non-real time approach, mixture approach. The devices used contain smart phone, PDAs, mobile devices, transportable computers and tablet PDAs. This system is to become a more capable student learning environment so that student can get student’s learning done more efficiently. The development of a collaborative learning combines the advantages of an adaptive learning environment with the advantages of mobile telecommunication and the suppleness of mobile devices.


title: ScalParC: a new scalable and efficient parallel classification algorithm for mining large datasets

abstract: We present ScalParC (Scalable Parallel Classifier), a new parallel formulation of a decision tree based classification process. Like other state-of-the-art decision tree classifiers such as SPRINT, ScalParC is suited for handling large datasets. We show that existing parallel formulation of SPRINT is unscalable, whereas ScalParC is shown to be scalable in both runtime and memory requirements. We present the experimental results of classifying up to 6.4 million records on up to 128 processors of Cray T3D, in order to demonstrate the scalable behavior of ScalParC. A key component of ScalParC is the parallel hash table. The proposed parallel hashing paradigm can be used to parallelize other algorithms that require many concurrent updates to a large hash table.


title: The Dangers of Verify PIN on Contactless Cards

abstract: Contactless / Near Field Communication (NFC) card payments are being introduced around the world, allowing customers to use a card to pay for small purchases by simply placing the card onto the Point of Sale terminal. Although the terminal needs to be able to verify a PIN, it is not clear if such PIN verification features should be available on the NFC card itself. We show that contactless Visa payment cards have (largely redundant) functionality, Verify PIN, which makes them vulnerable to new forms of wireless attack. Based on careful examination of the Europay, MasterCard and Visa (EMV) protocol and experiments with the Visa fast Dynamic Data Authentication transaction protocol, we provide a set of building blocks for possible attacks. These building blocks are data skimming, Verify PIN and transaction relay, which we implement and experiment with. Based on these building blocks, we propose a number of realistic attacks, including a denial-of-service attack and a newly developed realistic PIN guessing attack. The conclusion of our work is that implementing Verify PIN functionality on NFC cards has no demonstrated benefits and opens up new avenues of attack.


ste-GAN-ography: Generating Steganographic Images via Adversarial Training.


title: Image Processing and Machine Learning for Automated Fruit Grading System: A Technical Review

abstract: In India, demand for various fruits and vegetables are increasing as population grows.Automation in agriculture plays a vital role in increasing the productivity and economical growth of the Country, therefore there is a need for automated system for accurate, fast and quality fruits determination. Researchers have developed numerous algorithms for quality grading and sorting of fruit. Color is most striking feature for identifying disease and maturity of the fruit. In this paper; efficient algorithms for color feature extraction are reviewed. Then after, various classification techniques are compared based on their merits and demerits. The objective of the paper is to provide introduction to machine learning and color based grading algorithms, its components and current work reported on an automatic fruit grading system.


title: The neuroscience of consumer choice

abstract: We review progress and challenges relating to scientific and applied goals of the nascent field of consumer neuroscience. Scientifically, substantial progress has been made in understanding the neurobiology of choice processes. Further advances, however, require researchers to begin clarifying the set of developmental and cognitive processes that shape and constrain choices. First, despite the centrality of preferences in theories of consumer choice, we still know little about where preferences come from and the underlying developmental processes. Second, the role of attention and memory processes in consumer choice remains poorly understood, despite importance ascribed to them in interpreting data from the field. The applied goal of consumer neuroscience concerns our ability to translate this understanding to augment prediction at the population level. Although the use of neuroscientific data for market-level predictions remains speculative, there is growing evidence of superiority in specific cases over existing market research techniques.


title: CMOS Full-Adders for Energy-Efficient Arithmetic Applications

abstract: We present two high-speed and low-power full-adder cells designed with an alternative internal logic structure and pass-transistor logic styles that lead to have a reduced power-delay product (PDP). We carried out a comparison against other full-adders reported as having a low PDP, in terms of speed, power consumption and area. All the full-adders were designed with a 0.18-μm CMOS technology, and were tested using a comprehensive testbench that allowed to measure the current taken from the full-adder inputs, besides the current provided from the power-supply. Post-layout simulations show that the proposed full-adders outperform its counterparts exhibiting an average PDP advantage of 80%, with only 40% of relative area.


title: Growing pains for deep learning

abstract: Neural networks, which support online image search and speech recognition, eventually will drive more advanced services.


title: A survey of motion-parallax-based 3-D reconstruction algorithms

abstract: The task of recovering three-dimensional (3-D) geometry from two-dimensional views of a scene is called 3-D reconstruction. It is an extremely active research area in computer vision. There is a large body of 3-D reconstruction algorithms available in the literature. These algorithms are often designed to provide different tradeoffs between speed, accuracy, and practicality. In addition, even the output of various algorithms can be quite different. For example, some algorithms only produce a sparse 3-D reconstruction while others are able to output a dense reconstruction. The selection of the appropriate 3-D reconstruction algorithm relies heavily on the intended application as well as the available resources. The goal of this paper is to review some of the commonly used motion-parallax-based 3-D reconstruction techniques and make clear the assumptions under which they are designed. To do so efficiently, we classify the reviewed reconstruction algorithms into two large categories depending on whether a prior calibration of the camera is required. Under each category, related algorithms are further grouped according to the common properties they share.


title: Biochemical responses of Hyacinth bean (Lablab purpureus) to salinity stress

abstract: Effect of salinity on Hyacinth bean, Lablab purpureus (HA-4 cultivar) was evaluated in 10-day old seedlings with 100–500 mM NaCl over 72 h of exposure. The stress reduced dry and fresh weight, leaf surface area, root and shoot length, total chlorophyll, and RWC. Oxidative stress markers, H2O2, glutathione, TBARS, proline, ascorbic acid, total phenols, and total soluble sugar contents were significantly elevated. Salinity enhanced antioxidant enzymes, POX, and GR activities and reduced that of CAT in concentration and time dependent manner in leaves. Antioxidant enzymes in roots showed inverse relationship with concentration and time of exposure. Metabolic enzyme β-amylase activity increased in both leaves and roots. Acid phosphatase decreased in leaves and elevated in roots. Intensity of constitutive isozymes correlated with in vitro levels under stress, but the protein band patterns differed from controls. Lablab showed reasonable tolerance up to 300 mM NaCl, but leaves and roots differed in their response.


title: Identification of ischemic heart disease via machine learning analysis on magnetocardiograms

abstract: Ischemic heart disease (IHD) is predominantly the leading cause of death worldwide. Early detection of IHD may effectively prevent severity and reduce mortality rate. Recently, magnetocardiography (MCG) has been developed for the detection of heart malfunction. Although MCG is capable of monitoring the abnormal patterns of magnetic field as emitted by physiologically defective heart, data interpretation is time-consuming and requires highly trained professional. Hence, we propose an automatic method for the interpretation of IHD pattern of MCG recordings using machine learning approaches. Two types of machine learning techniques, namely back-propagation neural network (BNN) and direct kernel self-organizing map (DK-SOM), were applied to explore the IHD pattern recorded by MCG. Data sets were obtained by sequential measurement of magnetic field emitted by cardiac muscle of 125 individuals. Data were divided into training set and testing set of 74 cases and 51 cases, respectively. Predictive performance was obtained by both machine learning approaches. The BNN exhibited sensitivity of 89.7%, specificity of 54.5% and accuracy of 74.5%, while the DK-SOM provided relatively higher prediction performance with a sensitivity, specificity and accuracy of 86.2%, 72.7% and 80.4%, respectively. This finding suggests a high potential of applying machine learning approaches for high-throughput detection of IHD from MCG data.


title: Designing exploratory design games: a framework for participation in Participatory Design?

abstract: The dogma of Participatory Design is the direct involvement of people in the shaping of future artefacts. Thus central for designers within this field are the staging of a design process involving participation of people. Organising collaboration between people having various competencies and interests is challenging and therefore designers need frameworks, which can accommodate this work. This paper discusses the use of exploratory design games to organise participation in participatory design projects. Examples of different exploratory design games as sources of inspiration are presented. Through a comparison of different exploratory design games the paper sheds light on the repertoire of possibilities for designers to be aware of when creating their own exploratory design games.


title: An SVM learning approach to robotic grasping

abstract: Finding appropriate stable grasps for a hand (either robotic or human) on an arbitrary object has proved to be a challenging and difficult problem. The space of grasping parameters coupled with the degrees-of-freedom and geometry of the object to be grasped creates a high-dimensional, non-smooth manifold. Traditional search methods applied to this manifold are typically not powerful enough to find appropriate stable grasping solutions, let alone optimal grasps. We address this issue in this paper, which attempts to find optimal grasps of objects using a grasping simulator. Our unique approach to the problem involves a combination of numerical methods to recover parts of the grasp quality surface with any robotic hand, and contemporary machine learning methods to interpolate that surface, in order to find the optimal grasp.


title: Exploring the Concept of Mobile Viral Marketing through Case Study Research.

abstract: Mobile viral marketing has a tremendous potential for communication and distribution purposes. Although seen as an important issue from the viewpoint of practitioners there has been little research on the phenomenon so far. This paper represents a first step in filling this void. We examine 34 case studies in order to identify relevant characteristics of mobile viral marketing. The outcome of the paper is a description model of mobile viral marketing as well as a derivation of four mobile viral marketing standard types. The proposed scheme allows unambiguously characterizing any given mobile viral marketing strategy and providing recommendations for designing new mobile viral strategies.


title: An empirical Bayes formulation of cohort models in cancer epidemiology.

abstract: This paper concerns the incidence rates of malignant skin melanoma for several age-sex groups and time periods in three geographic regions, uses a method of cohort analysis and employs a two-stage random effects model. The first stage entails the assumption that the within-region variation in the frequency of disease incidence for a fixed age-sex-cohort group has a Poisson distribution with mean proportional to the population at risk. The second stage, after adjusting for age and sex, entails the assumption that the between-region geographic variation in the logarithm of the true incidence rate has a prior distribution with parameters estimated by the method of maximum likelihood. After adjusting for age effects, we estimate random geographic-specific cohort effects for each sex with use of an empirical Bayes method and compare the results with the usual multiplicative Poisson model that assumes fixed geographic-specific cohort effects for each sex. This comparison shows that the method presented here provides more stable estimates of geographic-specific cohort effects, and in addition the random effects model describes these data more adequately.


title: PCA-Initialized Deep Neural Networks Applied To Document Image Analysis

abstract: In this paper, we present a novel approach for initializing deep neural networks, i.e., by using Principal Component Analysis (PCA) to initialize neural layers. Usually, the initialization of the weights of a deep neural network is done in one of the three following ways: 1) with random values, 2) layer-wise, usually as Deep Belief Network or as auto-encoder, and 3) re-use of layers from another network (transfer learning). Therefore, typically, many training epochs are needed before meaningful weights are learned, or a rather similar dataset is required for seeding a fine-tuning of transfer learning. In this paper, we describe how to turn a PCA into an auto-encoder, by generating an encoder layer of the PCA parameters and furthermore adding a decoding layer. We analyze the initialization technique on real documents. First, we show that a PCA-based initialization is quick and leads to a very stable initialization. Furthermore, for the task of layout analysis we investigate the effectiveness of PCAbased initialization and show that it outperforms state-of-the-art random weight initialization methods.


title: Towards unsupervised weed scouting for agricultural robotics

abstract: Weed scouting is an important part of modern integrated weed management but can be time consuming and sparse when performed manually. Automated weed scouting and weed destruction has typically been performed using classification systems able to classify a set group of species known a priori. This greatly limits deployability as classification systems must be retrained for any field with a different set of weed species present within them. In order to overcome this limitation, this paper works towards developing a clustering approach to weed scouting which can be utilized in any field without the need for prior species knowledge. We demonstrate our system using challenging data collected in the field from an agricultural robotics platform. We show that considerable ::: improvements can be made by (i) learning low-dimensional ::: (bottleneck) features using a deep convolutional neural network to represent plants in general and (ii) tying views of the same area (plant) together . Deploying this algorithm on in-field data collected by AgBotII, we are able to successfully cluster cotton plants from grasses without prior knowledge or training for the specific plants in the field.


title: Early human communication helps in understanding language evolution.

abstract: Building a theory on extant species, as Ackermann et al. do, is a useful contribution to the field of language evolution. Here, I add another living model that might be of interest: human language ontogeny in the first year of life. A better knowledge of this phase might help in understanding two more topics among the "several building blocks of a comprehensive theory of the evolution of spoken language" indicated in their conclusion by Ackermann et al., that is, the foundation of the co-evolution of linguistic motor skills with the auditory skills underlying speech perception, and the possible phylogenetic interactions of protospeech production with referential capabilities.


title: Emotion regulation: concepts & practice in autism spectrum disorder.

abstract: Emotion Regulation Concepts ::: Imagine that you are driving to work and someone cuts you off. Your heart rate rapidly increases and you experience a wave of intense irritation, yet you manage to blare on your horn, simultaneously hit your brakes, and remain focused on safely driving. You have just engaged in effective emotion regulation (ER), which broadly encompasses the processes related to modifying your emotions to fit the context or meet one’s goals1,2 (in this case, staying safe). Although the distinction is widely debated,3–5 emotion regulation differs from the experience of emotion itself, in that ER involves an attempt to modify the intensity or temporal features of an emotion [e.g. after the initial startle when you are cut off, you quickly experience anger when you see the driver texting (emotion); but, you attempt to keep the anger from escalating so that you can remain focused on safely driving (ER)]. ER processes can occur at the unconscious level (without realizing you are doing so, you maintain a level of fear that will keep you alert but not unable to act) and at a conscious level (e.g. telling yourself you are okay after the incident is over). Further, ER can be response-focused as in this example, or antecedent-focused (prior to the emotion). ::: ::: Most people have a characteristic and fairly stable ER style. A person’s customary ER style can be generally adaptive or maladaptive, with the latter often associated with psychopathology and less appropriate behavior. Disrupted, or maladaptive, ER has been implicated as a mechanism underlying various psychiatric disorders, including depressive6,7 and anxiety8 disorders, and borderline personality disorder.9 Thus, poor ER is a transdiagnostic process that plays a role in many disorders in producing inappropriate emotional and behavioral reactions. The mechanisms that give rise to emotional dysregulation and how ER manifests itself, however, are more disorder-specific.9


title: Inferring Users’ Preferences from Crowdsourced Pairwise Comparisons: A Matrix Completion Approach

abstract: Inferring user preferences over a set of items is an important problem that has found numerous applications. This work focuses on the scenario where the explicit feature representation of items is unavailable, a setup that is similar to collaborative filtering. In order to learn a user's preferences from his/her response to only a small number of pairwise comparisons, we propose to leverage the pairwise comparisons made by many crowd users, a problem we refer to as crowdranking. The proposed crowdranking framework is based on the theory of matrix completion, and we present efficient algorithms for solving the related optimization problem. Our theoretical analysis shows that, on average, only O ( r log m ) pairwise queries are needed to accurately recover the ranking list of m items for the target user, where r is the rank of the unknown rating matrix, r << m . Our empirical study with two real-world benchmark datasets for collaborative filtering and one crowdranking dataset we collected via Amazon Mechanical Turk shows the promising performance of the proposed algorithm compared to the state-of-the-art approaches.


title: Exfoliative Cheilitis: A Case Report

abstract: Introduction: Exfoliative Cheilitis (EC) is a rare chronic inflammatory condition affecting the vermillion border of the lips and characterized by excessive production of keratin. In this paper, a case of chronic EC that was relatively resistant to usual treatment is to be introduced. Also, we explain its clinical course and treatment procedures and measures. Case Presentation: We report a 25-year-old Iranian woman presented with desquamation of her lips. History, clinical feature and histopathological examination revealed a diagnosis of EC. Conclusions: Although treatment of EC in the present study had considerable success in the short-term follow-up, it should be noted, due to the unknown of the exact cause, no specific treatment or protocol has still been identified.


Eastern Kutchin Warfare


title: A low-Cost, Open-Source, BCI- VR Game Control Development Environment Prototype for Game Based Neurorehabilitation

abstract: In this paper we present a low-cost and open-source brain-computer interface (BCI) virtual-reality (VR) Game Control Development Environment prototype, which we demonstrate using real-time signal processing of Electroencephalography (EEG) event-related desynchronization and synchronization changes (ERD/ERS) within the Precentral Gyrus (Motor Cortex), allowing a user to control a 3D object within a Virtual Reality Environment. This BCI-VR system prototype was functionally tested on multiple participants and demonstrated live before an audience during the 2017 ‘Hack the Brain’ at the Dublin Science Gallery. The availability of such an open-source, effective, BCI-VR Game Control Development Environment, at a level acceptable for industry experimentation, has the potential to open up this field to a much wider range of researchers and games developers and to assist the investigation of gaming experiences which both incorporate the specific control features available through BCI as a core element of the game play and the potential for its use in neurorehabilitation.


title: THE DISCOURSES AND LITERACY PRACTICES OF DOING SCIENCE IN ENGLISH

abstract: In January 2003, Malaysia re-adopted the English language as a medium of instruction for science and mathematics. This change in the medium of instruction brings with it challenges of its own. What does it mean to ‘do’ science in the Malaysian context and to do so in English? How does the change in the medium of instruction from Bahasa Malaysia to English impinge upon the instructional and literacy practices of teachers and learners? What kinds of changes are required of the communities and stakeholders involved in the teaching of science? This article will address these questions by critiquing some dominant assumptions behind the literacy practices of ‘doing’ science in the Malaysian context based upon the findings of a qualitative case study conducted to investigate how two teachers working in diverse and different contexts in Malaysian schools cope with the new medium of instruction.


title: A Survey on the Electrification of Transportation in a Smart Grid Environment

abstract: Economics and environmental incentives, as well as advances in technology, are reshaping the traditional view of industrial systems. The anticipation of a large penetration of plug-in hybrid electric vehicles (PHEVs) and plug-in electric vehicles (PEVs) into the market brings up many technical problems that are highly related to industrial information technologies within the next ten years. There is a need for an in-depth understanding of the electrification of transportation in the industrial environment. It is important to consolidate the practical and the conceptual knowledge of industrial informatics in order to support the emerging electric vehicle (EV) technologies. This paper presents a comprehensive overview of the electrification of transportation in an industrial environment. In addition, it provides a comprehensive survey of the EVs in the field of industrial informatics systems, namely: 1) charging infrastructure and PHEV/PEV batteries; 2) intelligent energy management; 3) vehicle-to-grid; and 4) communication requirements. Moreover, this paper presents a future perspective of industrial information technologies to accelerate the market introduction and penetration of advanced electric drive vehicles.


title: Awards as Incentives

abstract: Non-monetary incentives in the form of awards have so far escaped the attention of economists despite their widespread use. This paper presents an experiment conducted online at IBM to assess the impact of these kinds of extrinsic incentives. Introducing a hypothetical award has statistically significant effects on stated contributions to a public good. Our design allows the estimation of the impact of different award characteristics related to, for example, how public or how valuable the award is. We illustrate these findings by providing predictions about the behavior induced by a new award at IBM.


title: BCI - DPI Suscetibility Tests on CAN Networks at Equipment Level

abstract: This paper addresses the susceptibility of CAN networks to RF noise when applied to signal ports of a transceiver. The impact of RF interference on CAN is evaluated at equipment level in two measurement tests, injecting noise by means of DPI and BCI. The noise injection paths for the different tests are characterized and compared. The main factors contributing to noise rejection in CAN differential signaling are analyzed and communication failures due to a noisy environment are highlighted. The CAN bus immunity to interferences is characterized as a function of noise power vs. frequency, comparing transmitted and received signals from an analog point of view (IEC 62228).


DE TRAVAIL DT / 2006-14 Helping Hand ? Aid to Failing States


title: Experimental analysis of mode switching techniques in pen-based user interfaces

abstract: Inking and gesturing are two central tasks in pen-based user interfaces. Switching between modes for entry of uninterpreted ink and entry of gestures is required by many pen-based user interfaces. Without an appropriate mode switching technique, pen-based interactions in such situations may be inefficient and cumbersome. In this paper, we investigate five techniques for switching between ink and gesture modes in pen interfaces, including a pen-pressure based mode switching technique that allows implicit mode transition. A quantitative experimental study was conducted to evaluate the performance of these techniques. The results suggest that pressing a button with the non-preferred hand offers the fastest performance, while the technique of holding the pen still is significantly slower and more prone to error than the other techniques. Pressure, while promising, did not perform as well as the non-preferred hand button with our current implementation.


title: Comparing algorithm for dynamic speed-setting of a low-power CPU

abstract: To take advantage of the full potential of ubiquitous computing, we will need systems which minimize powerconsumption. Weiser et al. and others have suggested that this may be accomplished by a CPU which dynamically changes speed and voltage, thereby saving energy by spreading run cycles into idle time. Here we continue this research, using a simulation to compare a number of policies for dynamic speed-setting. Our work clarifies a fundamental power vs. delay tradeoff, as well as the role of prediction and of smoothing in dynamic speed-setting policies. We conclude that success seemingly depends more on simple smoothing algorithms than on sophisticated prediction techniques, but defer to the replication of these results on future variable-speed systems.


title: Data curation + process curation=data integration + science

abstract: In bioinformatics, we are familiar with the idea of curated data as a prerequisite for data integration. We neglect, often to our cost, the curation and cataloguing of the processes that we use to integrate and analyse our data. Programmatic access to services, for data and processes, means that compositions of services can be made that represent the in silico experiments or processes that bioinformaticians perform. Data integration through workflows depends on being able to know what services exist and where to find those services. The large number of services and the operations they perform, their arbitrary naming and lack of documentation, however, mean that they can be difficult to use. The workflows themselves are composite processes that could be pooled and reused but only if they too can be found and understood. Thus appropriate curation, including semantic mark-up, would enable processes to be found, maintained and consequently used more easily. This broader view on semantic annotation is vital for full data integration that is necessary for the modern scientific analyses in biology. This article will brief the community on the current state of the art and the current challenges for process curation, both within and without the Life Sciences.


title: Inferior glenohumeral ligament (IGHL) complex: anatomy, injuries, imaging features, and treatment options

abstract: The inferior glenohumeral ligament (IGHL) complex is comprised of three components supporting the inferior aspect of the shoulder. It consists of an anterior band, a posterior band, and an interposed axillary pouch. Injuries to the IGHL complex have a unifying clinical history of traumatic shoulder injury, which are often sports or fall-related, with the biomechanical mechanism, positioning of the arm, and individual patient factors determining the specific component of the ligamentous complex that is injured, the location of the injury of those components, and the degree of bone involvement. Several acronyms are employed to characterize these features, specifying whether there is involvement of a portion of the anterior band, posterior band, or midsubstance, and if there is avulsion from the humeral attachment, glenoid attachment, or both. Imaging recommendations for the evaluation of the IGHL complex include magnetic resonance imaging (MRI), and injuries to this complex are best visualized with magnetic resonance arthrography. Additionally, a brief description of clinical management of inferior glenohumeral ligament injuries is included.


title: Dual-band microstrip antenna for WiMAX applications using complementary split ring resonators

abstract: A new design of dual-band microstrip antenna is presented in this paper using metamaterial concept. By etching a metamaterial unit cells that have a resonance frequency of 5.3 GHz within the ground plane of a conventional patch resonates at 3.6 GHz, a dual resonance response is created to satisfy the requirement of covering the middle and upper bands of Wi-MAX.A detailed study of the placement of the unit cell is introduced and the most suitable one is chosen to be the place of our unit cell. Multi-unit cell concept is used for performance enhancement of the fabricated design to achieve a good matching with the simulated one.


title: A Curvature-Corrected Low-Voltage Bandgap Reference

abstract: A curvature-corrected bandgap reference that can function at supply voltages as low as 1 V, at a supply current of only 100 mu A, is presented. After trimming, this bandgap reference has a temperature coefficient (TC) of +or-4 p.p.m./ degrees C. The reference voltage is about 200 mV and it can easily be adjusted to higher values. The temperature range of this circuit is from 0 to 125 degrees C. This bandgap reference is realized using a standard bipolar process with base-diffused resistors. >


title: Biotechnological advances in neuro-electro-stimulation for the treatment of hyposalivation and xerostomia.

abstract: Treatment of xerostomia is a common clinical challenge in the oral medicine practice. Although some treatments have been used to improve the symptoms of xerostomia, none is completely satisfactory for the patients who suffer of this alteration. In the last years non-pharmacological treatments based on electro-stimulation for the treatment of xerostomia have been developed. This review is aimed at presenting new developments for the treatment of xerostomia, applying neuro-electro-stimulation by miniaturized intra-oral electro-stimulators. These devices increase salivary secretion and improve symptoms of oral dryness. Their effect is obtained by means of stimulation of the lingual nerve, in whose proximity the electrodes of the apparatus are placed. The objective of this mechanism is both to directly stimulate the salivary glands controlled by that nerve and to enhance the salivary reflex. Clinical studies have been carried out that have demonstrated the wetting effect of the method described in this article.


title: Competitive Advantages and Strategic Information Systems

abstract: Information edge and 3 rd millennium predisposed so many of revolutions. Business organization with emphasize on information systems is try to gathering desirable information for decision making. Because of comprehensive change in business background and emerge of computers and internet, the business structure and needed information had change, the competitiveness as a major factor for life of organizations in information edge is preyed of information technology challenges. In this article we have reviewed in the literature of information systems and discussed the concepts of information system as a strategic tool.


title: Importance-Driven Time-Varying Data Visualization

abstract: The ability to identify and present the most essential aspects of time-varying data is critically important in many areas of science and engineering. This paper introduces an importance-driven approach to time-varying volume data visualization for enhancing that ability. By conducting a block-wise analysis of the data in the joint feature-temporal space, we derive an importance curve for each data block based on the formulation of conditional entropy from information theory. Each curve characterizes the local temporal behavior of the respective block, and clustering the importance curves of all the volume blocks effectively classifies the underlying data. Based on different temporal trends exhibited by importance curves and their clustering results, we suggest several interesting and effective visualization techniques to reveal the important aspects of time-varying data.


title: Adaptive as-natural-as-possible image stitching

abstract: The goal of image stitching is to create natural-looking mosaics free of artifacts that may occur due to relative camera motion, illumination changes, and optical aberrations. In this paper, we propose a novel stitching method, that uses a smooth stitching field over the entire target image, while accounting for all the local transformation variations. Computing the warp is fully automated and uses a combination of local homography and global similarity transformations, both of which are estimated with respect to the target. We mitigate the perspective distortion in the non-overlapping regions by linearizing the homography and slowly changing it to the global similarity. The proposed method is easily generalized to multiple images, and allows one to automatically obtain the best perspective in the panorama. It is also more robust to parameter selection, and hence more automated compared with state-of-the-art methods. The benefits of the proposed approach are demonstrated using a variety of challenging cases.


title: A Template-based Abstractive Meeting Summarization: Leveraging Summary and Source Text Relationships

abstract: In this paper, we present an automatic abstractive summarization system of meeting conversations. Our system extends a novel multi-sentence fusion algorithm in order to generate abstract templates. It also leverages the relationship between summaries and their source meeting transcripts to select the best templates for generating abstractive summaries of meetings. Our manual and automatic evaluation results demonstrate the success of our system in achieving higher scores both in readability and informativeness.


title: A Theory Of Lexical Access In Speech Production

abstract: Preparing words in speech production is normally a fast and accurate process. We generate them two or three per second in fluent conversation; and overtly maining a clear picture of an object can casily be initiated within 600 msec after picture onset. The un-derlying process, however, is exceedingly complex. The theory reviewed in this target article analyzes this process as staged and feed-forward. After a first stage of conceptual preparation, word generation proceeds through lexical selection, morphological and phonological encoding, phonetic encoding, and articulation itself. In addition, the speaker exerts some degree of output control, by monitoring of self-produced internal and overt speech. The core of the theory, ranging from lexical selection to the initiation of phonetic encoding, is captured in a computational model, called WEAVER++. Both the theory and the computational model have been developed in interaction with reaction time experiments, particularly in picture naming or related word production paradigms, with the aim of accounting for the real-time processing in normal word production. A comprehensive review of theory, model, and experiments is presented. The model can handle some of the main observations in the domain of speech errors (the major empirical domain for most other theories of lexical access) and the theory opens new ways of approaching the cerebral organization of speech production by way of high-temporal-resolution imaging.


title: Two-Phase Malicious Web Page Detection Scheme Using Misuse and Anomaly Detection

abstract: Misuse detection method and anomaly detection method are widely used for the detection of malicious web pages. Both are based on machine learning. Misuse detection can detect known malicious web pages, but it cannot detect new ones. In contrast, anomaly detection can detect unknown malicious web pages, but it has a high false positive rate. In order to achieve a high detection rate through precisely detecting known and unknown malicious web pages, we propose a two-phase detection scheme. In the first phase, the misuse detection model is built based on the C4.5 decision tree algorithm, which allows known malicious web pages to be detected. In the second phase, the anomaly detection model with a one-class support vector machine is used to detect new types of malicious web pages. The experimental results show that our proposed method has significantly higher malicious web page detection rate than conventional ones with the expense of slightly higher false positive rate.


title: Values, Attitudes Toward Interpersonal Violence, and Interpersonal Violent Behavior

abstract: The relevance of human values for the study of the motivational sources of interpersonal violent behavior was investigated in various fields of the social sciences. However, several past studies mixed up values with other dimensions like attitudes, norms, or beliefs, and only a few systematically assessed the effect of values on violent behavior relying on a value theory. Furthermore, in other studies violence was often analyzed as a composite index of different forms of delinquent behavior rather than as violence per se. In the current study we address these gaps in the literature by building upon Schwartz' theory of basic human values. We use it to explain attitudes toward interpersonal violence and interpersonal violent behavior. We analyze data of young people (n = 1,810) drawn from a German study in Duisburg, Germany, which assessed various types of self-reported violent behavior as well as values and attitudes toward violence. We test structural equation models in which we explain interpersonal violent behavior with basic human values, and where attitudes toward interpersonal violent behavior mediate this relation. Results show that self-transcendence and conservation values are associated negatively and power and stimulation values positively with interpersonal violent behavior. In addition, attitudes operate as a partial mediator for the former and as a full mediator for the latter in the relation between values and violent behavior. Despite a dominant association between attitudes and behavior, values themselves can significantly contribute to the explanation of violent behavior.


title: Dynamic recon fi guration of human brain functional networks through neurofeedback

abstract: Recent fMRI studies demonstrated that functional connectivity is altered following cognitive tasks (e.g., learning) or due to various neurological disorders. We tested whether real-time fMRI-based neurofeedback can be a tool to voluntarily reconfigure brain network interactions. To disentangle learning-related from regulation-related effects, we first trained participants to voluntarily regulate activity in the auditory cortex (training phase) and subsequently asked participants to exert learned voluntary self-regulation in the-absence of feedback (transfer phase without learning). Using independent component analysis (ICA), we found network reconfigurations (increases in functional network connectivity) during the neurofeedback training phase between the auditory target region and (I) the auditory pathway; (2) visual regions related to visual feedback processing; (3) insula related to introspection and self-regulation and (4) working memory and high-level visual attention areas related to cognitive effort. Interestingly, the auditory target region was identified as the hub of the reconfigured functional networks without a-priori assumptions. During the transfer phase, we again found specific functional connectivity reconfiguration between auditory and attention network confirming the specific effect of self-regulation on functional connectivity. Functional connectivity to working memory related networks was no longer altered consistent with the absent demand on working memory. We demonstrate that neurofeedback learning is mediated by widespread changes in functional connectivity. In contrast, applying learned self-regulation involves more limited and specific network changes in an auditory setup intended as a model for tinnitus. Hence, neurofeedback training might be used to promote recovery from neurological disorders that are linked to abnormal patterns of brain connectivity. (C) 2013 Elsevier Inc. All rights reserved.


title: Titan: a toolset that connects software architecture with quality analysis

abstract: In this tool demo, we will illustrate our tool---Titan---that supports a new architecture model: design rule spaces (DRSpaces). We will show how Titan can capture both architecture and evolutionary structure and help to bridge the gap between architecture and defect prediction. We will demo how to use our toolset to capture hundreds of buggy files into just a few architecturally related groups, and to reveal architecture issues that contribute to the error-proneness and change-proneness of these groups. Our tool has been used to analyze dozens of large-scale industrial projects, and has demonstrated its ability to provide valuable direction on which parts of the architecture are problematic, and on why, when, and how to refactor. The video demo of Titan can be found at https://art.cs.drexel.edu/~lx52/titan.mp4


title: An image-schematic account of spatial categories

abstract: How we categorize certain objects depends on the processes they afford: something is a vehicle because it affords transportation, a house because it offers shelter or a watercourse because water can flow in it. The hypothesis explored here is that image schemas (such as LINK, CONTAINER, SUPPORT, and PATH) capture abstractions that are essential to model affordances and, by implication, categories. To test the idea, I develop an algebraic theory formalizing image schemas and accounting for the role of affordances in categorizing spatial entities.


title: An Interaction Design Framework for Social Robots

abstract: We present a novel design framework enabling the development of social robotics applications by cross-disciplinary teams of programmers and interaction designers. By combining a modular back-end software architecture with an easy-to-use graphical interface for developing interaction sequences, this system enables programmers and designers to work in parallel to develop robot applications and tune the subtle details of social behaviors. In this paper, we describe the structure of our design framework, and we present an experimental evaluation of our system showing that it increases the effectiveness of programmerdesigner teams developing social robot applications. Keywords-human-robot interaction; software development; social robotics; programming interfaces;


title: Binarized Knowledge Graph Embeddings

abstract: Tensor factorization has become an increasingly popular approach to knowledge graph completion(KGC), which is the task of automatically predicting missing facts in a knowledge graph. However, even with a simple model like CANDECOMP/PARAFAC(CP) tensor decomposition, KGC on existing knowledge graphs is impractical in resource-limited environments, as a large amount of memory is required to store parameters represented as 32-bit or 64-bit floating point numbers. This limitation is expected to become more stringent as existing knowledge graphs, which are already huge, keep steadily growing in scale. To reduce the memory requirement, we present a method for binarizing the parameters of the CP tensor decomposition by introducing a quantization function to the optimization problem. This method replaces floating point-valued parameters with binary ones after training, which drastically reduces the model size at run time. We investigate the trade-off between the quality and size of tensor factorization models for several KGC benchmark datasets. In our experiments, the proposed method successfully reduced the model size by more than an order of magnitude while maintaining the task performance. Moreover, a fast score computation technique can be developed with bitwise operations.


title: Design of a 50000 rpm high-speed high-power six-phase PMSM for use in aircraft applications

abstract: This paper deals with the challenge of designing a high-speed permanent-magnet synchronous machine (PMSM) with a continuous power of 700 kW as a turbo generator for the usage in aircrafts. For this application, high-power density above 5kW/kg is required. To identify the best topology and to fulfil the given requirements, various design solutions for the PMSM are taken into account. This contains various six-phase winding configurations to minimize losses or weight. Moreover, a possible magnet and various sleeve materials are taken into account in order to ensure mechanical stability. Furthermore, a thermal design is presented. Results given are based on analytical, as well as FE simulations.


title: A Survey on Cryptography and Steganography Methods for Information Security

abstract: This paper deals with the tidings of cryptography in history, how it has played a vital role in World War -1, World War-2. It also deals with the Substitution, Transposition Cryptographic techniques and Steganography principles which can be used to maintain the confidentiality of computerized and none computerized information files. A number of well known techniques have been adapted for computer usage including the Ceaser cipher, Mono alphabetic cipher, Homophonic substitution, Bale cipher, Play fair cipher, Poly alphabetic cipher, Vigenere Cipher, Onetime pad cipher, Vernam ciphers, Play Color Cipher and usage of rotor machine in Substitutions, Rail fence technique, more complex permutations for more secure transposition and some Steganography principle were briefly discussed with merits and demerits. Finally it gives the broad knowledge on almost all the cryptographic and Steganography principles where a reader or scholar have lot of scope for updating or invention of more secure algorithms to fulfill the global needs in information security.


Comparison of Modern Stochastic Optimization Algorithms


title: Mapping coral reefs using consumer-grade drones and structure from motion photogrammetry techniques

abstract: We propose a novel technique to measure the small-scale three-dimensional features of a shallow-water coral reef using a small drone equipped with a consumer-grade camera, a handheld GPS and structure from motion (SfM) algorithms. We used a GoPro HERO4 with a modified lens mounted on a DJI Phantom 2 drone (maximum total take-off weight <2 kg) to perform a 10 min flight and collect 306 aerial images with an overlap equal or greater than 90%. We mapped an area of 8380 m2, obtaining as output an ortho-rectified aerial photomosaic and a bathymetric digital elevation model (DEM) with a resolution of 0.78 and 1.56 cm pixel−1, respectively. Through comparison with airborne LiDAR data for the same area, we verified that the location of the ortho-rectified aerial photomosaic is accurate within ~1.4 m. The bathymetric difference between our DEM and the LiDAR dataset is −0.016 ± 0.45 m (1σ). Our results show that it is possible, in conditions of calm waters, low winds and minimal sun glint, to deploy consumer-grade drones as a relatively low-cost and rapid survey technique to produce multispectral and bathymetric data on shallow-water coral reefs. We discuss the utility of such data to monitor temporal changes in topographic complexity of reefs and associated biological processes.


title: Gene-smoking interactions identify several novel blood pressure loci in the Framingham Heart Study.

abstract: BACKGROUND ::: Cardiovascular diseases are among the most significant health problems in the United States. Blood pressure (BP) variability has a genetic component, and most of the genetic variance remains to be identified. One promising strategy for gene discovery is genome-wide analysis of interactions between single nucleotide polymorphisms (SNPs) and environmental factors related to cardiovascular diseases. ::: ::: ::: METHODS ::: We investigated SNP-smoking interaction effects on BP in genome-wide data in 6,889 participants from the Framingham Heart Study. We performed the standard 1 degree of freedom (df) test of the interaction effect and the joint 2 df test of main and interaction effects. Three smoking measures were used: cigarettes per day (CPD), pack years of smoking, and smoking status. ::: ::: ::: RESULTS ::: We identified 7 significant and 21 suggestive BP loci. Identified through the joint 2 df test, significant SBP loci include: rs12149862 (P = 3.65×10(-9)) in CYB5B, rs2268365 (P = 4.85×10(-8)) in LRP2, rs133980 (P = 1.71×10(-8) with CPD and P = 1.07×10(-8) with pack-years) near MN1, and rs12634933 (P = 4.05×10(-8)) in MECOM. Through 1 df interaction analysis, 1 suggestive SBP locus at SNP rs8010717 near NRXN3 was identified using all 3 smoking measures (P = 3.27×10(-7) with CPD, P = 1.03×10(-7) with pack-years, and P = 1.19×10(-7) with smoking status). ::: ::: ::: CONCLUSIONS ::: Several of these BP loci are biologically plausible, providing physiological connection to BP regulation. Our study demonstrates that SNP-smoking interactions can enhance gene discovery and provide insight into novel pathways and mechanisms regulating BP.


title: Accuracy of Clinical Judgment of the Chin-Down Posture for Dysphagia During the Clinical/Bedside Assessment as Corroborated by Videofluoroscopy in Adults with Acute Stroke

abstract: Speech-language pathologists tend to rely on the noninstrumental swallowing evaluation in making recommendations about a patient's diet and management plan. The present study was designed to examine the sensitivity and specificity of the accuracy of using the chin-down posture during the clinical/bedside swallowing assessment. In 15 patients with acute stroke and clinically suspected oropharyngeal dysphagia, the correlation between clinical and videofluoroscopic findings was examined. Results identified that there is a difference in outcome prediction using the chin-down posture during the clinical/bedside assessment of swallowing compared to assessment by videofluoroscopy. Results are discussed relative to statistical and clinical perspectives, including site of lesion and factors to be considered in the design of an overall treatment plan for a patient with disordered swallowing.


title: Non-Newtonian Flow and Applied Rheology: Engineering Applications

abstract: This book bridges the gap between the theoretical work of the rheologist, and the practical needs of those who have to design and operate the systems in which these materials are handled or processed. It is an established and important reference for senior level mechanical engineers, chemical and process engineers, as well as any engineer or scientist who needs to study or work with these fluids, including pharmaceutical engineers, mineral processing engineers, medical researchers, water and civil engineers. This new edition covers a considerably broader range of topics than its predecessor, including computational fluid dynamics modeling techniques, liquid/solid flows and applications to areas such as food processing, among others.Written by two of the world's leading experts, this is the only dedicated non-Newtonian flow reference in print. Since first publication significant advances have been made in almost all areas covered in this book, which are incorporated in the new edition, including developments in CFD and computational techniques, velocity profiles in pipes, liquid/solid flows and applications to food processing, and new heat/mass transfer methods and models. This book covers both basic rheology and the fluid mechanics of NN fluids. It is a truly self-contained reference for anyone studying or working with the processing and handling of fluids.


Fuzzy logic in control systems: fuzzy logic controller. I


Overview of the Second BUCC Shared Task: Spotting Parallel Sentences in Comparable Corpora


title: Multi-Label Zero-Shot Learning via Concept Embedding

abstract: Zero Shot Learning (ZSL) enables a learning model to classify instances of an unseen class during training. While most research in ZSL focuses on single-label classification, few studies have been done in multi-label ZSL, where an instance is associated with a set of labels simultaneously, due to the difficulty in modeling complex semantics conveyed by a set of labels. In this paper, we propose a novel approach to multi-label ZSL via concept embedding learned from collections of public users' annotations of multimedia. Thanks to concept embedding, multi-label ZSL can be done by efficiently mapping an instance input features onto the concept embedding space in a similar manner used in single-label ZSL. Moreover, our semantic learning model is capable of embedding an out-of-vocabulary label by inferring its meaning from its co-occurring labels. Thus, our approach allows both seen and unseen labels during the concept embedding learning to be used in the aforementioned instance mapping, which makes multi-label ZSL more flexible and suitable for real applications. Experimental results of multi-label ZSL on images and music tracks suggest that our approach outperforms a state-of-the-art multi-label ZSL model and can deal with a scenario involving out-of-vocabulary labels without re-training the semantics learning model.


title: A Practical Template Attack on MICKEY-128 2.0 Using PSO Generated IVs and LS-SVM

abstract: The reported power analysis attacks on hardware implementations of the MICKEY family of streams ciphers require a large number of power traces. The primary motivation of our work is to break an implementation of the cipher when only a limited number of power traces can be acquired by an adversary. In this paper, we propose a novel approach to mount a Template attack (TA) on MICKEY-128 2.0 stream cipher using Particle Swarm Optimization (PSO) generated initialization vectors (IVs). In addition, we report the results of power analysis against a MICKEY-128 2.0 implementation on a SASEBO-GII board to demonstrate our proposed attack strategy. The captured power traces were analyzed using Least Squares Support Vector Machine (LS-SVM) learning algorithm based binary classifiers to segregate the power traces into the respective Hamming distance (HD) classes. The outcomes of the experiments reveal that our proposed power analysis attack strategy requires a much lesser number of IVs compared to a standard Correlation Power Analysis (CPA) attack on MICKEY-128 2.0 during the key loading phase of the cipher.


title: An Active Power Control System for Wind Turbines Capable of Primary and Secondary Frequency Control for Supporting Grid Reliability

abstract: Wind energy is producing a larger share of power on many utility grids as more wind turbines are installed, providing motivation for wind turbines to provide ancillary services that are necessary for power grid reliability. The ancillary services considered in this paper consist of actively controlling the power output of the wind turbines to track power set-point commands, participate in frequency regulation, and provide frequency response. This paper focuses on the development of a wind turbine control system that is capable of varying the turbine’s active power output upon receiving de-rating power set-point commands, manual power commands, and automatic frequency regulation commands to meet the system operators’ needs in below-rated and above-rated wind speeds. The turbine is de-rated by operating at a higher than optimal tip speed ratio, storing additional inertia in the rotor, which can be used to assist in frequency regulation by providing a primary response to fluctuations in the grid frequency.


title: Swarm Intelligence: From Natural to Artificial Systems

abstract: 1. Introduction 2. Ant Foraging Behavior, Combinatorial Optimization, and Routing in Communications Networks 3. Division of Labor and Task Allocation 4. Cemetery Organization, Brood Sorting, Data Analysis, and Graph Partitioning 5. Self-Organization and Templates: Application to Data Analysis and Graph Partitioning 6. Nest Building and Self-Assembling 7. Cooperative Transport by Insects and Robots 8. Epilogue


title: An Efficient Algorithm for Enumerating Chordless Cycles and Chordless Paths

abstract: A chordless cycle (induced cycle) $C$ of a graph is a cycle without any chord, meaning that there is no edge outside the cycle connecting two vertices of the cycle. A chordless path is defined similarly. In this paper, we consider the problems of enumerating chordless cycles/paths of a given graph $G=(V,E),$ and propose algorithms taking $O(|E|)$ time for each chordless cycle/path. In the existing studies, the problems had not been deeply studied in the theoretical computer science area, and no output polynomial time algorithm has been proposed. Our experiments showed that the computation time of our algorithms is constant per chordless cycle/path for non-dense random graphs and real-world graphs. They also show that the number of chordless cycles is much smaller than the number of cycles. We applied the algorithm to prediction of NMR (Nuclear Magnetic Resonance) spectra, and increased the accuracy of the prediction.


title: Influence of Different Positive Emotions on Persuasion Processing: A Functional Evolutionary Approach

abstract: Much research has found that positive affect facilitates increased reliance on heuristics in cognition. However, theories proposing distinct evolutionary fitness-enhancing functions for specific positive emotions also predict important differences among the consequences of different positive emotion states. Two experiments investigated how six positive emotions influenced the processing of persuasive messages. Using different methods to induce emotions and assess processing, we showed that the positive emotions of anticipatory enthusiasm, amusement, and attachment love tended to facilitate greater acceptance of weak persuasive messages (consistent with previous research), whereas the positive emotions of awe and nurturant love reduced persuasion by weak messages. In addition, a series of mediation analyses suggested that the effects distinguishing different positive emotions from a neutral control condition were best accounted for by different mediators rather than by one common mediator. These findings build upon approaches that link affective valence to certain types of processing, documenting emotion-specific effects on cognition that are consistent with functional evolutionary accounts of discrete positive emotions.


title: Hormone receptor-related gene polymorphisms and prostate cancer risk in North Indian population

abstract: The purpose of this study was to analyse the frequency and type of mutations in the coding region of androgen receptor (AR) and to determine the role of polymorphisms in the intron 1 of ERalpha, exon 5 of ERbeta, intron 7 of progesterone, exon 7 of the aromatase (CYP19) and exon 9 of VDR genes in the risk of prostate cancer. PCR-RFLP analysis of all above the genes was on 100 prostate cancer patients and an equal number of matching controls. The study also included PCR-SSCP analyses of exons 2-8 of AR gene. The genotype containing -/- allele of ERalpha gene was statistically significant for the risk of prostate cancer pose (OR, 2.70; 95% CI, 1.08-6.70, P = 0.032) Rr genotype of ERbeta gene also have a higher risk (OR, 1.65; 95% CI, 0.52-5.23) for prostate cancer. The Cys allele of CYP19 gene was also associated with statistically significant increased risk of prostate cancer (OR; 2.28, 95% CI, 1.20-4.35, P = 0.012). tt genotype of codon 352 of VDR gene showed an OR of 0.43 for (95% CI, 0.13-1.39) and an OR for Tt genotype was 0.65 (95% CI, 0.36-1.16). Taken together, the results showed that in North Indian population, ERalpha and CYP19 genes may be playing a role in the risk of prostate cancer.


title: Structural Comparison of Executable Objects

abstract: A method to heuristically construct an isomorphism between the sets of functions in two similar but differing versions of the same executable file is presented. Such an isomorphism has multiple practical applications, specifically the ability to detect programmatic changes between the two executable versions. Moreover, information (function names) which is available for one of the two versions can also be made available for the other . A framework implementing the described methods is presented, along with empirical data about its performance when used to analyze patches to recent security vulnerabilities. As a more practical example, a security update which fixes a critical vulnerability in an H.323 parsing component is analyzed, the relevant vulnerability extracted and the implications of the vulnerability and the fix discussed.


title: On the development of a compact sub-nanosecond tunable monocycle pulse transmitter for UWB applications

abstract: Development of a new sub-nanosecond monocycle pulse transmitter with tunable pulse duration for short-range low-power ultra-wideband radar and communication systems is presented along with detailed design and analysis. The developed pulse transmitter is simple, compact, and can be realized using planar or uniplanar integrated circuits. A novel RC coupling circuit along with a high driving current, provided by a high-speed amplifier and buffers, are used to obtain an increase in the output power. A decoupling circuit is implemented to reduce ringing on the monocycle pulse and provide necessary pulse clamping. Tuning of the output monocycle-pulse duration is achieved by using two distributed delay lines, coupled together by the decoupling network, each spatially loaded with antiparallel p-i-n diodes that are alternately switched on and off. Measurement results show tunable monocycle pulse durations in range of 0.4-1.2 ns, approximately corresponding to the operating frequency range of 0.15-3.7 GHz, and 200-400 mW of pulse peak power. The calculated and measured pulse durations also agree reasonably well


title: Assessing student satisfaction in an asynchronous e-learning environment

abstract: Electronic learning systems and virtual learning environments for education or training are increasingly gaining attention in educational organizations as well as in industry. An important dimension in the evaluation of a virtual learning environment is the assessment of e-learner satisfaction. Critical elements in the assessment procedure are the learner interface, the course content, the access and interaction with the learning community and the personalization of learning. These elements provide the guidelines for developing questionnaires assessing quantitatively e-learner satisfaction. Furthermore, qualitative data are also necessary to give a deeper insight in the causes of learner satisfaction and dissatisfaction. A measuring tool combining both quantitative and qualitative techniques for learner-satisfaction assessment is presented. The role of the teacher in establishing and promoting new learning styles within the emerging virtual learning environments is also acknowledged and is incorporated in the model as an additional construct which is related with learner satisfaction.


title: Evaluation of image stabilization algorithms

abstract: Several techniques for electronic image stabilization have been proposed, but very little research has been done to compare and evaluate such techniques. We propose a set of measures to evaluate image stabilization algorithms based of their fidelity, displacement range, and performance. These measures do not require calibration or ground truth, making the evaluation procedure very simple and flexible, i.e., it provides the means to compare techniques based on different motion models. We have used this procedure to compare several image stabilization algorithms and also evaluate the sensitivity of these algorithms to some of its parameters. These same procedures could also be used for the comparison and evaluation of motion estimation and image registration techniques.


title: BUAA-iCC at ImageCLEF 2015 Scalable Concept Image Annotation Challenge

abstract: In this working note, we mainly focus on the image anno- tation subtask of ImageCLEF 2015 challenge that BUAA-iCC research group participated. For this task, we flrstly explore textual similarity information between each test sample and predeflned concept. Subse- quently, two difierent kinds of semantic information are extracted from visual images: visual tags using generic object recognition classiflers and visual tags relevant to human being related concepts. For the former information, the visual tags are predicted by using deep convolutional neural network (CNN) and a set of support vector machines trained on ImageNet, and flnally transferred to textual information. For the latter visual information, human related concepts are extracted via face and facial attribute detection, and flnally transferred to similarity informa- tion by using manually designed mapping rules, in order to enhance the performance of annotating human related concepts. Meanwhile, a late fusion strategy is developed to incorporate aforementioned various kinds of similarity information. Results validate that the combination of the textual and visual similarity information and the adopted late fusion strategy could yield signiflcantly better performance.


Visualizing Ontologies with VOWL 1