title: An adaptive streaming active learning strategy based on instance weighting

abstract: The concept of the "sufficient weight" that we use as an uncertainty measure.An efficient uncertainty-based active learning for classification.An adaptive uncertainty threshold for the streaming active learning. This paper addresses stream-based active learning for classification. We propose a new query strategy based on instance weighting that improves the performance of the active learner compared to the commonly used uncertainty strategies. The proposed strategy computes the smallest weight that should be associated with new instance, so that the classifier changes its prediction regarding this instance. If a small weight is sufficient to change the predicted label, then the classifier was uncertain about its prediction, and the true label is queried from a labeller. In order to determine whether the sufficient weight is "small enough", we propose an adaptive uncertainty threshold which is suitable for the streaming setting. The proposed adaptive threshold allows the stream-based active learner to achieve an accuracy which is similar to that of a fully supervised learner, while querying much less labels. Experiments on several public and real world data prove the effectiveness of the proposed method.


title: Why-oriented end-user debugging of naive Bayes text classification

abstract: Machine learning techniques are increasingly used in intelligent assistants, that is, software targeted at and continuously adapting to assist end users with email, shopping, and other tasks. Examples include desktop SPAM filters, recommender systems, and handwriting recognition. Fixing such intelligent assistants when they learn incorrect behavior, however, has received only limited attention. To directly support end-user “debugging” of assistant behaviors learned via statistical machine learning, we present a Why-oriented approach which allows users to ask questions about how the assistant made its predictions, provides answers to these “why” questions, and allows users to interactively change these answers to debug the assistant's current and future predictions. To understand the strengths and weaknesses of this approach, we then conducted an exploratory study to investigate barriers that participants could encounter when debugging an intelligent assistant using our approach, and the information those participants requested to overcome these barriers. To help ensure the inclusiveness of our approach, we also explored how gender differences played a role in understanding barriers and information needs. We then used these results to consider opportunities for Why-oriented approaches to address user barriers and information needs.


title: VizClick visualizing clickstream data

abstract: Clickstream data is ubiquitous in today's web-connected world. Such data displays the salient features of big data, that is, volume, velocity and variety. As with any big data, visualizations can play a central role in making sense and generating hypotheses from such data. In this paper, we present a systematic approach of visualizing clickstream data. There are three basic questions we aim to address. First, we explore the inter-dependence between the large number of dimensions that are measured in clickstream data. Next, we analyze spatial aspects of data collected in web-analytics. Finally, the web designers might be interested in getting a deeper understanding of the website's topography and how browsers are interacting with it. Our approach is designed for business analysts, web designers and marketers; and helps them draw actionable insights in the management and refinement of large websites.


title: A navigation system for robots operating in crowded urban environments

abstract: Over the past years, there has been a tremendous progress in the area of robot navigation. Most of the systems developed thus far, however, are restricted to indoor scenarios, non-urban outdoor environments, or road usage with cars. Urban areas introduce numerous challenges to autonomous mobile robots as they are highly complex and in addition to that dynamic. In this paper, we present a navigation system for pedestrian-like autonomous navigation with mobile robots in city environments. We describe different components including a SLAM system for dealing with huge maps of city centers, a planning approach for inferring feasible paths taking also into account the traversability and type of terrain, and a method for accurate localization in dynamic environments. The navigation system has been implemented and tested in several large-scale field tests in which the robot Obelix managed to autonomously navigate from our university campus over a 3.3 km long route to the city center of Freiburg.


title: Antialiasing recovery

abstract: We present a method for restoring antialiased edges that are damaged by certain types of nonlinear image filters. This problem arises with many common operations such as intensity thresholding, tone mapping, gamma correction, histogram equalization, bilateral filters, unsharp masking, and certain nonphotorealistic filters. We present a simple algorithm that selectively adjusts the local gradients in affected regions of the filtered image so that they are consistent with those in the original image. Our algorithm is highly parallel and is therefore easily implemented on a GPU. Our prototype system can process up to 500 megapixels per second and we present results for a number of different image filters.


title: Autonomous Deep Learning: A Genetic DCNN Designer for Image Classification

abstract: Abstract Recent years have witnessed the breakthrough success of deep convolutional neural networks (DCNNs) in image classification and other vision applications. DCNNs have distinct advantages over traditional solutions in providing a uniform feature extraction-classification framework to free users from troublesome handcrafted feature extraction. However, DCNNs are far from autonomous, since their performance relies heavily on the handcrafted architectures, which also requires a lot expertise and experience to design, and cannot be continuously improved once the tuning of hyper-parameters converges. In this paper, we propose an autonomous and continuous learning (ACL) algorithm to generate automatically a DCNN architecture for each given vision task. We first partition a DCNN into multiple stacked meta convolutional blocks and fully connected blocks, each of which may contain the operations of convolution, pooling, fully connection, batch normalization, activation and drop out, and thus convert the architecture into an integer code. Then, we use genetic evolutionary operations, including selection, mutation and crossover to evolve a population of DCNN architectures. We have evaluated this algorithm on six image classification tasks, i.e., MNIST, Fashion-MNIST, EMNIST-Letters, EMNIST-Digits, CIFAR10 and CIFAR100. Our results indicate that the proposed ACL algorithm is able to evolve the DCNN architecture continuously if more time cost is allowed and can find a suboptimal DCNN architecture, whose performance is comparable to the state of the art.


title: New challenges for feature selection On the Relationship Between Feature Selection and Classification Accuracy

abstract: Dimensionality reduction and feature subset selection are two techniques for reducing the attribute space of a feature set, which is an important component of both supervised and unsupervised classification or regression problems. While in feature subset selection a subset of the original attributes is extracted, dimensionality reduction in general produces linear combinations of the original attribute set. ::: ::: In this paper we investigate the relationship between several attribute space reduction techniques and the resulting classification accuracy for two very different application areas. On the one hand, we consider e-mail filtering, where the feature space contains various properties of e-mail messages, and on the other hand, we consider drug discovery problems, where quantitative representations of molecular structures are encoded in terms of information-preserving descriptor values. ::: ::: Subsets of the original attributes constructed by filter and wrapper techniques as well as subsets of linear combinations of the original attributes constructed by three different variants of the principle component analysis (PCA) are compared in terms of the classification performance achieved with various machine learning algorithms as well as in terms of runtime performance. We successively reduce the size of the attribute sets and investigate the changes in the classification results. Moreover, we explore the relationship between the variance captured in the linear combinations within PCA and the resulting classification accuracy. ::: ::: The results show that the classification accuracy based on PCA is highly sensitive to the type of data and that the variance captured the principal components is not necessarily a vital indicator for the classification performance.


title: Some map matching algorithms for personal navigation assistants

abstract: Third-generation personal navigation assistants (PNAs) (i.e., those that provide a map, the user's current location, and directions) must be able to reconcile the user's location with the underlying map. This process is known as map matching. Most existing research has focused on map matching when both the user's location and the map are known with a high degree of accuracy. However, there are many situations in which this is unlikely to be the case. Hence, this paper considers map matching algorithms that can be used to reconcile inaccurate locational data with an inaccurate map/network.


title: Behavioral screening for cocaine sensitivity in mutagenized zebrafish

abstract: Understanding the molecular basis of addiction could be greatly aided by using forward genetic manipulation to lengthen the list of candidate genes involved in this complex process. Here, we report that zebrafish exhibit cocaine-induced conditioned place preference. In a pilot screen of 18 F2 generation families of mutagenized fish, we found three with abnormally low responses to cocaine. This behavior was inherited by the F3 generation in a manner that suggests the abnormalities were because of dominant mutations in single genes. Performance profiles in secondary behavioral screens measuring visual dark-adaptation and learning suggest that the defects were the result of mutations in distinct genes that affect dopaminergic signaling in the retina and brain.


title: TCAS traffic display redesign

abstract: The Traffic alert and Collision Avoidance System (TCAS) is designed to reduce the number of mid-air collisions. TCAS monitors the nearby airspace for other aircraft which are shown to the pilot on a traffic situation display (TSD). In addition, TCAS alerts the pilot to the presence of nearby aircraft with a precautionary Traffic Advisory (TA). If separation continues to degrade, TCAS then provides a Resolution Advisory (RA) which will audibly and visually instruct the pilot to make a vertical avoidance maneuver. However, pilots do not always comply with the RA. Changing the TSD could help improve pilot compliance. During an RA, the pilot is asked to maneuver vertically, but the TSD provides a top-down horizontal view which does not project the flight-path of other traffic and does not show if the other traffic is also receiving an RA. This project performed a literature review, a work domain analysis, and pilot interviews to inform design changes. The design methods echoed a need to provide pilots with better altitude information during traffic events along with a better picture of traffic heading and velocity. From these results, a new display was designed and is described using an animation. Future work includes evaluation of the new display as part of an iterative design process.


title: www.elsevier.com/locate/prer Eye movements and the control of actions in everyday life

abstract: The patterns of eye movement that accompany static activities such as reading have been studied since the early 1900s, but it is only since head-mounted eye trackers became available in the 1980s that it has been possible to study active tasks such as walking, driving, playing ball games and ordinary everyday activities like food preparation. This review examines the ways that vision contributes to the organization of such activities, and in particular how eye movements are used to locate the information needed by the motor system in the execution of each act. Major conclusions are that the eyes are proactive, typically seeking out the information required in the second before each act commences, although occasional ‘look ahead’ fixations are made to establish the locations of objects for use further into the future. Gaze often moves on before the last act is complete, indicating the presence of an information buffer. Each task has a characteristic but flexible pattern of eye movements that accompanies it, and this pattern is similar between individuals. The eyes rarely visit objects that are irrelevant to the action, and the conspicuity of objects (in terms of low-level image statistics) is much less important than their role in the task. Gaze control may involve movements of eyes, head and trunk, and these are coordinated in a way that allows for both flexibility of movement and stability of gaze. During the learning of a new activity, the eyes first provide feedback on the motor performance, but as this is perfected they provide feed-forward direction, seeking out the next object to be acted upon.


title: Residual Features and Unified Prediction Network for Single Stage Detection

abstract: Recently, a lot of single stage detectors using multi-scale features have been actively proposed. They are much faster than two stage detectors that use region proposal networks (RPN) without much degradation in the detection performances. However, the feature maps in the lower layers close to the input which are responsible for detecting small objects in a single stage detector have a problem of insufficient representation power because they are too shallow. There is also a structural contradiction that the feature maps have to deliver low-level information to next layers as well as contain high-level abstraction for prediction. In this paper, we propose a method to enrich the representation power of feature maps using Resblock and deconvolution layers. In addition, a unified prediction module is applied to generalize output results and boost earlier layers' representation power for prediction. The proposed method enables more precise prediction, which achieved higher score than SSD on PASCAL VOC and MS COCO. In addition, it maintains the advantage of fast computation of a single stage detector, which requires much less computation than other detectors with similar performance. Code is available at this https URL


title: Fast and Accurate Thickness Determination of Unknown Materials using Terahertz Time Domain Spectroscopy

abstract: We present a fast and accurate time domain based algorithm which extracts simultaneously the thickness and refractive index of highly transparent samples from terahertz time domain spectroscopy data. The utilized transfer function considers the Fabry-Perot oscillations of the sample and enables to analyze data with multiple reflections. The algorithm can also be applied to signals corrupted by vapor absorption lines. Since the data extraction takes only fractions of a second, this computation method is well suited for real-time monitoring of industrial processes. We show that the accuracy of the new algorithm is comparable to that of sophisticated, highly accurate and time consumptive frequency domain algorithms.


title: Transfer Learning for Behavior Ranking

abstract: Intelligent recommendation has been well recognized as one of the major approaches to address the information overload problem in the big data era. A typical intelligent recommendation engine usually consists of three major components, that is, data as the main input, algorithms for preference learning, and system for user interaction and high-performance computation. We observe that the data (e.g., users’ behavior) are usually in different forms, such as examinations (e.g., browse and collection) and ratings, where the former are often much more abundant than the latter. Although the data are in different representations, they are both related to users’ true preferences and are also deemed complementary to each other for preference learning. However, very few ranking or recommendation algorithms have been developed to exploit such two types of user behavior. In this article, we focus on jointly modeling the examination behavior and rating behavior and develop a novel and efficient ranking-oriented recommendation algorithm accordingly. First, we formally define a new recommendation problem termed behavior ranking, which aims to build a ranking-oriented model by exploiting both the examination behavior and rating behavior. Second, we develop a simple and generic transfer to rank (ToR) algorithm for behavior ranking, which transfers knowledge of candidate items from a global preference learning task to a local preference learning task. Compared with the previous work on integrating heterogeneous user behavior, our ToR algorithm is the first ranking-oriented solution, which can effectively generate recommendations in a more direct manner than those regression-oriented methods. Extensive empirical studies show that our ToR algorithm performs significantly more accurately than the state-of-the-art methods in most cases. Furthermore, our ToR algorithm is very efficient in terms of the time complexity, which is similar to those for homogeneous user behavior alone.


title: A 3D printed soft gripper integrated with curvature sensor for studying soft grasping

abstract: The grasping dynamics between a soft gripper and a deformable object has not been investigated so far. To this end, a 3D printed soft robot gripper with modular design was proposed in this paper. The gripper consists of a rigid base and three modular soft fingers. A snap-lock mechanism was designed on each finger for easy attach-detach to the base. All components were 3D printed using the Objet260 Connex system. The soft finger is air-driven and the idea is based on the principle of fluidic elastomer actuator. A curvature sensor was integrated inside each finger to measure the curvature during grasping. The fingers integrated with sensors were calibrated under different pneumatic pressure inputs. Relationship between pressure loading and bending angle, and relationship between bending angle and sensor output (voltage) were derived. Experiments with the gripper grasping and lifting a paper container filled with peanuts were conducted and results were presented and discussed.


title: Fast single frame super-resolution using scale-invariant self-similarity

abstract: Example-based super-resolution (SR) attracts great interest due to its wide range of applications. However, these algorithms usually involve patch search in a large database or the input image, which is computationally intensive. In this paper, we propose a scale-invariant self-similarity (SiSS) based super-resolution method. Instead of searching patches, we select the patch according to the SiSS measurement, so that the computational complexity is significantly reduced. Multi-shaped and multi-sized patches are used to collect sufficient patches for high-resolution (HR) image reconstruction and a hybrid weighting method is used to suppress the artifacts. Experimental results show that the proposed algorithm is 20~1,800 times faster than several state-of-the-art approaches and can achieve comparable quality.


title: Continuous-Time Trajectory Estimation for Event-based Vision Sensors

abstract: Event-based vision sensors, such as the Dynamic Vision Sensor (DVS), do not output a sequence of video frames like standard cameras, but a stream of asynchronous events. An event is triggered when a pixel detects a change of brightness in the scene. An event contains the location, sign, and precise timestamp of the change. The high dynamic range and temporal resolution of the DVS, which is in the order of micro-seconds, make this a very promising sensor for high-speed applications, such as robotics and wearable computing. However, due to the fundamentally different structure of the sensor's output, new algorithms that exploit the high temporal resolution and the asynchronous nature of the sensor are required. In this paper, we address ego-motion estimation for an event-based vision sensor using a continuous-time framework to directly integrate the information conveyed by the sensor. The DVS pose trajectory is approximated by a smooth curve in the space of rigid-body motions using cubic splines and it is optimized according to the observed events. We evaluate our method using datasets acquired from sensor-in-the-loop simulations and onboard a quadrotor performing flips. The results are compared to the ground truth, showing the good performance of the proposed technique.


title: Online subscriptions with anonymous access

abstract: Online privacy is an increasingly important problem, as many services are now offered in a digital form. Privacy (or the lack thereof) is of a special concern in subscriptions to large data repositories with heterogeneous information, where the service provider can easily profile its users and sell that information to third parties. In this work we present the design and implementation of a system that closely resembles the current practice of subscriptions to many services such as newspapers, digital libraries, music collections, etc., but at the same time offers anonymous access to the service. As with current practice, in our solution a user subscribes to the service obtaining access to it for a certain period of time, at the end of which the subscription expires. In our system user access is always anonymous and no two transactions by the same user can be linked together. Moreover, the system assures a high level of protection to the service provider, as a user cannot share her subscription credentials with others without denying herself access to the service. We present experimental results showing that the design of our system results in only small computation overheads, in addition to having very low communication requirements. The main objective of this work is thus to illustrate the practically of integrating anonymity into today's subscription-based services.


title: Market basket analysis using artificial neural network

abstract: Market basket analysis is based upon the identification and analysis of purchasing patterns of the customers. The problem with market basket analysis is the varying needs of the customers with respect to seasons and time and so we need to perform it time and again. Another problem that arises while doing market basket analysis is with Apriori algorithm in which we need to find candidate sets and frequent item-sets time and again. In this paper, we are suggesting the use of artificial neural network technique to overcome these problems. We have used single layer feed-forward partially connected neural network technique for this purpose.


title: Combining logic and probability: P-log perspective

abstract: My research focuses on investigation and improvement of knowledge representation (KR) language P-log which was designed to reason about both logical and probabilistic knowledge. In particular, I aim to extend P-log with new constructs, clarify its semantics, develop a new efficient inference engine for it and establish its relationship with other related formalisms. Successful completion of this work will greatly increase the scope of practical applications of the language.


Constructivism: a psychological theory of learning


title: A survey of visual, mixed, and augmented reality gaming

abstract: Visual mixed and augmented realities have historically been applied to the gaming application domain. This article provides a survey of visual mixed and augmented reality gaming in both the academic and commercial contexts. There is an exploration of both indoor and outdoor mixed and augmented reality gaming. The different games are presented via the three major display technologies: head-mounted display, handheld display, and spatial immersive display. A number of academic mixed and augmented reality research projects are described that provide an overview of the current state of the art. A set of example commercial games are also examined to provide the context for the state of the games on the market.


title: High Voltage Power Supply for Ozone Generation Based on Piezoelectric Transformer

abstract: Even though, nowadays, piezoelectric transformers (PTs) are only available with low power rating, there exist several low-power applications of ozone generation in which the use of this novel technology could be advantageous. Hence, the aim of this paper is to evaluate the possibilities of using PTs in the implementation of high-voltage power supplies for ozone generation. First, the possible topologies that can be used to drive the PT are identified. Then, the half-bridge inverter operating under zero-voltage switching (ZVS) is investigated, and the effect of the silent discharge generator (SDG) on the converter operation is analyzed. A new control circuit that allows the ZVS operation is proposed. The control circuit operates in closed loop by measuring the phase between the PT's resonant current and the switching pattern and adjusting the switching frequency to the optimum value to assure ZVS. A laboratory prototype for a 6-W SDG was tested, and obtained experimental results are shown.


title: A comparative analysis of cascade measures for novelty and diversity

abstract: Traditional editorial effectiveness measures, such as nDCG, remain standard for Web search evaluation. Unfortunately, these traditional measures can inappropriately reward redundant information and can fail to reflect the broad range of user needs that can underlie a Web query. To address these deficiencies, several researchers have recently proposed effectiveness measures for novelty and diversity. Many of these measures are based on simple cascade models of user behavior, which operate by considering the relationship between successive elements of a result list. The properties of these measures are still poorly understood, and it is not clear from prior research that they work as intended. In this paper we examine the properties and performance of cascade measures with the goal of validating them as tools for measuring effectiveness. We explore their commonalities and differences, placing them in a unified framework; we discuss their theoretical difficulties and limitations, and compare the measures experimentally, contrasting them against traditional measures and against other approaches to measuring novelty. Data collected by the TREC 2009 Web Track is used as the basis for our experimental comparison. Our results indicate that these measures reward systems that achieve an balance between novelty and overall precision in their result lists, as intended. Nonetheless, other measures provide insights not captured by the cascade measures, and we suggest that future evaluation efforts continue to report a variety of measures.


title: GLMP- realtime pedestrian path prediction using global and local movement patterns

abstract: We present a novel real-time algorithm to predict the path of pedestrians in cluttered environments. Our approach makes no assumption about pedestrian motion or crowd density, and is useful for short-term as well as long-term prediction. We interactively learn the characteristics of pedestrian motion and movement patterns from 2D trajectories using Bayesian inference. These include local movement patterns corresponding to the current and preferred velocities and global characteristics such as entry points and movement features. Our approach involves no precomputation and we demonstrate the real-time performance of our prediction algorithm on sparse and noisy trajectory data extracted from dense indoor and outdoor crowd videos. The combination of local and global movement patterns can improve the accuracy of long-term prediction by 12–18% over prior methods in high-density videos.


title: Combining Multi-Objective Search and Constraint Solving for Configuring Large Software Product Lines

abstract: Software Product Line (SPL) feature selection involves the optimization of multiple objectives in a large and highly constrained search space. We introduce SATIBEA, that augments multi-objective search-based optimization with constraint solving to address this problem, evaluating it on five large real-world SPLs, ranging from 1,244 to 6,888 features with respect to three different solution quality indicators and two diversity metrics. The results indicate that SATIBEA statistically significantly outperforms the current state-of-the-art (p


title: Real-time hand tracking by invariant hough forest detection

abstract: This paper proposes a robust real-time hand tracking approach by combining a discriminative random forest classifier with generative color based cues using a particle filter. The proposed detector is scale and rotation invariant and is able to overcome ambiguities and local maxima in the color based likelihood function in real-time. A new hand tracking dataset with manually annotated groundtruths is created and made freely available for research purposes. Thorough evaluation shows the robustness and advantages of our proposal compared to other state of the art object tracking methods.


title: Touchplates: low-cost tactile overlays for visually impaired touch screen users

abstract: Adding tactile feedback to touch screens can improve their accessibility to blind users, but prior approaches to integrating tactile feedback with touch screens have either offered limited functionality or required extensive (and typically expensive) customization of the hardware. We introduce touchplates, carefully designed tactile guides that provide tactile feedback for touch screens in the form of physical guides that are overlaid on the screen and recognized by the underlying application. Unlike prior approaches to integrating tactile feedback with touch screens, touchplates are implemented with simple plastics and use standard touch screen software, making them versatile and inexpensive. Touchplates may be customized to suit individual users and applications, and may be produced on a laser cutter, 3D printer, or made by hand. We describe the design and implementation of touchplates, a "starter kit" of touchplates, and feedback from a formative evaluation with 9 people with visual impairments. Touchplates provide a low-cost, adaptable, and accessible method of adding tactile feedback to touch screen interfaces.


A Study on the Modeling and Analysis of an Electro-Hydraulic Power Steering System


title: Click Fraud Detection with Bot Signatures

abstract: Click Fraud Bots pose a significant threat to the online economy. To-date efforts to filter bots have been geared towards identifiable useragent strings, as epitomized by the IAB's Robots and Spiders list. However bots designed to perpetrate malicious activity or fraud, are designed to avoid detection with these kinds of lists, and many use very sophisticated schemes for cloaking their activities. In order to combat this emerging threat, we propose the creation of Bot Signatures for training and evaluation of candidate Click Fraud Detection Systems. Bot signatures comprise keyed records connected to case examples. We demonstrate the technique by developing 8 simulated examples of Bots described in the literature including Click Bot A.


title: Mapping and Localization from Planar Markers

abstract: Squared planar markers are a popular tool for fast, accurate and robust camera localization, but its use is frequently limited to a single marker, or at most, to a small set of them for which their relative pose is known beforehand. Mapping and localization from a large set of planar markers is yet a scarcely treated problem in favour of keypoint-based approaches. However, while keypoint detectors are not robust to rapid motion, large changes in viewpoint, or significant changes in appearance, fiducial markers can be robustly detected under a wider range of conditions. This paper proposes a novel method to simultaneously solve the problems of mapping and localization from a set of squared planar markers. First, a quiver of pairwise relative marker poses is created, from which an initial pose graph is obtained. The pose graph may contain small pairwise pose errors, that when propagated, leads to large errors. Thus, we distribute the rotational and translational error along the basis cycles of the graph so as to obtain a corrected pose graph. Finally, we perform a global pose optimization by minimizing the reprojection errors of the planar markers in all observed frames. The experiments conducted show that our method performs better than Structure from Motion and visual SLAM techniques.


title: Real-time usage forecasting for bike-sharing systems: A study on random forest and convolutional neural network applicability

abstract: In this paper, we present a system that has been developed to facilitate the collection and use of Bike-Sharing Systems data for research, notably to develop and compare bike usage forecasting algorithms. We collected internal and external data for six different European cities and developed a system providing short and long-term predictions of bikes and slots availabilities for bike-sharing stations in real-time. In order to provide the best predictions, we developed and compared the performances of two types of algorithm; the first one is based on the state-of-the-art Random Forest algorithm and the second one is based on Convolutional Neural Networks. Our study demonstrates their applicability, showing better accuracy for short-term predictions with the Random Forest algorithm and better long-term prediction accuracy with the Convolutional Neural Networks algorithm.


title: HyPer: A hybrid OLTP&OLAP main memory database system based on virtual memory snapshots

abstract: The two areas of online transaction processing (OLTP) and online analytical processing (OLAP) present different challenges for database architectures. Currently, customers with high rates of mission-critical transactions have split their data into two separate systems, one database for OLTP and one so-called data warehouse for OLAP. While allowing for decent transaction rates, this separation has many disadvantages including data freshness issues due to the delay caused by only periodically initiating the Extract Transform Load-data staging and excessive resource consumption due to maintaining two separate information systems. We present an efficient hybrid system, called HyPer, that can handle both OLTP and OLAP simultaneously by using hardware-assisted replication mechanisms to maintain consistent snapshots of the transactional data. HyPer is a main-memory database system that guarantees the ACID properties of OLTP transactions and executes OLAP query sessions (multiple queries) on the same, arbitrarily current and consistent snapshot. The utilization of the processor-inherent support for virtual memory management (address translation, caching, copy on update) yields both at the same time: unprecedentedly high transaction rates as high as 100000 per second and very fast OLAP query response times on a single system executing both workloads in parallel. The performance analysis is based on a combined TPC-C and TPC-H benchmark.


title: Cognition-Enabled Robot Manipulation in Human Environments: Requirements, Recent Work, and Open Problems

abstract: Service robots are expected to play an important role in our daily lives as our companions in home and work environments in the near future. An important requirement for fulfilling this expectation is to equip robots with skills to perform everyday manipulation tasks, the success of which is crucial for most home chores, such as cooking, cleaning, and shopping. Robots have been used successfully for manipulation tasks in wellstructured and controlled factory environments for decades. Designing skills for robots working in uncontrolled human environments raises many potential challenges in various subdisciplines, such as computer vision, automated planning, and human-robot interaction. In spite of the recent progress in these fields, there are still challenges to tackle. This article outlines problems in different research areas related to mobile manipulation from the cognitive perspective, reviews recently published works and the state-of-the-art approaches to address these problems, and discusses open problems to be solved to realize robot assistants that can be used in manipulation tasks in unstructured human environments.


title: Optimization of trading systems and portfolios

abstract: We propose to train trading systems and portfolios by optimizing objective functions that directly measure trading and investment performance. Rather than basing a trading system on forecasts or training via a supervised learning algorithm using labelled trading data, we train our systems using recurrent reinforcement learning algorithms. The objective functions that we consider as evaluation functions for reinforcement learning are profit or wealth, economic utility, the Sharpe ratio, and our proposed Differential Sharpe Ratio. The trading and portfolio management systems require prior decisions as input in order to properly take into account the effects of transactions costs, market impact, and taxes. This temporal dependence on system state requires the use of reinforcement versions of standard recurrent learning algorithms. We present empirical results in controlled experiments that demonstrate the efficacy of some of our methods. We find that maximizing the differential Sharpe ratio yields more consistent results than maximizing profits, and that both methods outperform a trading system based on forecasts that minimize MSE.


Audio Replay Attack Detection Using High-Frequency Features


title: Sample Efficient Actor-Critic with Experience Replay

abstract: This paper presents an actor-critic deep reinforcement learning agent with experience replay that is stable, sample efficient, and performs remarkably well on challenging environments, including the discrete 57-game Atari domain and several continuous control problems. To achieve this, the paper introduces several innovations, including truncated importance sampling with bias correction, stochastic dueling network architectures, and a new trust region policy optimization method.


title: ResearchGate versus Google Scholar: Which finds more early citations?

abstract: ResearchGate has launched its own citation index by extracting citations from documents uploaded to the site and reporting citation counts on article profile pages. Since authors may upload preprints to ResearchGate, it may use these to provide early impact evidence for new papers. This article assesses the whether the number of citations found for recent articles is comparable to other citation indexes using 2675 recently-published library and information science articles. The results show that in March 2017, ResearchGate found less citations than did Google Scholar but more than both Web of Science and Scopus. This held true for the dataset overall and for the six largest journals in it. ResearchGate correlated most strongly with Google Scholar citations, suggesting that ResearchGate is not predominantly tapping a fundamentally different source of data than Google Scholar. Nevertheless, preprint sharing in ResearchGate is substantial enough for authors to take seriously.


title: A mini unmanned aerial vehicle (UAV): system overview and image acquisition

abstract: In the last years UAV (Unmanned Aerial Vehicle)-systems became relevant for applications in precision farming and in infrastructure maintenance, like road maintenance and dam surveillance. This paper gives an overview about UAV (Unmanned Aerial Vehicle) systems and their application for photogrammetric recording and documentation of cultural heritage. First the historical development of UAV systems and the definition of UAV-helicopte rs will be given. The advantages of a photogrammetric system on-board a model helicopter will be briefly discussed and compared to standard aerial and terrestrial photogrammetry. UAVs are mostly low cost systems and flexible and therefore a suitable alternative solution compared to other mobile mapping systems. A mini UAV-system was used for photogrammetric image data acquisition near Palpa in Peru. A settlement from the 13 th century AD, which was presumably used as a mine, was flown with a model helicopter. Based on the image data, an accurate 3D-model will be generated in the future. With an orthophoto and a DEM derived from aerial images in a scale of 1:7 000, a flight planning was build up. The determined flying positions were implemented in the flight control system. Thus, the helicopter is able to fly to predefined pathpoints automatically. Tests in Switzerland and the flights in Pinchango Alto showed that using the built-in GPS/INS- and stabilization units of the flight control system, predefined positions could be reached exactly to acquire the images. The predicted strip crossings and flying height were kept accurately in the autonomous flying mode.


title: A new six-port junction based on substrate integrated waveguide technology

abstract: A six-port junction based on the substrate integrated waveguide (SIW) technology is proposed and presented. In this design of such a junction, the SIW is first converted to an equivalent rectangular waveguide, then regular rectangular waveguide design techniques are used. In this structure, an SIW power divider and SIW hybrid 3-dB coupler are designed as fundamental building blocks. A six-port junction circuit operating at 24 GHz is fabricated and measured. Good agreement between simulated and measured results is found for the proposed six-port junction.


title: A realizable modified tent map for true random number generation

abstract: Tent map is a discrete-time piecewise-affine I/O characteristic curve, which is used for chaos-based applications, such as true random number generation. However, tent map suffers from the inability to maintain the output state confined to the input range under noise and process variations. In this paper, we propose a modified tent map, which is interchangeable with the tent map for practical applications. In the proposed modified tent map, the confinement problem is solved while maintaining the functionality of the tent map. We also demonstrate simulation results for the circuit implementation of the presented modified tent map for true random number generation.


title: BHNN: A memory-efficient accelerator for compressing deep neural networks with blocked hashing techniques

abstract: In this paper, we propose a novel algorithm for compressing neural networks to reduce the memory requirements by using blocked hashing techniques. By adding blocked constraints on top of the conventional hashing technique, the test error rate is maintained while the spatial locality for the computations is preserved. Using this scheme, the synaptic connections are compressed by at least an order (10×) compared with the plain neural network with virtually no prediction accuracy loss. Compared with other compression techniques, the proposed algorithm achieves the best performance in the heavy compression regions. The blocked hashing techniques are also hardware friendly, of which the memory hierarchy of the hardware architecture can be efficiently implemented. To demonstrate the hardware efficiency, we implement the hardware architecture of the deep neural networks using the proposed blocked hashing techniques on a Xilinx Virtex-7 FPGA board. With a hardware parallelism of 32, the accelerator achieves a speed-up of 22× over the CPU, and 3∼5× over the GPU in the inference phase.


title: Distributed Intrusion Detection System in a Multi-Layer Network Architecture of Smart Grids

abstract: The advent of the smart grid promises to usher in an era that will bring intelligence, efficiency, and optimality to the power grid. Most of these changes will occur as an Internet-like communications network is superimposed on top of the current power grid using wireless mesh network technologies with the 802.15.4, 802.11, and WiMAX standards. Each of these will expose the power grid to cybersecurity threats. In order to address this issue, this work proposes a distributed intrusion detection system for smart grids (SGDIDS) by developing and deploying an intelligent module, the analyzing module (AM), in multiple layers of the smart grid. Multiple AMs will be embedded at each level of the smart grid-the home area networks (HANs), neighborhood area networks (NANs), and wide area networks (WANs)-where they will use the support vector machine (SVM) and artificial immune system (AIS) to detect and classify malicious data and possible cyberattacks. AMs at each level are trained using data that is relevant to their level and will also be able to communicate in order to improve detection. Simulation results demonstrate that this is a promising methodology for supporting the optimal communication routing and improving system security through the identification of malicious network traffic.


title: Gene replacements and insertions in rice by intron targeting using CRISPR–Cas9

abstract: Sequence-specific nucleases have been exploited to create targeted gene knockouts in various plants(1), but replacing a fragment and even obtaining gene insertions at specific loci in plant genomes remain a serious challenge. Here, we report efficient intron-mediated site-specific gene replacement and insertion approaches that generate mutations using the non-homologous end joining (NHEJ) pathway using the clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein 9 (Cas9) system. Using a pair of single guide RNAs (sgRNAs) targeting adjacent introns and a donor DNA template including the same pair of sgRNA sites, we achieved gene replacements in the rice endogenous gene 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS) at a frequency of 2.0%. We also obtained targeted gene insertions at a frequency of 2.2% using a sgRNA targeting one intron and a donor DNA template including the same sgRNA site. Rice plants harbouring the OsEPSPS gene with the intended substitutions were glyphosate-resistant. Furthermore, the site-specific gene replacements and insertions were faithfully transmitted to the next generation. These newly developed approaches can be generally used to replace targeted gene fragments and to insert exogenous DNA sequences into specific genomic sites in rice and other plants.


abstract: We introduce an effective model to overcome the problem of mode collapse when training Generative Adversarial Networks (GAN). Firstly, we propose a new generator objective that finds it better to tackle mode collapse. And, we apply an independent Autoencoders (AE) to constrain the generator and consider its reconstructed samples as "real" samples to slow down the convergence of discriminator that enables to reduce the gradient vanishing problem and stabilize the model. Secondly, from mappings between latent and data spaces provided by AE, we further regularize AE by the relative distance between the latent and data samples to explicitly prevent the generator falling into mode collapse setting. This idea comes when we find a new way to visualize the mode collapse on MNIST dataset. To the best of our knowledge, our method is the first to propose and apply successfully the relative distance of latent and data samples for stabilizing GAN. Thirdly, our proposed model, namely Generative Adversarial Autoencoder Networks (GAAN), is stable and has suffered from neither gradient vanishing nor mode collapse issues, as empirically demonstrated on synthetic, MNIST, MNIST-1K, CelebA and CIFAR-10 datasets. Experimental results show that our method can approximate well multi-modal distribution and achieve better results than state-of-the-art methods on these benchmark datasets. Our model implementation is published here: this https URL


title: Fusion of Multichannel Local and Global Structural Cues for Photo Aesthetics Evaluation

abstract: Photo aesthetic quality evaluation is a fundamental yet under addressed task in computer vision and image processing fields. Conventional approaches are frustrated by the following two drawbacks. First, both the local and global spatial arrangements of image regions play an important role in photo aesthetics. However, existing rules, e.g., visual balance, heuristically define which spatial distribution among the salient regions of a photo is aesthetically pleasing. Second, it is difficult to adjust visual cues from multiple channels automatically in photo aesthetics assessment. To solve these problems, we propose a new photo aesthetics evaluation framework, focusing on learning the image descriptors that characterize local and global structural aesthetics from multiple visual channels. In particular, to describe the spatial structure of the image local regions, we construct graphlets small-sized connected graphs by connecting spatially adjacent atomic regions. Since spatially adjacent graphlets distribute closely in their feature space, we project them onto a manifold and subsequently propose an embedding algorithm. The embedding algorithm encodes the photo global spatial layout into graphlets. Simultaneously, the importance of graphlets from multiple visual channels are dynamically adjusted. Finally, these post-embedding graphlets are integrated for photo aesthetics evaluation using a probabilistic model. Experimental results show that: 1) the visualized graphlets explicitly capture the aesthetically arranged atomic regions; 2) the proposed approach generalizes and improves four prominent aesthetic rules; and 3) our approach significantly outperforms state-of-the-art algorithms in photo aesthetics prediction.


title: Design of Resistive Loading Vivaldi Antenna

abstract: A novel Vivaldi antenna structure is proposed to broaden the bandwidth of the conventional ones. The theory of the equivalent circuit is adopted, and it is deduced that the bandwidth of the antenna can be enhanced by the high chip resistor and short pin in the new structure. An antenna of 62 mm (length) times 70 mm (width)times 0.5 mm (thickness) is designed and fabricated. The measure results show that the bandwidth is 1~ 20 GHz ( VSWR les2), while the gain varies between 0.9 and 7.8 dB. It is indicated that the antenna can be reduced to about a half of the conventional ones.


title: The Coordinating Role of Language in Real-Time Multimodal Learning of Cooperative Tasks

abstract: One of the defining characteristics of human cognition is our outstanding capacity to cooperate. A central requirement for cooperation is the ability to establish a “shared plan”—which defines the interlaced actions of the two cooperating agents—in real time, and even to negotiate this shared plan during its execution. In the current research we identify the requirements for cooperation, extending our earlier work in this area. These requirements include the ability to negotiate a shared plan using spoken language, to learn new component actions within that plan, based on visual observation and kinesthetic demonstration, and finally to coordinate all of these functions in real time. We present a cognitive system that implements these requirements, and demonstrate the system's ability to allow a Nao humanoid robot to learn a nontrivial cooperative task in real-time. We further provide a concrete demonstration of how the real-time learning capability can be easily deployed on a different platform, in this case the iCub humanoid. The results are considered in the context of how the development of language in the human infant provides a powerful lever in the development of cooperative plans from lower-level sensorimotor capabilities.


title: A real-time object detecting and tracking system for outdoor night surveillance

abstract: Autonomous video surveillance and monitoring has a rich history. Many deployed systems are able to reliably track human motion in indoor and controlled outdoor environments. However, object detection and tracking at night remain very important problems for visual surveillance. The objects are often distant, small and their signatures have low contrast against the background. Traditional methods based on the analysis of the difference between successive frames and a background frame will do not work. In this paper, a novel real time object detection algorithm is proposed for night-time visual surveillance. The algorithm is based on contrast analysis. In the first stage, the contrast in local change over time is used to detect potential moving objects. Then motion prediction and spatial nearest neighbor data association are used to suppress false alarms. Experiments on real scenes show that the algorithm is effective for night-time object detection and tracking.


title: Adaptive 360 VR Video Streaming: Divide and Conquer

abstract: While traditional multimedia applications such as games and videos are still popular, there has been a significant interest in the recent years towards new 3D media such as 3D immersion and Virtual Reality (VR) applications, especially 360 VR videos. 360 VR video is an immersive spherical video where the user can look around during playback. Unfortunately, 360 VR videos are extremely bandwidth intensive, and therefore are difficult to stream at acceptable quality levels. In this paper, we propose an adaptive bandwidth-efficient 360 VR video streaming system using a divide and conquer approach. In our approach, we propose a dynamic view-aware adaptation technique to tackle the huge streaming bandwidth demands of 360 VR videos. We spatially divide the videos into multiple tiles while encoding and packaging, use MPEG-DASH SRD to describe the spatial relationship of tiles in the 360-degree space, and prioritize the tiles in the Field of View (FoV). In order to describe such tiled representations, we extend MPEG-DASH SRD to the 3D space of 360 VR videos. We spatially partition the underlying 3D mesh, and construct an efficient 3D geometry mesh called hexaface sphere to optimally represent a tiled 360 VR video in the 3D space. Our initial evaluation results report up to 72% bandwidth savings on 360 VR video streaming with minor negative quality impacts compared to the baseline scenario when no adaptations is applied.


title: Multi-View Automatic Target Recognition using Joint Sparse Representation

abstract: We introduce a novel joint sparse representation based multi-view automatic target recognition (ATR) method, which can not only handle multi-view ATR without knowing the pose but also has the advantage of exploiting the correlations among the multiple views of the same physical target for a single joint recognition decision. Extensive experiments have been carried out on moving and stationary target acquisition and recognition (MSTAR) public database to evaluate the proposed method compared with several state-of-the-art methods such as linear support vector machine (SVM), kernel SVM, as well as a sparse representation based classifier (SRC). Experimental results demonstrate that the proposed joint sparse representation ATR method is very effective and performs robustly under variations such as multiple joint views, depression, azimuth angles, target articulations, as well as configurations.


title: A −8 mV/+15 mV Double Polarity Piezoelectric Transformer-Based Step-Up Oscillator for Energy Harvesting Applications

abstract: This paper presents two circuit topologies of battery-less integrated boost oscillators suitable for kick-starting electronic systems in fully discharged states with ultra-low input voltages, in the context of energy harvesting applications based on thermoelectric generators, by coupling a piezoelectric transformer in a feedback loop. With respect to the prior work, the first presented solution is a double polarity circuit designed in a $0.18~\mu \text{m}$ CMOS technology able to boost ultra-low positive and negative voltages without using switching matrixes. The circuit exploits a CMOS inverter made up of low threshold transistors, and also includes a hysteretic voltage monitor consuming only ~15 nW to enable an external circuit. The minimum achieved positive and negative oscillation voltages are +15 and −8 mV, which to the best of the authors’ knowledge, are among the lowest start-up voltages achieved in literature up to now without using magnetic components. Moreover, the input impedance in the range of several $\text{k}\Omega $ makes the presented solution suitable also for high impedances sources, such as rectennas. The second presented circuit, designed in a $0.32~\mu \text{m}$ CMOS technology, exploits an input stage based on depletion-mode MOSFETs in a common source stage configuration and achieves a maximum step ratio of ~60.


title: Surface passivation engineering strategy to fully-inorganic cubic CsPbI3 perovskites for high-performance solar cells

abstract: Owing to inevitable thermal/moisture instability for organic-inorganic hybrid perovskites, pure inorganic perovskite cesium lead halides with both inherent stability and prominent photovoltaic performance have become research hotspots as a promising candidate for commercial perovskite solar cells. However, it is still a serious challenge to synthesize desired cubic cesium lead iodides (CsPbI3) with superior photovoltaic performance for its thermodynamically metastable characteristics. Herein, polymer poly-vinylpyrrolidone (PVP)-induced surface passivation engineering is reported to synthesize extra-long-term stable cubic CsPbI3. It is revealed that acylamino groups of PVP induce electron cloud density enhancement on the surface of CsPbI3, thus lowering surface energy, conducive to stabilize cubic CsPbI3 even in micrometer scale. The cubic-CsPbI3 PSCs exhibit extra-long carrier diffusion length (over 1.5 μm), highest power conversion efficiency of 10.74% and excellent thermal/moisture stability. This result provides important progress towards understanding of phase stability in realization of large-scale preparations of efficient and stable inorganic PSCs.


title: A 10Gb/s Si-photonic transceiver with 150μW 120μs-lock-time digitally supervised analog microring wavelength stabilization for 1Tb/s/mm2 Die-to-Die Optical Networks

abstract: Silicon photonics has allowed cost reduction and performance improvement for optical interconnects for the past few years, and short-reach wavelength-division-multiplexed (WDM) links have recently emerged thanks to the introduction of microring modulators and filters [1-5]. Nevertheless, the promise of optical networks-on-chip foreseen in [1] has to face the integration challenges of scalable low-footprint elementary drivers and robust operation under heavy thermal stress due to self-heating of the cores with varying loads. This work presents a 3D-stacked CMOS-on-Si-photonic transceiver chip, which includes base building-blocks targeting die-to-die WDM optical communication for multicore processors: 10Gbps 2.5V pp OOK modulator driver, associated receiver, and digitally-supervised analog wavelength stabilization using microring heaters and remapping for 0-to-90°C operating range, for a total footprint of 0.01mm2 per microring.


title: Virtual item sales as a revenue model: identifying attributes that drive purchase decisions

abstract: The global market for virtual items, characters and currencies was estimated to exceed 2.1 Billion USD in 2007. Selling virtual goods for real money is an increasingly common revenue model not only for online games and virtual worlds, but for social networking sites and other mainstream online services as well. What drives consumer spending on virtual items is an increasingly relevant question, but little research has been devoted to the topic so far. Previous literature suggests that demand for virtual items is based on the items' ability to confer gameplay advantages on one hand, and on the items' decorative value on the other hand. In this paper, I adopt a perspective from the sociology of consumption and analyse examples from 14 virtual asset platforms to suggest a more detailed set of item attributes that drive virtual item purchase decisions, consisting of functional, hedonic and social attributes.


title: The Parameterized Complexity of k-Biclique

abstract: Given a graph $G$ and a parameter $k$, the $k$-biclique problem asks whether $G$ contains a complete bipartite subgraph $K_{k,k}$. This is the most easily stated problem on graphs whose parameterized complexity is still unknown. We provide an fpt-reduction from $k$-clique to $k$-biclique, thus solving this longstanding open problem. Our reduction use a class of bipartite graphs with a threshold property of independent interest. More specifically, for positive integers $n$, $s$ and $t$, we consider a bipartite graph $G=(A\;\dot\cup\;B, E)$ such that $A$ can be partitioned into $A=V_1\;\dot\cup \;V_2\;\dot\cup\cdots\dot\cup\; V_n$ and for every $s$ distinct indices $i_1\cdots i_s$, there exist $v_{i_1}\in V_{i_1}\cdots v_{i_s}\in V_{i_s}$ such that $v_{i_1}\cdots v_{i_s}$ have at least $t+1$ common neighbors in $B$; on the other hand, every $s+1$ distinct vertices in $A$ have at most $t$ common neighbors in $B$. Using the Paley-type graphs and Weil's character sum theorem, we show that for $t=(s+1)!$ and $n$ large enough, such threshold bipartite graphs can be computed in $n^{O(1)}$. One corollary of our reduction is that there is no $f(k)\cdot n^{o(k)}$ time algorithm to decide whether a graph contains a subgraph isomorphic to $K_{k!,k!}$ unless the ETH(Exponential Time Hypothesis) fails. We also provide a probabilistic construction with better parameters $t=\Theta(s^2)$, which indicates that $k$-biclique has no $f(k)\cdot n^{o(\sqrt{k})}$-time algorithm unless 3-SAT with $m$ clauses can be solved in $2^{o(m)}$-time with high probability. Our result also implies the dichotomy classification of the parameterized complexity of cardinality constrain satisfaction problem and the inapproximability of maximum $k$-intersection problem.


title: First-Order Mixed Integer Linear Programming

abstract: Mixed integer linear programming (MILP) is a powerful representation often used to formulate decision-making problems under uncertainty. However, it lacks a natural mechanism to reason about objects, classes of objects, and relations. First-order logic (FOL), on the other hand, excels at reasoning about classes of objects, but lacks a rich representation of uncertainty. While representing propositional logic in MILP has been extensively explored, no theory exists yet for fully combining FOL with MILP. We propose a new representation, called first-order programming or FOP, which subsumes both FOL and MILP. We establish formal methods for reasoning about first order programs, including a sound and complete lifted inference procedure for integer first order programs. Since FOP can offer exponential savings in representation and proof size compared to FOL, and since representations and proofs are never significantly longer in FOP than in FOL, we anticipate that inference in FOP will be more tractable than inference in FOL for corresponding problems.


Microcontroller based Constant Voltage Battery Charger with Soft switching Buck Converter for Solar Home Lighting System


title: On the evaluation of generative models in music

abstract: The modeling of artificial, human-level creativity is becoming more and more achievable. In recent years, neural networks have been successfully applied to different tasks such as image and music generation, demonstrating their great potential in realizing computational creativity. The fuzzy definition of creativity combined with varying goals of the evaluated generative systems, however, makes subjective evaluation seem to be the only viable methodology of choice. We review the evaluation of generative music systems and discuss the inherent challenges of their evaluation. Although subjective evaluation should always be the ultimate choice for the evaluation of creative results, researchers unfamiliar with rigorous subjective experiment design and without the necessary resources for the execution of a large-scale experiment face challenges in terms of reliability, validity, and replicability of the results. In numerous studies, this leads to the report of insignificant and possibly irrelevant results and the lack of comparability with similar and previous generative systems. Therefore, we propose a set of simple musically informed objective metrics enabling an objective and reproducible way of evaluating and comparing the output of music generative systems. We demonstrate the usefulness of the proposed metrics with several experiments on real-world data.


title: Bag-of-Words Image Representation: Key Ideas and Further Insight

abstract: In the context of object and scene recognition, state-of-the-art performances are obtained with visual Bag-of-Words (BoW) models of mid-level representations computed from dense sampled local descriptors (e.g., Scale-Invariant Feature Transform (SIFT)). Several methods to combine low-level features and to set mid-level parameters have been evaluated recently for image classification. In this chapter, we study in detail the different components of the BoW model in the context of image classification. Particularly, we focus on the coding and pooling steps and investigate the impact of the main parameters of the BoW pipeline. We show that an adequate combination of several low (sampling rate, multiscale) and mid-level (codebook size, normalization) parameters is decisive to reach good performances. Based on this analysis, we propose a merging scheme that exploits the specificities of edge-based descriptors. Low and high contrast regions are pooled separately and combined to provide a powerful representation of images. We study the impact on classification performance of the contrast threshold that determines whether a SIFT descriptor corresponds to a low contrast region or a high contrast region. Successful experiments are provided on the Caltech-101 and Scene-15 datasets.


title: Energy-Efficient Neural Network Accelerator Based on Outlier-Aware Low-Precision Computation

abstract: Owing to the presence of large values, which we call outliers, conventional methods of quantization fail to achieve significantly low precision, e.g., four bits, for very deep neural networks, such as ResNet-101. In this study, we propose a hardware accelerator, called the outlier-aware accelerator (OLAccel). It performs dense and low-precision computations for a majority of data (weights and activations) while efficiently handling a small number of sparse and high-precision outliers (e.g., amounting to 3% of total data). The OLAccel is based on 4-bit multiply-accumulate (MAC) units and handles outlier weights and activations in a different manner. For outlier weights, it equips SIMD lanes of MAC units with an additional MAC unit, which helps avoid cycle overhead for the majority of outlier occurrences, i.e., a single occurrence in the SIMD lanes. The OLAccel performs computations using outlier activation on dedicated, high-precision MAC units. In order to avoid coherence problem due to updates from low- and high-precision computation units, both units update partial sums in a pipelined manner. Our experiments show that the OLAccel can reduce by 43.5% (27.0%), 56.7% (36.3%), and 62.2% (49.5%) energy consumption for AlexNet, VGG-16, and ResNet-18, respectively, compared with a 16-bit (8-bit) state-of-the-art zero-aware accelerator. The energy gain mostly comes from the memory components, the DRAM, and on-chip memory due to reduced precision.


title: Process-driven data quality management: A critical review on the application of process modeling languages

abstract: Data quality is critical to organizational success. In order to improve and sustain data quality in the long term, process-driven data quality management (PDDQM) seeks to redesign processes that create or modify data. Consequently, process modeling is mandatory for PDDQM. Current research examines process modeling languages with respect to representational capabilities. However, there is a gap, since process modeling languages for PDDQM are not considered. We address this research gap by providing a synthesis of the varying applications of process modeling languages for PDDQM. We conducted a keyword-based literature review in conferences as well as 74 highranked information systems and computer science journals, reviewing 1,555 articles from 1995 onwards. For practitioners, it is possible to integrate the quality perspective within broadly applied process models. For further research, we derive representational requirements for PDDQM that should be integrated within existing process modeling languages. However, there is a need for further representational analysis to examine the adequacy of upcoming process modeling languages. New or enhanced process modeling languages may substitute for PDDQM-specific process modeling languages and facilitate development of a broadly applicable and accepted process modeling language for PDDQM.


title: Retrospective analysis of mortality and Candida isolates of 75 patients with candidemia: a single hospital experience

abstract: The mortality rate for candidemia is approximately 30%–60%. However, prognostic factors in patients with candidemia have not yet been elucidated in detail. The aim of the present study was to analyze prognostic factors for candidemia using the mortality rate and Candida isolates of patients with candidemia. Seventy-five patients with candidemia were analyzed between January 2007 and December 2013. The main outcome of this study was the 30-day mortality rate after the diagnosis of candidemia. The acute physiology and chronic health evaluation II score (APACHE II score) was measured in 34 patients (45.3%). Odds ratios (ORs) for death due to candidemia were analyzed using a multivariate stepwise logistic regression analysis. Twenty (26.6%) patients died within 30 days of being diagnosed with candidemia. Non-survivors had a significantly higher APACHE II score (n=7, mean; 18.9±4.5) than that of survivors (n=27, mean; 14.0±5.0). Advanced age (OR =1.1, 95% confidence interval =1.01–1.23, P=0.04) was a significant risk factor for a high mortality rate, whereas removal of a central venous catheter (OR =0.03, 95% confidence interval =0.002–0.3, P=0.01) was associated with a lower mortality rate. Seventy-six Candida spp. were isolated from blood cultures: Candida albicans 28 (36.8%), Candida parapsilosis 23 (30.2%), Candida guilliermondii 16 (21.0%), Candida glabrata four (5.2%), Candida tropicalis two (2.6%), and Candida spp. three (3.9%) that could not be identified. C. parapsilosis was the most frequently isolated species in younger patients (<65 years), whereas C. albicans was the most frequently isolated in elderly patients (≥65 years). Physicians who treat candidemia need to consider removing the central venous catheter and pay attention to the general condition of patients, particularly that of elderly patients.


title: Multimodal selection techniques for dense and occluded 3D virtual environments

abstract: Object selection is a primary interaction technique which must be supported by any interactive three-dimensional virtual reality application. Although numerous techniques exist, few have been designed to support the selection of objects in dense target environments, or the selection of objects which are occluded from the user's viewpoint. There is, thus, a limited understanding on how these important factors will affect selection performance. In this paper, we present a set of design guidelines and strategies to aid the development of selection techniques which can compensate for environment density and target visibility. Based on these guidelines, we present new forms of the ray casting and bubble cursor selection techniques, which are augmented with visual, audio, and haptic feedback, to support selection within dense and occluded 3D target environments. We perform a series of experiments to evaluate these new techniques, varying both the environment density and target visibility. The results provide an initial understanding of how these factors affect selection performance. Furthermore, the results showed that our new techniques adequately allowed users to select targets which were not visible from their initial viewpoint. The audio and haptic feedback did not provide significant improvements, and our analysis indicated that our introduced visual feedback played the most critical role in aiding the selection task.


Effects of lumbar stabilization exercise on functional disability and lumbar lordosis angle in patients with chronic low back pain


title: Automatic Segmentation of Acute Ischemic Stroke From DWI Using 3-D Fully Convolutional DenseNets

abstract: Acute ischemic stroke is recognized as a common cerebral vascular disease in aging people. Accurate diagnosis and timely treatment can effectively improve the blood supply of the ischemic area and reduce the risk of disability or even death. Understanding the location and size of infarcts plays a critical role in the diagnosis decision. However, manual localization and quantification of stroke lesions are laborious and time-consuming. In this paper, we propose a novel automatic method to segment acute ischemic stroke from diffusion weighted images (DWIs) using deep 3-D convolutional neural networks (CNNs). Our method can efficiently utilize 3-D contextual information and automatically learn very discriminative features in an end-to-end and data-driven way. To relieve the difficulty of training very deep 3-D CNN, we equip our network with dense connectivity to enable the unimpeded propagation of information and gradients throughout the network. We train our model with Dice objective function to combat the severe class imbalance problem in data. A DWI data set containing 242 subjects (90 for training, 62 for validation, and 90 for testing) with various types of acute ischemic stroke was constructed to evaluate our method. Our model achieved high performance on various metrics (Dice similarity coefficient: 79.13%, lesionwise precision: 92.67%, and lesionwise F1 score: 89.25%), outperforming the other state-of-the-art CNN methods by a large margin. We also evaluated the model on ISLES2015-SSIS data set and achieved very competitive performance, which further demonstrated its generalization capacity. The proposed method is fast and accurate, demonstrating a good potential in clinical routines.


title: Contact dermatitis caused by panthenol used for aftercare treatment of a new tattoo

abstract: Keywords: ::: ::: allergic contact dermatitis; ::: CAS no. 81-13-0; ::: case report; ::: d-panthenol; ::: dexpanthenol; ::: panthenol; ::: tattoo; ::: vitamin B5


title: Canonicalizing Open Knowledge Bases

abstract: Open information extraction approaches have led to the creation of large knowledge bases from the Web. The problem with such methods is that their entities and relations are not canonicalized, leading to redundant and ambiguous facts. For example, they may store {Barack Obama, was born, Honolulu and {Obama, place of birth, Honolulu}. In this paper, we present an approach based on machine learning methods that can canonicalize such Open IE triples, by clustering synonymous names and phrases. We also provide a detailed discussion about the different signals, features and design choices that influence the quality of synonym resolution for noun phrases in Open IE KBs, thus shedding light on the middle ground between "open" and "closed" information extraction systems.


title: Extreme Multi-label Loss Functions for Recommendation, Tagging, Ranking & Other Missing Label Applications

abstract: The choice of the loss function is critical in extreme multi-label learning where the objective is to annotate each data point with the most relevant subset of labels from an extremely large label set. Unfortunately, existing loss functions, such as the Hamming loss, are unsuitable for learning, model selection, hyperparameter tuning and performance evaluation. This paper addresses the issue by developing propensity scored losses which: (a) prioritize predicting the few relevant labels over the large number of irrelevant ones; (b) do not erroneously treat missing labels as irrelevant but instead provide unbiased estimates of the true loss function even when ground truth labels go missing under arbitrary probabilistic label noise models; and (c) promote the accurate prediction of infrequently occurring, hard to predict, but rewarding tail labels. Another contribution is the development of algorithms which efficiently scale to extremely large datasets with up to 9 million labels, 70 million points and 2 million dimensions and which give significant improvements over the state-of-the-art. This paper's results also apply to tagging, recommendation and ranking which are the motivating applications for extreme multi-label learning. They generalize previous attempts at deriving unbiased losses under the restrictive assumption that labels go missing uniformly at random from the ground truth. Furthermore, they provide a sound theoretical justification for popular label weighting heuristics used to recommend rare items. Finally, they demonstrate that the proposed contributions align with real world applications by achieving superior clickthrough rates on sponsored search advertising in Bing.


title: Are image quality metrics adequate to evaluate the quality of geometric objects?

abstract: Geometric objects are often represented by many millions of triangles or polygons, which limits the ease with which they can be transmitted and displayed electronically. This has lead to the development of many algorithms for simplifying geometric models, and to the recognition that metrics are required to evaluate their success. The goal is to create computer graphic renderings of the object that do not appear to be degraded to a human observer. The perceptual evaluation of simplified objects is a new topic. One approach has been to sue image-based metrics to predict the perceived degradation of simplified 3D models. Since that 2D images of 3D objects can have significantly different perceived quality, depending on the direction of the illumination, 2D measures of image quality may not adequately capture the perceived quality of 3D objects. To address this question, we conducted experiments in which we explicitly compared the perceived quality of animated 3D objects and their corresponding 2D still image projections. Our results suggest that 2D judgements do not provide a good predictor of 3D image quality, and identify a need to develop 'object quality metrics.'


title: Design of a Notched-Band Vivaldi Antenna With High Selectivity

abstract: In this letter, a notched-band Vivaldi antenna with high-frequency selectivity is designed and investigated. To obtain two notched poles inside the stopband, an open-circuited half-wavelength resonator and a short-circuited stepped impedance resonator are properly introduced into the traditional Vivaldi antenna. By theoretically calculating the resonant frequencies of the two loaded resonators, the frequency locations of the two notched poles can be precisely determined, thus achieving a wideband antenna with a desired notched band. To validate the feasibility of this new approach, a notched band antenna with a fractional bandwidth of 145.8% is fabricated and tested. Results indicate that good frequency selectivity of the notched band from 4.9 to 6.6 GHz is realized, and the antenna exhibits good impedance match, high radiation gain, and excellent radiation directivity in the passband. Both the simulation and measurement results are provided with good agreement.


title: Ultra-Low Power Applications under Technology Scaling

abstract: Ultra-low power applications often require several kb of embedded memory and are typically operated at the lowest possible operating voltage (VDD) to minimize both dynamic and static power consumption. Embedded memories can easily dominate the overall silicon area of these systems, and their leakage currents often dominate the total power consumption. Gain-cell based embedded DRAM arrays provide a high-density, low-leakage alternative to SRAM for such systems; however, they are typically designed for operation at nominal or only slightly scaled supply voltages. This paper presents a gain-cell array which, for the first time, targets aggressively scaled supply voltages, down into the subthreshold (sub-VT) domain. Minimum VDD design of gain-cell arrays is evaluated in light of technology scaling, considering both a mature 0.18 μm CMOS node, as well as a scaled 40 nm node. We first analyze the trade-offs that characterize the bitcell design in both nodes, arriving at a best-practice design methodology for both mature and scaled technologies. Following this analysis, we propose full gain-cell arrays for each of the nodes, operated at a minimum VDD. We find that an 0.18 μm gain-cell array can be robustly operated at a sub-VT supply voltage of 400mV, providing read/write availability over 99% of the time, despite refresh cycles. This is demonstrated on a 2 kb array, operated at 1 MHz, exhibiting full functionality under parametric variations. As opposed to sub-VT operation at the mature node, we find that the scaled 40 nm node requires a near-threshold 600mV supply to achieve at least 97% read/write availability due to higher leakage currents that limit the bitcell’s retention time. Monte Carlo simulations show that a 600mV 2 kb 40 nm gain-cell array is fully functional at frequencies higher than 50 MHz.


title: Processing complex aggregate queries over data streams

abstract: Recent years have witnessed an increasing interest in designing algorithms for querying and analyzing streaming data (i.e., data that is seen only once in a fixed order) with only limited memory. Providing (perhaps approximate) answers to queries over such continuous data streams is a crucial requirement for many application environments; examples include large telecom and IP network installations where performance data from different parts of the network needs to be continuously collected and analyzed.In this paper, we consider the problem of approximately answering general aggregate SQL queries over continuous data streams with limited memory. Our method relies on randomizing techniques that compute small "sketch" summaries of the streams that can then be used to provide approximate answers to aggregate queries with provable guarantees on the approximation error. We also demonstrate how existing statistical information on the base data (e.g., histograms) can be used in the proposed framework to improve the quality of the approximation provided by our algorithms. The key idea is to intelligently partition the domain of the underlying attribute(s) and, thus, decompose the sketching problem in a way that provably tightens our guarantees. Results of our experimental study with real-life as well as synthetic data streams indicate that sketches provide significantly more accurate answers compared to histograms for aggregate queries. This is especially true when our domain partitioning methods are employed to further boast the accuracy of the final estimates.


title: Instance-level Facial Attributes Transfer with Geometry-Aware Flow

abstract: We address the problem of instance-level facial attribute transfer without paired training data, e.g., faithfully transferring the exact mustache from a source face to a target face. This is a more challenging task than the conventional semantic-level attribute transfer, which only preserves the generic attribute style instead of instance-level traits. We propose the use of geometry-aware flow, which serves as a wellsuited representation for modeling the transformation between instance-level facial attributes. Specifically, we leverage the facial landmarks as the geometric guidance to learn the differentiable flows automatically, despite of the large pose gap existed. Geometry-aware flow is able to warp the source face attribute into the target face context and generate a warp-and-blend result. To compensate for the potential appearance gap between source and target faces, we propose a hallucination sub-network that produces an appearance residual to further refine the warp-and-blend result. Finally, a cycle-consistency framework consisting of both attribute transfer module and attribute removal module is designed, so that abundant unpaired face images can be used as training data. Extensive evaluations validate the capability of our approach in transferring instance-level facial attributes faithfully across large pose and appearance gaps. Thanks to the flow representation, our approach can readily be applied to generate realistic details on high-resolution images1.


title: Movement and Mind: A Functional Imaging Study of Perception and Interpretation of Complex Intentional Movement Patterns

abstract: We report a functional neuroimaging study with positron emission tomography (PET) in which six healthy adult volunteers were scanned while watching silent computer-presented animations. The characters in the animations were simple geometrical shapes whose movement patterns selectively evoked mental state attribution or simple action description. Results showed increased activation in association with mental state attribution in four main regions: medial prefrontal cortex, temporoparietal junction (superior temporal sulcus), basal temporal regions (fusiform gyrus and temporal poles adjacent to the amygdala), and extrastriate cortex (occipital gyrus). Previous imaging studies have implicated these regions in self-monitoring, in the perception of biological motion, and in the attribution of mental states using verbal stimuli or visual depictions of the human form. Ne suggest that these regions form a network for processing information about intentions, and speculate that the ability to make inferences about other people's mental states evolved from the ability to make inferences about other creatures' actions. (C) 2000 Academic Press.


title: High-Performance SIFT Hardware Accelerator for Real-Time Image Feature Extraction

abstract: Feature extraction is an essential part in applications that require computer vision to recognize objects in an image processed. To extract the features robustly, feature extraction algorithms are often very demanding in computation so that the performance achieved by pure software is far from real-time. Among those feature extraction algorithms, scale-invariant feature transform (SIFT) has gained a lot of popularity recently. In this paper, we propose an all-hardware SIFT accelerator-the fastest of its kind to our knowledge. It consists of two interactive hardware components, one for key point identification, and the other for feature descriptor generation. We successfully developed a segment buffer scheme that could not only feed data to the computing modules in a data-streaming manner, but also reduce about 50% memory requirement than a previous work. With a parallel architecture incorporating a three-stage pipeline, the processing time of the key point identification is only 3.4 ms for one video graphics array (VGA) image. Taking also into account the feature descriptor generation part, the overall SIFT processing time for a VGA image can be kept within 33 ms (to support real-time operation) when the number of feature points to be extracted is fewer than 890.


title: Text zone classification using unsupervised feature learning

abstract: Text zone classification is a vital step in the digitization process, without which OCR systems perform poorly. Prior methods to document zone classification have relied on large sets of hand-crafted features for training zone classifiers. Such features are usually database-dependent, and their computation is time consuming. In this work we propose a novel method for text zone classification that relies on the approach of unsupervised feature learning. Within our method, feature vectors of document zones are automatically learned by patches extraction, encoding and pooling, where feature encoding is based on a codebook of visual words. The training phase of the text classifier takes into consideration the unbalance between text zones and non-text zones of all types. The proposed method has been tested on publicly available standard databases, and achieved competitive or better results compared to state-of-the-art methods. The results show that our approach matches well the task of text classification, and is robust to zone shapes, orientations and size.


title: Improved pulse detection from head motions using DCT

abstract: The heart pulsation sends out the blood throughout the body. The rate in which the heart performs this vital task, heartbeat rate, is of curial importance to the body. Therefore, measuring heartbeat rate, a.k.a. pulse detection, is very important in many applications, especially the medical ones. To measure it, physicians traditionally, either sense the pulsations of some blood vessels or install some sensors on the body. In either case, there is a need for a physical contact between the sensor and the body to obtain the heartbeat rate. This might not be always feasible, for example, for applications like remote patient monitoring. In such cases, contactless sensors, mostly based on computer vision techniques, are emerging as interesting alternatives. This paper proposes such a system, in which the heartbeats (pulses) are detected by subtle motions that appear on the face due to blood circulation. The proposed system has been tested in different facial expressions. The experimental results show that the proposed system is correct and robust and outperforms state-of-the-art.


title: Inadequate Knowledge of Neonatal Danger Signs among Recently Delivered Women in Southwestern Rural Uganda: A Community Survey

abstract: Early detection of neonatal illness is an important step towards improving newborn survival. Every year an estimated 3.07 million children die during their first month of life and about one-third of these deaths occur during the first 24 hours. Ninety-eight percent of all neonatal deaths occur in low- and middle-income countries like Uganda. Inadequate progress has been made globally to reduce the amount of neonatal deaths that would be required to meet Millennium Development Goal 4. Poor knowledge of newborn danger signs delays care seeking. The aim of this study was to explore the knowledge of key newborn danger signs among mothers in southwestern Uganda.


title: HMM-KNN word recognition engine for bank cheque processing

abstract: Describes the mixed HMM-KNN word recognition module of a bank cheque processing system developed at CENPARMI. It uses a combination of 2 segmentation free word recognition schemes. The first scheme uses a set of global features associated to a modified K nearest neighbour classifier; while the second one uses a set of directional contour features as input to an HMM. The system has been designed to be modular and independent of specific languages as in Canada one has to deal with at least 2 languages, namely English and French. It can be easily adapted to read other European languages based on the Roman alphabet. The system is continuously tested on data from the local phone company, and we report here the results on a database of approximately 4,500 cheques.


title: Lambda Dependency-Based Compositional Semantics

abstract: This short note presents a new formal language, lambda dependency-based compositional semantics (lambda DCS) for representing logical forms in semantic parsing. By eliminating variables and making existential quantification implicit, lambda DCS logical forms are generally more compact than those in lambda calculus.


title: Structure-Based Statistical Features and Multivariate Time Series Clustering

abstract: We propose a new method for clustering multivariate time series. A univariate time series can be represented by a fixed-length vector whose components are statistical features of the time series, capturing the global structure. These descriptive vectors, one for each component of the multivariate time series, are concatenated, before being clustered using a standard fast clustering algorithm such as k-means or hierarchical clustering. Such statistical feature extraction also serves as a dimension-reduction procedure for multivariate time series. We demonstrate the effectiveness and simplicity of our proposed method by clustering human motion sequences: dynamic and high-dimensional multivariate time series. The proposed method based on univariate time series structure and statistical metrics provides a novel, yet simple and flexible way to cluster multivariate time series data efficiently with promising accuracy. The success of our method on the case study suggests that clustering may be a valuable addition to the tools available for human motion pattern recognition research.


title: Microstrip antenna array with four port Butler matrix for switched beam base station application

abstract: In this paper, the design of a microstrip antenna array with four port Butler matrix is presented. The Butler matrix is used as a beamforming network and it produces orthogonal beams that can be steered in different directions. Simulated butler matrix has 10 dB return loss bandwidth of 20%. This matrix feeds four single element microstrip antennas that can be operated from 2.412GHz to 2.484GHz. The circuit is designed by considering a single layer microstrip structure that makes it simpler. The design of wide band microwave devices such as branch-line coupler; cross-coupler and phase-shifters are also incorporated. The switched beam antenna is designed for 2.4GHz band Wi-Fi (Wireless Fidelity) system.


Pilonidal sinus treated by conservative surgery and the local application of phenol.


Avatar Idealization in Video Games


title: Behavioral detection of spam URL sharing: Posting patterns versus click patterns

abstract: Social media systems like Twitter and Facebook provide a global infrastructure for sharing information, and in one popular direction, of sharing web hyperlinks. Understanding the behavioral signals of both how URLs are inserted into these systems (via posting by users) and how URLs are received by social media users (via clicking) can provide new insights into social media search, recommendation, and user profiling, among many others. Such studies, however, have traditionally been difficult due to the proprietary (and sometimes private) nature of much URL-related data. Hence, in this paper, we begin a behavioral examination of URL sharing through two distinct perspectives: (i) the first is via a study of how these links are posted through publicly-accessible Twitter data; (ii) the second is via a study of how these links are received by measuring their click patterns through the publicly-accessible Bitly click API. We examine the differences between posting and click patterns in a sample application domain: the classification of spam URLs. We find that these behavioral signals - posting versus clicking - provide overlapping but fundamentally different perspectives on URLs, and that these perspectives can inform the design of future applications of spam link detection and link sharing.


title: NTRU-KE: A Lattice-based Public Key Exchange Protocol.

abstract: Public key exchange protocol is identified as an important application in the field of public-key cryptography. Most of the existing public key exchange schemes are Diffie-Hellman (DH)-type, whose security is based on DH problems over different groups. Note that there exists Shor’s polynomial-time algorithm to solve these DH problems when a quantum computer is available, we are therefore motivated to seek for a non-DHtype and quantum resistant key exchange protocol. To this end, we turn our attention to lattice-based cryptography. The higher methodology behind our roadmap is that in analogy to the link between ElGamal, DSA, and DH, one should expect a NTRU lattice-based key exchange primitive in related to NTRU-ENCRYPT and NTRU-SIGN. However, this excepted key exchange protocol is not presented yet and still missing. In this paper, this missing key exchange protocol is found, hereafter referred to as NTRU-KE, which is studied in aspects of security and key-mismatch failure. In comparison with ECDH (Elliptic Curve-based Diffie-Hellman), NTRU-KE features faster computation speed, resistance to quantum attack, and more communication overhead. Accordingly, we come to the conclusion that NTRU-KE is currently comparable with ECDH. However, decisive advantage of NTRU-KE will occur when quantum computers become a reality.


title: Ant colony Optimization: A Solution of Load balancing in Cloud

abstract: As the cloud computing is a new style of computing over internet. It has many advantages along with some crucial issues to be resolved in order to improve reliability of cloud environment. These issues are related with the load management, fault tolerance and different security issues in cloud environment. In this paper the main concern is load balancing in cloud computing. The load can be CPU load, memory capacity, delay or network load. Load balancing is the process of distributing the load among various nodes of a distributed system to improve both resource utilization and job response time while also avoiding a situation where some of the nodes are heavily loaded while other nodes are idle or doing very little work. Load balancing ensures that all the processor in the system or every node in the network does approximately the equal amount of work at any instant of time. Many methods to resolve this problem has been came into existence like Particle Swarm Optimization, hash method, genetic algorithms and several scheduling based algorithms are there. In this paper we are proposing a method based on Ant Colony optimization to resolve the problem of load balancing in cloud environment.