title: Advances in Wireless Power Transfer Systems for Roadway-Powered Electric Vehicles

abstract: Roadway-powered electric vehicles (RPEVs) are attractive candidates for future transportation because they do not rely on large and heavy batteries but directly and efficiently get power while moving along a road. The inductive power transfer systems (IPTSs) that have been widely used for the wireless powering of RPEVs are reviewed in this paper. The development history of the IPTS is tracked from the origin of the RPEV in the 1890s to the recent RPEV. Throughout its 100-year history, the size, weight, efficiency, air gap, lateral tolerance, electromagnetic force, and cost of the IPTS have been substantially improved, and now RPEVs are becoming more widely commercialized. Important milestones of the developments of the IPTS and RPEVs are summarized in this paper, focusing on recent developments of on-line electric vehicles that were first commercialized in 2013.


McPAT 1 . 0 : An Integrated Power , Area , and Timing Modeling Framework for Multicore Architectures ∗


Word Sense Disambiguation with Recurrent Neural Networks


title: LPRNet: License Plate Recognition via Deep Neural Networks

abstract: This paper proposes LPRNet - end-to-end method for Automatic License Plate Recognition without preliminary character segmentation. Our approach is inspired by recent breakthroughs in Deep Neural Networks, and works in real-time with recognition accuracy up to 95% for Chinese license plates: 3 ms/plate on nVIDIA GeForce GTX 1080 and 1.3 ms/plate on Intel Core i7-6700K CPU. LPRNet consists of the lightweight Convolutional Neural Network, so it can be trained in end-to-end way. To the best of our knowledge, LPRNet is the first real-time License Plate Recognition system that does not use RNNs. As a result, the LPRNet algorithm may be used to create embedded solutions for LPR that feature high level accuracy even on challenging Chinese license plates.


title: Preparation and evaluation of dl-praeruptorin A microemulsion based hydrogel for dermal delivery.

abstract: AbstractThe purpose of the present investigation was to develop and optimize the microemulsion (ME) as a transdermal system for Pd-Ia, a poor water soluble and low bioavailable drug. The pseudo-ternary phase diagrams were constructed for various ME formulations including oleic acid as the oil phase, Cremophor RH40 as the surfactant, ethanol as the cosurfactant, and water. The maximum cumulative amount permeated through rat abdominal skins per unit area in 32 h (Q32), and the maximum flux were evaluated using the Franz diffusion cell in order to optimize the ME formulation. The results indicated that the optimized ME formulation was composed of oleic acid (5%, W/W), Cremophor RH40 (13.33%, W/W), ethanol (26.67%, W/W), and water (55%, W/W); the maximum cumulative amount of Pd-Ia was 354.330 ± 12.006 μg cm−2, the maximum flux was 11.467 ± 0.500 μg cm−2 h−1. ME-gel was administered transdermally to rats. The mean plasma concentration of Pd-Ia following transdermal application of ME-gel could be maintained for...


title: Multi-Language Identification Using Convolutional Recurrent Neural Network

abstract: Language Identification, being an important aspect of Automatic Speaker Recognition has had many changes and new approaches to ameliorate performance over the last decade. We compare the performance of using audio spectrum in the log scale and using Polyphonic sound sequences from raw audio samples to train the neural network and to classify speech as either English or Spanish. To achieve this, we use the novel approach of using a Convolutional Recurrent Neural Network using Long Short Term Memory (LSTM) or a Gated Recurrent Unit (GRU) for forward propagation of the neural network. Our hypothesis is that the performance of using polyphonic sound sequence as features and both LSTM and GRU as the gating mechanisms for the neural network outperform the traditional MFCC features using a unidirectional Deep Neural Network.


title: FigureSeer: Parsing Result-Figures in Research Papers

abstract: ‘Which are the pedestrian detectors that yield a precision above 95 % at 25 % recall?’ Answering such a complex query involves identifying and analyzing the results reported in figures within several research papers. Despite the availability of excellent academic search engines, retrieving such information poses a cumbersome challenge today as these systems have primarily focused on understanding the text content of scholarly documents. In this paper, we introduce FigureSeer, an end-to-end framework for parsing result-figures, that enables powerful search and retrieval of results in research papers. Our proposed approach automatically localizes figures from research papers, classifies them, and analyses the content of the result-figures. The key challenge in analyzing the figure content is the extraction of the plotted data and its association with the legend entries. We address this challenge by formulating a novel graph-based reasoning approach using a CNN-based similarity metric. We present a thorough evaluation on a real-word annotated dataset to demonstrate the efficacy of our approach.


title: Event-based control as a cloud service

abstract: Event-based control has gained significant interest from the research community in recent years because it allows better resource utilization in networked control systems. In this paper, we propose an architecture for offering event-based control as a service from the cloud, which not only improves resource utilization but also reduces the cost and setup time of large-scale industrial automation systems. Providing event-based control from the cloud, however, poses multiple research challenges. We address two of the main challenges, which are the network delays and failures introduced because of moving the controller far away from the plant. We present a delay mitigation technique that maintains the stability and performance of the control system. Our results using commercial clouds show that our delay mitigation technique can handle large communication delays up to several seconds with practically zero effect on the main performance metrics of the system. Moreover, our proposed fault tolerance approach can effectively handle network failures even if the controlled system is thousands of miles away from its cloud controllers.


title: MARGOT: A web server for argumentation mining

abstract: Abstract Argumentation mining is a recent challenge concerning the automatic extraction of arguments from unstructured textual corpora. Argumentation mining technologies are rapidly evolving and show a clear potential for application in diverse areas such as recommender systems, policy-making and the legal domain. There is a long-recognised need for tools that enable users to browse, visualise, search, and manipulate arguments and argument structures. There is, however, a lack of widely accessible tools. In this article we describe the technology behind MARGOT, the first online argumentation mining system designed to reach out to the wider community of potential users of these new technologies. We evaluate its performance and discuss its possible application in the analysis of content from various domains.


title: Platelet-rich fibrin in the treatment of localized gingival recessions: a split-mouth randomized clinical trial

abstract: Objectives ::: The aim of this study is to evaluate the clinical efficacy of platelet-rich fibrin (PRF) in combination with coronally advanced flap (CAF) in the treatment of localized gingival recessions.


title: Can mindfulness influence weight management related eating behaviors? If so, how?

abstract: Mindfulness is increasingly being used for weight management. However, the strength of the evidence for such an approach is unclear; although mindfulness-based weight management programs have had some success, it is difficult to conclude that the mindfulness components were responsible. Research in this area is further complicated by the fact that the term ‘mindfulness’ is used to refer to a range of different practices. Additionally, we have little understanding of the mechanisms by which mindfulness might exert its effects. This review addresses these issues by examining research that has looked at the independent effects of mindfulness and mindfulness-related strategies on weight loss and weight management related eating behaviors. As well as looking at evidence for effects, the review also considers whether effects may vary with different types of strategy, and the kinds of mechanisms that may be responsible for any change. It is concluded that there is some evidence to support the effects of (a) present moment awareness, when applied to the sensory properties of food, and (b) decentering. However, research in these areas has yet to be examined in a controlled manner in relation to weight management.


title: Thermoelectric generator emulator for MPPT testing

abstract: This paper presents a novel approach to use a DC power supply as a thermoelectric generator (TEG) emulator to perform static and dynamic maximum power point tracking (MPPT). First, the electrical characterization of a calcium-manganese-oxide module is performed on a TEG test rig. Afterwards, the experimentally obtained I–V characteristics of the TEG are emulated using a programmable solar array simulator (SAS). The detailed implementation procedure of the TEG emulator is described. In this work, the SAS has been successfully used to test static and dynamic efficiencies of the MPPT algorithm. For dynamic testing, a trapezoidal temperature profile with 1 °C/s slope is used as equivalent to a realistic profile for a TEG integrated into a stove.


title: Graph Embedding Based Recommendation Techniques on the Knowledge Graph

abstract: This paper presents a novel, graph embedding based recommendation technique. The method operates on the knowledge graph, an information representation technique alloying content-based and collaborative information. To generate recommendations, a two dimensional embedding is developed for the knowledge graph. As the embedding maps the users and the items to the same vector space, the recommendations are then calculated on a spatial basis. Regarding to the number of cold start cases, precision, recall, normalized Cumulative Discounted Gain and computational resource need, the evaluation shows that the introduced technique delivers a higher performance compared to collaborative filtering on top-n recommendation lists. Our further finding is that graph embedding based methods show a more stable performance in the case of an increasing amount of user preference information compared to the benchmark method.


Microwave and Millimeter-Wave Technologies and Their Applications SIW-Like Guided Wave Structures and Applications


title: Flexible Developable Surfaces

abstract: We introduce a discrete paradigm for developable surface modeling. Unlike previous attempts at interactive developable surface modeling, our system is able to enforce exact developability at every step, ensuring that users do not inadvertently suggest configurations that leave the manifold of admissible folds of a flat two-dimensional sheet. With methods for navigation of this highly nonlinear constraint space in place, we show how to formulate a discrete mean curvature bending energy measuring how far a given discrete developable surface is from being flat. This energy enables relaxation of user-generated configurations and suggests a straightforward subdivision scheme that produces admissible smoothed versions of bent regions of our discrete developable surfaces. © 2012 Wiley Periodicals, Inc.


Effects of music type on reading comprehension performance and other physiological factors


title: Feature extraction for lesion margin characteristic classification from CT Scan lungs image

abstract: Lung cancer is one of the common cancer which occurred in both male and female. Revealed by WHO data, in 2012, this disease become one of the major cause of death in worldwide with the mortality rate about 1.59 million. An early detection of lung cancer by using Computed Tomography (CT) Scan can provide more opportunity to survive. However, the diagnosis of lung cancer by reading the CT scan image which performed by radiologists may lead to an error. A computer-based digital image processing is a solution to improve the accuracy and consistency in reading the CT Scan image result. This study aim is to identify the morphological characteristic of regular and irregular margins by using feature extraction method. In this research, image processing divided into several stages refer to the segmentation process with Otsu method, feature extraction with number of features such as convexity, solidity, circularity, and compactness, and the last is classification by using Multi Layer Perceptron (MLP). The classification process of features convexity, solidity, circularity, and compactness, resulted in the accuracy value of 85%, sensitivity of 85%, and specificity of 85%.


title: Humanoid Multimodal Tactile-Sensing Modules

abstract: In this paper, we present a new generation of active tactile modules (i.e., HEX-O-SKIN), which are developed in order to approach multimodal whole-body-touch sensation for humanoid robots. To better perform like humans, humanoid robots need the variety of different sensory modalities in order to interact with their environment. This calls for certain robustness and fault tolerance as well as an intelligent solution to connect the different sensory modalities to the robot. Each HEX-O-SKIN is a small hexagonal printed circuit board equipped with multiple discrete sensors for temperature, acceleration, and proximity. With these sensors, we emulate the human sense of temperature, vibration, and light touch. Off-the-shelf sensors were utilized to speed up our development cycle; however, in general, we can easily extend our design with new discrete sensors, thereby making it flexible for further exploration. A local controller on each HEX-O-SKIN preprocesses the sensor signals and actively routes data through a network of modules toward the closest PC connection. Local processing decreases the necessary network and high-level processing bandwidth, while a local analog-to-digital conversion and digital-data transfers are less sensitive to electromagnetic interference. With an active data-routing scheme, it is also possible to reroute the data around broken connections-yielding robustness throughout the global structure while minimizing wirings. To support our approach, multiple HEX-O-SKIN are embedded into a rapid-prototyped elastomer skin material and redundantly connected to neighboring modules by just four ports. The wiring complexity is shifted to each HEX-O-SKIN such that a power and data connection between two modules is reduced to four noncrossing wires. Thus, only a very simple robot-specific base frame is needed to support and wire the HEX-O-SKIN to a robot. The potential of our multimodal sensor modules is demonstrated experimentally on a robot platform.


title: Factors Determining Quality of Care in Family Planning Services in Africa: A Systematic Review of Mixed Evidence

abstract: Background ::: Improving use of family planning services is key to improving maternal health in Africa, and provision of quality of care in family planning services is critical to support higher levels of contraceptive uptake. The objective of this systematic review was to synthesize the available evidence on factors determining the quality of care in family planning services in Africa. ::: Methods ::: Quantitative and qualitative studies undertaken in Africa, published in English, in grey and commercial literature, between 1990 and 2015 were considered. Methodological quality of included studies was assessed using standardized tools. Findings from the quantitative studies were summarized using narrative and tables. Client satisfaction was used to assess the quality of care in family planning services in the quantitative component of the review. Meta-aggregation was used to synthesize the qualitative study findings. ::: Results ::: From 4334 records, 11 studies (eight quantitative, three qualitative) met the review eligibility criteria. The review found that quality of care was influenced by client, provider and facility factors, and structural and process aspects of the facilities. Client’s waiting time, provider competency, provision/prescription of injectable methods, maintaining privacy and confidentiality were the most commonly identified process factors. The quality of stock inventory was the most commonly identified structural factor. The quality of care was also positively associated with privately-owned facilities. The qualitative synthesis revealed additional factors including access related factors such as ‘pre-requisites to be fulfilled by the clients and cost of services, provider workload, and providers’ behaviour. ::: Conclusion ::: There is limited evidence on factors determining quality of care in family planning services in Africa that shows quality of care is influenced by multiple factors. The evidence suggests that lowering access barriers and avoiding unnecessary pre-requisites for taking contraceptive methods are important to improve the quality of care in family planning services. Strategies to improve provider behavior and competency are important. Moreover, strategies that minimize client waiting time and ensure client confidentiality should be implemented to ensure quality of care in family planning services. However, no strong evidence based conclusions and recommendations may be drawn from the evidence. Future studies are needed to identify the most important factors associated with quality of care in family planning services in a wider range of African countries.


title: Quantitative Methods: Triangulation in Action*

abstract: December 1979, volume 24 There is a distinct tradition in the literature on social science research methods that advocates the use of multiple methods. This form of research strategy is usually described as one of convergent methodology, multimethod/multitrait (Campbell and Fiske, 1959), convergent validation or, what has been called "triangulation" (Webb et al., 1 966). These various notions share the conception that qualitative and quantitative methods should be viewed as complementary rather than as rival camps. In fact, most textbooks underscore the desirability of mixing methods given the strengths and weaknesses found in single method designs.


title: Community-based classification of noun phrases in twitter

abstract: Many event monitoring systems rely on counting known keywords in streaming text data to detect sudden spikes in frequency. But the dynamic and conversational nature of Twitter makes it hard to select known keywords for monitoring. Here we consider a method of automatically finding noun phrases (NPs) as keywords for event monitoring in Twitter. Finding NPs has two aspects, identifying the boundaries for the subsequence of words which represent the NP, and classifying the NP to a specific broad category such as politics, sports, etc. To classify an NP, we define the feature vector for the NP using not just the words but also the author's behavior and social activities. Our results show that we can classify many NPs by using a sample of training data from a knowledge-base.


Changes in the level of fetal DNA in blood serum of women with non-developing pregnancy


title: Fast Spectral Clustering With Anchor Graph for Large Hyperspectral Images

abstract: The large-scale hyperspectral image (HSI) clustering problem has attracted significant attention in the field of remote sensing. Most traditional graph-based clustering methods still face challenges in the successful application of the large-scale HSI clustering problem mainly due to their high computational complexity. In this letter, we propose a novel approach, called fast spectral clustering with anchor graph (FSCAG), to efficiently deal with the large-scale HSI clustering problem. Specifically, we consider the spectral and spatial properties of HSI in the anchor graph construction. The proposed FSCAG algorithm first constructs anchor graph and then performs spectral analysis on the graph. With this, the computational complexity can be reduced to $O(ndm)$ , which is a significant improvement compared to conventional graph-based clustering methods that need at least $O(n^{2}d)$ , where $n$ , $d$ , and $m$ are the number of samples, features, and anchors, respectively. Several experiments are conducted to demonstrate the efficiency and effectiveness of the proposed FSCAG algorithm.


title: A compact six-order folded-waveguide resonator filter

abstract: A new compact 6-order bandpass filter design based on a folded waveguide (FWG) resonator structure is presented. The ridge waveguide is an important transmission line for broadband communication systems. This reduces the size of the filter. Then fold this filter along the center coordinates. It has no coupling or tuning screws. A full-wave simulator has been employed for designing the filter. The filter expands the 3dB fractional bandwidth to 8.2% at the center frequency of 3.5 GHz. This filter also provides an improved selectivity characteristic with compact size compared with the conventional resonator filters.


title: A dual circular polarized K/Ka-band feed chain for satellite communication applications

abstract: This paper presents a dual circular polarized feed system, which comprises an enhanced multiport turnstile junction and a corrugated circular horn with high aperture efficiency. The feed system operates in the K/Ka bands with polarization diversity, achieving both right- and left-hand circular polarizations, which is particularly suitable in space reflector antennas for satellite communications applications. The feed system is designed, manufactured and measured. Measured results are in very good agreement with simulated ones. The results show that in both K- and Ka-bands, the return losses, LHCP to RHCP isolations, port isolations, and cross polarization levels are better than 25 dB, 22 dB, 58 dB, and 32dB, respectively.


title: Dynamic Prediction Length for Time Series with Sequence to Sequence Networks

abstract: Recurrent neural networks and sequence to sequence models require a predetermined length for prediction output length. Our model addresses this by allowing the network to predict a variable length output in inference. A new loss function with a tailored gradient computation is developed that trades off prediction accuracy and output length. The model utilizes a function to determine whether a particular output at a time should be evaluated or not given a predetermined threshold. We evaluate the model on the problem of predicting the prices of securities. We find that the model makes longer predictions for more stable securities and it naturally balances prediction accuracy and length.


title: Combination of 16S rRNA-targeted oligonucleotide probes with flow cytometry for analyzing mixed microbial populations.

abstract: Fluorescent oligonucleotide hybridization probes were used to label bacterial cells for analysis by flow cytometry. The probes, complementary to short sequence elements within the 16S rRNA common to phylogenetically coherent assemblages of microorganisms, were labeled with tetramethylrhodamine and hybridized to suspensions of fixed cells. Flow cytometry was used to resolve individual target and nontarget bacteria (1 to 5 microns) via probe-conferred fluorescence. Target cells were quantified in an excess of nontarget cells. The intensity of fluorescence was increased additively by the combined use of two or three fluorescent probes complementary to different regions of the same 16S rRNA.


title: The Numerics of GANs

abstract: In this paper, we analyze the numerics of common algorithms for training Generative Adversarial Networks (GANs). Using the formalism of smooth two-player games we analyze the associated gradient vector field of GAN training objectives. Our findings suggest that the convergence of current algorithms suffers due to two factors: i) presence of eigenvalues of the Jacobian of the gradient vector field with zero real-part, and ii) eigenvalues with big imaginary part. Using these findings, we design a new algorithm that overcomes some of these limitations and has better convergence properties. Experimentally, we demonstrate its superiority on training common GAN architectures and show convergence on GAN architectures that are known to be notoriously hard to train.


title: Procrastination, conscientiousness, anxiety, and goals: Exploring the measurement and correlates of procrastination among school-aged children

abstract: We explore the reliability and validity of a self-report measure of procrastination and conscientiousness designed for use with third- to fifth-grade students. The responses of 120 students are compared with teacher and parent ratings of the student. Confirmatory and exploratory factor analyses were also used to examine the structure of the scale. Procrastination and conscientiousness are highly correlated (inversely); evidence suggests that procrastination and conscientiousness are aspects of the same construct. Procrastination and conscientiousness are correlated with the Physiological Anxiety subscale of the Revised Children’s Manifest Anxiety Scale, and with the Task (Mastery) and Avoidance (Task Aversiveness) subscales of Skaalvik’s (1997) Goal Orientation Scales. Both theoretical implications and implications for interventions are discussed. © 2002 Wiley Periodicals, Inc.


title: A high precision micropower operational amplifier

abstract: An operational amplifier configuration implemented as a true micropower high precision op amp is described. It includes a well controlled and predictable DC biasing network that is insensitive to variations in temperature, supply voltages, and process. Also, it permits single supply operation. Excellent DC precision characteristics, comparable to or better than the very best precision op amps currently available, are realized yet at micropower levels. By simply increasing the biasing currents, a version of this design operates in general purpose applications without any degradation in its high precision characteristics. Thus, the AC performance levels of general purpose op amps are attained at a fraction of supply current. This device is fabricated using a standard bipolar IC process; an ion-implanted JFET is added to simplify biasing.


title: Predictive control of 5 DOF robot arm of autonomous mobile robotic system motion control employing mathematical model of the robot arm dynamics

abstract: The paper deals with a design of model predictive control (MPC) as an example of the advanced local motion control of articulated robot arms in the scope of manipulation operations within the intradepartmental transportation among workplaces. Initially, the use of articulated robotic arms as a part of mobile robotic systems is discussed. Then, the convenient composition of mathematical models of kinematics and dynamics of the aforementioned robot arms is introduced. Thereafter, MPC design is explained. The proposed theoretical methods of the mathematical modeling and control design are demonstrated by the simulation of the 5 degrees of freedom robot arm composed of drive, joint and arm modules of the Schunk Co.


title: The effect of periodontitis on biting abilities

abstract: Background: The aim of this study was to compare the biting abilities (bite force [BF] and occlusal contact area [OCA]) of chronic periodontitis patients with those of control patients with healthy periodontia by using pressure-sensitive sheets. Methods: The study population consisted of 20 patients with chronic periodontitis whose initial periodontal therapy was completed and 20 control subjects with healthy periodontia. Periodontitis was diagnosed by radiographs and measurements of probing depth and clinical attachment level. Pressure-sensitive sheets were used for the quantitative analysis of BF and OCA. Results: Both mean BF and OCA values were greater in the healthy control group compared to those of the study group. The differences between the groups were statistically significant at the 95% confidence level. Conclusion: Reduced periodontal support in the absence of inflammation negatively affected biting abilities.


title: Developing Age and Gender Predictive Lexica over Social Media

abstract: Demographic lexica have potential for widespread use in social science, economic, and business applications. We derive predictive lexica (words and weights) for age and gender using regression and classification models from word usage in Facebook, blog, and Twitter data with associated demographic labels. The lexica, made publicly available,1 achieved state-of-the-art accuracy in language based age and gender prediction over Facebook and Twitter, and were evaluated for generalization across social media genres as well as in limited message situations.


title: Techniques for visualizing LSTMs applied to electrocardiograms.

abstract: This paper explores four different visualization techniques for long short-term memory (LSTM) networks applied to continuous-valued time series. On the datasets analysed, we find that the best visualization technique is to learn an input deletion mask that optimally reduces the true class score. With a specific focus on single-lead electrocardiograms from the MIT-BIH arrhythmia dataset, we show that salient input features for the LSTM classifier align well with medical theory.


title: Improved phase unwrapping algorithm based on NVIDIA CUDA

abstract: The paper presents an improved version of the region-growing algorithm for the interferometric phase unwrapping of the radar images. A method for packing/unpacking indices of points in a photograph that is based on bitwise operations is developed. A fast algorithm for generating a growth ring of target pixels and an algorithm for merging regions are created. Software implementation of the algorithm can be run on platforms using graphics devices that support the NVIDIA CUDA technology.


title: Could mindfulness decrease anger, hostility, and aggression by decreasing rumination?

abstract: Research suggests that rumination increases anger and aggression. Mindfulness, or present-focused and intentional awareness, may counteract rumination. Using structural equation modeling, we examined the relations between mindfulness, rumination, and aggression. In a pair of studies, we found a pattern of correlations consistent with rumination partially mediating a causal link between mindfulness and hostility, anger, and verbal aggression. The pattern was not consistent with rumination mediating the association between mindfulness and physical aggression. Although it is impossible with the current nonexperimental data to test causal mediation, these correlations support the idea that mindfulness could reduce rumination, which in turn could reduce aggression. These results suggest that longitudinal work and experimental manipulations mindfulness would be worthwhile approaches for further study of rumination and aggression. We discuss possible implications of these results. Language: en


title: Extensions of error exponent analysis in hypothesis testing

abstract: The classical characterization of achievable error exponents in binary hypothesis testing is generalized in two different directions. First, in M-ary hypothesis testing, the tradeoff of all M(M - 1) types of error exponents and corresponding optimal decision schemes are explored. Then, motivated by a power-constrained distributed detection scenario, binary hypothesis testing is revisited, and the tradeoff of power consumption versus error exponents is fully characterized. In the latter scenario, sensors are allowed to make random decisions as to whether they should remain silent and save power, or transmit and improve detection quality. It is then shown by an example that optimal sensor decisions may indeed be random


title: Local feature view clustering for 3D object recognition

abstract: There have been important recent advances in object recognition through the matching of invariant local image features. However, the existing approaches are based on matching to individual training images. This paper presents a method for combining multiple images of a 3D object into a single model representation. This provides for recognition of 3D objects from any viewpoint, the generalization of models to non-rigid changes, and improved robustness through the combination of features acquired under a range of imaging conditions. The decision of whether to cluster a training image into an existing view representation or to treat it as a new view is based on the geometric accuracy of the match to previous model views. A new probabilistic model is developed to reduce the false positive matches that would otherwise arise due to loosened geometric constraints on matching 3D and non-rigid models. A system has been developed based on these approaches that is able to robustly recognize 3D objects in cluttered natural images in sub-second times.


title: RFID-based location tracking system using a RSS and DA

abstract: In recent years, the Global Positioning System (GPS) explosive growth in outdoor location tracking. Other hand, another interest in developing indoor location tracking systems. Additionally in radio frequency identification (RFID) technology make it a precise technology for use in indoor location tracking systems. In this paper, we present an RFID-based location tracking system using the Received Signal Strength and Directional Antenna (RSS and DA) architecture, which can provide more flexibility for system implementation and cost-effectiveness for system maintenance.


title: Evaluation of machine learning-based information extraction algorithms: criticisms and recommendations

abstract: We survey the evaluation methodology adopted in information extraction (IE), as defined in a few different efforts applying machine learning (ML) to IE. We identify a number of critical issues that hamper comparison of the results obtained by different researchers. Some of these issues are common to other NLP-related tasks: e.g., the difficulty of exactly identifying the effects on performance of the data (sample selection and sample size), of the domain theory (features selected), and of algorithm parameter settings. Some issues are specific to IE: how leniently to assess inexact identification of filler boundaries, the possibility of multiple fillers for a slot, and how the counting is performed. We argue that, when specifying an IE task, these issues should be explicitly addressed, and a number of methodological characteristics should be clearly defined. To empirically verify the practical impact of the issues mentioned above, we perform a survey of the results of different algorithms when applied to a few standard datasets. The survey shows a serious lack of consensus on these issues, which makes it difficult to draw firm conclusions on a comparative evaluation of the algorithms. Our aim is to elaborate a clear and detailed experimental methodology and propose it to the IE community. Widespread agreement on this proposal should lead to future IE comparative evaluations that are fair and reliable. To demonstrate the way the methodology is to be applied we have organized and run a comparative evaluation of ML-based IE systems (the Pascal Challenge on ML-based IE) where the principles described in this article are put into practice. In this article we describe the proposed methodology and its motivations. The Pascal evaluation is then described and its results presented.


title: An empirical evaluation of test case filtering techniques based on exercising complex information flows

abstract: Some software defects trigger failures only when certain complex information flows occur within the software. Profiling and analyzing such flows therefore provides a potentially important basis for filtering test cases. We report the results of an empirical evaluation of several test case filtering techniques that are based on exercising complex information flows. Both coverage-based and profile-distribution-based filtering techniques are considered. They are compared to filtering techniques based on exercising basic blocks, branches, function calls, and def-use pairs, with respect to their effectiveness for revealing defects.


Sensor network localization via received signal strength measurements with directional antennas


title: Backward path following control of an articulated vehicle

abstract: Recently, the need for autonomous or highly functional agricultural equipment has increased because of fewer and aging laborers involved in farming. Vehicles are used to pull trailers when picking fruits in an orchard. It has to be driven in reverse when being parked in a garage and when changing its course to prevent it from running into people or other such obstacles. However, precise control of its movement in reverse is very difficult for an unskilled driver. In this study, we propose a new backward path following control method for an unmanned ground vehicle with a trailer for operation in an orchard. The experiments confirmed the effectiveness of our proposed method.


title: Development of Acute Megakaryoblastic Leukemia with Isochromosome (12p) after a Primary Mediastinal Germ Cell Tumor in Korea

abstract: The association of hematological malignancies with a mediastinal germ cell tumor (GCT) is very rare. We report one case of a young adult male with primary mediastinal GCT who subsequently developed acute megakaryoblastic leukemia involving isochromosome (12p). A 25-yr-old man had been diagnosed with a mediastinal GCT and underwent surgical resection and adjuvant chemotherapy. At 1 week after the last cycle of chemotherapy, his peripheral blood showed leukocytosis with blasts. A bone marrow study confirmed the acute megakaryoblastic leukemia. A cytogenetic study revealed a complex karyotype with i(12p). Although additional chemotherapy was administered, the patient could not attain remission and died of septic shock. This case was definitely distinct from therapy-related secondary leukemia in terms of clinical, morphologic, and cytogenetic features. To our knowledge, this is the first case report of a patient with mediastinal GCT subsequently developing acute megakaryoblastic leukemia involving i(12p) in Korea.


title: Sim-to-Real Transfer Learning using Robustified Controllers in Robotic Tasks involving Complex Dynamics

abstract: Learning robot tasks or controllers using deep reinforcement learning has been proven effective in simulations. Learning in simulation has several advantages. For example, one can fully control the simulated environment, including halting motions while performing computations. Another advantage when robots are involved, is that the amount of time a robot is occupied learning a task—rather than being productive—can be reduced by transferring the learned task to the real robot. Transfer learning requires some amount of fine-tuning on the real robot. For tasks which involve complex (non-linear) dynamics, the fine-tuning itself may take a substantial amount of time. In order to reduce the amount of fine-tuning we propose to learn robustified controllers in simulation. Robustified controllers are learned by exploiting the ability to change simulation parameters (both appearance and dynamics) for successive training episodes. An additional benefit for this approach is that it alleviates the precise determination of physics parameters for the simulator, which is a non-trivial task. We demonstrate our proposed approach on a real setup in which a robot aims to solve a maze game, which involves complex dynamics due to static friction and potentially large accelerations. We show that the amount of fine-tuning in transfer learning for a robustified controller is substantially reduced compared to a non-robustified controller.


title: Zero-Shot Cross-lingual Classification Using Multilingual Neural Machine Translation

abstract: Transferring representations from large supervised tasks to downstream tasks has shown promising results in AI fields such as Computer Vision and Natural Language Processing (NLP). In parallel, the recent progress in Machine Translation (MT) has enabled one to train multilingual Neural MT (NMT) systems that can translate between multiple languages and are also capable of performing zero-shot translation. However, little attention has been paid to leveraging representations learned by a multilingual NMT system to enable zero-shot multilinguality in other NLP tasks. In this paper, we demonstrate a simple framework, a multilingual Encoder-Classifier, for cross-lingual transfer learning by reusing the encoder from a multilingual NMT system and stitching it with a task-specific classifier component. Our proposed model achieves significant improvements in the English setup on three benchmark tasks - Amazon Reviews, SST and SNLI. Further, our system can perform classification in a new language for which no classification data was seen during training, showing that zero-shot classification is possible and remarkably competitive. In order to understand the underlying factors contributing to this finding, we conducted a series of analyses on the effect of the shared vocabulary, the training data type for NMT, classifier complexity, encoder representation power, and model generalization on zero-shot performance. Our results provide strong evidence that the representations learned from multilingual NMT systems are widely applicable across languages and tasks.


Data Analytics on Consumer Behavior in Omni-channel Retail Banking, Card and Payment Services


title: Context-Aware Single-Shot Detector

abstract: SSD is one of the state-of-the-art object detection algorithms, and it combines high detection accuracy with real-time speed. However, it is widely recognized that SSD is less accurate in detecting small objects compared to large objects, because it ignores the context from outside the proposal boxes. In this paper, we present CSSD--a shorthand for context-aware single-shot multibox object detector. CSSD is built on top of SSD, with additional layers modeling multi-scale contexts. We describe two variants of CSSD, which differ in their context layers, using dilated convolution layers (DiCSSD) and deconvolution layers (DeCSSD) respectively. The experimental results show that the multi-scale context modeling significantly improves the detection accuracy. In addition, we study the relationship between effective receptive fields (ERFs) and the theoretical receptive fields (TRFs), particularly on a VGGNet. The empirical results further strengthen our conclusion that SSD coupled with context layers achieves better detection results especially for small objects ($+3.2\% {\rm AP}_{@0.5}$ on MS-COCO compared to the newest SSD), while maintaining comparable runtime performance.


title: Measurement of human service staff satisfaction: Development of the Job Satisfaction Survey

abstract: The development of the Job Satisfaction Survey (JSS), a nine-subscale measure of employee job satisfaction applicable specifically to human service, public, and nonprofit sector organizations, is described. The item selection, item analysis, and determination of the final 36-item scale are also described, and data on reliability and validity and the instrument's norms are summarized. Included are a multitrait-multimethod analysis of the JSS and the Job Descriptive Index (JDI), factor analysis of the JSS, and scale intercorrelations. Correlation of JSS scores with criteria of employee perceptions and behaviors for multiple samples were consistent with findings involving other satisfaction scales and with findings from the private sector. The strongest correlations were with perceptions of the job and supervisor, intention of quitting, and organizational commitment. More modest correlations were found with salary, age, level, absenteeism, and turnover.


title: A Time, Energy, and Area Efficient Domain Wall Memory-Based SPM for Embedded Systems

abstract: Applications that run in the embedded systems normally should be finished within a timing constraint in energy-efficient fashion. Due to these two requirements, the embedded systems often employ software-controlled scratch pad memory (SPM) instead of hardware-controlled cache as their on-chip memory. The data accesses in SPMs are controlled purely by the software, which provides better time-predictability and precise time-control. In this paper, we propose a time, energy, and area efficient domain wall memory (DWM)-based SPM for embedded systems. To efficiently manage this type of novel SPM, an integer nonlinear programming formulation and the instructions group schedule algorithm are proposed to generate memory access instruction scheduling and data placement. In addition, the longest move reduce algorithm is also proposed to configure different types of DWM memory cells to achieve minimal area size. Experimental results show that the proposed techniques can generate a configuration of DWM-based SPM with minimal area size while satisfying time constraint.


title: Self-calibrating multi-sensor fusion with probabilistic measurement validation for seamless sensor switching on a UAV

abstract: Fusing data from multiple sensors on-board a mobile platform can significantly augment its state estimation abilities and enable autonomous traversals of different domains by adapting to changing signal availabilities. However, due to the need for accurate calibration and initialization of the sensor ensemble as well as coping with erroneous measurements that are acquired at different rates with various delays, multi-sensor fusion still remains a challenge. In this paper, we introduce a novel multi-sensor fusion approach for agile aerial vehicles that allows for measurement validation and seamless switching between sensors based on statistical signal quality analysis. Moreover, it is capable of self-initialization of its extrinsic sensor states. These initialized states are maintained in the framework such that the system can continuously self-calibrate. We implement this framework on-board a small aerial vehicle and demonstrate the effectiveness of the above capabilities on real data. As an example, we fuse GPS data, ultra-wideband (UWB) range measurements, visual pose estimates, and IMU data. Our experiments demonstrate that our system is able to seamlessly filter and switch between different sensors modalities during run time.


title: Space Robotics—DLR's Telerobotic Concepts, Lightweight Arms and Articulated Hands

abstract: The paper briefly outlines DLR's experience with real space robot missions (ROTEX and ETS VII). It then discusses forthcoming projects, e.g., free-flying systems in low or geostationary orbit and robot systems around the space station ISS, where the telerobotic system MARCO might represent a common baseline. Finally it describes our efforts in developing a new generation of “mechatronic” ultra-light weight arms with multifingered hands. The third arm generation is operable now (approaching present-day technical limits). In a similar way DLR's four-fingered hand II was a big step towards higher reliability and yet better performance. Artificial robonauts for space are a central goal now for the Europeans as well as for NASA, and the first verification tests of DLR's joint components are supposed to fly already end of 93 on the space station.


title: Neural correlates of written emotion word processing: A review of recent electrophysiological and hemodynamic neuroimaging studies

abstract: A growing body of literature investigating the neural correlates of emotion word processing has emerged in recent years. Written words have been shown to represent a suitable means to study emotion processing and most importantly to address the distinct and interactive contributions of the two dimensions of emotion: valence and arousal. The aim of the present review is to integrate findings from electrophysiological (ERP) and hemodynamic neuroimaging (fMRI) studies in order to provide a better understanding of emotion word processing. It provides an up-to-date review of recent ERP studies since the review by Kissler et al. (2006) as well as the first review of hemodynamic brain imaging studies in the field. A discussion of theoretical and methodological issues is also presented, along with suggestions for future research.


title: Beyond Tree Structure Models: A New Occlusion Aware Graphical Model for Human Pose Estimation

abstract: Occlusion is a main challenge for human pose estimation, which is largely ignored in popular tree structure models. The tree structure model is simple and convenient for exact inference, but short in modeling the occlusion coherence especially in the case of self-occlusion. We propose an occlusion aware graphical model which is able to model both self-occlusion and occlusion by the other objects simultaneously. The proposed model structure can encodes the interactions between human body parts and objects, and hence enables it to learn occlusion coherence from data discriminatively. We evaluate our model on several public benchmarks for human pose estimation including challenging subsets featuring significant occlusion. The experimental results show that our method obtains comparable accuracy with the state-of-the-arts, and is robust to occlusion for 2D human pose estimation.


title: Importance of Online Product Reviews from a Consumer's Perspective

abstract: Product reviews and ratings are popular tools to support buying decisions of consumers. These tools are also valuable for online retailers, who use rating systems in order to build trust and reputation in the online market. Many online shops oer quantitative ratings, textual reviews or a combination of both. This paper examines the acceptance and usage of ratings and reviews in the context of e-commerce transactions. A survey among 104 German online shoppers was conducted to examine how consumer reviews and rat- ings are used to support buying decisions. The survey shows that reviews and ratings are an important source of information for consumers. However, qualitative feedback from the survey indicates that the perceived helpfulness of rating systems varies. Especially the comparison of user reviews is a very time consuming process for the customer, because of the unstructured nature of textual user reviews. In this paper we summarize similar problems and show corresponding examples to them. This will give new insight for future research in the area of user ratings and reviews.


title: Breaking the memory secrecy assumption

abstract: Many countermeasures exist that attempt to protect against buffer overflow attacks on applications written in C and C++. The most widely deployed countermeasures rely on artificially introducing randomness in the memory image of the application. StackGuard and similar systems, for instance, will insert a random value before the return address on the stack, and Address Space Layout Randomization (ASLR) will make the location of stack and/or heap less predictable for an attacker. A critical assumption in these probabilistic countermeasures is that attackers cannot read the contents of memory. In this paper we show that this assumption is not always justified. We identify a new class of vulnerabilities -- buffer overreads -- that occur in practice and that can be exploited to read parts of the memory contents of a process running a vulnerable application. We describe in detail how to exploit an application protected by both ASLR and stack canaries, if the application contains both a buffer overread and a buffer overflow vulnerability. We also provide a detailed discussion of how this vulnerability affects other, less widely deployed probabilistic countermeasures such as memory obfuscation and instruction set randomization.


title: H+-dependent inorganic phosphate transporter in breast cancer cells: Possible functions in the tumor microenvironment

abstract: Abstract Tumor microenvironment has a high concentration of inorganic phosphate (Pi), which is actually a marker for tumor progression. Regarding Pi another class of transporter has been recently studied, an H+-dependent Pi transporter, that is stimulated at acidic pH in Caco2BBE human intestinal cells. In this study, we characterized the H+-dependent Pi transport in breast cancer cell (MDA-MB-231) and around the cancer tissue. MDA-MB-231 cell line presented higher levels of H+-dependent Pi transport as compared to other breast cell lines, such as MCF-10A, MCF-7 and T47-D. The Pi transport was linear as a function of time and exhibited a Michaelis-Menten kinetic of Km = 1.387 ± 0.1674 mM Pi and Vmax = 198.6 ± 10.23 Pi × h−1 × mg protein−1 hence reflecting a low affinity Pi transport. H+-dependent Pi uptake was higher at acidic pH. FCCP, Bafilomycin A1 and SCH28080, which deregulate the intracellular levels of protons, inhibited the H+-dependent Pi transport. No effect on pHi was observed in the absence of inorganic phosphate. PAA, an H+-dependent Pi transport inhibitor, reduced the Pi transport activity, cell proliferation, adhesion, and migration. Arsenate, a structural analog of Pi, inhibited the Pi transport. At high Pi conditions, the H+-dependent Pi transport was five-fold higher than the Na+-dependent Pi transport, thus reflecting a low affinity Pi transport. The occurrence of an H+-dependent Pi transporter in tumor cells may endow them with an alternative path for Pi uptake in situations in which Na+-dependent Pi transport is saturated within the tumor microenvironment, thus regulating the energetically expensive tumor processes.


title: Active learning for sentiment analysis on data streams: Methodology and workflow implementation in the ClowdFlows platform

abstract: Abstract Sentiment analysis from data streams is aimed at detecting authors’ attitude, emotions and opinions from texts in real-time. To reduce the labeling effort needed in the data collection phase, active learning is often applied in streaming scenarios, where a learning algorithm is allowed to select new examples to be manually labeled in order to improve the learner’s performance. Even though there are many on-line platforms which perform sentiment analysis, there is no publicly available interactive on-line platform for dynamic adaptive sentiment analysis, which would be able to handle changes in data streams and adapt its behavior over time. This paper describes ClowdFlows, a cloud-based scientific workflow platform, and its extensions enabling the analysis of data streams and active learning. Moreover, by utilizing the data and workflow sharing in ClowdFlows, the labeling of examples can be distributed through crowdsourcing. The advanced features of ClowdFlows are demonstrated on a sentiment analysis use case, using active learning with a linear Support Vector Machine for learning sentiment classification models to be applied to microblogging data streams.


title: PayBreak: Defense Against Cryptographic Ransomware

abstract: Similar to criminals in the physical world, cyber-criminals use a variety of illegal and immoral means to achieve monetary gains. Recently, malware known as ransomware started to leverage strong cryptographic primitives to hold victims' computer files "hostage" until a ransom is paid. Victims, with no way to defend themselves, are often advised to simply pay. Existing defenses against ransomware rely on ad-hoc mitigations that target the incorrect use of cryptography rather than generic live protection. To fill this gap in the defender's arsenal, we describe the approach, prototype implementation, and evaluation of a novel, automated, and most importantly proactive defense mechanism against ransomware. Our prototype, called PayBreak, effectively combats ransomware, and keeps victims' files safe. PayBreak is based on the insight that secure file encryption relies on hybrid encryption where symmetric session keys are used on the victim computer. PayBreak observes the use of these keys, holds them in escrow, and thus, can decrypt files that would otherwise only be recoverable by paying the ransom. Our prototype leverages low overhead dynamic hooking techniques and asymmetric encryption to realize the key escrow mechanism which allows victims to restore the files encrypted by ransomware. We evaluated PayBreak for its effectiveness against twenty hugely successful families of real-world ransomware, and demonstrate that our system can restore all files that are encrypted by samples from twelve of these families, including the infamous CryptoLocker, and more recent threats such as Locky and SamSam. Finally, PayBreak performs its protection task at negligible performance overhead for common office workloads and is thus ideally suited as a proactive online protection system.


title: Which is the best algorithm for virtual machine placement optimization

abstract: Summary ::: One of the key problems for Infrastructure-as-a-Service providers is finding the optimal allocation of virtual machines on the physical machines available in the provider's data center. Since the allocation has significant impact on operational costs as well as on the performance of the accommodated applications, several algorithms have been proposed for the virtual machine placement problem. So far, no objective comparison of the proposed algorithms has been provided; therefore, it is not known which one works best or what factors influence the performance of the algorithms. In this paper, we present an environment and methodology for such comparisons and compare 7 different algorithms using the proposed environment and methodology. Our results showcase differences of up to 66% between the effectiveness of different algorithms on the same real-world workload traces, thus underlining the importance of objectively comparing the performance of competing algorithms.


title: A valid two-item food security questionnaire for screening HIV-1 infected patients in a clinical setting

abstract: Objective: To validate a two-item food security questionnaire (FSQ) for use in a clinical setting to screen HIV-1 infected patients for food insecurity. Design: The present study was a questionnaire-based survey of forty-nine subjects attending an HIV clinic. Subjects completed a two-item questionnaire and a six-item validated FSQ contemporaneously. Results: A strong correlation was found between the two-item and six-item FSQ (p = 0·895; 95 % CI 0·821, 0-940; P<0·0001). Cronbach's α coefficient was found to be 0·94 and 0·90 for the two-item and six-item FSQ, respectively. The two-item FSQ yielded a sensitivity of 100 % (95 % CI 75, 100) and a specificity of 78 % (95 % CI 61, 90). The negative predictive value was found to be 100 % (95 % CI 88, 100). Conclusions: The results of the present study suggest that the two-item FSQ is a valid, reliable and sensitive screening tool of food insecurity in people living with HIV in a clinical setting.


title: Semi-supervised outlier detection with only positive and unlabeled data based on fuzzy clustering

abstract: The task of semi-supervised outlier detection is to find the instances that are exceptional from other data, using some labeled examples. In many applications such as fraud detection and intrusion detection, this issue becomes more important. Most existing techniques are unsupervised. On the other hand, semi-supervised approaches use both negative and positive instances to detect outliers. However, in many real world applications, very few positive labeled examples are available. This paper proposes an innovative approach to address this problem. The proposed method works as follows. First, some reliable negative instances are extracted by a kNN-based algorithm. Afterwards, fuzzy clustering using both negative and positive examples is utilized to detect outliers. Experimental results on real data sets demonstrate that the proposed approach outperforms the previous unsupervised state-of-the-art methods in detecting outliers.


title: Comparing models of rule-based classification learning: A replication and extension of Shepard, . . .

abstract: We partially replicate and extend Shepard, Hovland, and Jenkins's (1961) classic study of task difficulty for learning six fundamental types of rule-based categorization problems. Our main results mirrored those of Shepard et al., with the ordering of task difficulty being the same as in the original study. A much richer data set was collected, however, which enabled the generation of block-by-block learning curves suitable for quantitative fitting. Four current computational models of classification learning were fitted to the learning data: ALCOVE (Kruschke, 1992), the rational model (Anderson, 1991), the configural-cue model (Gluck & Bower, 1988b), and an extended version of the configural-cue model with dimensionalized, adaptive learning rate mechanisms. Although all of the models captured important qualitative aspects of the learning data, ALCOVE provided the best overall quantitative fit. The results suggest the need to incorporate some form of selective attention to dimensions in category-learning models based on stimulus generalization and cue conditioning.


title: Learning Structure and Strength of CNN Filters for Small Sample Size Training

abstract: Convolutional Neural Networks have provided state-of-the-art results in several computer vision problems. However, due to a large number of parameters in CNNs, they require a large number of training samples which is a limiting factor for small sample size problems. To address this limitation, we propose SSF-CNN which focuses on learning the "structure" and "strength" of filters. The structure of the filter is initialized using a dictionary based filter learning algorithm and the strength of the filter is learned using the small sample training data. The architecture provides the flexibility of training with both small and large training databases, and yields good accuracies even with small size training data. The effectiveness of the algorithm is first demonstrated on MNIST, CIFAR10, and NORB databases, with varying number of training samples. The results show that SSF-CNN significantly reduces the number of parameters required for training while providing high accuracies on the test databases. On small sample size problems such as newborn face recognition and Omniglot, it yields state-of-the-art results. Specifically, on the IIITD Newborn Face Database, the results demonstrate improvement in rank-1 identification accuracy by at least 10%.


title: Coupled Binary Embedding for Large-Scale Image Retrieval

abstract: Visual matching is a crucial step in image retrieval based on the bag-of-words (BoW) model. In the baseline method, two keypoints are considered as a matching pair if their SIFT descriptors are quantized to the same visual word. However, the SIFT visual word has two limitations. First, it loses most of its discriminative power during quantization. Second, SIFT only describes the local texture feature. Both drawbacks impair the discriminative power of the BoW model and lead to false positive matches. To tackle this problem, this paper proposes to embed multiple binary features at indexing level. To model correlation between features, a multi-IDF scheme is introduced, through which different binary features are coupled into the inverted file. We show that matching verification methods based on binary features, such as Hamming embedding, can be effectively incorporated in our framework. As an extension, we explore the fusion of binary color feature into image retrieval. The joint integration of the SIFT visual word and binary features greatly enhances the precision of visual matching, reducing the impact of false positive matches. Our method is evaluated through extensive experiments on four benchmark datasets (Ukbench, Holidays, DupImage, and MIR Flickr 1M). We show that our method significantly improves the baseline approach. In addition, large-scale experiments indicate that the proposed method requires acceptable memory usage and query time compared with other approaches. Further, when global color feature is integrated, our method yields competitive performance with the state-of-the-arts.


title: Technology for sub-50nm DRAM and NAND flash manufacturing

abstract: This paper discusses whether memory technologies can continue advances beyond sub-50nm node especially for DRAM and NAND flash memories. First, the barriers to shrink technology are addressed for DRAM and NAND flash memories, depending on their inherent operation principles. Then, details of technology solutions are introduced and its manufacturability is examined. Beyond 30nm node, It is expected that 3-dimensional transistor scheme is needed for both logic and memory array in addition to the development of new materials and structural technologies


title: An LSTM-Based Dynamic Customer Model for Fashion Recommendation

abstract: Online fashion sales present a challenging use case for personalized recommendation: Stores offer a huge variety of items in multiple sizes. Small stocks, high return rates, seasonality, and changing trends cause continuous turnover of articles for sale on all time scales. Customers tend to shop rarely, but often buy multiple items at once. We report on backtest experiments with sales data of 100k frequent shoppers at Zalando, Europe's leading online fashion platform. To model changing customer and store environments, our recommendation method employs a pair of neural networks: To overcome the cold start problem, a feedforward network generates article embeddings in "fashion space," which serve as input to a recurrent neural network that predicts a style vector in this space for each client, based on their past purchase sequence. We compare our results with a static collaborative filtering approach, and a popularity ranking baseline.


title: Beyond Views: Measuring and Predicting Engagement on YouTube Videos

abstract: The share of videos in the internet traffic has been growing, therefore understanding how videos capture attention on a global scale is also of growing importance. Most current research focus on modeling the number of views, but we argue that video engagement, or time spent watching is a more appropriate measure for resource allocation problems in attention, networking, and promotion activities. In this paper, we present a first large-scale measurement of video-level aggregate engagement from publicly available data streams, on a collection of 5.3 million YouTube videos published over two months in 2016. We study a set of metrics including time and the average percentage of a video watched. We define a new metric, relative engagement, that is calibrated against video properties and strongly correlate with recognized notions of quality. Moreover, we find that engagement measures of a video are stable over time, thus separating the concerns for modeling engagement and those for popularity -- the latter is known to be unstable over time and driven by external promotions. We also find engagement metrics predictable from a cold-start setup, having most of its variance explained by video context, topics and channel information -- R2=0.77. Our observations imply several prospective uses of engagement metrics -- choosing engaging topics for video production, or promoting engaging videos in recommender systems.


title: Object Detection in Videos by Short and Long Range Object Linking

abstract: Compared with object detection in static images, object detection in videos is more challenging due to degraded image qualities. An effective way to address this problem is to exploit temporal contexts by linking the same object across video to form tubelets and aggregating classification scores in the tubelets. In this paper, we focus on obtaining high quality object linking results for better classification. Unlike previous methods that link objects by checking boxes between neighboring frames, we propose to link in the same frame. To achieve this goal, we extend prior methods in following aspects: (1) a cuboid proposal network that extracts spatio-temporal candidate cuboids which bound the movement of objects; (2) a short tubelet detection network that detects short tubelets in short video segments; (3) a short tubelet linking algorithm that links temporally-overlapping short tubelets to form long tubelets. Experiments on the ImageNet VID dataset show that our method outperforms both the static image detector and the previous state of the art. In particular, our method improves results by 8.8% over the static image detector for fast moving objects.


title: Why Do Partisan Media Polarize Viewers

abstract: The recent increase in partisan media has generated interest in whether such outlets polarize viewers. I draw on theories of motivated reasoning to explain why partisan media polarize viewers, why these programs affect some viewers much more strongly than others, and how long these effects endure. Using a series of original experiments, I find strong support for my theoretical expectations, including the argument that these effects can still be detected several days postexposure. My results demonstrate that partisan media polarize the electorate by taking relatively extreme citizens and making them even more extreme. Though only a narrow segment of the public watches partisan media programs, partisan media’s effects extend much more broadly throughout the political arena.


title: Whisper: Tracing the Spatiotemporal Process of Information Diffusion in Real Time

abstract: When and where is an idea dispersed? Social media, like Twitter, has been increasingly used for exchanging information, opinions and emotions about events that are happening across the world. Here we propose a novel visualization design, “Whisper”, for tracing the process of information diffusion in social media in real time. Our design highlights three major characteristics of diffusion processes in social media: the temporal trend, social-spatial extent, and community response of a topic of interest. Such social, spatiotemporal processes are conveyed based on a sunflower metaphor whose seeds are often dispersed far away. In Whisper, we summarize the collective responses of communities on a given topic based on how tweets were retweeted by groups of users, through representing the sentiments extracted from the tweets, and tracing the pathways of retweets on a spatial hierarchical layout. We use an efficient flux line-drawing algorithm to trace multiple pathways so the temporal and spatial patterns can be identified even for a bursty event. A focused diffusion series highlights key roles such as opinion leaders in the diffusion process. We demonstrate how our design facilitates the understanding of when and where a piece of information is dispersed and what are the social responses of the crowd, for large-scale events including political campaigns and natural disasters. Initial feedback from domain experts suggests promising use for today's information consumption and dispersion in the wild.


title: TRACE OPTIMIZATION AND EIGENPROBLEMS IN DIMENSION REDUCTION METHODS

abstract: This paper gives an overview of the eigenvalue problems encountered in areas of data mining that are related to dimension reduction. Given some input high-dimensional data, the goal of dimension reduction is to map them to a low-dimensional space such that certain properties of the original data are preserved. Optimizing these properties among the reduced data can be typically posed as a trace optimization problem that leads to an eigenvalue problem. There is a rich variety of such problems and the goal of this paper is to unravel relations between them as well as to discuss effective solution techniques. First, we make a distinction between projective methods that determine an explicit linear mapping from the high-dimensional space to the low-dimensional space, and nonlinear methods where the mapping between the two is nonlinear and implicit. Then, we show that all of the eigenvalue problems solved in the context of explicit linear projections can be viewed as the projected analogues of the nonlinear or implicit projections. We also discuss kernels as a means of unifying linear and nonlinear methods and revisit some of the equivalences between methods established in this way. Finally, we provide some illustrative examples to showcase the behavior and the particular characteristics of the various dimension reduction techniques on real world data sets. Copyright c © 200 John Wiley & Sons, Ltd.


title: The effect of network topology on the spread of epidemics

abstract: Many network phenomena are well modeled as spreads of epidemics through a network. Prominent examples include the spread of worms and email viruses, and, more generally, faults. Many types of information dissemination can also be modeled as spreads of epidemics. In this paper we address the question of what makes an epidemic either weak or potent. More precisely, we identify topological properties of the graph that determine the persistence of epidemics. In particular, we show that if the ratio of cure to infection rates is larger than the spectral radius of the graph, then the mean epidemic lifetime is of order log n, where n is the number of nodes. Conversely, if this ratio is smaller than a generalization of the isoperimetric constant of the graph, then the mean epidemic lifetime is of order e/sup na/, for a positive constant a. We apply these results to several network topologies including the hypercube, which is a representative connectivity graph for a distributed hash table, the complete graph, which is an important connectivity graph for BGP, and the power law graph, of which the AS-level Internet graph is a prime example. We also study the star topology and the Erdos-Renyi graph as their epidemic spreading behaviors determine the spreading behavior of power law graphs.


Crisis in a Networked World Features of Computer-Mediated Communication in the April 16 , 2007 , Virginia Tech Event


title: A Split Gate Power FINFET With Improved ON-Resistance and Switching Performance

abstract: A split gate power FINFET (SG-FINFET) with reduced ON-resistance and improved switching performance is proposed and its mechanism is investigated by simulations. It is characterized by a fin gate (FG) and split gate: the FG surrounds the p-well region from three dimensions; the split gate tied to the source is embedded beside the drift region and isolated with slant oxide. First, the FG enlarges the channel width and modulates the current distribution, which leads to a low specific ON-resistance ( $R_{\mathrm {\mathrm{\scriptscriptstyle ON},sp}})$ and improved transconductance ( $g_{m})$ . Second, the split gate acts as a source field plate to assist in depleting the drift region, which increases the drift doping concentration ( $N_{d})$ and further reduces $R_{\mathrm {\mathrm{\scriptscriptstyle ON},sp}}$ . Third, the split gate reduces the gate–drain capacitance ( $C_{\mathrm {GD}})$ and switching power dissipation. Compared with the conventional LDMOS and superjunction LDMOS, the SG-FINFET decreases $R_{\mathrm {\mathrm{\scriptscriptstyle ON},sp}}$ by 60% and 47% at the same 80 V class breakdown voltage. The SG-FINFET also exhibits 55% reduction in the gate–drain charge $Q_{\mathrm {GD}}$ in comparison with the FG LDMOS (without the split gate).


title: An Efficient Control Configuration Development for a High-speed Robotic Palletizing System

abstract: This article presents a newly developed robotized palletizer control configuration consisting of a graphical application running on a human-machine interface (HMI) touch screens, a PLC program, and a servo system that executes palletizer reconfiguration and makes high-speed robotic palletizing possible. The servo system of the palletizer allows the PLC to reconfigure the palletizer automatically, based on the configuration data, and to control the product feed to the robots that execute the palletizing function. This allows the robots to handle several boxes at a time and therefore achieve high-speed palletizing. Significant downtime reduction during batch changes is achieved due to simplification of operator tasks, which makes the configuration and control method ideally suitable for production environments with frequent batch changes. Packaging and materials handling industries may be financially benefited using this configuration and control method. These include low palletizer downtime, high palletizer throughput, low training costs, no rework due to configuration errors and wider availability of potential operators.


title: Compact and wideband modified wilkinson power dividers

abstract: A Wilkinson power divider has ideal transmission characteristics and it enables easy realization in planar transmission line technology. However, the conventional Wilkinson power divider features a large component footprint because of its quarter-wavelength line components and narrow bandwidth. In this paper, two modified Wilkinson power divider are proposed in order to achieve size reduction and wide bandwidth. The first structure presents Wilkinson power divider using compact folded step impedance transmission lines (FSITL) rather than the uniform microstrip line design for operating center frequency of 3 GHz. While the second structure presents the FSITL with delta-stub for 2.4 GHz. The proposed folded microstrip line structure is capable of reducing the size of the power divider circuit. The impedance ratio of SITLs is taken into account as the total electrical length of the lines for every wavelength 0 1 and 0 2 in order to enhance size reduction to the optimum. The study managed to get an overall dimension of 15 mm × 9.5 mm for the first proposed design achieving a reduction of 75.6 % and bandwidth of 4 GHz. For the second proposed structure, the size was 15 mm × 15 mm with a reduction of 56 %. The proposed structures provided an extra 80 % fractional bandwidth based on the −15 dB return loss as a reference. The proposed power divider used RT/duroid 5880 substrate with 0.38 mm thickness. Simulation and measurement results indicated that the modified power divider showed equal power division, good phase balance, high isolation between output ports, and good return loss better than −15 dB covering the operating frequency range.


Mobile Robot Positioning & Sensors and Techniques


title: Fund Manager Use of Public Information: New Evidence on Managerial Skills

abstract: We show theoretically that the responsiveness of a fund manager's portfolio allocations to changes in public information decreases in the manager's skill. We go on to estimate this sensitivity ("RPI") as the "R"-super-2 of the regression of changes in a manager's portfolio holdings on changes in public information using a panel of U.S. equity funds. Consistent with "RPI" containing information related to managerial skills, we find a strong inverse relationship between "RPI" and various existing measures of performance, and between "RPI" and fund flows. We also document that both fund- and manager-specific attributes affect "RPI". Copyright 2007 by The American Finance Association.


title: The Temporal Winner-Take-All Readout

abstract: How can the central nervous system make accurate decisions about external stimuli at short times on the basis of the noisy responses of nerve cell populations? It has been suggested that spike time latency is the source of fast decisions. Here, we propose a simple and fast readout mechanism, the temporal Winner-Take-All (tWTA), and undertake a study of its accuracy. The tWTA is studied in the framework of a statistical model for the dynamic response of a nerve cell population to an external stimulus. Each cell is characterized by a preferred stimulus, a unique value of the external stimulus for which it responds fastest. The tWTA estimate for the stimulus is the preferred stimulus of the cell that fired the first spike in the entire population. We then pose the questions: How accurate is the tWTA readout? What are the parameters that govern this accuracy? What are the effects of noise correlations and baseline firing? We find that tWTA sensitivity to the stimulus grows algebraically fast with the number of cells in the population, N, in contrast to the logarithmic slow scaling of the conventional rate-WTA sensitivity with N. Noise correlations in first-spike times of different cells can limit the accuracy of the tWTA readout, even in the limit of large N, similar to the effect that has been observed in population coding theory. We show that baseline firing also has a detrimental effect on tWTA accuracy. We suggest a generalization of the tWTA, the n-tWTA, which estimates the stimulus by the identity of the group of cells firing the first n spikes and show how this simple generalization can overcome the detrimental effect of baseline firing. Thus, the tWTA can provide fast and accurate responses discriminating between a small number of alternatives. High accuracy in estimation of a continuous stimulus can be obtained using the n-tWTA.


title: First-in-human study to assess guselkumab (anti-IL-23 mAb) pharmacokinetics/safety in healthy subjects and patients with moderate-to-severe psoriasis

abstract: PURPOSE ::: The purpose of this study is to evaluate the pharmacokinetics, immunogenicity, safety, and tolerability of guselkumab, a human monoclonal antibody with high affinity and specificity for binding to interleukin-23. ::: ::: ::: METHODS ::: In this first-in-human, phase 1, randomized study, a single intravenous (IV; 0.03-10 mg/kg) or subcutaneous (SC; 10-300 mg) dose of guselkumab was administered to 47 healthy subjects, and a single SC dose (placebo, 10, 30, 100, 300 mg) was administered to 24 patients with moderate-to-severe psoriasis. ::: ::: ::: RESULTS ::: Mean maximum observed serum concentration and area under the zero-to-infinity serum concentration-time curve of guselkumab increased in an approximately dose-proportional manner over the dose range of 0.03-10 mg/kg following a single IV administration or 10-300 mg following a single SC administration. Mean clearance and volume of distribution ranged from 3.62-6.03 mL/day/kg and 99.38-123.22 mL/kg, respectively. Mean half-life ranged from 12 to 19 days in healthy subjects and patients with psoriasis. Among guselkumab-treated subjects/patients, 1/30 (3.3 %) healthy subjects in the IV group, 0/6 healthy subjects in the SC group, and 1/20 (5.0 %) patients with psoriasis tested positive for antibodies to guselkumab. No clinically significant adverse events were identified in this study. ::: ::: ::: CONCLUSION ::: Guselkumab pharmacokinetic profiles were generally comparable between healthy subjects and patients with psoriasis. Guselkumab, administered as an IV infusion or SC injection, was well tolerated in healthy subjects and patients with psoriasis.


title: News-based trading strategies

abstract: The marvel of markets lies in the fact that dispersed information is instantaneously processed and used to adjust the price of goods, services and assets. Financial markets are particularly efficient when it comes to processing information; such information is typically embedded in textual news that is then interpreted by investors. Quite recently, researchers have started to automatically determine news sentiment in order to explain stock price movements. Interestingly, this so-called news sentiment works fairly well in explaining stock returns. In this paper, we design trading strategies that utilize textual news in order to obtain profits on the basis of novel information entering the market. We thus propose approaches for automated decision-making based on supervised and reinforcement learning. Altogether, we demonstrate how news-based data can be incorporated into an investment system. Financial disclosures are the main source for the decision-making in finance.Sentiment analysis of financial disclosures can provide decision support.We design and compare different strategies for news trading.These can outperform our benchmarks in terms of profits but at the cost of risk.Especially viable approaches are supervised and reinforcement learning.


title: Depth-aware neural style transfer

abstract: Neural style transfer has recently received significant attention and demonstrated amazing results. An efficient solution proposed by Johnson et al. trains feed-forward convolutional neural networks by defining and optimizing perceptual loss functions. Such methods are typically based on high-level features extracted from pre-trained neural networks, where the loss functions contain two components: style loss and content loss. However, such pre-trained networks are originally designed for object recognition, and hence the high-level features often focus on the primary target and neglect other details. As a result, when input images contain multiple objects potentially at different depths, the resulting images are often unsatisfactory because image layout is destroyed and the boundary between the foreground and background as well as different objects becomes obscured. We observe that the depth map effectively reflects the spatial distribution in an image and preserving the depth map of the content image after stylization helps produce an image that preserves its semantic content. In this paper, we introduce a novel approach for neural style transfer that integrates depth preservation as additional loss, preserving overall image layout while performing style transfer.


title: Standards for vehicular communication—from IEEE 802.11p to 5G

abstract: Standardization for wireless vehicular communication ensures, as in other domains, interoperability, supports regulations and legislation, and creates larger markets. For the initial deployment of vehicular communication, consistent sets of standards have been created, commonly named C-ITS in Europe and DSRC in the U.S., both relying on the WiFi standard IEEE 802.11. These initial standard sets specify vehicle-to-vehicle and vehicle-to-infrastructure communication and enable applications primarily for driver information and warnings. The article provides an overview of the key C-ITS and DSRC protocols from a standardization perspective. The article analyzes automated driving as the potential new application domain for vehicular communication, discusses its requirements on communication, and derives potential directions for future releases of the vehicular communication standards.


title: Learning Adversarial Networks for Semi-Supervised Text Classification via Policy Gradient

abstract: Semi-supervised learning is a branch of machine learning techniques that aims to make fully use of both labeled and unlabeled instances to improve the prediction performance. The size of modern real world datasets is ever-growing so that acquiring label information for them is extraordinarily difficult and costly. Therefore, deep semi-supervised learning is becoming more and more popular. Most of the existing deep semi-supervised learning methods are built under the generative model based scheme, where the data distribution is approximated via input data reconstruction. However, this scheme does not naturally work on discrete data, e.g., text; in addition, learning a good data representation is sometimes directly opposed to the goal of learning a high performance prediction model. To address the issues of this type of methods, we reformulate the semi-supervised learning as a model-based reinforcement learning problem and propose an adversarial networks based framework. The proposed framework contains two networks: a predictor network for target estimation and a judge network for evaluation. The judge network iteratively generates proper reward to guide the training of predictor network, and the predictor network is trained via policy gradient. Based on the aforementioned framework, we propose a recurrent neural network based model for semi-supervised text classification. We conduct comprehensive experimental analysis on several real world benchmark text datasets, and the results from our evaluations show that our method outperforms other competing state-of-the-art methods.


title: Random number generation with multiple streams for sequential and parallel computing

abstract: We provide a review of the state of the art on the design and implementation of random number generators (RNGs) for simulation, on both sequential and parallel computing environments. We focus on the need for multiple independent streams and substreams of random numbers, explain how they can be constructed and managed, review software libraries that offer them, and illustrate their usefulness via examples. We also review the basic quality criteria for good random number generators and their theoretical and empirical testing.


title: Cooperative Localization for Autonomous Underwater Vehicles

abstract: This paper describes an algorithm for distributed acoustic navigation for Autonomous Underwater Vehicles (AUVs). Whereas typical AUV navigation systems utilize pre-calibrated arrays of static transponders, our work seeks to create a fully mobile network of AUVs that perform acoustic ranging and data exchange with one another to achieve cooperative positioning for extended duration missions over large areas. The algorithm enumerates possible solutions for the AUV trajectory based on dead-reckoning and range-only measurements provided by acoustic modems that are mounted on each vehicle, and chooses the trajectory via minimization of a cost function based on these constraints. The resulting algorithm is computationally efficient, meets the strict bandwidth requirements of available AUV modems, and has potential to scale well to networks of large numbers of vehicles. The method has undergone extensive experimentation, and results from three different scenarios are reported in this paper, each of which utilizes MIT SCOUT Autonomous Surface Craft (ASC) as convenient platforms for testing. In the first experiment, we utilize three ASCs, each equipped with a Woods Hole acoustic modem, as surrogates for AUVs. In this scenario, two ASCs serve as Communication/Navigation Aids (CNAs) for a third ASC that computes its position based exclusively on GPS positions of the CNAs and acoustic range measurements between platforms. In the second scenario, an undersea glider is used in conjunction with two ASCs serving as CNAs. Finally, in the third experiment, a Bluefin12 AUV serves as the target vehicle. All three experiments demonstrate the successful operation of the technique with real ocean data.


title: Deep Multimodal Distance Metric Learning Using Click Constraints for Image Ranking

abstract: How do we retrieve images accurately? Also, how do we rank a group of images precisely and efficiently for specific queries? These problems are critical for researchers and engineers to generate a novel image searching engine. First, it is important to obtain an appropriate description that effectively represent the images. In this paper, multimodal features are considered for describing images. The images unique properties are reflected by visual features, which are correlated to each other. However, semantic gaps always exist between images visual features and semantics. Therefore, we utilize click feature to reduce the semantic gap. The second key issue is learning an appropriate distance metric to combine these multimodal features. This paper develops a novel deep multimodal distance metric learning (Deep-MDML) method. A structured ranking model is adopted to utilize both visual and click features in distance metric learning (DML). Specifically, images and their related ranking results are first collected to form the training set. Multimodal features, including click and visual features, are collected with these images. Next, a group of autoencoders is applied to obtain initially a distance metric in different visual spaces, and an MDML method is used to assign optimal weights for different modalities. Next, we conduct alternating optimization to train the ranking model, which is used for the ranking of new queries with click features. Compared with existing image ranking methods, the proposed method adopts a new ranking model to use multimodal features, including click features and visual features in DML. We operated experiments to analyze the proposed Deep-MDML in two benchmark data sets, and the results validate the effects of the method.


title: Statistical hypothesis testing in positive unlabelled data

abstract: We propose a set of novel methodologies which enable valid statistical hypothesis testing when we have only positive and unlabelled (PU) examples. This type of problem, a special case of semi-supervised data, is common in text mining, bioinformatics, and computer vision. Focusing on a generalised likelihood ratio test, we have 3 key contributions: (1) a proof that assuming all unlabelled examples are negative cases is sufficient for independence testing, but not for power analysis activities; (2) a new methodology that compensates this and enables power analysis, allowing sample size determination for observing an effect with a desired power; and finally, (3) a new capability, supervision determination, which can determine a-priori the number of labelled examples the user must collect before being able to observe a desired statistical effect. Beyond general hypothesis testing, we suggest the tools will additionally be useful for information theoretic feature selection, and Bayesian Network structure learning.


title: A Generalized Coupling Matrix Extraction Technique for Bandpass Filters With Uneven-Qs

abstract: In this paper, a vector fitting (VF) based analytical extraction method, which is capable of accurately extracting the coupling matrix and the uneven unloaded Qs of each electric resonators of a filter, is presented. Having had the complex poles and residues determined using VF, the coupling matrix can be obtained by a sequence of complex orthogonal transformations. As a side product, the unloaded Qs for each resonator will be directly obtained from the complex diagonal elements of the coupling matrix. To demonstrate the effectiveness of the proposed method, an ideal demonstrative example along with two practical challenging filter tuning examples, namely, an eight-pole dual-mode dielectric filter and an eight-pole dual-mode predistortion filter, are demonstrated. An excellent match between the responses of the measured data and those from the extracted coupling matrix with actual unloaded Q factors can be seen.


title: Baselines and Bigrams: Simple, Good Sentiment and Topic Classification

abstract: Variants of Naive Bayes (NB) and Support Vector Machines (SVM) are often used as baseline methods for text classification, but their performance varies greatly depending on the model variant, features used and task/dataset. We show that: (i) the inclusion of word bigram features gives consistent gains on sentiment analysis tasks; (ii) for short snippet sentiment tasks, NB actually does better than SVMs (while for longer documents the opposite result holds); (iii) a simple but novel SVM variant using NB log-count ratios as feature values consistently performs well across tasks and datasets. Based on these observations, we identify simple NB and SVM variants which outperform most published results on sentiment analysis datasets, sometimes providing a new state-of-the-art performance level.


title: Roles of Information Security Awareness and Perceived Fairness in Information Security Policy Compliance

abstract: Drawing on the Theory of Planned Behavior (TPB), this research investigates two factors that drive an employee to comply with requirements of the information security policy (ISP) of her organization with regards to protecting information and technology resources: an employee’s information security awareness (ISA) and her perceived fairness of the requirements of the ISP. Our results, which is based on the PLS analysis of data collected from 464 participants, show that ISA and perceived fairness positively affect attitude, and in turn attitude positively affects intention to comply. ISA also has an indirect impact on attitude since it positively influences perceived fairness. As organizations strive to get their employees to follow their information security rules and regulations, our study sheds light on the role of an employee’s ISA and procedural fairness with regards to security rules and regulations in the workplace.


title: Using low resolution position sensors in bumpless position/speed estimation methods for low cost PMSM drives

abstract: A bumpless position/speed estimation method which uses only a low-resolution position sensor is presented for low cost permanent magnet machine (PMSM) drives. By integrating methods, i.e. using a speed reference for zero speed startup, a low resolution Hall sensor for low speed acceleration, and back-EMF for high speed operation, the rotor position can be estimated and controlled over the entire speed range. In order to achieve bumpless transitions, the three techniques are combined with a crossover function based on the speed reference. The proposed method can be used in applications with unpredictable variation in loads, such as horizontal drum washing machines.


title: A Virtual Try-On System for Prescription Eyeglasses

abstract: Corrective lenses introduce distortion caused by the refraction effect, which changes the wear’s appearance. To give users a more realistic experience, a virtual try-on system for prescription eyeglasses modifies an input video and virtually inserts prescription eyeglasses, producing an output similar to a virtual mirror. The proposed system generates a 3D representation of the corrective lenses mounted into the eyeglasses frame and modifies the video sequence to virtually insert the eyeglasses through image-based rendering. Unlike existing virtual try-on systems, this approach simulates the refraction effects due to the corrective lens and takes into account reflections and shading.