title: Low-Latency Video Semantic Segmentation

abstract: Recent years have seen remarkable progress in semantic segmentation. Yet, it remains a challenging task to apply segmentation techniques to video-based applications. Specifically, the high throughput of video streams, the sheer cost of running fully convolutional networks, together with the low-latency requirements in many real-world applications, e.g. autonomous driving, present a significant challenge to the design of the video segmentation framework. To tackle this combined challenge, we develop a framework for video semantic segmentation, which incorporates two novel components: (1) a feature propagation module that adaptively fuses features over time via spatially variant convolution, thus reducing the cost of per-frame computation: and (2) an adaptive scheduler that dynamically allocate computation based on accuracy prediction. Both components work together to ensure low latency while maintaining high segmentation quality. On both Cityscapes and CamVid, the proposed framework obtained competitive performance compared to the state of the art, while substantially reducing the latency, from 360 ms to 119 ms.


title: Why go logarithmic if we can go linear?: Towards effective distinct counting of search traffic

abstract: Estimating the number of distinct elements in a large multiset has several applications, and hence has attracted active research in the past two decades. Several sampling and sketching algorithms have been proposed to accurately solve this problem. The goal of the literature has always been to estimate the number of distinct elements while using minimal resources. However, in some modern applications, the accuracy of the estimate is of primal importance, and businesses are willing to trade more resources for better accuracy. Throughout our experience with building a distinct count system at a major search engine, Ask.com, we reviewed the literature of approximating distinct counts, and compared most algorithms in the literature. We deduced that Linear Counting, one of the least used algorithms, has unique and impressive advantages when the accuracy of the distinct count is critical to the business. For other estimators to attain comparable accuracy, they need more space than Linear Counting. We have supported our analytical results through comprehensive experiments. The experimental results highly favor Linear Counting when the number of distinct elements is large and the error tolerance is low.


title: Models of Semantic Representation with Visual Attributes

abstract: We consider the problem of grounding the meaning of words in the physical world and focus on the visual modality which we represent by visual attributes. We create a new large-scale taxonomy of visual attributes covering more than 500 concepts and their corresponding 688K images. We use this dataset to train attribute classifiers and integrate their predictions with text-based distributional models of word meaning. We show that these bimodal models give a better fit to human word association data compared to amodal models and word representations based on handcrafted norming data.


title: How Instructional Methods Influence Skill Development in Management Education

abstract: Research concerning why and how to promote social interaction and learner reflection in management education and training is somewhat underdeveloped. In this investigation, we used a predictive, quasi-experimental design with 246 students from a business school in Colombia who were enrolled in 10 sections of a leadership course to examine expected effects of instructional methods that promoted different levels of social interaction and reflection on self-reported learning behaviors (dialogue and reflection activities), self-efficacy for class performance, and instructors’ assessments of students’ skill demonstration (team work, communication, influence, and work proficiency and effort). In comparisons to students participating in instructional conditions with less social interaction and fewer reflective activities, students participating in an instructional condition that promoted higher levels of these activities exhibited considerably greater student–student dialogue, instructor–student dialogue, and re...


title: Exploiting FrameNet for Content-Based Book Recommendation

abstract: Adding semantic knowledge to a content-based recommender helps to better understand the items and user representations. Most recent research has focused on examining the added value of adding semantic features based on structured web data, in particular Linked Open Data (LOD). In this paper, we focus in contrast on semantic feature construction from text, by incorporating features based on semantic frames into a book recommendation classifier. To this purpose we leverage the semantic frames based on parsing the plots of the items under consideration with a state-of-the-art semantic parser. By investigating this type of semantic information, we show that these frames are also able to represent information about a particular book, but without the need of having explicitly structured data describing the books available. We reveal that exploiting frame information outperforms a basic bag-of-words approach and that especially the words relating to those frames are beneficial for classification. In a final step we compare and combine our system with the LOD features from a system leveraging DBpedia as knowledge resource. We show that both ::: approaches yield similar results and reveal that combining semantic information from these two different sources might even be beneficial.


title: Decentralized Control of Unmanned Aerial Vehicle Collaborative Sensing Missions

abstract: Cooperative unmanned aerial vehicle (UAV) teams can serve as a mobile sensor networks to autonomously execute sensing tasks in uncertain and dynamic environments. We have implemented a UAV system that performs collaborative sensing missions under the supervision of a single user. Decentralized task allocation and autonomous mission execution are enabled by onboard computing and ad-hoc wireless communication and provide robustness to communication and resource losses in quickly evolving scenarios. The collaboration algorithm combines shared and local information to produce multi-step plans with the goal of minimizing the global cost of the mission. Missions are constructed in real time from tasks such as patrolling an area or searching for an intruder, and may include constraints such as a restricted airspace. Algorithms for decentralized planning, guaranteed search and avoiding a restricted airspace were demonstrated using a team of four UAVs. Experimental data shows that tasks were allocated to the appropriate UAVs and successfully executed.


title: Applicability of RF-based methods for emotion recognition: A survey

abstract: Human emotion recognition has attracted a lot of research in recent years. However, conventional methods for sensing human emotions are either expensive or privacy intrusive. In this paper, we explore a connection between emotion recognition and RF-based activity recognition that can lead to a novel ubiquitous emotion sensing technology. We discuss the latest literature from both domains, highlight the potential of body movements for accurate emotion detection and focus on how emotion recognition could be done using inexpensive, less privacy intrusive, device-free RF sensing methods. Applications include environment and crowd behaviour tracking in real time, assisted living, health monitoring, or also domestic appliance control. As a result of this survey, we propose RF-based device free recognition for emotion detection based on body movements. However, it requires overcoming challenges, such as accuracy, to outperform classical methods.


title: Differentiated Sentiment Analysis of Corporate Social Media Accounts.

abstract: Social media managers as well as analysts use social media messages as an additional channel to manage and measure opinions towards brands. Currently, however, sentiment tools have been predominantly focused on the binary positive-negative valence of emotions and thereby neglected the multi-dimensional structure of human emotions. Moreover, while practitioners try to address the issue of undifferentiated customer requests towards a brand by operating more interest group-specific accounts, research still lacks understanding regarding the impact of different account types. We approach this research gap by developing a classification of social media accounts and, subsequently, deploy a sentiment analysis that differentiates between seven emotions within 532,363 thousand tweets towards 641 accounts from 33 S&P 100 companies. Our results confirm the assumed necessity of considering different account types when studying corporate social media presence and assessing differentiated emotions in social media analytics.


title: Cross-domain Feature Selection for Language Identification

abstract: We show that transductive (cross-domain) learning is an important consideration in building a general-purpose language identification system, and develop a feature selection method that generalizes across domains. Our results demonstrate that our method provides improvements in transductive transfer learning for language identification. We provide an implementation of the method and show that our system is faster than popular standalone language identification systems, while maintaining competitive accuracy.


title: Color Based Object Recognition

abstract: Abstract The purpose is to arrive at recognition of multicolored objects invariant to a substantial change in viewpoint, object geometry and illumination. Assuming dichromatic reflectance and white illumination, it is shown that normalized color rgb, saturation S and hue H, and the newly proposed color models c 1 c 2 c 3 and l 1 l 2 l 3 are all invariant to a change in viewing direction, object geometry and illumination. Further, it is shown that hue H and l1l2l3 are also invariant to highlights. Finally, a change in spectral power distribution of the illumination is considered to propose a new color constant color model m1m2m3. To evaluate the recognition accuracy differentiated for the various color models, experiments have been carried out on a database consisting of 500 images taken from 3-D multicolored man-made objects. The experimental results show that highest object recognition accuracy is achieved by l1l2l3 and hue H followed by c1c2c3, normalized color rgb and m1m2m3 under the constraint of white illumination. Also, it is demonstrated that recognition accuracy degrades substantially for all color features other than m1m2m3 with a change in illumination color. The recognition scheme and images are available within the PicToSeek and Pic2Seek systems on-line at: http: //www.wins.uva.nl/research/isis/zomax/.


title: Scan Detection: A Data Mining Approach

abstract: A precursor to many attacks on networks is often a reconnaissance operation, more commonly referred to as a scan. Despite the vast amount of attention focused on methods for scan detection, the state-ofthe-art methods suffer from high rate of false alarms and low rate of scan detection. In this paper, we formalize the problem of scan detection as a data mining problem. We show how the network traffic data sets can be converted into a data set that is appropriate for running off-the-shelf classifiers on. Our method successfully demonstrates that data mining models can encapsulate expert knowledge to create an adaptable algorithm that can substantially outperform state-ofthe-art methods for scan detection in both coverage and precision.


title: Curvature-augmented tensor voting for shape inference from noisy 3D data

abstract: Improves the basic tensor voting formalism to infer the sign and direction of principal curvatures at each input site from noisy 3D data. Unlike most previous approaches, no local surface fitting, partial derivative computation, nor oriented normal vector recovery is performed in our method. These approaches are known to be noise-sensitive, since accurate partial derivative information is often required, which is usually unavailable from real data. Also, unlike approaches that detect signs of Gaussian curvature, we can handle points with zero Gaussian curvature uniformly, without first localizing them in a separate process. The tensor-voting curvature estimation is non-iterative, does not require initialization, and is robust to a considerable amount of outlier noise, as its effect is reduced by collecting a large number of tensor votes. Qualitative and quantitative results on synthetic and real complex data are presented.


title: Action recognition with exemplar based 2.5d graph matching

abstract: This paper deals with recognizing human actions in still images. We make two key contributions. (1) We propose a novel, 2.5D representation of action images that considers both view-independent pose information and rich appearance information. A 2.5D graph of an action image consists of a set of nodes that are key-points of the human body, as well as a set of edges that are spatial relationships between the nodes. Each key-point is represented by view-independent 3D positions and local 2D appearance features. The similarity between two action images can then be measured by matching their corresponding 2.5D graphs. (2) We use an exemplar based action classification approach, where a set of representative images are selected for each action class. The selected images cover large within-action variations and carry discriminative information compared with the other classes. This exemplar based representation of action classes further makes our approach robust to pose variations and occlusions. We test our method on two publicly available datasets and show that it achieves very promising performance.


title: On the optimal partitioning of data with K-means, growing K-means, neural gas, and growing neural gas.

abstract: In this paper, the performance of new clustering methods such as Neural Gas (NG) and Growing Neural Gas (GNG) is compared with the K-means method for real and simulated data sets. Moreover, a new algorithm called growing K-means, GK, is introduced as the alternative to Neural Gas and Growing Neural Gas. It has small input requirements and is conceptually very simple. The GK leads to nearly optimal values of the cost function, and, contrary to K-means, it is independent of the initial data set partition. The incremental property of GK additionally helps to estimate the number of "natural" clusters in data, i.e., the well-separated groups of objects in the data space.


title: Genetic algorithm based modeling and control of a two wheeled vehicle with an extended rod, a Lagrangian based dynamic approach

abstract: This work carries out design and implementation of a PID control algorithm utilizing genetic algorithm as an optimization technique for a novel design of a two-wheeled vehicle with an extended rod. The vehicle design offers an additional feature to the conventional inverted pendulum on two wheels. The intermediate body (IB) of the vehicle is composed of two co-axial parts connected by a linear actuator and with a payload attached to the end of the upper part. The linear actuator allows the payload to move up and down along the IB of the vehicle. Considering the various positions, speeds and different sizes of a payload, carried by the vehicle, while maintaining the entire vehicle balanced is the main challenge of the current study. Dynamic modeling of the system is based on utilizing Lagrangian formulation to drive the system equations of motion.


title: Mindfulness and meditation: treating cognitive impairment and reducing stress in dementia

abstract: Abstract This study investigates the relationship between mindfulness, meditation, cognition and stress in people with Alzheimer’s disease (AD), dementia, mild cognitive impairment and subjective cognitive decline. Accordingly, we explore how the use of meditation as a behavioural intervention can reduce stress and enhance cognition, which in turn ameliorates some dementia symptoms. A narrative review of the literature was conducted with any studies using meditation as an intervention for dementia or dementia-related memory conditions meeting inclusion criteria. Studies where moving meditation was the main intervention were excluded due to the possible confounding of exercise. Ten papers were identified and reviewed. There was a broad use of measures across all studies, with cognitive assessment, quality of life and perceived stress being the most common. Three studies used functional magnetic resonance imaging to measure functional changes to brain regions during meditation. The interventions fell into the following three categories: mindfulness, most commonly mindfulness-based stress reduction (six studies); Kirtan Kriya meditation (three studies); and mindfulness-based Alzheimer’s stimulation (one study). Three of these studies were randomised controlled trials. All studies reported significant findings or trends towards significance in a broad range of measures, including a reduction of cognitive decline, reduction in perceived stress, increase in quality of life, as well as increases in functional connectivity, percent volume brain change and cerebral blood flow in areas of the cortex. Limitations and directions for future studies on meditation-based treatment for AD and stress management are suggested.


title: Integrating heterogeneous information via flexible regularization framework for recommendation

abstract: Recently, there is a surge of social recommendation, which leverages social relations among users to improve recommendation performance. However, in many applications, social relations are very sparse or absent. Meanwhile, the attribute information of users or items may be rich. It is a big challenge to exploit this attribute information for the improvement of recommendation performance. In this paper, we organize objects and relations in recommender system as a heterogeneous information network and introduce meta-path-based similarity measure to evaluate the similarity of users or items. Furthermore, a matrix factorization-based dual regularization framework SimMF is proposed to flexibly integrate different types of information through adopting users' and items' similarities as regularization on latent factors of users and items. Extensive experiments not only validate the effectiveness of SimMF but also reveal some interesting findings. We find that attribute information of users and items can significantly improve recommendation accuracy, and their contribution seems more important than that of social relations. The experiments also reveal that different regularization models have obviously different impacts on users and items.


title: Compositional analysis: a valid approach to analyze microbiome high-throughput sequencing data

abstract: A workshop held at the 2015 annual meeting of the Canadian Society of Microbiologists highlighted compositional data analysis methods and the importance of exploratory data analysis for the analysis of microbiome data sets generated by high-throughput DNA sequencing. A summary of the content of that workshop, a review of new methods of analysis, and information on the importance of careful analyses are presented herein. The workshop focussed on explaining the rationale behind the use of compositional data analysis, and a demonstration of these methods for the examination of 2 microbiome data sets. A clear understanding of bioinformatics methodologies and the type of data being analyzed is essential, given the growing number of studies uncovering the critical role of the microbiome in health and disease and the need to understand alterations to its composition and function following intervention with fecal transplant, probiotics, diet, and pharmaceutical agents.


title: Clustering in the Presence of Background Noise

abstract: We address the problem of noise management in clustering algorithms. Namely, issues that arise when on top of some cluster structure the data also contains an unstructured set of points. We consider how clustering algorithms can be "robustified" so that they recover the cluster structure in spite of the unstructured part of the input. We introduce some quantitative measures of such robustness that take into account the strength of the embedded cluster structure as well as the mildness of the noise subset. We propose a simple and efficient method to turn any centroid-based clustering algorithm into a noise-robust one, and prove robustness guarantees for our method with respect to these measures. We also prove that more straightforward ways of "robustifying" clustering algorithms fail to achieve similar guarantees.


title: Mindfulness and Emotion Regulation Difficulties in Generalized Anxiety Disorder: Preliminary Evidence for Independent and Overlapping Contributions

abstract: Abstract Diminished levels of mindfulness (awareness and acceptance/nonjudgment) and difficulties in emotion regulation have both been proposed to play a role in symptoms of generalized anxiety disorder (GAD); the current studies investigated these relationships in nonclinical and clinical samples. In the first study, among a sample of 395 individuals at an urban commuter campus, self-reports of both emotion regulation difficulties and aspects of mindfulness accounted for unique variance in GAD symptom severity, above and beyond variance shared with depressive and anxious symptoms, as well as variance shared with one another. In the second study, individuals with GAD ( n = 16) reported significantly lower levels of mindfulness and significantly higher levels of difficulties in emotion regulation than individuals in a nonanxious control group ( n = 16). Results are discussed in terms of directions for future research and potential implications for treatment development.


title: Generative Semantic Manipulation with Mask-Contrasting GAN

abstract: Despite the promising results on paired/unpaired image-to-image translation achieved by Generative Adversarial Networks (GANs), prior works often only transfer the low-level information (e.g. color or texture changes), but fail to manipulate high-level semantic meanings (e.g., geometric structure or content) of different object regions. On the other hand, while some researches can synthesize compelling real-world images given a class label or caption, they cannot condition on arbitrary shapes or structures, which largely limits their application scenarios and interpretive capability of model results. In this work, we focus on a more challenging semantic manipulation task, aiming at modifying the semantic meaning of an object while preserving its own characteristics (e.g. viewpoints and shapes), such as cow\(\rightarrow \)sheep, motor\(\rightarrow \)bicycle, cat\(\rightarrow \)dog. To tackle such large semantic changes, we introduce a contrasting GAN (contrast-GAN) with a novel adversarial contrasting objective which is able to perform all types of semantic translations with one category-conditional generator. Instead of directly making the synthesized samples close to target data as previous GANs did, our adversarial contrasting objective optimizes over the distance comparisons between samples, that is, enforcing the manipulated data be semantically closer to the real data with target category than the input data. Equipped with the new contrasting objective, a novel mask-conditional contrast-GAN architecture is proposed to enable disentangle image background with object semantic changes. Extensive qualitative and quantitative experiments on several semantic manipulation tasks on ImageNet and MSCOCO dataset show considerable performance gain by our contrast-GAN over other conditional GANs.


title: A Feature-Based Approach for Group-Wise Grid Map Registration

abstract: For autonomous vehicles and advanced driver assistance systems, information on the actual state of the environment is fundamental for localization and mapping tasks. Localization benefits from multiple observations of the same location at different times as these may provide important information on static and mobile objects. For efficient mapping, the environment may be explored in parallel. For these purposes, multiple observations represented by grid maps have to be aligned into one mutual frame. This paper addresses the problem of group-wise grid map registration using an image processing approach. For registration, a rotational-invariant descriptor is proposed in order to provide the correspondences of points of interest in radar-based occupancy grid maps. As pairwise registration of multiple grid maps suffers from bias, this paper proposes a graph-based approach for robust registration of multiple grid maps. This will facilitate highly accurate range sensor maps for the aforementioned purposes. Large-scale experiments show the benefit of the proposed methods and compare it to state-of-the-art algorithms on radar measurements.


title: Rapid and Agile Locomotion with Power-dense Millirobots

abstract: Author(s): Haldane, Duncan | Advisor(s): Fearing, Ronald S; Agogino, Alice M | Abstract: The development of legged robots can serve two purposes. The first is to enable more mobility for robotic platforms and allow them greater flexibility for moving through complex real-world environments. The second is that the legged robot is a scientific tool. It can be used to design new experiments that drive insights both for the development of new robotic platforms and the characteristic of animal locomotors from which they are inspired. This work presents a design methodology that targets the creation of extreme robotic locomotors. These are robots that outperform all others at a particular task. They are used to study locomotion at the edge of the current performance envelope for robotic systems. The design methodology focuses on maximizing the power-density of the platform. We apply it to create first a rapid running robot, the X2-VelociRoACH, and two versions of a jumping robot, Salto and Salto-1P. In all of these robots, we centralize the actuation such that one actuator provides all the power for the energetic locomotory tasks. A kinematic coupling is designed for each platform, such that the correct behavior (running or jumping) happens by default when the energetic actuator is driven open-loop. The design methodology successfully created two robots at the edge of their respective performance envelopes.The X2-VelociRoACH is a 54 gram experimental legged robot developed with this methodology that was developed to test hypotheses about running with unnaturally high stride frequencies. It is capable of running at stride frequencies up to 45 Hz, and velocities up to 4.9 m/s, making it the fastest legged robot relative to size. The top speed of the robot was limited by structural failure. High-frequency running experiments with the robot shows that the power required to cycle its running appendages increase cubically with the stride rate. Our findings show that although it is possible to further increase the maximum velocity of a legged robot with the simple strategy of increasing stride frequency, considerations must be made for the energetic demands of high stride rates. For the development of the jumping robot Salto, we first devise the vertical jumping agility metric to identify a model animal system for inspiration. We found the most agile animals outperform the most agile robots by a factor of two. The animal with the highest vertical jumping agility, the galago (Galago senegalensis), is known to use a power-modulating strategy to obtain higher peak power than that of muscle alone. Few previous robots have used series-elastic power modulation (achieved by combining series-elastic actuation with variable mechanical advantage), and because of motor power limits, the best current robot has a vertical jumping agility of only 55% of a galago. Through use of a specialized leg mechanism designed to enhance power modulation, we constructed a jumping robot that achieved 78% of the vertical jumping agility of a galago. The leg mechanism also has constraints which assure rotation-free jumping motion by default. Agile robots can explore venues of locomotion that were not previously attainable. We demonstrate this with a wall jump, where the robot leaps from the floor to a wall and then springs off the wall to reach a net height that is greater than that accessible by a single jump. Our results show that series-elastic power modulation is an actuation strategy that enables a clade of vertically agile robots.We extend the work with Salto to see how the locomotory capacity of an extreme robotic locomotor can be extended without compromising the power density of the platform. Salto-1P uses aerodynamic thrusters and an inertial tail to control its attitude in the air. A linearized Raibert step controller was sufficient to enable unconstrained in-place hopping and forwards-backwards locomotion with external position feedback. We present studies of extreme jumping locomotion in which the robot spends just 7.7% of its time on the ground, experiencing accelerations of 14 times earth gravity in its stance phase. An experimentally collected dataset of 772 observed jumps was used to establish the range of achievable horizontal and vertical impulses for Salto-1P.


title: Max-Pooling Loss Training of Long Short-Term Memory Networks for Small-Footprint Keyword Spotting

abstract: We propose a max-pooling based loss function for training Long Short-Term Memory (LSTM) networks for small-footprint keyword spotting (KWS), with low CPU, memory, and latency requirements. The max-pooling loss training can be further guided by initializing with a cross-entropy loss trained network. A posterior smoothing based evaluation approach is employed to measure keyword spotting performance. Our experimental results show that LSTM models trained using cross-entropy loss or max-pooling loss outperform a cross-entropy loss trained baseline feed-forward Deep Neural Network (DNN). In addition, max-pooling loss trained LSTM with randomly initialized network performs better compared to cross-entropy loss trained LSTM. Finally, the max-pooling loss trained LSTM initialized with a cross-entropy pre-trained network shows the best performance, which yields 67:6% relative reduction compared to baseline feed-forward DNN in Area Under the Curve (AUC) measure.


title: A Smart Kitchen for Ambient Assisted Living

abstract: The kitchen environment is one of the scenarios in the home where users can benefit from Ambient Assisted Living (AAL) applications. Moreover, it is the place where old people suffer from most domestic injuries. This paper presents a novel design, implementation and assessment of a Smart Kitchen which provides Ambient Assisted Living services; a smart environment that increases elderly and disabled people's autonomy in their kitchen-related activities through context and user awareness, appropriate user interaction and artificial intelligence. It is based on a modular architecture which integrates a wide variety of home technology (household appliances, sensors, user interfaces, etc.) and associated communication standards and media (power line, radio frequency, infrared and cabled). Its software architecture is based on the Open Services Gateway initiative (OSGi), which allows building a complex system composed of small modules, each one providing the specific functionalities required, and can be easily scaled to meet our needs. The system has been evaluated by a large number of real users (63) and carers (31) in two living labs in Spain and UK. Results show a large potential of system functionalities combined with good usability and physical, sensory and cognitive accessibility.


Fully Associative Ensemble Learning for Hierarchical Multi-Label Classification.


title: Effective Use of Word Order for Text Categorization with Convolutional Neural Networks

abstract: Convolutional neural network (CNN) is a neural network that can make use of the internal structure of data such as the 2D structure of image data. This paper studies CNN on text categorization to exploit the 1D structure (namely, word order) of text data for accurate prediction. Instead of using low-dimensional word vectors as input as is often done, we directly apply CNN to high-dimensional text data, which leads to directly learning embedding of small text regions for use in classification. In addition to a straightforward adaptation of CNN from image to text, a simple but new variation which employs bag-of-word conversion in the convolution layer is proposed. An extension to combine multiple convolution layers is also explored for higher accuracy. The experiments demonstrate the effectiveness of our approach in comparison with state-of-the-art methods.


title: NEuro COgnitive REhabilitation for Disease of Addiction (NECOREDA) Program: From Development to Trial

abstract: Despite extensive evidence for cognitive deficits associated with drug use and multiple publications supporting the efficacy of cognitive rehabilitation treatment (CRT) services for drug addictions, there are a few well-structured tools and organized programs to improve cognitive abilities in substance users. Most published studies on cognitive rehabilitation for drug dependent patients used rehabilitation tools, which have been previously designed for other types of brain injuries such as schizophrenia or traumatic brain injuries and not specifically designed for drug dependent patients. These studies also suffer from small sample size, lack of follow-up period assessments and or comprehensive treatment outcome measures. To address these limitations, we decided to develop and investigate the efficacy of a paper and pencil cognitive rehabilitation package called NECOREDA (Neurocognitive Rehabilitation for Disease of Addiction) to improve neurocognitive deficits associated with drug dependence particularly caused by stimulants (e.g. amphetamine type stimulants and cocaine) and opiates. To evaluate the feasibility of NECOREDA program, we conducted a pilot study with 10 opiate and methamphetamine dependent patients for 3 months in outpatient setting. NECOREDA was revised based on qualitative comments received from clients and treatment providers. Final version of NECOREDA is composed of brain training exercises called "Brain Gym" and psychoeducational modules called "Brain Treasures" which is implemented in 16 training sessions interleaved with 16 review and practice sessions. NECOREDA will be evaluated as an add-on intervention to methadone maintenance treatment in a randomized clinical trial among opiate dependent patients starting from August 2015. We discuss methodological features of NECOREDA development and evaluation in this article.


title: Statistical behavior and consistency of classification methods based on convex risk minimization

abstract: We study how closely the optimal Bayes error rate can be approximately reached using a classification algorithm that computes a classifier by minimizing a convex upper bound of the classification error function. The measurement of closeness is characterized by the loss function used in the estimation. We show that such a classification scheme can be generally regarded as a (nonmaximum-likelihood) conditional in-class probability estimate, and we use this analysis to compare various convex loss functions that have appeared in the literature. Furthermore, the theoretical insight allows us to design good loss functions with desirable properties. Another aspect of our analysis is to demonstrate the consistency of certain classification methods using convex risk minimization. This study sheds light on the good performance of some recently proposed linear classification methods including boosting and support vector machines. It also shows their limitations and suggests possible improvements.


title: Modelling and analysis of convergence of wireless sensor network and passive optical network using queueing theory

abstract: Wireless sensor networks (WSNs) are being deployed for an ever-widening range of applications, particularly due to their low power consumption and reliability. In most applications, the sensor data must be sent over the Internet or core network, which results in the WSN backhaul problem. Passive optical networks (PONs), a next-generation access network technology, are well suited form a backhaul which interfaces WSNs to the core network. In this paper a new structure is proposed which converges WSNs and PONs. It is modelled and analyzed through queuing theory by employing two M/M/1 queues in tandem. Numerical results show how the WSN and PON dimensions affect the average queue length, thus serving as a guideline for future resource allocation and scheduling of such a converged network.


title: Regularized and Retrofitted models for Learning Sentence Representation with Context

abstract: Vector representation of sentences is important for many text processing tasks that involve classifying, clustering, or ranking sentences. For solving these tasks, bag-of-word based representation has been used for a long time. In recent years, distributed representation of sentences learned by neural models from unlabeled data has been shown to outperform traditional bag-of-words representations. However, most existing methods belonging to the neural models consider only the content of a sentence, and disregard its relations with other sentences in the context. In this paper, we first characterize two types of contexts depending on their scope and utility. We then propose two approaches to incorporate contextual information into content-based models. We evaluate our sentence representation models in a setup, where context is available to infer sentence vectors. Experimental results demonstrate that our proposed models outshine existing models on three fundamental tasks, such as, classifying, clustering, and ranking sentences.


title: DepthNet: A Recurrent Neural Network Architecture for Monocular Depth Prediction

abstract: Predicting the depth map of a scene is often a vital component of monocular SLAM pipelines. Depth prediction is fundamentally ill-posed due to the inherent ambiguity in the scene formation process. In recent times, convolutional neural networks (CNNs) that exploit scene geometric constraints have been explored extensively for supervised single-view depth prediction and semi-supervised 2-view depth prediction. In this paper we explore whether recurrent neural networks (RNNs) can learn spatio-temporally accurate monocular depth prediction from video sequences, even without explicit definition of the inter-frame geometric consistency or pose supervision. To this end, we propose a novel convolutional LSTM (ConvLSTM)-based network architecture for depth prediction from a monocular video sequence. In the proposed ConvLSTM network architecture, we harness the ability of long short-term memory (LSTM)-based RNNs to reason sequentially and predict the depth map for an image frame as a function of the appearances of scene objects in the image frame as well as image frames in its temporal neighborhood. In addition, the proposed ConvLSTM network is also shown to be able to make depth predictions for future or unseen image frame(s). We demonstrate the depth prediction performance of the proposed ConvLSTM network on the KITTI dataset and show that it gives results that are superior in terms of accuracy to those obtained via depth-supervised and self-supervised methods and comparable to those generated by state-of-the-art pose-supervised methods.


Memetics : a Darwinian pseudo-science .


title: Investigation of a New Ironless-Stator Self-Bearing Axial Flux Permanent Magnet Motor

abstract: In this paper, a new type of self-bearing axial flux permanent magnet (SBAFPM) motor with ironless stator is proposed. The torque windings and the suspension windings are integrated into a cohesive whole with the same pole number for both torque and radial force generation. The fundamental structure and the working principle of the presented ironless-stator SBAFPM motor with ironless stator are illustrated. The control method and the implementation for the integrated windings system to produce force and torque independently are presented, as well as an FEA model for calculating the achievable drive torque and suspension force. Results indicate that the bearing force and the drive torque depend linearly on the respective current. The presented ironless-stator SBAFPM motor is free from tradeoff between force and torque, which will simplify the control of the torque and the levitation force.


title: Autonomous overtaking decision making of driverless bus based on deep Q-learning method

abstract: The autonomous overtaking maneuver is a valuable technology in unmanned vehicle field. However, overtaking is always perplexed by its security and time cost. Now, an autonomous overtaking decision making method based on deep Q-learning network is proposed in this paper, which employs a deep neural network(DNN) to learn Q function from action chosen to state transition. Based on the trained DNN, appropriate action is adopted in different environments for higher reward state. A series of experiments are performed to verify the effectiveness and robustness of our proposed approach for overtaking decision making based on deep Q-learning method. The results support that our approach achieves better security and lower time cost compared with traditional reinforcement learning methods.


title: Harnessing the power of crowdsourcing and Internet of Things in disaster response

abstract: Crowdsourcing and Internet of Things (IoT) are gaining more and more attention both in industry and academia in order to explore their effects on disaster relief. The current state of the literature shows a clear focus on the extent to which crowdsourcing on one hand, or IoT on the other hand, can individually make a difference regarding disaster response, but very few studies have considered the integration of both crowdsourcing and internet of things in order to link them with disaster response. Accordingly, in this paper, the authors have attempted to develop a crowdsourcing and IoT integration model which could help improving disaster response by using important value derived from using both social media and RFID technology. Furthermore, despite the fact that disaster relief offers similarities with epidemic transmission, (especially the SIR model), the application of SIR model in disaster relief still remains unexplored, which has led the authors to conduct a series of SIR model-based simulations to investigate the extent to which such integration model helps improving disaster response.


title: Online minimization knapsack problem

abstract: In this paper, we address the online minimization knapsack problem, i.e., the items are given one by one over time and the goal is to minimize the total cost of items that covers a knapsack. We study the removable model, where it is allowed to remove old items from the knapsack in order to accept a new item. We obtain the following results.(i)We propose a 8-competitive deterministic and memoryless algorithm for the problem, which contrasts with the result for the online maximization knapsack problem that no online algorithm has a bounded competitive ratio 14.(ii)We propose a 2e-competitive randomized algorithm for the problem.(iii)We derive a lower bound of 2 for deterministic algorithms for the problem.(iv)We propose a 1.618-competitive deterministic algorithm for the case in which each item has size equal to its cost, and show that this is best possible.


title: Data semantics revisited

abstract: The problem of data semantics is establishing and maintaining the correspondence between a data source and its intended subject matter. We review the long history of the problem in Databases, and contrast it with recent research on the Semantic Web. We then propose two new directions for research on the problem and sketch some open research questions.


title: Factor graph based multi-source data fusion for wireless localization

abstract: Multi-source fusion localization is an effective approach when a single source is unavailable or the positioning accuracy is unsatisfied, and it can take advantages of different location sources to achieve a better result. Data fusion is a process of merging different solutions and techniques with disparate types of information. In order to provide users with better location based services, this paper proposes a factor graph based multi-source data fusion algorithm for wireless localization. Different fusion sources are divided into multi-levels by adopting confidence level estimate algorithm. By using sum-product algorithm, the soft-information is calculated to complete the fusion process. Through some simulations, we can see that the proposed algorithm can improve the positioning accuracy greatly. At the same time, it has low complexity and a plug and play capability.


Making Fast Strategic Decisions in High-Velocity Environments


title: Revisiting Self-Supervised Visual Representation Learning

abstract: Unsupervised visual representation learning remains a largely unsolved problem in computer vision research. Among a big body of recently proposed approaches for unsupervised learning of visual representations, a class of self-supervised techniques achieves superior performance on many challenging benchmarks. A large number of the pretext tasks for self-supervised learning have been studied, but other important aspects, such as the choice of convolutional neural networks (CNN), has not received equal attention. Therefore, we revisit numerous previously proposed self-supervised models, conduct a thorough large scale study and, as a result, uncover multiple crucial insights. We challenge a number of common practices in self-supervised visual representation learning and observe that standard recipes for CNN design do not always translate to self-supervised representation learning. As part of our study, we drastically boost the performance of previously proposed techniques and outperform previously published state-of-the-art results by a large margin. We will release the code for reproducing our experiments when the anonymity requirements are lifted.


title: SPINE: Structural Identity Preserved Inductive Network Embedding

abstract: Recent advances in the field of network embedding have shown that low-dimensional network representation is playing a critical role in network analysis. Most existing network embedding methods encode the local proximity of a node, such as the first- and second-order proximities. While being efficient, these methods are short of leveraging the global structural information between nodes distant from each other. In addition, most existing methods learn embeddings on one single fixed network, and thus cannot be generalized to unseen nodes or networks without retraining. In this paper we present SPINE, a method that can jointly capture the local proximity and proximities at any distance, while being inductive to efficiently deal with unseen nodes or networks. Specifically, we identify the structural identity of each node based on a high-order proximity of the network named Rooted PageRank, followed by a novel biased Skip-Gram Negative Sampling algorithm and a carefully designed objective function to encode local proximities and global structural identities of nodes simultaneously. Extensive experimental results on benchmark datasets demonstrate the superiority of the proposed framework over the state of the art.


title: Adversarial Metric Learning

abstract: In the past decades, intensive efforts have been put to design various loss functions and metric forms for metric learning problem. These improvements have shown promising results when the test data is similar to the training data. However, the trained models often fail to produce reliable distances on the ambiguous test pairs due to the distribution bias between training set and test set. To address this problem, the Adversarial Metric Learning (AML) is proposed in this paper, which automatically generates adversarial pairs to remedy the distribution bias and facilitate robust metric learning. Specifically, AML consists of two adversarial stages, i.e. confusion and distinguishment. In confusion stage, the ambiguous but critical adversarial data pairs are adaptively generated to mislead the learned metric. In distinguishment stage, a metric is exhaustively learned to try its best to distinguish both the adversarial pairs and the original training pairs. Thanks to the challenges posed by the confusion stage in such competing process, the AML model is able to grasp plentiful difficult knowledge that has not been contained by the original training pairs, so the discriminability of AML can be significantly improved. The entire model is formulated into optimization framework, of which the global convergence is theoretically proved. The experimental results on toy data and practical datasets clearly demonstrate the superiority of AML to the representative state-of-the-art metric learning methodologies.


title: Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes

abstract: Importance A deep learning system (DLS) is a machine learning technology with potential for screening diabetic retinopathy and related eye diseases. Objective To evaluate the performance of a DLS in detecting referable diabetic retinopathy, vision-threatening diabetic retinopathy, possible glaucoma, and age-related macular degeneration (AMD) in community and clinic-based multiethnic populations with diabetes. Design, Setting, and Participants Diagnostic performance of a DLS for diabetic retinopathy and related eye diseases was evaluated using 494 661 retinal images. A DLS was trained for detecting diabetic retinopathy (using 76 370 images), possible glaucoma (125 189 images), and AMD (72 610 images), and performance of DLS was evaluated for detecting diabetic retinopathy (using 112 648 images), possible glaucoma (71 896 images), and AMD (35 948 images). Training of the DLS was completed in May 2016, and validation of the DLS was completed in May 2017 for detection of referable diabetic retinopathy (moderate nonproliferative diabetic retinopathy or worse) and vision-threatening diabetic retinopathy (severe nonproliferative diabetic retinopathy or worse) using a primary validation data set in the Singapore National Diabetic Retinopathy Screening Program and 10 multiethnic cohorts with diabetes. Exposures Use of a deep learning system. Main Outcomes and Measures Area under the receiver operating characteristic curve (AUC) and sensitivity and specificity of the DLS with professional graders (retinal specialists, general ophthalmologists, trained graders, or optometrists) as the reference standard. Results In the primary validation dataset (n = 14 880 patients; 71 896 images; mean [SD] age, 60.2 [2.2] years; 54.6% men), the prevalence of referable diabetic retinopathy was 3.0%; vision-threatening diabetic retinopathy, 0.6%; possible glaucoma, 0.1%; and AMD, 2.5%. The AUC of the DLS for referable diabetic retinopathy was 0.936 (95% CI, 0.925-0.943), sensitivity was 90.5% (95% CI, 87.3%-93.0%), and specificity was 91.6% (95% CI, 91.0%-92.2%). For vision-threatening diabetic retinopathy, AUC was 0.958 (95% CI, 0.956-0.961), sensitivity was 100% (95% CI, 94.1%-100.0%), and specificity was 91.1% (95% CI, 90.7%-91.4%). For possible glaucoma, AUC was 0.942 (95% CI, 0.929-0.954), sensitivity was 96.4% (95% CI, 81.7%-99.9%), and specificity was 87.2% (95% CI, 86.8%-87.5%). For AMD, AUC was 0.931 (95% CI, 0.928-0.935), sensitivity was 93.2% (95% CI, 91.1%-99.8%), and specificity was 88.7% (95% CI, 88.3%-89.0%). For referable diabetic retinopathy in the 10 additional datasets, AUC range was 0.889 to 0.983 (n = 40 752 images). Conclusions and Relevance In this evaluation of retinal images from multiethnic cohorts of patients with diabetes, the DLS had high sensitivity and specificity for identifying diabetic retinopathy and related eye diseases. Further research is necessary to evaluate the applicability of the DLS in health care settings and the utility of the DLS to improve vision outcomes.


title: Smart Contracts for Bribing Miners

abstract: We present three smart contracts that allow a briber to fairly exchange bribes to miners who pursue a mining strategy benefiting the briber. The first contract, CensorshipCon, highlights that Ethereum’s uncle block reward policy can directly subsidise the cost of bribing miners. The second contract, HistoryRevisionCon, rewards miners via an in-band payment for reversing transactions or enforcing a new state of another contract. The third contract, GoldfingerCon, rewards miners in one cryptocurrency for reducing the utility of another cryptocurrency. This work is motivated by the need to understand the extent to which smart contracts can impact the incentive mechanisms involved in Nakamoto-style consensus protocols.


title: Visual servo control. I. Basic approaches

abstract: This paper is the first of a two-part series on the topic of visual servo control using computer vision data in the servo loop to control the motion of a robot. In this paper, we describe the basic techniques that are by now well established in the field. We first give a general overview of the formulation of the visual servo control problem. We then describe the two archetypal visual servo control schemes: image-based and position-based visual servo control. Finally, we discuss performance and stability issues that pertain to these two schemes, motivating the second article in the series, in which we consider advanced techniques


title: Appropriability Mechanisms and the Platform Partnership Decision: Evidence from Enterprise Software

abstract: We examine whether ownership of intellectual property rights IPR or downstream capabilities is effective in encouraging entry into markets complementary to a proprietary platform by preventing the platform owner from expropriating rents from start-ups. We study this question in the context of the software industry, an environment where evidence of the efficacy of IPR as a mechanism to appropriate the returns from innovation has been mixed. Entry, in our context, is measured by an independent software vendor's ISV's decision to become certified by a platform owner and produce applications compatible with the platform. We find that ISVs with a greater stock of formal IPR such as patents and copyrights, and those with stronger downstream capabilities as measured by trademarks and consulting services are more likely to join the platform, suggesting that these mechanisms are effective in protecting ISVs from the threat of expropriation. We also find that the effects of IPR on the likelihood of partnership are greater when an ISV has weak downstream capabilities or when the threat of imitation is greater, such as when the markets served by the ISV are growing quickly. ::: ::: This paper was accepted by Gerard P. Cachon, information systems.


title: A TaLISMAN: automatic text and line segmentation of historical manuscripts

abstract: Historical and artistic handwritten books are valuable cultural heritage (CH) items, as they provide information about tangible and intangible cultural aspects from the past. Massive digitization projects have made these kind of data available to a world-wide population, and pose real challenges for automatic processing. In this scenario, document layout analysis plays a significant role, being a fundamental step of any document image understanding system. In this paper, we present a completely automatic algorithm to perform a robust text segmentation of old handwritten manuscripts on a per-book basis, and we show how to exploit this outcome to find two layout elements, i.e., text blocks and text lines. Our proposed technique have been evaluated on a large and heterogeneous corpus content, and our experimental results demonstrate that this approach is efficient and reliable, even when applied to very noisy and damaged books.


title: Revisiting Unreasonable Effectiveness of Data in Deep Learning Era

abstract: The success of deep learning in vision can be attributed to: (a) models with high capacity; (b) increased computational power; and (c) availability of large-scale labeled data. Since 2012, there have been significant advances in representation capabilities of the models and computational capabilities of GPUs. But the size of the biggest dataset has surprisingly remained constant. What will happen if we increase the dataset size by 10x or 100x? This paper takes a step towards clearing the clouds of mystery surrounding the relationship between `enormous data' and visual deep learning. By exploiting the JFT-300M dataset which has more than 375M noisy labels for 300M images, we investigate how the performance of current vision tasks would change if this data was used for representation learning. Our paper delivers some surprising (and some expected) findings. First, we find that the performance on vision tasks increases logarithmically based on volume of training data size. Second, we show that representation learning (or pre-training) still holds a lot of promise. One can improve performance on many vision tasks by just training a better base model. Finally, as expected, we present new state-of-the-art results for different vision tasks including image classification, object detection, semantic segmentation and human pose estimation. Our sincere hope is that this inspires vision community to not undervalue the data and develop collective efforts in building larger datasets.


title: PILCO: A Model-Based and Data-Efficient Approach to Policy Search

abstract: In this paper, we introduce PILCO, a practical, data-efficient model-based policy search method. PILCO reduces model bias, one of the key problems of model-based reinforcement learning, in a principled way. By learning a probabilistic dynamics model and explicitly incorporating model uncertainty into long-term planning, PILCO can cope with very little data and facilitates learning from scratch in only a few trials. Policy evaluation is performed in closed form using state-of-the-art approximate inference. Furthermore, policy gradients are computed analytically for policy improvement. We report unprecedented learning efficiency on challenging and high-dimensional control tasks.


title: Leveraging SDN to conserve energy in WSN-An analysis

abstract: Energy conservation is one of the serious problems faced by WSN as the sensor nodes have limited battery power and are expected to perform data aggregation and actuation functions in addition to sensing data. Literature has plenty of solutions proposed to reduce energy consumption and usage. With the recent upcoming technology of introducing network programmability that centralizes network management tasks using software defined architecture (SDN), network trafficking is a prominent domain for applicability of SDN. Inherent traffic issues in WSN like data forwarding, aggregation of the data, path break and energy consumption can be efficiently handled by SDN, which provides a platform in which the data plane and the control plane are separated. By integrating SDN in WSN, the sensor nodes perform only forwarding and don't take any routing decision, due to which energy usage will be reduced. We propose a general framework for a software-defined wireless sensor network where the controller will be implemented at the base station, centre nodes in the cluster acts as switches and communication between the switch and the controller is via OpenFlow protocol. We realize the energy saving in the proposed architecture with the results obtained using NS2 and mininet emulator environments.


title: The first record of a soft-shelled turtle (Testudines: Pan-Trionychidae) from southern Balkans (Pliocene, Gefira, N. Greece) and new information from bone histology

abstract: Soft-shelled turtles (Pan-Trionychidae) are not included in the present-day chelonian fauna of Greece and have been unknown in the Greek fossil record up to now. Here, we report the first fossil occurrence of a soft-shelled turtle from Greece, originating from the Pliocene Gefira Member (Angelochori Formation), in the lower Axios valley. The corresponding specimens were discovered with several mammalian remains, most of them attributable to the mastodon of Auvergne, Anancus arvernensis. The chelonian material includes five carapacial fragments that belong to the same individual and can be attributed to Pan-Trionychidae based on the typical sculpturing on the dorsal side of the carapace. Most of these bony plates were histologically sampled and thereby provide evidence for the “plywood” structure, another characteristic of pan-trionychids. They represent the first extended sampling of trionychid plates that belong to the same individual, allowing the documentation of the variation of the relevant trionychid morphologies in the carapace. These findings expand the paleobiogeographic range of this taxon to the southern Balkans and Greece and allow a better estimation of the chelonian paleo-fauna of the area. They are also important for the temporal distribution of this clade in the Paleoarctic, as they join specimens from Italy as being the last trionychids in Europe.


title: A statistical model for angle of arrival in indoor multipath propagation

abstract: Multiple antenna systems are a useful way of overcoming the effects of multipath interference, and can allow more efficient use of spectrum. In order to test the effectiveness of various algorithms such as diversity combining, phased array processing, and adaptive array processing in an indoor environment, a channel model is needed which models both the time and angle of arrival in indoor environments. Some data has been collected indoors and some temporal models have been proposed, but no existing model accounts for both time and angle of arrival. This paper discusses existing models for the time of arrival, experimental data (form the 6.75-7.25 GHz band) that were collected indoors, and a proposed extension of the Saleh-Valenzuela model (1987), which accounts for the angle of arrival. Model parameters measured in two different buildings are compared with the parameters presented in the paper by Saleh and Valenzuela, and some statistical validation of the model is presented.


title: When in Rome ... Learn Why the Romans Do What They Do: How Multicultural Learning Experiences Facilitate Creativity

abstract: Research suggests that living in and adapting to foreign cultures facilitates creativity. The current research investigated whether one aspect of the adaptation process—multicultural learning—is a critical component of increased creativity. Experiments 1-3 found that recalling a multicultural learning experience: (a) facilitates idea flexibility (e.g., the ability to solve problems in multiple ways), (b) increases awareness of underlying connections and associations, and (c) helps overcome functional fixedness. Importantly, Experiments 2 and 3 specifically demonstrated that functional learning in a multicultural context (i.e., learning about the underlying meaning or function of behaviors in that context) is particularly important for facilitating creativity. Results showed that creativity was enhanced only when participants recalled a functional multicultural learning experience and only when participants had previously lived abroad. Overall, multicultural learning appears to be an important mechanism by which foreign living experiences lead to creative enhancement.


title: Extracting foreground ensemble features to detect abnormal crowd behavior in intelligent video-surveillance systems

abstract: Public safety is a matter of national security and people’s livelihoods. In recent years, intelligent video-surveillance systems have become important active-protection systems. A surveillance system that provides early detection and threat assessment could protect people from crowd-related disasters and ensure public safety. Image processing is commonly used to extract features, e.g., people, from a surveillance video. However, little research has been conducted on the relationship between foreground detection and feature extraction. Most current video-surveillance research has been developed for restricted environments, in which the extracted features are limited by having information from a single foreground; they do not effectively represent the diversity of crowd behavior. This paper presents a general framework based on extracting ensemble features from the foreground of a surveillance video to analyze a crowd. The proposed method can flexibly integrate different foreground-detection technologies to adapt to various monitored environments. Furthermore, the extractable representative features depend on the heterogeneous foreground data. Finally, a classification algorithm is applied to these features to automatically model crowd behavior and distinguish an abnormal event from normal patterns. The experimental results demonstrate that the proposed method’s performance is both comparable to that of state-of-the-art methods and satisfies the requirements of real-time applications.


title: Attention-Based Convolutional Neural Network for Machine Comprehension

abstract: Understanding open-domain text is one of the primary challenges in natural language processing (NLP). Machine comprehension benchmarks evaluate the system's ability to understand text based on the text content only. In this work, we investigate machine comprehension on MCTest, a question answering (QA) benchmark. Prior work is mainly based on feature engineering approaches. We come up with a neural network framework, named hierarchical attention-based convolutional neural network (HABCNN), to address this task without any manually designed features. Specifically, we explore HABCNN for this task by two routes, one is through traditional joint modeling of passage, question and answer, one is through textual entailment. HABCNN employs an attention mechanism to detect key phrases, key sentences and key snippets that are relevant to answering the question. Experiments show that HABCNN outperforms prior deep learning approaches by a big margin.


title: PDF articles metadata harvester

abstract: Scientific journals are very important in recording the finding from researchers around the world. The recent media to disseminate scientific journals is PDF. On scheme to find the scientific journals over the internet is via metadata. Metadata stores information about article summary. Embedding metadata into PDF of scientific article will grant the consistency of metadata readness. Harvesting the metadata from scientific journal is very interesting field at the moment. This paper will discuss about scientific journal metadata harvesters involving XMP.


title: Sexual assault of women.

abstract: Sexual violence affects up to one third of women during their lifetime. Sexual assault is underreported, and more than one half of assaults are committed by someone known to the survivor. Although both men and women can be sexually assaulted, women are at greatest risk. Some groups are more vulnerable, including adolescents; survivors of childhood sexual or physical abuse; persons who are disabled; persons with substance abuse problems; sex workers; persons who are poor or homeless; and persons living in prisons, institutions, or areas of military conflict. Family physicians care for sexual assault survivors immediately and years after the assault. Immediate care includes the treatment of injuries, prophylaxis for sexually transmitted infections, administration of emergency contraception to prevent pregnancy, and the sensitive management of psychological issues. Family physicians should collect evidence for a "rape kit" only if they are experienced in treating persons who have been sexually assaulted because of the legal ramifications of improper collection and storage of evidence. Sexual assault may result in long-term mental and physical health problems. Presentations to the family physician may include self-destructive behaviors, chronic pelvic pain, and difficulty with pelvic examinations. Prevention of sexual assault is societal and should focus on public health education. Safety and support programs have been shown to reduce sexual assaults.


title: Objects and attention: the state of the art

abstract: What are the units of attention? In addition to standard models holding that attention can select spatial regions and visual features, recent work suggests that in some cases attention can directly select discrete objects. This paper reviews the state of the art with regard to such ‘object-based’ attention, and explores how objects of attention relate to locations, reference frames, perceptual groups, surfaces, parts, and features. Also discussed are the dynamic aspects of objecthood, including the question of how attended objects are individuated in time, and the possibility of attending to simple dynamic motions and events. The final sections of this review generalize these issues beyond vision science, to other modalities and fields such as auditory objects of attention and the infant’s ‘object concept’. q 2001 Elsevier Science B.V. All rights reserved.


title: CoinShuffle: Practical Decentralized Coin Mixing for Bitcoin

abstract: The decentralized currency network Bitcoin is emerging as a potential new way of performing financial transactions across the globe. Its use of pseudonyms towards protecting users' privacy has been an attractive feature to many of its adopters. Nevertheless, due to the inherent public nature of the Bitcoin transaction ledger, users' privacy is severely restricted to linkable anonymity, and a few transaction deanonymization attacks have been reported thus far. ::: ::: In this paper we propose CoinShuffle, a completely decentralized Bitcoin mixing protocol that allows users to utilize Bitcoin in a truly anonymous manner. CoinShuffle is inspired by the accountable anonymous group communication protocol Dissent and enjoys several advantages over its predecessor Bitcoin mixing protocols. It does not require any trusted, accountable or untrusted third party and it is perfectly compatible with the current Bitcoin system. CoinShuffle introduces only a small communication overhead for its users, while completely avoiding additional anonymization fees and minimalizing the computation and communication overhead for the rest of the Bitcoin system.


title: How a diverse research ecosystem has generated new rehabilitation technologies: Review of NIDILRR’s Rehabilitation Engineering Research Centers

abstract: Over 50 million United States citizens (1 in 6 people in the US) have a developmental, acquired, or degenerative disability. The average US citizen can expect to live 20% of his or her life with a disability. Rehabilitation technologies play a major role in improving the quality of life for people with a disability, yet widespread and highly challenging needs remain. Within the US, a major effort aimed at the creation and evaluation of rehabilitation technology has been the Rehabilitation Engineering Research Centers (RERCs) sponsored by the National Institute on Disability, Independent Living, and Rehabilitation Research. As envisioned at their conception by a panel of the National Academy of Science in 1970, these centers were intended to take a “total approach to rehabilitation”, combining medicine, engineering, and related science, to improve the quality of life of individuals with a disability. Here, we review the scope, achievements, and ongoing projects of an unbiased sample of 19 currently active or recently terminated RERCs. Specifically, for each center, we briefly explain the needs it targets, summarize key historical advances, identify emerging innovations, and consider future directions. Our assessment from this review is that the RERC program indeed involves a multidisciplinary approach, with 36 professional fields involved, although 70% of research and development staff are in engineering fields, 23% in clinical fields, and only 7% in basic science fields; significantly, 11% of the professional staff have a disability related to their research. We observe that the RERC program has substantially diversified the scope of its work since the 1970’s, addressing more types of disabilities using more technologies, and, in particular, often now focusing on information technologies. RERC work also now often views users as integrated into an interdependent society through technologies that both people with and without disabilities co-use (such as the internet, wireless communication, and architecture). In addition, RERC research has evolved to view users as able at improving outcomes through learning, exercise, and plasticity (rather than being static), which can be optimally timed. We provide examples of rehabilitation technology innovation produced by the RERCs that illustrate this increasingly diversifying scope and evolving perspective. We conclude by discussing growth opportunities and possible future directions of the RERC program.


title: Managing temporal change of cities with CityGML

abstract: An increasing number of cities are developing digital models. It becomes thus necessary to take into account changes over time. Interoperability and thus the use of standards is also recommended. In this paper, we propose a new method, based on CityGML to take into account changes in the objects which compose the city. This method is efficient for any kind of changes of the city objects (semantic, geometry, topology or appearance). We then propose an extension of our method in order to consider more frequent changes as it is the case with sensors data that can be linked with part of city objects.


abstract: We have recently developed an algorithm for generating complex dynamically-feasible maneuvers for autonomous vehicles traveling at high speeds over large distances. Our approach is based on performing anytime incremental search on a multi-resolution, dynamically-feasible lattice state space. It has been implemented on an autonomous passenger vehicle that competed in, and won, the Urban Challenge. Much of the speed and robustness of our approach owes to the clever design and use of grid-based cost maps that were used throughout the planning process. In this paper, we explain the design and use of these various grid-based cost maps. Comments Dave Ferguson and Maxim Likhachev, " Efficiently Using Cost Maps For Planning Complex Maneuvers, " Proceedings of International Conference on Robotics and Automation Workshop on Planning with Cost Maps, 2008. This conference paper is available at ScholarlyCommons: http://repository.upenn.edu/grasp_papers/20 Efficiently Using Cost Maps For Planning Complex Maneuvers


title: Software fault prediction metrics

abstract: ContextSoftware metrics may be used in fault prediction models to improve software quality by predicting fault location. ObjectiveThis paper aims to identify software metrics and to assess their applicability in software fault prediction. We investigated the influence of context on metrics' selection and performance. MethodThis systematic literature review includes 106 papers published between 1991 and 2011. The selected papers are classified according to metrics and context properties. ResultsObject-oriented metrics (49%) were used nearly twice as often compared to traditional source code metrics (27%) or process metrics (24%). Chidamber and Kemerer's (CK) object-oriented metrics were most frequently used. According to the selected studies there are significant differences between the metrics used in fault prediction performance. Object-oriented and process metrics have been reported to be more successful in finding faults compared to traditional size and complexity metrics. Process metrics seem to be better at predicting post-release faults compared to any static code metrics. ConclusionMore studies should be performed on large industrial software systems to find metrics more relevant for the industry and to answer the question as to which metrics should be used in a given context.


title: Vasculature Detection In Angiograms Using Active Contours

abstract: Active contour models, or “snakes,” have shown themselves to be a powerful and flexible paradigm for many problems in image understanding. Active contour models are now applied to the problem of detecting coronary blood vessels in cardiac angiographic images, an important medical image understanding problem. Given two endpoints along a vessel, active contour models are used to find the vessel, resulting in greater consistency and less computation than traditional vessel detection algorithms. Snakes are also employed to detect vessels acrm multiple frames, a task which involves predicting the location of the snake in the current frame based on ita location in previous frames.


title: A PHD filter for tracking closely spaced objects with elliptic Random Hypersurface models

abstract: It is one of the difficulties for space-based optical sensors to track a group of closely spaced objects (CSOs). A suitable way is to treat the CSOs as extended targets, tracking the centroid states and extensions jointly instead of individual objects. The target-generated measurements are modeled by the elliptic Random Hypersurface model (RHM) which can be easily embedded into other Bayesian inference algorithms. Here, the RHM is incorporated with the Gaussian-mixture probability hypothesis density (GM-PHD) for extended target tracking, which enables estimating shape-varying targets in presence of clutter and uncertain associations. Compared with the Gaussian inverse Wishart (GIW) PHD, simulation results demonstrate the capabilities and limitations of the proposed method. At last, a ballistic missile defense scene is established to depict how the method is to handle the CSOs tracking issue.


title: Interactive example-based urban layout synthesis

abstract: We present an interactive system for synthesizing urban layouts by example. Our method simultaneously performs both a structure-based synthesis and an image-based synthesis to generate a complete urban layout with a plausible street network and with aerial-view imagery. Our approach uses the structure and image data of real-world urban areas and a synthesis algorithm to provide several high-level operations to easily and interactively generate complex layouts by example. The user can create new urban layouts by a sequence of operations such as join, expand, and blend without being concerned about low-level structural details. Further, the ability to blend example urban layout fragments provides a powerful way to generate new synthetic content. We demonstrate our system by creating urban layouts using example fragments from several real-world cities, each ranging from hundreds to thousands of city blocks and parcels.


title: Predictive-corrective incompressible SPH

abstract: We present a novel, incompressible fluid simulation method based on the Lagrangian Smoothed Particle Hydrodynamics (SPH) model. In our method, incompressibility is enforced by using a prediction-correction scheme to determine the particle pressures. For this, the information about density fluctuations is actively propagated through the fluid and pressure values are updated until the targeted density is satisfied. With this approach, we avoid the computational expenses of solving a pressure Poisson equation, while still being able to use large time steps in the simulation. The achieved results show that our predictive-corrective incompressible SPH (PCISPH) method clearly outperforms the commonly used weakly compressible SPH (WCSPH) model by more than an order of magnitude while the computations are in good agreement with the WCSPH results.


title: Arabic Natural Language Processing: Challenges and Solutions

abstract: The Arabic language presents researchers and developers of natural language processing (NLP) applications for Arabic text and speech with serious challenges. The purpose of this article is to describe some of these challenges and to present some solutions that would guide current and future practitioners in the field of Arabic natural language processing (ANLP). We begin with general features of the Arabic language in Sections 1, 2, and 3 and then we move to more specific properties of the language in the rest of the article. In Section 1 of this article we highlight the significance of the Arabic language today and describe its general properties. Section 2 presents the feature of Arabic Diglossia showing how the sociolinguistic aspects of the Arabic language differ from other languages. The stability of Arabic Diglossia and its implications for ANLP applications are discussed and ways to deal with this problematic property are proposed. Section 3 deals with the properties of the Arabic script and the explosion of ambiguity that results from the absence of short vowel representations and overt case markers in contemporary Arabic texts. We present in Section 4 specific features of the Arabic language such as the nonconcatenative property of Arabic morphology, Arabic as an agglutinative language, Arabic as a pro-drop language, and the challenge these properties pose to ANLP. We also present solutions that have already been adopted by some pioneering researchers in the field. In Section 5 we point out to the lack of formal and explicit grammars of Modern Standard Arabic which impedes the progress of more advanced ANLP systems. In Section 6 we draw our conclusion.


title: Scalable learning of collective behavior based on sparse social dimensions

abstract: The study of collective behavior is to understand how individuals behave in a social network environment. Oceans of data generated by social media like Facebook, Twitter, Flickr and YouTube present opportunities and challenges to studying collective behavior in a large scale. In this work, we aim to learn to predict collective behavior in social media. In particular, given information about some individuals, how can we infer the behavior of unobserved individuals in the same network? A social-dimension based approach is adopted to address the heterogeneity of connections presented in social media. However, the networks in social media are normally of colossal size, involving hundreds of thousands or even millions of actors. The scale of networks entails scalable learning of models for collective behavior prediction. To address the scalability issue, we propose an edge-centric clustering scheme to extract sparse social dimensions. With sparse social dimensions, the social-dimension based approach can efficiently handle networks of millions of actors while demonstrating comparable prediction performance as other non-scalable methods.


title: Context-aware citation recommendation

abstract: When you write papers, how many times do you want to make some citations at a place but you are not sure which papers to cite? Do you wish to have a recommendation system which can recommend a small number of good candidates for every place that you want to make some citations? In this paper, we present our initiative of building a context-aware citation recommendation system. High quality citation recommendation is challenging: not only should the citations recommended be relevant to the paper under composition, but also should match the local contexts of the places citations are made. Moreover, it is far from trivial to model how the topic of the whole paper and the contexts of the citation places should affect the selection and ranking of citations. To tackle the problem, we develop a context-aware approach. The core idea is to design a novel non-parametric probabilistic model which can measure the context-based relevance between a citation context and a document. Our approach can recommend citations for a context effectively. Moreover, it can recommend a set of citations for a paper with high quality. We implement a prototype system in CiteSeerX. An extensive empirical evaluation in the CiteSeerX digital library against many baselines demonstrates the effectiveness and the scalability of our approach.


title: MAVR: Code Reuse Stealthy Attacks and Mitigation on Unmanned Aerial Vehicles

abstract: As embedded systems have increased in performance and reliability, their applications have expanded into new domains such as automated drone-based delivery mechanisms. Security of these drones, also referred to as unmanned aerial vehicles (UAVs), is crucial due to their use in many different domains. In this paper, we present a stealthy attack strategy that allows the attacker to change sensor values and modify the UAV navigation path. As the attack is stealthy, the system will continue to execute normally and thus the ground station or other monitoring entities and systems will not be able to detect that an attack is undergoing. With respect to defense, we propose a strategy that combines software and hardware techniques. At software level, we propose a fine grained randomization based approach that modifies the layout of the executable code and hinders code-reuse attack. To strengthen the security of our defense, we leverage a custom hardware platform designed and built by us. The platform isolates the code binary and randomized binary in such a way that the actual code being executed is never exposed for an attacker to analyze. We have implemented a prototype of this defense technique and present results to demonstrate the effectiveness and efficiency of this defense strategy.


title: Ultrathin cameras using annular folded optics

abstract: We present a reflective multiple-fold approach to visible imaging for high-resolution, large aperture cameras of significantly reduced thickness. This approach allows for reduced bulk and weight compared with large high-quality camera systems and improved resolution and light collection compared with miniature conventional cameras. An analysis of the properties of multiple-fold imagers is presented along with the design, fabrication, and testing of an eightfold prototype camera. This demonstration camera has a 35 mm effective focal length, 0.7 NA, and 27 mm effective aperture folded into a 5 mm total thickness.


title: The Business of Unethical Weapons

abstract: If recourse to war is sometimes justifiable, then weapons need to be available. But is all fair in war, or can and should some weapons be banned as unethical? If so, how will this affect the arms business in a shrinking market demanding continuous product innovation? The author is completing his MBA degree at London Business School and has a background in electronic engineering and computer science.


Ontology Languages for the Semantic Web


title: Maximum Likelihood Frequency Estimation and Preamble Identification in OFDMA-based WiMAX Systems

abstract: In multi-cellular WiMAX systems based on orthogonal frequency-division multiple-access (OFDMA), the training preamble is chosen from a set of known sequences so as to univocally identify the transmitting base station. Therefore, in addition to timing and frequency synchronization, preamble index identification is another fundamental task that a mobile terminal must successfully complete before establishing a communication link with the base station. In this work we investigate the joint maximum likelihood (ML) estimation of the carrier frequency offset (CFO) and preamble index in a multicarrier system compliant with the WiMAX specifications, and derive a novel expression of the relevant Cramer-Rao bound (CRB). Since the exact ML solution is prohibitively complex in its general formulation, suboptimal algorithms are developed which can provide a reasonable trade-off between estimation accuracy and processing load. Specifically, we show that the fractional CFO can be recovered by combining the ML estimator with an existing algorithm that attains the CRB in all practical scenarios. The integral CFO and preamble index are subsequently retrieved by a suitable approximation of their joint ML estimator. Compared to existing alternatives, the resulting scheme exhibits improved accuracy and reduced sensitivity to residual timing errors. The price for these advantages is a certain increase of the system complexity.


title: Detection of postnatal depression. Development of the 10-item Edinburgh Postnatal Depression Scale

abstract: The development of a 10-item self-report scale (EPDS) to screen for Postnatal Depression in the community is described. After extensive pilot interviews a validation study was carried out on 84 mothers using the Research Diagnostic Criteria for depressive illness obtained from Goldberg's Standardised Psychiatric Interview. The EPDS was found to have satisfactory sensitivity and specificity, and was also sensitive to change in the severity of depression over time. The scale can be completed in about 5 minutes and has a simple method of scoring. The use of the EPDS in the secondary prevention of Postnatal Depression is discussed.


title: A Complete Tamil Optical Character Recognition System

abstract: Document Image processing and Optical Character Recognition (OCR) have been a frontline research area in the field of human-machine interface for the last few decades. Recognition of Indian language characters has been a topic of interest for quite some time. The earlier contributions were reported in [1] and [2]. A more recent work is reported in [3] and [9]. The need for efficient and robust algorithms and systems for recognition is being felt in India, especially in the post and telegraph department where OCR can assist the staff in sorting mail. Character recognition can also form a part in applications like intelligent scanning machines, text to speech converters, and automatic language-to-language translators.


title: Embodiment and presence in virtual worlds: a review

abstract: The multimodal, 3D-graphical communication platforms known as virtual worlds have their historical roots in multi-user domains/dungeons (MUDs) and virtual reality (VR). Given the extensive research on these technologies and the novelty of virtual worlds as a topic of study in information systems (IS), it behooves us to learn from the concepts, theories and insights generated primarily by other disciplines that have focused on these technologies. Because neither MUDs nor VR have significant organizational application, thus locating them outside of the IS discipline’s purview, very little of this literature has found its way into IS research thus far. This article reviews the extant literature on virtual environments and seeks to make its insights accessible to IS research on virtual worlds. In particular, this will focus on concepts, theories and insights regarding embodiment and presence, which are afforded by the avatar, a distinguishing technological artifact of virtual worlds.


title: Augmented Variational Autoencoders for Collaborative Filtering with Auxiliary Information

abstract: Recommender systems offer critical services in the age of mass information. A good recommender system selects a certain item for a specific user by recognizing why the user might like the item. This awareness implies that the system should model the background of the items and the users. This background modeling for recommendation is tackled through the various models of collaborative filtering with auxiliary information. This paper presents variational approaches for collaborative filtering to deal with auxiliary information. The proposed methods encompass variational autoencoders through augmenting structures to model the auxiliary information and to model the implicit user feedback. This augmentation includes the ladder network and the generative adversarial network to extract the low-dimensional representations influenced by the auxiliary information. These two augmentations are the first trial in the venue of the variational autoencoders, and we demonstrate their significant improvement on the performances in the applications of the collaborative filtering.


title: A review on Gabor wavelets for face recognition

abstract: Due to the robustness of Gabor features against local distortions caused by variance of illumination, expression and pose, they have been successfully applied for face recognition. The Facial Recognition Technology (FERET) evaluation and the recent Face Verification Competition (FVC2004) have seen the top performance of Gabor feature based methods. This paper aims to give a detailed survey of state of the art 2D face recognition algorithms using Gabor wavelets for feature extraction. Existing problems are covered and possible solutions are suggested.


title: Multi-language re-documentation to support a COBOL to Java migration project

abstract: Software migration projects need precise and up-to-date documentation of the software system to be migrated. Missing or outdated documentation hampers the migration process and compromises the overall quality of the resulting new software system. Moreover, if documentation is missing in the first place and no additional effort is undertaken to document the new software system, future maintenance and evolution tasks are burdened right from the beginning. Therefore, we apply an automatic re-documentation approach that uses a single tool chain to generate documentation for the software to be migrated and the transformed software system. By this, we not only support an ongoing COBOL to Java migration project at one of our industry partners but as well create the foundations to continuously generate up-to-date documentation for the new software system.


title: Enabling Next-Generation RFID Applications: Solutions and Challenges

abstract: Radio-frequency identification technology provides promising benefits such as inventory visibility and business process automation. However, if companies are to realize these benefits, researchers must address major challenges such as data processing, integration architecture design, security, and privacy.


Experimental Behavior of Composite Shear Walls Having L Shape Steel Sections in Boundary Regions


title: Gated Word-Character Recurrent Language Model

abstract: We introduce a recurrent neural network language model (RNN-LM) with long short-term memory (LSTM) units that utilizes both character-level and word-level inputs. Our model has a gate that adaptively finds the optimal mixture of the character-level and word-level inputs. The gate creates the final vector representation of a word by combining two distinct representations of the word. The character-level inputs are converted into vector representations of words using a bidirectional LSTM. The word-level inputs are projected into another high-dimensional space by a word lookup table. The final vector representations of words are used in the LSTM language model which predicts the next word given all the preceding words. Our model with the gating mechanism effectively utilizes the character-level inputs for rare and out-of-vocabulary words and outperforms word-level language models on several English corpora.


title: A Survey: Time Travel in Deep Learning Space: An Introduction to Deep Learning Models and How Deep Learning Models Evolved from the Initial Ideas

abstract: This report will show the history of deep learning evolves. It will trace back as far as the initial belief of connectionism modelling of brain, and come back to look at its early stage realization: neural networks. With the background of neural network, we will gradually introduce how convolutional neural network, as a representative of deep discriminative models, is developed from neural networks, together with many practical techniques that can help in optimization of neural networks. On the other hand, we will also trace back to see the evolution history of deep generative models, to see how researchers balance the representation power and computation complexity to reach Restricted Boltzmann Machine and eventually reach Deep Belief Nets. Further, we will also look into the development history of modelling time series data with neural networks. We start with Time Delay Neural Networks and move further to currently famous model named Recurrent Neural Network and its extension Long Short Term Memory. We will also briefly look into how to construct deep recurrent neural networks. Finally, we will conclude this report with some interesting open-ended questions of deep neural networks.


title: Detecting Morphological Filtering of Binary Images

abstract: Morphological operators are widely used in binary image processing for several purposes, such as removing noise, detecting contours or particular structures, and regularizing shapes. In particular, morphological filters are largely adopted in scanned documents to correct the artifacts caused by acquisition and binarization, as well as other processing. In this paper, we propose a novel approach for forensics detection of morphological filtering on binary images. The proposed technique exploits some mathematical properties of the two basic morphologic operators, erosion and dilation, to define an algorithm able not only to detect the application of the filter, but also to estimate the shape of the relevant structuring element. Experimental tests demonstrate that the technique is effective and robust to the most common operations performed on binary image documents.