title: Data-intensive applications, challenges, techniques and technologies: A survey on Big Data

abstract: It is already true that Big Data has drawn huge attention from researchers in information sciences, policy and decision makers in governments and enterprises. As the speed of information growth exceeds Moore’s Law at the beginning of this new century, excessive data is making great troubles to human beings. However, there are so much potential and highly useful values hidden in the huge volume of data. A new scientific paradigm is born as dataintensive scientific discovery (DISD), also known as Big Data problems. A large number of fields and sectors, ranging from economic and business activities to public administration, from national security to scientific researches in many areas, involve with Big Data problems. On the one hand, Big Data is extremely valuable to produce productivity in businesses and evolutionary breakthroughs in scientific disciplines, which give us a lot of opportunities to make great progresses in many fields. There is no doubt that the future competitions in business productivity and technologies will surely converge into the Big Data explorations. On the other hand, Big Data also arises with many challenges, such as difficulties in data capture, data storage, data analysis and data visualization. This paper is aimed to demonstrate a close-up view about Big Data, including Big Data applications, Big Data opportunities and challenges, as well as the state-of-the-art techniques and technologies we currently adopt to deal with the Big Data problems. We also discuss several underlying methodologies to handle the data deluge, for example, granular computing, cloud computing, bio-inspired computing, and quantum computing.


title: An intelligent personal assistant for task and time management

abstract: We describe an intelligent personal assistant that has been developed to aid a busy knowledge worker in managing time commitments and performing tasks. The design of the system was motivated by the complementary objectives of (1) relieving the user of routine tasks, thus allowing her to focus on tasks that critically require human problem-solving skills, and (2) intervening in situations where cognitive overload leads to oversights or mistakes by the user. The system draws on a diverse set of AI technologies that are linked within a Belief-Desire-Intention (BDI) agent system. Although the system provides a number of automated functions, the overall framework is highly user centric in its support for human needs, responsiveness to human inputs, and adaptivity to user working style and preferences.


title: Convolutional Experts Constrained Local Model for 3D Facial Landmark Detection

abstract: Constrained Local Models (CLMs) are a well-established family of methods for facial landmark detection. CE-CLM, the newest member of CLMs, brings CLMs back to state of the art performance. This is done through CE-CLMs ability to model the very complex individual landmark appearance that is affected by expression, illumination, facial hair, makeup, and accessories. A crucial component of CE-CLM is a novel local detector - Convolutional Experts Network (CEN) - that brings together the advantages of neural architectures and mixtures of experts in an end-to-end framework. In this paper we use CE-CLM to learn position of dense 84 landmark positions. To achieve best performance on the Menpo3D dense landmark detection challenge, we use two complementary networks alongside CE-CLM: a network that maps the output of CE-CLM to 84 landmarks called Adjustment Network, and a Deep Residual Network called Correction Networks that learns dataset specific corrections for CE-CLM.


title: A review of computer simulation for fraud detection research in financial datasets

abstract: The investigation of fraud in the financial domain has been restricted to those who have access to relevant data. However, customer financial records are protected by law and internal policies, therefore they are not available for most of the researchers in the area of fraud detection. This paper aims to present the work of those researchers who have had access to data and present an interesting approach to fraud detection research; which is the generation of a synthetic data set to work on fraud detection research. Some of the domains covered in this review include mobile money payments, e-payments, retail stores, online bank services and credit card payments. We also cover some of the most relevant surveys in the field and point out the impossibility to compare this work due to the lack of common public data set to test different results.


title: Ultra-low-power Wireless Streaming Cameras

abstract: Wireless video streaming has traditionally been considered an extremely power-hungry operation. Existing approaches optimize the camera and communication modules individually to minimize their power consumption. However, the joint redesign and optimization of wireless communication as well as the camera is what that provides more power saving. We present an ultra-low-power wireless video streaming camera. To achieve this, we present a novel "analog" video backscatter technique that feeds analog pixels from the photo-diodes directly to the backscatter hardware, thereby eliminating power consuming hardware components such as ADCs and amplifiers. We prototype our wireless camera using off-the-shelf hardware and show that our design can stream video at up to 13 FPS and can operate up to a distance of 150 feet from the access point. Our COTS prototype consumes 2.36mW. Finally, to demonstrate the potential of our design, we built two proof-of-concept applications: video streaming for micro-robots and security cameras for face detection.


title: Back to the Basics: Bayesian extensions of IRT outperform neural networks for proficiency estimation

abstract: Estimating student proficiency is an important task for computer based learning systems. We compare a family of IRT-based proficiency estimation methods to Deep Knowledge Tracing (DKT), a recently proposed recurrent neural network model with promising initial results. We evaluate how well each model predicts a student's future response given previous responses using two publicly available and one proprietary data set. We find that IRT-based methods consistently matched or outperformed DKT across all data sets at the finest level of content granularity that was tractable for them to be trained on. A hierarchical extension of IRT that captured item grouping structure performed best overall. When data sets included non-trivial autocorrelations in student response patterns, a temporal extension of IRT improved performance over standard IRT while the RNN-based method did not. We conclude that IRT-based models provide a simpler, better-performing alternative to existing RNN-based models of student interaction data while also affording more interpretability and guarantees due to their formulation as Bayesian probabilistic models.


title: MetaLDA: A Topic Model that Efficiently Incorporates Meta Information

abstract: Besides the text content, documents and their associated words usually come with rich sets of meta informa- tion, such as categories of documents and semantic/syntactic features of words, like those encoded in word embeddings. Incorporating such meta information directly into the generative process of topic models can improve modelling accuracy and topic quality, especially in the case where the word-occurrence information in the training data is insufficient. In this paper, we present a topic model, called MetaLDA, which is able to leverage either document or word meta information, or both of them jointly. With two data argumentation techniques, we can derive an efficient Gibbs sampling algorithm, which benefits from the fully local conjugacy of the model. Moreover, the algorithm is favoured by the sparsity of the meta information. Extensive experiments on several real world datasets demonstrate that our model achieves comparable or improved performance in terms of both perplexity and topic quality, particularly in handling sparse texts. In addition, compared with other models using meta information, our model runs significantly faster.


title: Mesoporous, Three-Dimensional Wood Membrane Decorated with Nanoparticles for Highly Efficient Water Treatment

abstract: Wood, an earth-abundant material, is widely used in our everyday life. With its mesoporous structure, natural wood is comprised of numerous long, partially aligned channels (lumens) as well as nanochannels that stretch along its growth direction. This wood mesostructure is suitable for a range of emerging applications, especially as a membrane/separation material. Here, we report a mesoporous, three-dimensional (3D) wood membrane decorated with palladium nanoparticles (Pd NPs/wood membrane) for efficient wastewater treatment. The 3D Pd NPs/wood membrane possesses the following advantages: (1) the uniformly distributed lignin within the wood mesostructure can effectively reduce Pd(II) ions to Pd NPs; (2) cellulose, with its abundant hydroxyl groups, can immobilize Pd NPs; (3) the partially aligned mesoporous wood channels as well as their inner ingenious microstructures increase the likelihood of wastewater contacting Pd NPs decorating the wood surface; (4) the long, Pd NP-decorated channels facilitate bul...


title: DeepCancer: Detecting Cancer through Gene Expressions via Deep Generative Learning

abstract: Transcriptional profiling on microarrays to obtain gene expressions has been used to facilitate cancer diagnosis. We propose a deep generative machine learning architecture (called DeepCancer) that learn features from unlabeled microarray data. These models have been used in conjunction with conventional classifiers that perform classification of the tissue samples as either being cancerous or non-cancerous. The proposed model has been tested on two different clinical datasets. The evaluation demonstrates that DeepCancer model achieves a very high precision score, while significantly controlling the false positive and false negative scores.


title: Aggression recognition using overlapping speech

abstract: Automatic recognition of negative affect and aggression is key in many safety critical domains such as surveillance and health care. In this paper we explore the potential of overlapping speech for predicting aggression levels. As a first step we consider 3 categories of overlapping speech based on literature. Having an annotation of these overlap categories, we examine whether overlapping speech is a good feature for predicting aggression by using it in classification together with a set of acoustic features typically used for this purpose. Next, we explore if this fine categorization of overlap is necessary in predicting aggression levels or a more coarse representation is sufficient. Finally, we check the additive values of automatically predicted overlapping speech for aggression recognition. The experiments are performed on a dataset of dyadic interactions between professional aggression training actors (actors) and naive participants (students) interacting freely based on short role descriptions. Our findings show that overlapping speech is a key feature for predicting aggression levels, that discriminating only severe cases of overlap is a sufficient feature and that automatically predicted overlap is improving aggression recognition as well.


title: Indoor 5G 3GPP-like Channel Models for Office and Shopping Mall Environments

abstract: Future mobile communications systems are likely to be very different to those of today with new service innovations driven by increasing data traffic demand, increasing processing power of smart devices and new innovative applications. To meet these service demands the telecommunications industry is converging on a common set of 5G requirements which includes network speeds as high as 10 Gbps, cell edge rate greater than 100 Mbps, and latency of less than 1 msec. To reach these 5G requirements the industry is looking at new spectrum bands in the range up to 100 GHz where there is spectrum availability for wide bandwidth channels. For the development of new 5G systems to operate in bands up to 100 GHz there is a need for accurate radio propagation models which are not addressed by existing channel models developed for bands below 6 GHz. This paper presents a preliminary overview of the 5G channel models for bands up to 100 GHz in indoor offices and shopping malls, derived from extensive measurements across a multitude of bands. These studies have found some extensibility of the existing 3GPP models (e.g. 3GPP TR36.873) to the higher frequency bands up to 100 GHz. The measurements indicate that the smaller wavelengths introduce an increased sensitivity of the propagation models to the scale of the environment and show some frequency dependence of the path loss as well as increased occurrence of blockage. Further, the penetration loss is highly dependent on the material and tends to increase with frequency. The small-scale characteristics of the channel such as delay spread and angular spread and the multipath richness is somewhat similar over the frequency range, which is encouraging for extending the existing 3GPP models to the wider frequency range. Further work will be carried out to complete these models, but this paper presents the first steps for an initial basis for the model development.


title: Statistical Model for the Forecast of Hydropower Production in Ecuador

abstract: The main sources of electricity generation are hydroelectric and thermo-fossil type in Ecuador. Gross hydroelectric production have sometimes exceeded 50% of national production. However, this type of hydroelectric is often threatened by droughts and their effect on the water reservoirs which our country has experienced on some occasions, such as in 2009. On the other hand, the National Plan for Good Living 2013-2017, in conjunction with the National Master Plan for Electrification 2013-2022, has planned new hydroelectric generation projects, which are expected to exceed 90% of the national balance. The objective of this article is to model the monthly production of hydroelectric energy for prediction purposes, by implementing five stochastic process models on a historical series of monthly hydroelectric energy production in Ecuador, during the period 2000-2015. The results show that the model that best fit the data of this time series is the ARIMA model (1, 1, 1)x(0, 0, 1) 12 with seasonality. This model shows that the energy monthly production can be forecasted to one and twelve months. The range used was from 2000 to 2014 and it was validated with data from January to December of 2015. With this model, the forecast is made for the year 2020, proving an increase of monthly production. The real values are in the confidence interval of the predicted values of the ARIMA model with annual seasonality. This model will help to describe and predict hydroelectric energy generation of Ecuador. In other words, it could be used in future planning studies of the electric sector.


title: Features for image retrieval: an experimental comparison

abstract: An experimental comparison of a large number of different image descriptors for content-based image retrieval is presented. Many of the papers describing new techniques and descriptors for content-based image retrieval describe their newly proposed methods as most appropriate without giving an in-depth comparison with all methods that were proposed earlier. In this paper, we first give an overview of a large variety of features for content-based image retrieval and compare them quantitatively on four different tasks: stock photo retrieval, personal photo collection retrieval, building retrieval, and medical image retrieval. For the experiments, five different, publicly available image databases are used and the retrieval performance of the features is analyzed in detail. This allows for a direct comparison of all features considered in this work and furthermore will allow a comparison of newly proposed features to these in the future. Additionally, the correlation of the features is analyzed, which opens the way for a simple and intuitive method to find an initial set of suitable features for a new task. The article concludes with recommendations which features perform well for what type of data. Interestingly, the often used, but very simple, color histogram performs well in the comparison and thus can be recommended as a simple baseline for many applications.


title: Harmonically Informed Multi-Pitch Tracking

abstract: This paper presents a novel system for multi-pitch tracking, i.e. estimate the pitch trajectory of each monophonic source in a mixture of harmonic sounds. The system consists of two stages: multi-pitch estimation and pitch trajectory formation. In the first stage, we propose a new approach based on modeling spectral peaks and non-peak regions to estimate pitches and polyphony in each single frame. In the second stage, we view the pitch trajectory formation problem as a constrained clustering problem of pitch estimates in all the frames. Constraints are imposed on some pairs of pitch estimates, according to time and frequency proximity. In clustering, harmonic structure is employed as the feature. The proposed system is tested on 10 recorded four-part J. S. Bach chorales. Both multi-pitch estimation and tracking results are very promising. In addition, for multi-pitch estimation, the proposed system is shown to outperform a state-of-the-art multi-pitch estimation approach.


title: Has this prepubertal girl been sexually abused?

abstract: CONTEXT ::: The legal and social sequelae of interpreting genital findings as indicative of sexual abuse are significant. While the absence of genital trauma does not rule out sexual abuse, the physical examination can identify genital findings compatible with sexual abuse. ::: ::: ::: OBJECTIVES ::: To determine the diagnostic utility of the genital examination in prepubertal girls for identifying nonacute sexual abuse. ::: ::: ::: DATA SOURCES ::: Published articles (1966-October 2008) that appeared in the MEDLINE database and were indexed under the search terms of child abuse, sexual or child abuse and either physical examination; genitalia; female, diagnosis; or sensitivity and specificity; and bibliographies of retrieved articles and textbooks. ::: ::: ::: STUDY SELECTION ::: Three of the authors independently reviewed titles of articles obtained from MEDLINE and selected articles for full-text review. ::: ::: ::: DATA EXTRACTION ::: Two authors independently abstracted data to calculate sensitivity, specificity, and likelihood ratios for the diagnosis of nonacute genital trauma caused by sexual abuse in prepubertal girls. ::: ::: ::: RESULTS ::: Data were not pooled due to study heterogeneity. The presence of vaginal discharge (positive likelihood ratio, 2.7; 95% confidence interval, 1.2-6.0) indicates an increased likelihood of sexual abuse. In the posterior hymen, hymenal transections, deep notches, and perforations prompt concerns for genital trauma from sexual abuse, but the sensitivity is unknown. Without a history of genital trauma from sexual abuse, the majority of prepubertal girls will not have a hymenal transection (specificity close to 100%). ::: ::: ::: CONCLUSIONS ::: Vaginal discharge as well as posterior hymenal transections, deep notches, and perforations raise the suspicion for sexual abuse in a prepubertal girl, but the findings do not independently confirm the diagnosis. Given the broad 95% confidence intervals around the likelihood ratios for the presence of findings along with the low or unknown sensitivity of all physical examination findings evaluated, the physical examination cannot independently confirm or exclude nonacute sexual abuse as the cause of genital trauma in prepubertal girls.


title: Coupling 3D eulerian, heightfield and particle methods for interactive simulation of large scale liquid phenomena

abstract: We propose a new method to simulate large scale water phenomena by combining particle, 3D grid and height field methods. In contrast to most hybrid approaches that use particles to simulate foam and spray only, we also represent the bulk of water near the surface with both particles and a grid depending on the regions of interest and switch between those two representations during the course of the simulation. For the coupling we leverage the recent idea of tracking the water surface with a density field in grid based methods. Combining particles and a grid simulation then amounts to adding the density field of the particles and the one stored on the grid. For open scenes, we simulate the water outside of the 3D grid domain by solving the Shallow Water Equations on a height field. We propose new methods to couple these two domains such that waves travel naturally across the border. We demonstrate the effectiveness of our approach in various scenarios including a whale breaching simulation, all running in real-time or at interactive rates.


title: Reconfigurable Routers for Low Power and High Performance

abstract: Network-on-chip (NoC) designs are based on a compromise among latency, power dissipation, or energy, and the balance is usually defined at design time. However, setting all parameters, such as buffer size, at design time can cause either excessive power dissipation (originated by router under utilization), or a higher latency. The situation worsens whenever the application changes its communication pattern, e.g., a portable phone downloads a new service. Large buffer sizes can ensure performance during the execution of different applications, but unfortunately, these same buffers are mainly responsible for the router total power dissipation. Another aspect is that by sizing buffers for the worst case latency incurs extra dissipation for the mean case, which is much more frequent. In this paper we propose the use of a reconfigurable router, where the buffer slots are dynamically allocated to increase router efficiency in an NoC, even under rather different communication loads. In the proposed architecture, the depth of each buffer word used in the input channels of the routers can be reconfigured at run time. The reconfigurable router allows up to 52% power savings, while maintaining the same performance as that of a homogeneous router, but using a 64% smaller buffer size.


title: Cluster Based Routing Protocols: A Comparative Study

abstract: Wireless Sensor Network is an infrastructure less network consisting of small, lightweight, low power, and inexpensive wireless sensor nodes which gather and propagate the environmental data. Different routing protocols have been proposed for wireless sensor networks for efficient routing. Each protocol has its own way of routing which vary from each other in terms of the parameters selected or the approach. Cluster based routing is one of the famous routing approaches in which the head node gathers data from all other nodes in the cluster, performs certain aggregation function and then sends that aggregated data to the base station. In this paper i will present the comparative study of few cluster based routing protocols.


title: Automatic music genre classification using ensemble of classifiers

abstract: This paper presents a novel approach to the task of automatic music genre classification which is based on multiple feature vectors and ensemble of classifiers. Multiple feature vectors are extracted from a single music piece. First, three 30-second music segments, one from the beginning, one from the middle and one from end part of a music piece are selected and feature vectors are extracted from each segment. Individual classifiers are trained to account for each feature vector extracted from each music segment. At the classification, the outputs provided by each individual classifier are combined through simple combination rules such as majority vote, max, sum and product rules, with the aim of improving music genre classification accuracy. Experiments carried out on a large dataset containing more than 3,000 music samples from ten different Latin music genres have shown that for the task of automatic music genre classification, the features extracted from the middle part of the music provide better results than using the segments from the beginning or end part of the music. Furthermore, the proposed ensemble approach, which combines the multiple feature vectors, provides better accuracy than using single classifiers and any individual music segment.


Fabrication and Characterization of Nano-Crystalline TiO2 Thin Film Electrodes for Lithium Ion Batteries


title: BanglaLekha-Isolated: A Comprehensive Bangla Handwritten Character Dataset

abstract: Bangla handwriting recognition is becoming a very important issue nowadays. It is potentially a very important task specially for Bangla speaking population of Bangladesh and West Bengal. By keeping that in our mind we are introducing a comprehensive Bangla handwritten character dataset named BanglaLekha-Isolated. This dataset contains Bangla handwritten numerals, basic characters and compound characters. This dataset was collected from multiple geographical location within Bangladesh and includes sample collected from a variety of aged groups. This dataset can also be used for other classification problems i.e: gender, age, district. This is the largest dataset on Bangla handwritten characters yet.


title: Robust Fake Iris Detection Based on Variation of the Reflectance Ratio Between the IRIS and the Sclera

abstract: In this paper, we propose a new fake iris detection method based on the changes in the reflectance ratio between the iris and the sclera. The proposed method has four advantages over previous works. First, it is possible to detect fake iris images with high accuracy. Second, our method does not cause inconvenience to users since it can detect fake iris images at a very fast speed. Third, it is possible to show the theoretical background of using the variation of the reflectance ratio between the iris and the sclera. To compare fake iris images with live ones, three types of fake iris images were produced: a printed iris, an artificial eye, and a fake contact lens. In the experiments, we prove that the proposed fake iris detection method achieves high performance when distinguishing between live and fake iris.


title: A Novel Un-differenced PPP-RTK Concept

abstract: In this contribution, a novel un-differenced (UD) (PPP-RTK) concept, i.e. a synthesis of Precise Point Positioning and Network-based Real-Time Kinematic concept, is introduced. In the first step of our PPP-RTK approach, the UD GNSS observations from a regional reference network are processed based upon re-parameterised observation equations, corrections for satellite clocks, phase biases and (interpolated) atmospheric delays are calculated and provided to users. In the second step, these network-based corrections are used at the user site to restore the integer nature of his UD phase ambiguities, which makes rapid and high accuracy user positioning possible. The proposed PPP-RTK approach was tested using two GPS CORS networks with inter-station distances ranging from 60 to 100 km. The first test network is the northern China CORS network and the second is the Australian Perth CORS network. In the test of the first network, a dual-frequency PPP-RTK user receiver was used, while in the test of the second network, a low-cost, single-frequency PPP-RTK user receiver was used. The performance of fast ambiguity resolution and the high accuracy positioning of the PPP-RTK results are demonstrated.


title: Towards Robust Deep Neural Networks with BANG

abstract: Machine learning models, including state-of-the-art deep neural networks, are vulnerable to small perturbations that cause unexpected classification errors. This unexpected lack of robustness raises fundamental questions about their generalization properties and poses a serious concern for practical deployments. As such perturbations can remain imperceptible - the formed adversarial examples demonstrate an inherent inconsistency between vulnerable machine learning models and human perception - some prior work casts this problem as a security issue. Despite the significance of the discovered instabilities and ensuing research, their cause is not well understood and no effective method has been developed to address the problem. In this paper, we present a novel theory to explain why this unpleasant phenomenon exists in deep neural networks. Based on that theory, we introduce a simple, efficient, and effective training approach, Batch Adjusted Network Gradients (BANG), which significantly improves the robustness of machine learning models. While the BANG technique does not rely on any form of data augmentation or the utilization of adversarial images for training, the resultant classifiers are more resistant to adversarial perturbations while maintaining or even enhancing the overall classification performance.


title: Cyber Assessment Methods for SCADA Security

abstract: The terrorist attacks of September 11, 2001 brought to light threats and vulnerabilities that face the United States. In response, the U.S. Government is directing the effort to secure the nation's critical infrastructure by creating programs to implement the National Strategy to Secure Cyberspace (1). One part of this effort involves assessing Supervisory Control and Data Acquisition (SCADA) systems. These systems are essential to the control of critical elements of our national infrastructure, such as electric power, oil, and gas production and distribution. Since their incapacitation or destruction would have a debilitating impact on the defense or economic security of the United States, one of the main objectives of this program is to identify vulnerabilities and encourage the public and private sectors to work together to design secure control systems that resolve these weaknesses. This paper describes vulnerability assessment methodologies used in ongoing research and assessment activities designed to identify and resolve vulnerabilities so as to improve the security of the nation's critical infrastructure.


title: CREDIT RISK MANAGEMENT IN COMMERCIAL BANKS

abstract: The article proposes a model of credit risk assessment on the basis of factor analysis of retail clients / borrowers in order to ensure predictive control of the level of risk posed by potential clients in commercial banks engaged in consumer lending. The aim of the study is to determine the level of risk represented by different groups (classes) of retail clients (borrowers) in order to reduce and prevent credit risk in the future as well as to improve the management of banking risks. The main results of the study are the creation of a model of borrowers’ internal credit ratings and the development of the methods of improving credit risk management in commercial banks.


title: Entrepreneurs traits and e-commerce adoption: an empirical study

abstract: Entrepreneurs in Malaysia are playing an important role for the economic development of the country. Due to the potential benefits of e-commerce, entrepreneurs of Malaysia have the opportunity to expand their market in the world. The purpose of the study is to find out the extent of the effect of entrepreneurial personality traits towards the e-commerce adoption in the context of small and medium enterprise industries in Malaysia. In selecting the survey sample, the convenient sampling of non-probability sampling was adopted. Most of the major towns in Peninsular Malaysia have been chosen for the purpose of collecting the data. During the data collection, a total of 1480 questionnaire were distributed and 1416 returned usable questionnaire (96.67 percent response rate). The study revealed that the personality traits did have some significant impact on the e-commerce adoption among the entrepreneurs in Malaysia. The recommendation, limitation and conclusion are then discussed at the end of the paper.


title: POST-ADOPTION SWITCHING BETWEEN TECHNOLOGY SUBSTITUTES: THE CASE OF WEB BROWSERS

abstract: In this study, we examine factors that influence users’ post-adoption switching between technology products that are near perfect substitutes. The recent introduction of Mozilla Firefox Web browser provided an ideal empirical setting for this study. Drawing upon literature on post-adoption user behavior, consumer behavior, and online consumer research, we proposed a research model and validated it using cross-sectional field data collected from 306 users on their decisions to switch from Microsoft Internet Explorer to Mozilla Firefox. Findings suggest that user satisfaction and breadth of use of the incumbent product are negatively associated with switching behavior, and perceived ease of use, relative advantage, and perceived security of the substitute product are positively associated with switching behavior. This study contributes to both research and practice by advancing our understanding of users’ post-adoption behavior in general and their switching behavior on Web-related technology products in specific.


title: ACTIVITY RECOGNITION ON SMART DEVICES: Dealing with diversity in the wild

abstract: Smart consumer devices are omnipresent in our everyday lives. We find novel interesting applications of smart devices, such as mobile phones, tablets, smartwatches and smartglasses, in monitoring personal health, tracking sporting performances, identifying physical activities and obtaining navigation information among many others. These novel applications make use of a large variety of the smart devices' sensors, such as accelerometer, gyroscope and GPS. However, the usefulness of these applications relies often primarily on the abilities of interpreting noisy, and often biased, measurements from said sensors in order to extract high-level context information, such as the activity currently performed by the user.


title: Child sexual abuse and subsequent psychopathology: results from the National Comorbidity Survey

abstract: OBJECTIVES: This study examined the relationship between child sexual abuse (CSA) and subsequent onset of psychiatric disorders, accounting for other childhood adversities, CSA type, and chronicity of the abuse. METHODS: Retrospective reports of CSA, other adversities, and psychiatric disorders were obtained by the National Comorbidity Survey, a nationally representative survey of the United States (n = 5877). Reports were analyzed by multivariate methods. RESULTS: CSA was reported by 13.5% of women and 2.5% of men. When other childhood adversities were controlled for, significant associations were found between CSA and subsequent onset of 14 mood, anxiety, and substance use disorders among women and 5 among men. In a subsample of respondents reporting no other adversities, odds of depression and substance problems associated with CSA were higher. Among women, rape (vs molestation), knowing the perpetrator (vs strangers), and chronicity of CSA (vs isolated incidents) were associated with higher odds of some disorders. CONCLUSIONS: CSA usually occurs as part of a larger syndrome of childhood adversities. Nonetheless, CSA, whether alone or in a larger adversity cluster, is associated with substantial increased risk of subsequent psychopathology.


title: Human hand gesture recognition using a convolution neural network

abstract: Automatic human gesture recognition from camera images is an interesting topic for developing intelligent vision systems. In this paper, we propose a convolution neural network (CNN) method to recognize hand gestures of human task activities from a camera image. To achieve the robustness performance, the skin model and the calibration of hand position and orientation are applied to obtain the training and testing data for the CNN. Since the light condition seriously affects the skin color, we adopt a Gaussian Mixture model (GMM) to train the skin model which is used to robustly filter out non-skin colors of an image. The calibration of hand position and orientation aims at translating and rotating the hand image to a neutral pose. Then the calibrated images are used to train the CNN. In our experiment, we provided a validation of the proposed method on recognizing human gestures which shows robust results with various hand positions and orientations and light conditions. Our experimental evaluation of seven subjects performing seven hand gestures with average recognition accuracies around 95.96% shows the feasibility and reliability of the proposed method.


title: Tracking-error model-based predictive control for mobile robots in real time

abstract: In this paper, a model-predictive trajectory-tracking control applied to a mobile robot is presented. Linearized tracking-error dynamics is used to predict future system behavior and a control law is derived from a quadratic cost function penalizing the system tracking error and the control effort. Experimental results on a real mobile robot are presented and a comparison of the control obtained with that of a time-varying state-feedback controller is given. The proposed controller includes velocity and acceleration constraints to prevent the mobile robot from slipping and a Smith predictor is used to compensate for the vision-system dead-time. Some ideas for future work are also discussed.


title: Dual-Band Circularly Polarized Antennas Using Stacked Patches With Asymmetric U-Slots

abstract: In this letter, a new design for single-feed dual-band circularly polarized microstrip antennas is presented. A stacked- patch configuration is used for the antenna, and circular polarization is achieved by designing asymmetrical U-slots on the patches. The dimensions of the U-slots are optimized to achieve circular polarization in both bands. A prototype has been designed to operate at two frequencies with a ratio of 1.66. Both experimental and theoretical results are presented and discussed. The circularly polarized bandwidth of the antenna is 1.0% at 3.5 GHz (WiMax) and 3.1% at 5.8 GHz (HiperLAN).


title: Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation

abstract: Image segmentation is a fundamental problem in biomedical image analysis. Recent advances in deep learning have achieved promising results on many biomedical image segmentation benchmarks. However, due to large variations in biomedical images (different modalities, image settings, objects, noise, etc.), to utilize deep learning on a new application, it usually needs a new set of training data. This can incur a great deal of annotation effort and cost, because only biomedical experts can annotate effectively, and often there are too many instances in images (e.g., cells) to annotate. In this paper, we aim to address the following question: With limited effort (e.g., time) for annotation, what instances should be annotated in order to attain the best performance? We present a deep active learning framework that combines fully convolutional network (FCN) and active learning to significantly reduce annotation effort by making judicious suggestions on the most effective annotation areas. We utilize uncertainty and similarity information provided by FCN and formulate a generalized version of the maximum set cover problem to determine the most representative and uncertain areas for annotation. Extensive experiments using the 2015 MICCAI Gland Challenge dataset and a lymph node ultrasound image segmentation dataset show that, using annotation suggestions by our method, state-of-the-art segmentation performance can be achieved by using only 50% of training data.


title: Magnesium and mood disorders: systematic review and meta-analysis

abstract: Background Magnesium (Mg 2+ ) has received considerable attention with regards to its potential role in the pathophysiology of the mood disorders, but the available evidence seems inconclusive. Aims To review and quantitatively summarise the human literature on Mg 2+ intake and Mg 2+ blood levels in the mood disorders and the effects of Mg 2+ supplements on mood. Method Systematic review and meta-analyses. Results Adherence to a Mg 2+ -rich diet was negatively associated with depression in cross-sectional (odds ratio = 0.66) but not in prospective studies. Mg 2+ levels in bodily fluids were on average higher in patients with a mood disorder (Hedge's g = 0.19), but only in patients treated with antidepressants and/or mood stabilisers. There was no evident association between Mg 2+ levels and symptom severity. Mg 2+ supplementation was associated with a decline in depressive symptoms in uncontrolled ( g = −1.60) but not in placebo-controlled trials ( g = −0.21). Conclusion Our results provide little evidence for the involvement of Mg 2+ in the mood disorders. Declaration of interest None.


title: Initial sequencing and analysis of the human genome

abstract: The human genome holds an extraordinary trove of information about human development, physiology, medicine and evolution. Here we report the results of an international collaboration to produce and make freely available a draft sequence of the human genome. We also present an initial analysis of the data, describing some of the insights that can be gleaned from the sequence.


Predicting Categorical Emotions by Jointly Learning Primary and Secondary Emotions through Multitask Learning


title: Hierarchical Relational Attention for Video Question Answering

abstract: Video Question Answering (VideoQA) tasks require understanding of the connection of context specific video parts which are temporally distributed. Humans are capable of focusing on temporally distributed video scenes and also to find correspondence or relationships among these segments. To achieve similar capability, a hierarchical relational attention mechanism is proposed in this paper. The proposed VideoQA model derives attention on temporal segments i.e. video features based on each of the question words. Also, contextual relevance of these temporal segments are captured to derive the final video representation which leads to a better reasoning capability. We evaluate the performance of the proposed approach on the MSRVTT-QA and the MSVD-QA datasets to establish its superior performance over the state of the art.


title: Joint Syntactic and Semantic Parsing with Combinatory Categorial Grammar

abstract: We present an approach to training a joint syntactic and semantic parser that combines syntactic training information from CCGbank with semantic training information from a knowledge base via distant supervision. The trained parser produces a full syntactic parse of any sentence, while simultaneously producing logical forms for portions of the sentence that have a semantic representation within the parser’s predicate vocabulary. We demonstrate our approach by training a parser whose semantic representation contains 130 predicates from the NELL ontology. A semantic evaluation demonstrates that this parser produces logical forms better than both comparable prior work and a pipelined syntax-then-semantics approach. A syntactic evaluation on CCGbank demonstrates that the parser’s dependency Fscore is within 2.5% of state-of-the-art.


Robot Learning from Demonstration : Kinesthetic Teaching vs . Teleoperation


title: Learning from Linked Open Data Usage: Patterns & Metrics

abstract: Although the cloud of Linked Open Data has been growing continuously for several years, little is known about the particular features of linked data usage. Motivating why it is important to understand the usage of Linked Data, we describe typical linked data usage scenarios and contrast the so derived requirement with conventional server access analysis. Then, we report on usage patterns found through an indepth analysis of access logs of four popular LOD datasets. Eventually, based on the usage patterns we found in the analysis, we propose metrics for assessing Linked Data usage from the human and the machine perspective, taking into account dierent agent types and resource representations.


title: A self-adaptive estimation of distribution algorithm with differential evolution strategy for supermarket location problem

abstract: In modern production systems, an ever-rising product variety has imposed great challenges for in-plant part supply systems used to feed mixed-model assembly lines with required parts. In recent years, many automotive manufacturers have identified the supermarket concept as an efficient part feeding strategy to enable JIT (Just-in-time) deliveries at low costs. This paper studies a discrete supermarket location problem which considers the utilization rate and capacity constraint of the supermarkets simultaneously. Firstly, a mathematical model is developed with the objective of minimizing the total system cost consisting of operating cost and transportation cost. Then, a self-adaptive estimation of distribution algorithm with differential evolution strategy, named DE/AEDA, is proposed to solve the problem. Finally, computational experiments are carried out to analyze the performance of the proposed algorithm compared with the benchmark algorithm by using a non-parametric test method. The results indicate that the proposed algorithm is valid and efficient.


Poisoning Network Visibility in Software-Defined Networks: New Attacks and Countermeasures


title: Context-aware Learning for Sentence-level Sentiment Analysis with Posterior Regularization

abstract: This paper proposes a novel context-aware method for analyzing sentiment at the level of individual sentences. Most existing machine learning approaches suffer from limitations in the modeling of complex linguistic structures across sentences and often fail to capture nonlocal contextual cues that are important for sentiment interpretation. In contrast, our approach allows structured modeling of sentiment while taking into account both local and global contextual information. Specifically, we encode intuitive lexical and discourse knowledge as expressive constraints and integrate them into the learning of conditional random field models via posterior regularization. The context-aware constraints provide additional power to the CRF model and can guide semi-supervised learning when labeled data is limited. Experiments on standard product review datasets show that our method outperforms the state-of-theart methods in both the supervised and semi-supervised settings.


title: WordSup: Exploiting Word Annotations for Character Based Text Detection

abstract: Imagery texts are usually organized as a hierarchy of several visual elements, i.e. characters, words, text lines and text blocks. Among these elements, character is the most basic one for various languages such as Western, Chinese, Japanese, mathematical expression and etc. It is natural and convenient to construct a common text detection engine based on character detectors. However, training character detectors requires a vast of location annotated characters, which are expensive to obtain. Actually, the existing real text datasets are mostly annotated in word or line level. To remedy this dilemma, we propose a weakly supervised framework that can utilize word annotations, either in tight quadrangles or the more loose bounding boxes, for character detector training. When applied in scene text detection, we are thus able to train a robust character detector by exploiting word annotations in the rich large-scale real scene text datasets, e.g. ICDAR15 and COCO-text. The character detector acts as a key role in the pipeline of our text detection engine. It achieves the state-of-the-art performance on several challenging scene text detection benchmarks. We also demonstrate the flexibility of our pipeline by various scenarios, including deformed text detection and math expression recognition.


title: Surfing the money tides: Understanding the foreign exchange market through metaphors

abstract: This study describes metaphorical conceptualizations of the foreign exchange market held by market participants and examines how these metaphors socially construct the financial market. Findings are based on 55 semi-structured interviews with senior foreign exchange experts at banks and at financial news providers in Europe. We analysed interview transcripts by metaphor analysis, a method based on cognitive linguistics. Results indicate that market participants' understanding of financial markets revolves around seven metaphors, namely the market as a bazaar, as a machine, as gambling, as sports, as war, as a living being and as an ocean. Each of these metaphors highlights and conceals certain aspects of the foreign exchange market and entails a different set of implications on crucial market dimensions, such as the role of other market participants and market predictability. A correspondence analysis supports our assumption that metaphorical thinking corresponds with implicit assumptions about market predictability. A comparison of deliberately generated and implicitly used metaphors reveals notable differences. In particular, implicit metaphors are predominantly organic rather than mechanical. In contrast to academic models, interactive and organic metaphors, and not the machine metaphor, dominate the market accounts of participants.


title: [Chemical constituents from the root of Rubus chroosepalus].

abstract: AIM ::: To investigate the chemical constituents of Rubus chroosepalus Focke. ::: ::: ::: METHODS ::: The methanol extracts were suspended in H2O and then extracted with EtOAc. Column chromatography was used for separation and purification, while spectral analysis was used for identification. ::: ::: ::: RESULTS ::: Seven compounds were isolated and identified as 2 alpha, 3 beta-dihydroxy-urs-12, 19-dien-23, 28-oic acid (I), 2 alpha, 3 beta, 23-trihydroxy-urs-12,18-dien-28-oic acid (IIa), 2 alpha, 3 beta, 23-trihydroxy-urs-12,19-dien-28-oic acid (IIb), 2 alpha, 3 alpha-dihydroxy-urs-12,18-dien-28-oic acid (IIIa), 2 alpha, 3 alpha-dihydroxy-urs-12,19-dien-28-oic acid (IIIb), and the acetonide of IIIa and IIIb (IVa and IVb). ::: ::: ::: CONCLUSION ::: I was found to be a new pentacyclic triterpenoid acid.


title: CAPTURE: A New Predictive Anti-Poaching Tool for Wildlife Protection

abstract: Wildlife poaching presents a serious extinction threat to many animal species. Agencies ("defenders") focused on protecting such animals need tools that help analyze, model and predict poacher activities, so they can more effectively combat such poaching; such tools could also assist in planning effective defender patrols, building on the previous security games research. ::: ::: To that end, we have built a new predictive anti-poaching tool, CAPTURE (Comprehensive Anti-Poaching tool with Temporal and observation Uncertainty REasoning). CAPTURE provides four main contributions. First, CAPTURE's modeling of poachers provides significant advances over previous models from behavioral game theory and conservation biology. This accounts for:(i) the defender's imperfect detection of poaching signs; (ii) complex temporal dependencies in the poacher's behaviors; (iii) lack of knowledge of numbers of poachers. Second, we provide two new heuristics: parameter separation and target abstraction to reduce the computational complexity in learning the poacher models. Third, we present a new game-theoretic algorithm for computing the defender's optimal patrolling given the complex poacher model. Finally, we present detailed models and analysis of real-world poaching data collected over 12 years in Queen Elizabeth National Park in Uganda to evaluate our new model's prediction accuracy. This paper thus presents the largest dataset of real-world defender-adversary interactions analyzed in the security games literature. CAPTURE will be tested in Uganda in early 2016.


title: A General Framework for Agglomerative Hierarchical Clustering Algorithms

abstract: This paper presents a general framework for agglomerative hierarchical clustering based on graphs. Different hierarchical agglomerative clustering algorithms can be obtained from this framework, by specifying an inter-cluster similarity measure, a subgraph of the 13-similarity graph, and a cover routine. We also describe two methods obtained from this framework called hierarchical compact algorithm and hierarchical star algorithm. These algorithms have been evaluated using standard document collections. The experimental results show that our methods are faster and obtain smaller hierarchies than traditional hierarchical algorithms while achieving a similar clustering quality


title: Keyword search over RDF graphs

abstract: Large knowledge bases consisting of entities and relationships between them have become vital sources of information for many applications. Most of these knowledge bases adopt the Semantic-Web data model RDF as a representation model. Querying these knowledge bases is typically done using structured queries utilizing graph-pattern languages such as SPARQL. However, such structured queries require some expertise from users which limits the accessibility to such data sources. To overcome this, keyword search must be supported. In this paper, we propose a retrieval model for keyword queries over RDF graphs. Our model retrieves a set of subgraphs that match the query keywords, and ranks them based on statistical language models. We show that our retrieval model outperforms the-state-of-the-art IR and DB models for keyword search over structured data using experiments over two real-world datasets.


title: A Compact Highly Reconfigurable CMOS MMIC Directional Coupler

abstract: This paper presents a tunable CMOS directional coupler that utilizes lumped-element L-C sections. The lumped-element approach used to build the directional coupler makes it possible to integrate the coupler onto a single monolithic microwave integrated circuit (MMIC), as it occupies a small area compared to printed designs. The directional coupler uses varactors and tunable active inductors (TAIs) to synthesize the series and shunt reactances, respectively, which allows extensive electronic control over the coupling coefficient, while insuring a low return loss and a very high isolation. Furthermore, using varactors and TAIs allows the directional coupler to be reconfigured for operation over a wide range of frequencies. Moreover, the symmetric configuration of the coupler allows it to switch from forward to backward operation by simply exchanging the bias voltages applied across the series varactors. The MMIC coupler was fabricated in a standard 0.13-mum CMOS process and operates from a 1.5-V supply. The circuit occupies 730 mum times 600 mum, and is capable of achieving tunable coupling coefficients from 1.4 to 7.1 dB, while maintaining an isolation higher than 41 dB. The MMIC coupler is also capable of operating at any center frequency over the 2.1-3.1-GHz frequency range with higher than 40-dB isolation. The coupler achieves a -4.1-dBm 1-dB compression point while operating from a 1.5-V supply.


title: Ultrawideband Metasurface Lenses Based on Off-Shifted Opposite Layers

abstract: Metasurfaces have demonstrated to be a low-cost solution for the development of directive antennas at high frequency. One of the opportunities of metasurfaces is the possibility to produce planar l ...


title: Parallelizing Skip Lists for In-Memory Multi-Core Database Systems

abstract: Due to the coarse granularity of data accesses and the heavy use of latches, indices in the B-tree family are not efficient for in-memory databases, especially in the context of today's multi-core architecture. In this paper, we study the parallelizability of skip lists for the parallel and concurrent environment, and present PSL, a Parallel in-memory Skip List that lends itself naturally to the multi-core environment, particularly with non-uniform memory access. For each query, PSL traverses the index in a Breadth-First-Search (BFS) to find the list node with the matching key, and exploits SIMD processing to speed up this process. Furthermore, PSL distributes incoming queries among multiple execution threads disjointly and uniformly to eliminate the use of latches and achieve a high parallelizability. The experimental results show that PSL is comparable to a readonly index, FAST, in terms of read performance, and outperforms ART and Masstree respectively by up to 30% and 5x for a variety of workloads.


title: A replicated study on correlating agile team velocity measured in function and story points

abstract: Since the rapid growth of agile development methods for software engineering, more and more organizations measure the size of iterations, releases, and projects in both function points and story points. In 2011 Santana et al. performed a case study on the relation between function points and story points, from data collected in a Brazilian Government Agency. In this paper we replicate this study, using data collected in a Dutch banking organization. Based on a statistical correlation test we find that a comparison between function points and story points as measured in our repository indicates a moderate negative linear relation, where Santana et al. concluded a strong positive linear relation between both size metrics in their case study. Based on the outcome of our study we conclude that it appears too early to make generic claims on the relation between function points and story points; in fact FSM-theory seems to underpin that such a relationship is a spurious one.


title: Autonomous Robot Navigation in Highly Populated Pedestrian Zones

abstract: In the past, there has been a tremendous amount of progress in the area of autonomous robot navigation, and a large variety of robots have been developed that demonstrated robust navigation capabilities indoors, in nonurban outdoor environments, or on roads; relatively few approaches have focused on navigation in urban environments such as city centers. Urban areas, however, introduce numerous challenges for autonomous robots as they are rather unstructured and dynamic. In this paper, we present a navigation system for mobile robots designed to operate in crowded city environments and pedestrian zones. We describe the different components of this system, including a simultaneous localization and mapping module for dealing with huge maps of city centers, a planning component for inferring feasible paths, taking into account the traversability and type of terrain, a module for accurate localization in dynamic environments, and the means for calibrating and monitoring the platform. Our navigation system has been implemented and tested in several large-scale field tests, in which a real robot autonomously navigated over several kilometers in a complex urban environment. This also included a public demonstration, during which the robot autonomously traveled along a more than 3-km-long route through the city center of Freiburg, Germany.


title: Exploiting Syntactico-Semantic Structures for Relation Extraction

abstract: In this paper, we observe that there exists a second dimension to the relation extraction (RE) problem that is orthogonal to the relation type dimension. We show that most of these second dimensional structures are relatively constrained and not difficult to identify. We propose a novel algorithmic approach to RE that starts by first identifying these structures and then, within these, identifying the semantic type of the relation. In the real RE problem where relation arguments need to be identified, exploiting these structures also allows reducing pipelined propagated errors. We show that this RE framework provides significant improvement in RE performance.


title: Securing the Internet of Things: A Standardization Perspective

abstract: The Internet of Things (IoT) is the next wave of innovation that promises to improve and optimize our daily life based on intelligent sensors and smart objects working together. Through Internet Protocol (IP) connectivity, devices can now be connected to the Internet, thus allowing them to be read, controlled, and managed at any time and at any place. Security is an important aspect for IoT deployments. However, proprietary security solutions do not help in formulating a coherent security vision to enable IoT devices to securely communicate with each other in an interoperable manner. This paper gives an overview of the efforts in the Internet Engineering Task Force (IETF) to standardize security solutions for the IoT ecosystem. We first provide an in-depth review of the communication security solutions for IoT, specifically the standard security protocols to be used in conjunction with the Constrained Application Protocol (CoAP), an application protocol specifically tailored to the needs of adapting to the constraints of IoT devices. Since Datagram Transport Layer Security (DTLS) has been chosen as the channel security underneath CoAP, this paper also discusses the latest standardization efforts to adapt and enhance the DTLS for IoT applications. This includes the use of 1) raw public key in DTLS; 2) extending DTLS record Layer to protect group (multicast) communication; and 3) profiling DTLS for reducing the size and complexity of implementations on embedded devices. We also provide an extensive review of compression schemes that are being proposed in IETF to mitigate message fragmentation issues in DTLS.


title: Evaluation of pelvic floor muscle function in a random group of adult women in Austria

abstract: Despite an increasing clinical interest in female pelvic floor function, there is a lack of data with respect to the knowledge of average adult women about the physiological role of the pelvic floor and their ability to contract pelvic floor muscles (PFM) voluntarily. It was the aim of our study to evaluate the percentage of PFM dysfunction in adult women and the impact of risk factors, such as age, body mass index (BMI), number of children delivered, and the influence of previous PFM training. A total of 343 Austrian adult women (mean age, 41.2 ± 14.6 years; range, 18–79 years), selected at random, were examined to test their ability to contract the PFM. The examination was carried out by three independent gynecologists during the course of a routine gynecological visit. The ability to contract the PFM voluntarily or involuntarily was assessed by digital intravaginal palpation with the patients in a supine position. The muscle strength was graded according to the Modified Oxford Grading Scale by Laycock. A high percentage (44.9%) of the women was not able to voluntarily perform a normal PFM contraction. In only 26.5%, an involuntary contraction of the pelvic floor was present before an increase in intra-abdominal pressure. The inability to contract the PFM did not correlate with women’s age but revealed a weak relationship with the number of childbirths and the patient’s BMI. A significant correlation was found between the Oxford Grading Scale rating and the patient’s report about previous PFM training.


title: Simulation of Vision-Based For Automatic Takeoff and Landing Hexacopter on a Moving Ground Vehicle

abstract: In this paper presents Software In the Loop Simulation (SILS) and explain about the existing systems such as frame hexacopter, flight dynamics, on-board sensors, such as the IMU, external imaging sensor and the environment presented integrated with ROS and Gazebo simulator. After this simulation is done in the Gazebo simulator, the author will be implemented into the real hardware. The choice of platform for UAV is tarot t-680 hexacopter, it have six rotor aerial vehicle. To perform a precise landing add an external imaging sensor camera 3D intel realsense R200 static mounted facing downwards. Where the hexacopter will estimate pose and then proceed to altitude control its for autonomous landing on a moving UGV.


title: The Hierarchical Operational Space Formulation: Stability Analysis for the Regulation Case

abstract: The Operational Space Formulation (OSF) from the 1980s is probably the most frequently applied task-space controller in robotics. In multipriority control of redundant robots via the OSF, a feedback linearization is performed on the first hierarchy level while lower-priority tasks are executed in the dynamically consistent null space of the Jacobian matrices of all higher-priority tasks without disturbing them. However, it has been observed in the past that a formal stability analysis for the overall closed loop is rather difficult, especially for the null space dynamics. Except for exponential stability on the main task level, a complete proof is still missing when the tasks conflict with each other. Here, we provide this formal proof of asymptotic stability for the regulation case of a passivity-based OSF controller by means of conditional stability theory and semidefinite Lyapunov functions. Simulations support the intuitive, energy-based interpretation of the proof. This stability analysis lifts the widely used OSF onto a more solid foundation.


title: Learning from Clinical Judgments: Semi-Markov-Modulated Marked Hawkes Processes for Risk Prognosis

abstract: Critically ill patients in regular wards are vulnerable to unanticipated adverse events which require prompt transfer to the intensive care unit (ICU). To allow for accurate prognosis of deteriorating patients, we develop a novel continuous-time probabilistic model for a monitored patient's temporal sequence of physiological data. Our model captures"informatively sampled"patient episodes: the clinicians' decisions on when to observe a hospitalized patient's vital signs and lab tests over time are represented by a marked Hawkes process, with intensity parameters that are modulated by the patient's latent clinical states, and with observable physiological data (mark process) modeled as a switching multi-task Gaussian process. In addition, our model captures"informatively censored"patient episodes by representing the patient's latent clinical states as an absorbing semi-Markov jump process. The model parameters are learned from offline patient episodes in the electronic health records via an EM-based algorithm. Experiments conducted on a cohort of patients admitted to a major medical center over a 3-year period show that risk prognosis based on our model significantly outperforms the currently deployed medical risk scores and other baseline machine learning algorithms.


title: Gate-variants of Gated Recurrent Unit (GRU) neural networks

abstract: The paper evaluates three variants of the Gated Recurrent Unit (GRU) in recurrent neural networks (RNN) by reducing parameters in the update and reset gates. We evaluate the three variant GRU models on MNIST and IMDB datasets and show that these GRU-RNN variant models perform as well as the original GRU RNN model while reducing the computational expense.


title: Highly Directive Hybrid Metal–Dielectric Yagi-Uda Nanoantennas

abstract: A hybrid metal–dielectric nanoantenna promises to harness the large Purcell factor of metallic nanostructures while taking advantage of the high scattering directivity and low dissipative losses of dielectric nanostructures. Here, we investigate a compact hybrid metal–dielectric nanoantenna that is inspired by the Yagi-Uda design. It comprises a metallic gold bowtie nanoantenna feed element and three silicon nanorod directors, exhibiting high unidirectional in-plane directivity and potential beam redirection capability in the visible spectral range. The entire device has a footprint of only 0.38 λ2, and its forward directivity is robust against fabrication imperfections. We use the photoluminescence from the gold bowtie nanoantenna itself as an elegant emitter to characterize the directivity of the device and experimentally demonstrate a directivity of ∼49.2. In addition, we demonstrate beam redirection with our device, achieving a 5° rotation of the main emission lobe with a feed element displacement of ...


Big data analytics in healthcare: promise and potential


title: CLVQ: cross-language video question/answering system

abstract: Multilanguage information retrieval promotes users to browse documents in the form of their mother language, and more and more peoples interested in retrieves short answers rather than a full document. In this paper, we present a cross-language video QA system i.e. CLVQ, which could process the English questions, and find answers in Chinese videos. The main contribution of this research are: (1) the application of QA technology into different media; and (2) adopt a new answer finding approach without human-made rules; (3) the combination of several techniques of passage retrieval algorithms. The experimental result shows 56% of answer finding. The testing collection was consists of six discovery movies, and questions are from the School of Discovery Web site.


title: Using Imagery to Simplify Perceptual Abstraction in Reinforcement Learning Agents

abstract: In this paper, we consider the problem of reinforcement learning in spatial tasks. These tasks have many states that can be aggregated together to improve learning efficiency. In an agent, this aggregation can take the form of selecting appropriate perceptual processes to arrive at a qualitative abstraction of the underlying continuous state. However, for arbitrary problems, an agent is unlikely to have the perceptual processes necessary to discriminate all relevant states in terms of such an abstraction. ::: ::: To help compensate for this, reinforcement learning can be integrated with an imagery system, where simple models of physical processes are applied within a low-level perceptual representation to predict the state resulting from an action. Rather than abstracting the current state, abstraction can be applied to the predicted next state. Formally, it is shown that this integration broadens the class of perceptual abstraction methods that can be used while preserving the underlying problem. Empirically, it is shown that this approach can be used in complex domains, and can be beneficial even when formal requirements are not met.


title: Low complexity frequency-response masking filters using modified structure based on serial masking

abstract: This paper presents a technique to synthesize low-complexity high-selectivity arbitrary-bandwidth finite impulse response (FIR) digital filters using the frequency-response masking (FRM) technique. A serial masking scheme is used to perform the masking task in two stages lowering the complexity of the masking filters. The bandedge shaping filter is implemented as a prefilter-equalizer cascade lowering the complexity of the band-edge shaping filter. The design of the FRM filter is formulated as a nonlinear nonconvex semi-infinite programming optimization problem whereby the maximum frequency response magnitude error of the overall filter is minimized. A constraint transcription method and a smoothing technique are employed to transform the continuous inequality constraints into equality constraints. By using the concept of penalty function, the transformed constraints are incorporated into the cost function to form a new cost function. The nonlinear optimization problem with inequality constraints is approximated by a sequence of unconstraint optimization problems which can be solved efficiently by a limited-memory BFGS (L-BFGS) method [1]. An example taken from the literature demonstrates that this technique yields sharp filter having reduced magnitude error and total amount of nonzero coefficients.


title: Meta-modelling and graph grammars for multi-paradigm modelling in AToM3

abstract: This paper presents the combined use of meta-modelling and graph grammars for the generation of visual modelling tools for simulation formalisms. In meta-modelling, formalisms are described at a meta-level. This information is used by a meta-model processor to generate modelling tools for the described formalisms. We combine meta-modelling with graph grammars to extend the model manipulation capabilities of the generated modelling tools: edit, simulate, transform into another formalism, optimize and generate code. We store all (meta-)models as graphs, and thus, express model manipulations as graph grammars. ::: ::: We present the design and implementation of these concepts in AToM3 (A_To_ol for M_ulti-formalism, M_eta-M_odelling). AToM3 supports modelling of complex systems using different formalisms, all meta-modelled in their own right. Models in different formalisms may be transformed into a single common formalism for further processing. These transformations are specified by graph grammars. Mosterman and Vangheluwe [18] introduced the term multi-paradigm modelling to denote the combination of multiple formalisms, multiple abstraction levels, and meta-modelling. As an example of multi-paradigm modelling we present a meta-model for the Object-Oriented Continuous Simulation Language OOCSMP, in which we combine ideas from UML class diagrams (to express the OOCSMP model structure), Causal Block Diagrams (CBDs), and Statecharts (to specify the methods of the OOCSMP classes). A graph grammar is able to generate OOCSMP code, and then a compiler for this language (C-OOL) generates Java applets for the simulation execution.


title: Automatic Detection of Acute Bacterial Pneumonia from Chest X-ray Reports

abstract: OBJECTIVE ::: To evaluate the performance of a natural language processing system in extracting pneumonia-related concepts from chest x-ray reports. ::: ::: ::: METHODS ::: ::: ::: ::: DESIGN ::: Four physicians, three lay persons, a natural language processing system, and two keyword searches (designated AAKS and KS) detected the presence or absence of three pneumonia-related concepts and inferred the presence or absence of acute bacterial pneumonia from 292 chest x-ray reports. Gold standard: Majority vote of three independent physicians. Reliability of the gold standard was measured. ::: ::: ::: OUTCOME MEASURES ::: Recall, precision, specificity, and agreement (using Finn's R: statistic) with respect to the gold standard. Differences between the physicians and the other subjects were tested using the McNemar test for each pneumonia concept and for the disease inference of acute bacterial pneumonia. ::: ::: ::: RESULTS ::: Reliability of the reference standard ranged from 0.86 to 0.96. Recall, precision, specificity, and agreement (Finn R:) for the inference on acute bacterial pneumonia were, respectively, 0.94, 0.87, 0.91, and 0.84 for physicians; 0.95, 0.78, 0.85, and 0.75 for natural language processing system; 0.46, 0.89, 0.95, and 0.54 for lay persons; 0.79, 0.63, 0.71, and 0.49 for AAKS; and 0.87, 0.70, 0.77, and 0.62 for KS. The McNemar pairwise comparisons showed differences between one physician and the natural language processing system for the infiltrate concept and between another physician and the natural language processing system for the inference on acute bacterial pneumonia. The comparisons also showed that most physicians were significantly different from the other subjects in all pneumonia concepts and the disease inference. ::: ::: ::: CONCLUSION ::: In extracting pneumonia related concepts from chest x-ray reports, the performance of the natural language processing system was similar to that of physicians and better than that of lay persons and keyword searches. The encoded pneumonia information has the potential to support several pneumonia-related applications used in our institution. The applications include a decision support system called the antibiotic assistant, a computerized clinical protocol for pneumonia, and a quality assurance application in the radiology department.


Meshless Approximation Methods and Applications in Physics Based Modeling and Animation


title: Sparse-Representation-Based Graph Embedding for Traffic Sign Recognition

abstract: Researchers have proposed various machine learning algorithms for traffic sign recognition, which is a supervised multicategory classification problem with unbalanced class frequencies and various appearances. We present a novel graph embedding algorithm that strikes a balance between local manifold structures and global discriminative information. A novel graph structure is designed to depict explicitly the local manifold structures of traffic signs with various appearances and to intuitively model between-class discriminative information. Through this graph structure, our algorithm effectively learns a compact and discriminative subspace. Moreover, by using L2, 1-norm, the proposed algorithm can preserve the sparse representation property in the original space after graph embedding, thereby generating a more accurate projection matrix. Experiments demonstrate that the proposed algorithm exhibits better performance than the recent state-of-the-art methods.


title: The Impact of Game Patterns on Player Experience and Social Interaction in Co-Located Multiplayer Games

abstract: Outstanding multiplayer games engage players by providing rich social interactions. Yet, it is still not clear how to purposefully design these interactions. Our paper tackles this issue by establishing a research model for social player interaction highlighting the impact of the game design, the player group, and the gaming setting. Based on that model, we investigated the influence of three particular game design patterns - player interdependence, time pressure, and shared control - on the interaction of players. For that purpose, we developed a co-located game to systematically test variations of those patterns. We analyzed the resulting player experience and social interaction by applying questionnaires and videotaping game play sessions. Results indicate that high player interdependence implies more communication and less frustration, whereas shared control results in less perceived competence and autonomy. Moreover, individual player characteristics also impact the social interaction.


title: Back to the Past: Source Identification in Diffusion Networks from Partially Observed Cascades

abstract: When a piece of malicious information becomes rampant in an information diffusion network, can we identify the source node that originally introduced the piece into the network and infer the time when it initiated this? Being able to do so is critical for curtailing the spread of malicious information, and reducing the potential losses incurred. This is a very challenging problem since typically only incomplete traces are observed and we need to unroll the incomplete traces into the past in order to pinpoint the source. In this paper, we tackle this problem by developing a two-stage framework, which first learns a continuous-time diffusion network model based on historical diffusion traces and then identifies the source of an incomplete diffusion trace by maximizing the likelihood of the trace under the learned model. Experiments on both large synthetic and real-world data show that our framework can effectively go back to the past, and pinpoint the source node and its initiation time significantly more accurately than previous state-of-the-arts.


title: Machine-Learning-Guided Typestate Analysis for Static Use-After-Free Detection

abstract: Typestate analysis relies on pointer analysis for detecting temporal memory safety errors, such as use-after-free (UAF). For large programs, scalable pointer analysis is usually imprecise in analyzing their hard "corner cases", such as infeasible paths, recursion cycles, loops, arrays, and linked lists. Due to a sound over-approximation of the points-to information, a large number of spurious aliases will be reported conservatively, causing the corresponding typestate analysis to report a large number of false alarms. Thus, the usefulness of typestate analysis for heap-intensive clients, like UAF detection, becomes rather limited, in practice. We introduce Tac, a static UAF detector that bridges the gap between typestate and pointer analyses by machine learning. Tac learns the correlations between program features and UAF-related aliases by using a Support Vector Machine (SVM) and applies this knowledge to further disambiguate the UAF-related aliases reported imprecisely by the pointer analysis so that only the ones validated by its SVM classifier are further investigated by the typestate analysis. Despite its unsoundness, Tac represents a practical typestate analysis approach for UAF detection. We have implemented Tac in LLVM-3.8.0 and evaluated it using a set of eight open-source C/C++ programs. The results show that Tac is effective (in terms of finding 5 known CVE vulnerabilities, 1 known bug, and 8 new bugs with a low false alarm rate) and scalable (in terms of analyzing a large codebase with 2,098 KLOC in just over 4 hours).


title: Transparent, Conductive Graphene Electrodes for Dye-Sensitized Solar Cells

abstract: Transparent, conductive, and ultrathin graphene films, as an alternative to the ubiquitously employed metal oxides window electrodes for solid-state dye-sensitized solar cells, are demonstrated. These graphene films are fabricated from exfoliated graphite oxide, followed by thermal reduction. The obtained films exhibit a high conductivity of 550 S/cm and a transparency of more than 70% over 1000−3000 nm. Furthermore, they show high chemical and thermal stabilities as well as an ultrasmooth surface with tunable wettability.


title: Static analysis for detecting taint-style vulnerabilities in web applications

abstract: The number and the importance of web applications have increased rapidly over the last years. At the same time, the quantity and impact of security vulnerabilities in such applications have grown as well. Since manual code reviews are time-consuming, error-prone and costly, the need for automated solutions has become evident. ::: ::: In this paper, we address the problem of vulnerable web applications by means of static source code analysis. More precisely, we use flow-sensitive, interprocedural and context-sensitive data flow analysis to discover vulnerable points in a program. In addition to the taint analysis at the core of our engine, we employ a precise alias analysis targeted at the unique reference semantics commonly found in scripting languages. Moreover, we enhance the quality and quantity of the generated vulnerability reports by employing an iterative two-phase algorithm for fast and precise resolution of file inclusions. The presented concepts are targeted at the general class of taint-style vulnerabilities and can be easily applied to the detection of vulnerability types such as SQL injection, cross-site scripting (XSS), and command injection. ::: ::: We implemented the presented concepts in Pixy, a high-precision static analysis tool aimed at detecting cross-site scripting and SQL injection vulnerabilities in PHP programs. To demonstrate the effectiveness of our techniques, we analyzed a number of popular, open-source web applications and discovered hundreds of previously unknown vulnerabilities. Both the high analysis speed as well as the low number of generated false positives show that our techniques can be used for conducting effective security audits.


title: HTML Automatic Table Layout

abstract: Automatic layout of tables is required in online applications because of the need to tailor the layout to the viewport width, choice of font, and dynamic content. However, if the table contains text, minimizing the height of the table for a fixed maximum width is NP-hard. Thus, more efficient heuristic algorithms are required. We evaluate the HTML table layout recommendation and find that while it generally produces quite compact layout it is brittle and can lead to quite uncompact layout. We present an alternate heuristic algorithm. It uses a greedy strategy that starts from the widest reasonable layout and repeatedly chooses to narrow the column for which narrowing leads to the least increase in table height. The algorithm is simple, fast enough to be used in online applications, and gives significantly more compact layout than is obtained with HTML’s recommended table layout algorithm.


title: Recent Advances in Oncology Acupuncture and Safety Considerations in Practice

abstract: Oncology acupuncture is a new and emerging field of research. Recent advances from published clinical trials have added evidence to support the use of acupuncture for symptom management in cancer patients. Recent new developments include (1) pain and dysfunction after neck dissection; (2) radiation-induced xerostomia in head and neck cancer; (3) aromatase inhibitor-associated arthralgia in breast cancer; (4) hot flashes in breast cancer and prostate cancer; and (5) chemotherapyinduced neutropenia in ovarian cancer. Some interventions are becoming a non-pharmaceutical option for cancer patients, while others still require further validation and confirmation. Meanwhile, owing to the rapid development of the field and increased demands from cancer patients, safety issues concerning oncology acupuncture practice have become imperative. Patients with cancer may be at higher risk developing adverse reactions from acupuncture. Practical strategies for enhancing safety measures are discussed and recommended.


News from the Hemingway Collection


Solid State Disk forensics: Is there a path forward?


title: Load shedding for aggregation queries over data streams

abstract: Systems for processing continuous monitoring queries over data streams must be adaptive because data streams are often bursty and data characteristics may vary over time. We focus on one particular type of adaptivity: the ability to gracefully degrade performance via "load shedding" (dropping unprocessed tuples to reduce system load) when the demands placed on the system cannot be met in full given available resources. Focusing on aggregation queries, we present algorithms that determine at what points in a query plan should load shedding be performed and what amount of load should be shed at each point in order to minimize the degree of inaccuracy introduced into query answers. We report the results of experiments that validate our analytical conclusions.


title: Is Cetacean Intelligence Special? New Perspectives on the Debate

abstract: In recent years, the interpretation of our observations of animal behaviour, in particular that of cetaceans, has captured a substantial amount of attention in the scientific community. The traditional view that supports a special intellectual status for this mammalian order has fallen under significant scrutiny, in large part due to problems of how to define and test the cognitive performance of animals. This paper presents evidence supporting complex cognition in cetaceans obtained using the recently developed intelligence and embodiment hypothesis. This hypothesis is based on evolutionary neuroscience and postulates the existence of a common information-processing principle associated with nervous systems that evolved naturally and serves as the foundation from which intelligence can emerge. This theoretical framework explaining animal intelligence in neural computational terms is supported using a new mathematical model. Two pathways leading to higher levels of intelligence in animals are identified, each reflecting a trade-off either in energetic requirements or the number of neurons used. A description of the evolutionary pathway that led to increased cognitive capacities in cetacean brains is detailed and evidence supporting complex cognition in cetaceans is presented. This paper also provides an interpretation of the adaptive function of cetacean neuronal traits.


Mapping Large Loops with a Single Hand-Held Camera


Zero-Shot Learning for Natural Language Understanding Using Domain-Independent Sequential Structure and Question Types


title: Coronary spasm, prostaglandin and HLA factors.

abstract: To elucidate the contribution of prostanoids in coronary spasm, plasma levels of thromboxane B2 (TXB2) and 6-keto PGF1 alpha at the coronary sinus and ascending aorta in 21 patients with variant angina were measured, as compared with findings in 20 with effort angina and 13 subjects with normal coronaries. In the coronary sinus blood, plasma TXB2 in patients with effort angina exhibited statistically significant high levels, as compared with data in the controls. On the contrary, the data obtained from patients with variant angina were not statistically significant. However, eight patients whose coronary angiogram revealed more than 50% of coronary stenoses had statistically significant high levels of TXB2 and other patients with normal coronaries or less than 50% of narrowing showed almost the same levels of TXB2 as the controls. In contrast to TXB2, the plasma levels of 6-keto PGF1 alpha in patients with variant angina were very low in both groups with variant angina. These data suggest that high levels of TXB2 observed in patients with atherosclerotic coronaries may be an accelerating factor while low levels of prostacyclin may be an essential factor leading to spasm. HLA analysis of 23 patients with variant angina was performed to search for genetic factors, under the hypothesis that such may contribute to the low levels in prostacyclin. This preliminary study revealed statistically significant high frequencies of Bw52 and B-40 in the patients, as compared with frequencies among 152 normal Japanese. Genetic studies are ongoing in our clinic.


title: An Adaptive Wearable Parallel Robot for the Treatment of Ankle Injuries

abstract: This paper presents the development of a novel adaptive wearable ankle robot for the treatments of ankle sprain through physical rehabilitation. The ankle robot has a bioinspired design, devised after a careful study of the improvement opportunities in the existing ankle robots. Robot design is adaptable to subjects of varying physiological abilities and age groups. Ankle robot employs lightweight but powerful pneumatic muscle actuators (PMA) which mimics skeletal muscles in actuation. To address nonlinear characteristics of PMA, a fuzzy-based disturbance observer (FBDO) has been developed. Another instance of an adaptive fuzzy logic controller based on Mamdani inference has been developed and appended with the FBDO to compensate for the transient nature of the PMA. With the proposed control scheme, it is possible to simultaneously control four parallel actuators of the ankle robot and achieve three rotational degrees of freedom. To evaluate the robot design, the disturbance observer, and the adaptive fuzzy logic controller, experiments were performed. The ankle robot was used by a neurologically intact subject. The robot-human interaction was kept as active-passive while the robot was operated on predefined trajectories commonly adopted by the therapists. Trajectory tracking results are reported in the presence of an unpredicted human user intervention, use of compliant and nonlinear actuators, and parallel kinematic structure of the ankle robot.


title: End-to-End Video Classification with Knowledge Graphs

abstract: Video understanding has attracted much research attention especially since the recent availability of large-scale video benchmarks. In this paper, we address the problem of multi-label video classification. We first observe that there exists a significant knowledge gap between how machines and humans learn. That is, while current machine learning approaches including deep neural networks largely focus on the representations of the given data, humans often look beyond the data at hand and leverage external knowledge to make better decisions. Towards narrowing the gap, we propose to incorporate external knowledge graphs into video classification. In particular, we unify traditional "knowledgeless" machine learning models and knowledge graphs in a novel end-to-end framework. The framework is flexible to work with most existing video classification algorithms including state-of-the-art deep models. Finally, we conduct extensive experiments on the largest public video dataset YouTube-8M. The results are promising across the board, improving mean average precision by up to 2.9%.


title: Power quality of smart grids with Plug-in Electric Vehicles considering battery charging profile

abstract: The impact of different battery charging rates of Plug-in Electric Vehicles (PEVs) on the power quality of smart grid distribution systems is studied in this paper. PEV battery chargers are high power nonlinear devices that can generate significant amount of current harmonics. PEVs will be an integral component to the operation of smart grids and therefore their power quality impacts must be thoroughly analyzed. Based on decoupled harmonic load flow analysis, different PEV charging scenarios (e.g., time zone scheduling, charging rate and penetration level) are tested for a typical large distribution network topology. The impacts of PEV charge rate on voltage profile, fundamental and harmonic losses, transformer loading and total harmonic distortions are demonstrated.


title: Inferring Mood Instability on Social Media by Leveraging Ecological Momentary Assessments

abstract: Active and passive sensing technologies are providing powerful mechanisms to track, model, and understand a range of health behaviors and well-being states. Despite yielding rich, dense and high fidelity data, current sensing technologies often require highly engineered study designs and persistent participant compliance, making them difficult to scale to large populations and to data acquisition tasks spanning extended time periods. This paper situates social media as a new passive, unobtrusive sensing technology. We propose a semi-supervised machine learning framework to combine small samples of data gathered through active sensing, with large-scale social media data to infer mood instability (MI) in individuals. Starting from a theoretically-grounded measure of MI obtained from mobile ecological momentary assessments (EMAs), we show that our model is able to infer MI in a large population of Twitter users with 96% accuracy and F-1 score. Additionally, we show that, our model predicts self-identifying Twitter users with bipolar and borderline personality disorder to exhibit twice the likelihood of high MI, compared to that in a suitable control. We discuss the implications and the potential for integrating complementary sensing capabilities to address complex research challenges in precision medicine.


title: Precision psychiatry: a neural circuit taxonomy for depression and anxiety.

abstract: Summary Although there have been tremendous advances in the understanding of human dysfunctions in the brain circuitry for self-reflection, emotion, and cognitive control, a brain-based taxonomy for mental disease is still lacking. As a result, these advances have not been translated into actionable clinical tools, and the language of brain circuits has not been incorporated into training programmes. To address this gap, I present this synthesis of published work, with a focus on functional imaging of circuit dysfunctions across the spectrum of mood and anxiety disorders. This synthesis provides the foundation for a taxonomy of putative types of dysfunction, which cuts across traditional diagnostic boundaries for depression and anxiety and includes instead distinct types of neural circuit dysfunction that together reflect the heterogeneity of depression and anxiety. This taxonomy is suited to specifying symptoms in terms of underlying neural dysfunction at the individual level and is intended as the foundation for building mechanistic research and ultimately guiding clinical practice.


title: Comparative evaluation of various MFCC implementations on the speaker verification task

abstract: Making no claim of being exhaustive, a review of the most popular MFCC (Mel Frequency Cepstral Coefficients) implementations is made. These differ mainly in the particular approximation of the nonlinear pitch perception of human, the filter bank design, and the compression of the filter bank output. Then, a comparative evaluation of the presented implementations is performed on the task of text-independent speaker verification, by means of the well-known 2001 NIST SRE (speaker recognition evaluation) one-speaker detection database.


title: Event and Anomaly Detection Using Tucker3 Decomposition

abstract: Failure detection in telecommunication networks is a vital task. So far, several supervised and unsupervised solutions have been provided for discovering failures in such networks. Among them unsupervised approaches has attracted more attention since no label data is required. Often, network devices are not able to provide information about the type of failure. In such cases the type of failure is not known in advance and the unsupervised setting is more appropriate for diagnosis. Among unsupervised approaches, Principal Component Analysis (PCA) is a well-known solution which has been widely used in the anomaly detection literature and can be applied to matrix data (e.g. Users-Features). However, one of the important properties of network data is their temporal sequential nature. So considering the interaction of dimensions over a third dimension, such as time, may provide us better insights into the nature of network failures. In this paper we demonstrate the power of three-way analysis to detect events and anomalies in time-evolving network data.


title: PanoContext: A Whole-Room 3D Context Model for Panoramic Scene Understanding

abstract: The field-of-view of standard cameras is very small, which is one of the main reasons that contextual information is not as useful as it should be for object detection. To overcome this limitation, we advocate the use of 360° full-view panoramas in scene understanding, and propose a whole-room context model in 3D. For an input panorama, our method outputs 3D bounding boxes of the room and all major objects inside, together with their semantic categories. Our method generates 3D hypotheses based on contextual constraints and ranks the hypotheses holistically, combining both bottom-up and top-down context information. To train our model, we construct an annotated panorama dataset and reconstruct the 3D model from single-view using manual annotation. Experiments show that solely based on 3D context without any image region category classifier, we can achieve a comparable performance with the state-of-the-art object detector. This demonstrates that when the FOV is large, context is as powerful as object appearance. All data and source code are available online.