title: Modeling and control of a saucer type Coandä effect UAV

abstract: The concept of utilizing the Coandia effect to produce stable flight in saucer type Aerial Vehicles (AV) was first conceived in 1935 by Henri Marie Coanda. Coanda's proposed AV design remained a relatively unexplored curiosity for decades until the recent surge in popularity of small Unmanned Aerial Vehicles (UAV) revitalized interest in the topic. Coanda effect UAV offer some distinct advantages over standard multirotors due to their robust frame design, which offers a unique mix of crash resistance, flight safety, and flight performance making them ideal for Human-Robot Interaction (HRI) applications. While much work has been performed in the area of characterization for Coanda surfaces and improved mechanical design of saucer type UAV, little work has been found in the literature discussing stability and attitude control of the platform. This work seeks to contribute to the study of these drones by introducing an approximate servo mapping for desired control effort of a particular prototype, and an experimental control law implemented within the framework of a modified commercially available flight stack.


title: Container Rebalancing: Towards Proactive Linux Containers Placement Optimization in a Data Center

abstract: Similar to Virtualization, Linux Containers (LXC) provides high-performance, lightweight computing resource allocation and isolation. Each LXC container has a resource overhead smaller than that of a virtual machine, leading to significantly lower container migration time and making frequent container placement modification a viable optimization technique. Traditional container scheduling mechanisms do not leverage this property of LXC. Generally, a scheduler tries to find the most optimal placement for a new container, the allocated host then executes the scheduled container until the end of the container's life cycle. This strategy works fine for short-lived containers. With a long-lived container such as a server process becoming more and more common, and the container placement calculated at the beginning of the execution may not remain optimal during the container's lifetime, since the other containers are moving in and out of the cluster. This research proposes container rebalancing, a novel scheduling mechanism with a rebalancing process working alongside a scheduling process. The container rebalancing method increases LXC cluster utilization while maintaining minimal interference with the scheduling process. This is done by continuously modifying container placement, by using the rebalancing process, in order to load-balance utilization of each host in the LXC cluster. LXC cluster simulation driven by Google's cluster data is used to verify the feasibility of container rebalancing. Simulation results show an observable increase in container scheduled rate and cluster utilization with no drawback, suggesting that container rebalancing is a promising method.


Detecting Early Risk of Depression from Social Media User-generated Content


title: Inertial Navigation System/Doppler Velocity Log (INS/DVL) Fusion with Partial DVL Measurements

abstract: The Technion autonomous underwater vehicle (TAUV) is an ongoing project aiming to develop and produce a small AUV to carry on research missions, including payload dropping, and to demonstrate acoustic communication. Its navigation system is based on an inertial navigation system (INS) aided by a Doppler velocity log (DVL), magnetometer, and pressure sensor (PS). In many INSs, such as the one used in TAUV, only the velocity vector (provided by the DVL) can be used for aiding the INS, i.e., enabling only a loosely coupled integration approach. In cases of partial DVL measurements, such as failure to maintain bottom lock, the DVL cannot estimate the vehicle velocity. Thus, in partial DVL situations no velocity data can be integrated into the TAUV INS, and as a result its navigation solution will drift in time. To circumvent that problem, we propose a DVL-based vehicle velocity solution using the measured partial raw data of the DVL and additional information, thereby deriving an extended loosely coupled (ELC) approach. The implementation of the ELC approach requires only software modification. In addition, we present the TAUV six degrees of freedom (6DOF) simulation that includes all functional subsystems. Using this simulation, the proposed approach is evaluated and the benefit of using it is shown.


title: 3D velocity model and ray tracing of antenna array GPR

abstract: Migration is an important signal processing method that can improve signal-clutter ratio and reconstruct subsurface image. Diffraction stacking migration and Kirchhoff migration sum amplitudes along the migration trajectory, which generally is hyperbolic. But when the ground surface varies acutely, the migration trajectory is not hyperbolic. To computer the migration trajectory need the technique of ray tracing. We introduce a method of ray tracing based on 3D velocity model. Firstly, we build the 3D velocity model depending on the estimation of both ground surface topography and velocities. Then we compute the travel time between transmitter, receiver and each subsurface scattering point, and search the propagation ray depending on the Fermat's principle. The method is tested by an experiment data acquired by the stepped-frequency (SF) CMP antenna GPR system. The target is a metal ball that is buried under a sand mound. A nice result of ray tracing is shown in the case.


title: Invisible marker tracking for AR

abstract: We introduce a novel tracking system based on invisible markers which are created/drawn with an IR fluorescent pen. The tracking system consists of one scene camera, one IR camera, and one half mirror. The two cameras are positioned in each side of half mirror so that their optical centers coincide with each other. We track the invisible markers using the IR camera and visualize AR in the view of the scene camera. Thus, it works as a robust marker-less tracking system. Experimental results are given to demonstrate the viability of the proposed system.


title: E2: a framework for NFV applications

abstract: By moving network appliance functionality from proprietary hardware to software, Network Function Virtualization promises to bring the advantages of cloud computing to network packet processing. However, the evolution of cloud computing (particularly for data analytics) has greatly benefited from application-independent methods for scaling and placement that achieve high efficiency while relieving programmers of these burdens. NFV has no such general management solutions. In this paper, we present a scalable and application-agnostic scheduling framework for packet processing, and compare its performance to current approaches.


title: A Generative Word Embedding Model and its Low Rank Positive Semidefinite Solution

abstract: Most existing word embedding methods can be categorized into Neural Embedding Models and Matrix Factorization (MF)-based methods. However some models are opaque to probabilistic interpretation, and MF-based methods, typically solved using Singular Value Decomposition (SVD), may incur loss of corpus information. In addition, it is desirable to incorporate global latent factors, such as topics, sentiments or writing styles, into the word embedding model. Since generative models provide a principled way to incorporate latent factors, we propose a generative word embedding model, which is easy to interpret, and can serve as a basis of more sophisticated latent factor models. The model inference reduces to a low rank weighted positive semidefinite approximation problem. Its optimization is approached by eigendecomposition on a submatrix, followed by online blockwise regression, which is scalable and avoids the information loss in SVD. In experiments on 7 common benchmark datasets, our vectors are competitive to word2vec, and better than other MF-based methods.


title: Patina: dynamic heatmaps for visualizing application usage

abstract: We present Patina, an application independent system for collecting and visualizing software application usage data. Patina requires no instrumentation of the target application, all data is collected through standard window metrics and accessibility APIs. The primary visualization is a dynamic heatmap overlay which adapts to match the content, location, and shape of the user interface controls visible in the active application. We discuss a set of design guidelines for the Patina system, describe our implementation of the system, and report on an initial evaluation based on a short-term deployment of the system.


title: Describing bibliographic references in RDF

abstract: In this paper we present two ontologies, i.e., BiRO and C4O, that allow users to describe bibliographic references in an accurate way, and we introduce REnhancer, a proof-of-concept implementation of a converter that takes as input a raw-text list of references and produces an RDF dataset according to the BiRO and C4O ontologies.


title: BLE-Based Accurate Indoor Location Tracking for Home and Office

abstract: Nowadays the use of smart mobile devices and the accompanying needs for emerging services relying on indoor location-based services (LBS) for mobile devices are rapidly increasing. For more accurate location tracking using Bluetooth Low Energy (BLE), this paper proposes a novel trilateration-based algorithm and presents experimental results that demonstrate its effectiveness.


title: Improved interference approximation method for preamble-based channel estimation in FBMC/OQAM

abstract: Filter bank-based multicarrier modulation (FBMC) using offset quadrature amplitude modulation (OQAM), known as FBMC/OQAM (or OFDM/OQAM), provides an attractive alternative to the conventional cyclic prefix-based orthogonal frequency division multiplexing (CP-OFDM), especially in terms of increased robustness to frequency offset and Doppler spread, and high bandwidth efficiency. It suffers, however, from an inherent (intrinsic) imaginary intercarrier/ inter-symbol interference that complicates signal processing tasks such as channel estimation (CE). Recently, the so-called interference approximation method (IAM) was proposed for preamble-based CE. It relies on the knowledge of the pilot's neighborhood to approximate this interference and constructively exploit it in simplifying CE and improving its performance. The IAM preamble with nulls at the neighboring time instants that results in optimum CE performance was recently reported. This paper investigates an extended version of it, which can provide significant improvement through an appropriate exploitation of the interfering symbols from neighboring time instants as well. Simulation results are presented that compare the methods, with each other and with CP-OFDM, in both mildly and highly frequencyselective channels.


title: An introduction to Markov logic networks and application in video activity analysis

abstract: A Markov logic network (MLN) is a compact combination of logic representation of knowledge and probabilistic reasoning in Markov networks. We have seen its applications in different domains, however, few tried to explain or demonstrate the underneath reasons why MLN works. This paper gives an introduction to MLN using examples in the hope to help understand its elegance and booster the application. Application in video activity analysis was designed to demonstrate how MLN can be used in a specific domain, including feature extraction, logic predicate/formula design, and activity recognition through probabilistic reasoning.


title: Energy-Assisted Information Detection for Simultaneous Wireless Information and Power Transfer: Performance Analysis and Case Studies

abstract: In simultaneous wireless information and power transfer (SWIPT), practical receiver architectures consisting of an information receiver and an energy harvester have been proposed in place of an ideal receiver capable of performing two tasks simultaneously using the same circuits. In this paper, we present a novel receiver architecture design incorporating an interplay between the information receiver and the energy harvester to enhance the performance of the practical SWIPT receiver. In particular, the energy level of the received signal monitored at the energy harvester is fed back to the information receiver to assist information decoding at the information receiver. The symbol-error-rate (SER) and diversity analyses show that the proposed receiver architecture could yield a higher diversity order for unconventional constellations where any two distinct symbols have distinct energy levels, and the same diversity order for conventional modulations. Simulation of PAM and QAM modulations verifies the analysis, shows the improved SER performance of the proposed receiver architecture, and illustrates the energy-dimension-augmented decision regions. Some insights into designing enhanced practical SWIPT receivers are provided as a result.


title: Botnet Economics: Uncertainty Matters

abstract: Botnets have become an increasing security concern in today’s Internet. Thus far the mitigation to botnet attacks is a never ending arms race focusing on technical approaches. In this chapter, we model botnet-related cybercrimes as a result of profit-maximizing decision-making from the perspectives of both botnet masters and renters/attackers. From this economic model, we can understand the effective rental size and the optimal botnet size that can maximize the profits of botnet masters and attackers. We propose the idea of using virtual bots (honeypots running on virtual machines) to create uncertainty in the level of botnet attacks. The uncertainty introduced by virtual bots has a deep impact on the profit gains on the botnet market. With decreasing profitability, botnet-related attacks such as DDoS are reduced if not eliminated from the root cause, i.e. economic incentives.


title: Graphical Methods for Visualizing Comparative Data on Phylogenies

abstract: Phylogenies have emerged as central in evolutionary biology over the past three decades or more, and an extraordinary expansion in the breadth and sophistication of phylogenetic comparative methods has played a large role in this growth. In this chapter, I focus on a somewhat neglected area: the use of graphical methods to simultaneously represent comparative data and trees. As this research area is theoretically very broad, I have concentrated on new methods developed by me, or techniques devised by others and implemented by me as part of my R phylogenetics package, phytools. I describe a variety of methods in this chapter, including approaches that can be used to map reconstructed discrete or continuous character evolution on trees; techniques for projecting phylogenetic trees into morphospace; and methods for visualizing phylogenies in the context of a global or regional geographic map. In this chapter, my intention is not merely to showcase new methods that I have developed. Rather, I have also dedicated considerable attention to detailing the algorithms and computational techniques required for these approaches with the hope that this chapter will become a resource or jumping-off point for researchers interested in building new, more advanced approaches and methods in this area.


title: A 360-degree panoramic video system design

abstract: In this paper, a low-complexity video stitching algorithm and its system prototype are proposed. With the novel design, users can obtain a high-resolution, high quality and seamless 360-degree panoramic video immediately by stitching the images with overlapped regions. Most of the present works are focused on image stitching instead of video stitching. In the proposed design, we develop some novel methods to solve the problems encountered in video stitching. First, we provide a new blending method to remove the color difference in video stitching. Moreover, we avoid the moving objects in the overlapped area by using the dynamic seam adjustment scheme. Finally, we remove the drift problem and obtain a better visual quality while displaying the 360 degree panoramic video scenes. The implementation results show that the entire system achieves 4-channel D1 30fps real-time video stitching on an Intel i7 3930K CPU 2.3GHz machine with 8GB DDR3 memory and Linux Ubuntu 12.10 operation system.


Ontologies in knowledge management support: A case study


Underground: The London alternative press, 1966-74


title: Cloud-based adaptive compression and secure management services for 3D healthcare data

abstract: Several studies show that the lack of access to resources and shared data is one of the main causes of errors in the healthcare sector. In particular, 3D medical images play a fundamental role in healthcare environment, but they are typically very large in size. Therefore, their management, which should be performed also by means of devices with limited characteristics, requires complex network protocols along with advanced compression and security techniques. This work concerns the secure management of 3D medical images, with the main aim that such management must take place in an almost completely transparent manner for the end-user, regardless of the computational and networking capabilities he may use. In particular, our contribution is twofold: first, we propose an engine for lossless dynamic and adaptive compression of 3D medical images, which also allows the embedding of security watermarks within them. Furthermore, in order to provide effective, secure and flexible access to healthcare resources that need to be managed by medical applications, we define the architecture of a SaaS Cloud system, which is based on the aforementioned engine. The resulting architecture allows devices with totally different and heterogeneous hardware and software characteristics to interact among them, so that these differences are almost completely transparent to the end-user. A Cloud-based solution for lossless dynamic and adaptive compression of 3D medical images.Management of such data may be considered as an atypical Big Data problem.It provides SaaS services based on an elastic and on-demand peer to peer overlay infrastructure.It also provides effective, secure and flexible access to healthcare resources that need to be managed by medical applications.Allows devices with totally different and heterogeneous hardware and software characteristics to interact among them.


title: Transductive Face Sketch-Photo Synthesis

abstract: Face sketch-photo synthesis plays a critical role in many applications, such as law enforcement and digital entertainment. Recently, many face sketch-photo synthesis methods have been proposed under the framework of inductive learning, and these have obtained promising performance. However, these inductive learning-based face sketch-photo synthesis methods may result in high losses for test samples, because inductive learning minimizes the empirical loss for training samples. This paper presents a novel transductive face sketch-photo synthesis method that incorporates the given test samples into the learning process and optimizes the performance on these test samples. In particular, it defines a probabilistic model to optimize both the reconstruction fidelity of the input photo (sketch) and the synthesis fidelity of the target output sketch (photo), and efficiently optimizes this probabilistic model by alternating optimization. The proposed transductive method significantly reduces the expected high loss and improves the synthesis performance for test samples. Experimental results on the Chinese University of Hong Kong face sketch data set demonstrate the effectiveness of the proposed method by comparing it with representative inductive learning-based face sketch-photo synthesis methods.


title: Hashing and Data Fingerprinting in Digital Forensics

abstract: Hashing is a primary, yet under appreciated, tool in digital forensic investigations. Recent R&D has demonstrated that, with clever design, we can construct robust fingerprinting and similarity hashes that can significantly speed up an investigation.


title: Upsampling range data in dynamic environments

abstract: We present a flexible method for fusing information from optical and range sensors based on an accelerated high-dimensional filtering approach. Our system takes as input a sequence of monocular camera images as well as a stream of sparse range measurements as obtained from a laser or other sensor system. In contrast with existing approaches, we do not assume that the depth and color data streams have the same data rates or that the observed scene is fully static. Our method produces a dense, high-resolution depth map of the scene, automatically generating confidence values for every interpolated depth point. We describe how to integrate priors on object motion and appearance and how to achieve an efficient implementation using parallel processing hardware such as GPUs.


title: Hyperbaric oxygen therapy. Part 2: application in disease

abstract: OBJECTIVE ::: Review the mechanisms of action and clinical application of hyperbaric oxygen therapy (HBOT) based on human and veterinary clinical and experimental literature. ::: ::: ::: DATA SOURCES ::: Pubmed and Veterinary Information Network databases were searched for human and veterinary journal articles on hyperbaric therapy in clinically applicable situations. Historical reference searches on several articles in addition to basic physiologic concepts were also reviewed. ::: ::: ::: HUMAN DATA SYNTHESIS ::: HBOT has gained acceptance as an adjunctive treatment in clinical conditions other than diving-related injuries, such as select problem wounds and central nervous system diseases, in human medicine. Access to hyperbaric therapy has increased and ongoing research has furthered understanding of the mechanisms and potential therapeutic uses of HBOT. ::: ::: ::: VETERINARY DATA SYNTHESIS ::: Several animal models have been utilized to examine the effects of HBOT; primarily rodents (mice, rats) and rabbits but also dogs, cats, and pigs. Data related to animal model research as it pertains to clinical application of HBOT is reviewed. ::: ::: ::: CONCLUSIONS ::: There is a substantial body of literature that has examined the adverse and beneficial effects of HBOT in animal models. As technology becomes more readily available to clinical practice and more clinical trials are performed to define its effectiveness, HBOT may be considered as an additional therapeutic option in many conditions including select problem wounds, spinal cord injury, and cerebral ischemic injury. Understanding the mechanisms by which HBOT exerts its effects will help guide research and use of the modality in clinical patients.


title: Rethinking Data-Intensive Science Using Scalable Analytics Systems

abstract: "Next generation" data acquisition technologies are allowing scientists to collect exponentially more data at a lower cost. These trends are broadly impacting many scientific fields, including genomics, astronomy, and neuroscience. We can attack the problem caused by exponential data growth by applying horizontally scalable techniques from current analytics systems to accelerate scientific processing pipelines. In this paper, we describe ADAM, an example genomics pipeline that leverages the open-source Apache Spark and Parquet systems to achieve a 28x speedup over current genomics pipelines, while reducing cost by 63%. From building this system, we were able to distill a set of techniques for implementing scientific analyses efficiently using commodity "big data" systems. To demonstrate the generality of our architecture, we then implement a scalable astronomy image processing system which achieves a 2.8--8.9x improvement over the state-of-the-art MPI-based system.


title: Elastic Provisioning of Virtual Machines for Container Deployment

abstract: Docker containers enable to package an application together with all its dependencies and easily run it in any environment. Thanks to their ease of use and portability, containers are gaining an increasing interest and promise to change the way how Cloud platforms are designed and managed. For their execution in the Cloud, we need to solve the container deployment problem, which deals with the identification of an elastic set of computing machines that can host and execute those containers, while considering the diversity of their requirements. In this paper, we provide a general formulation of the Elastic provisioning of Virtual machines for Container Deployment (for short, EVCD) as an Integer Linear Programming problem, which takes explicitly into account the heterogeneity of container requirements and virtual machine resources. Besides optimizing multiple QoS metrics, EVCD can reallocate containers at runtime, when a QoS improvement can be achieved. Using the proposed formulation as benchmark, we evaluate two well-known heuristics, i.e., greedy first-fit and round-robin, that are usually adopted for solving the container deployment problem.


title: Residual Error Based Anomaly Detection Using Auto-Encoder in SMD Machine Sound

abstract: Detecting an anomaly or an abnormal situation from given noise is highly useful in an environment where constantly verifying and monitoring a machine is required. As deep learning algorithms are further developed, current studies have focused on this problem. However, there are too many variables to define anomalies, and the human annotation for a large collection of abnormal data labeled at the class-level is very labor-intensive. In this paper, we propose to detect abnormal operation sounds or outliers in a very complex machine along with reducing the data-driven annotation cost. The architecture of the proposed model is based on an auto-encoder, and it uses the residual error, which stands for its reconstruction quality, to identify the anomaly. We assess our model using Surface-Mounted Device (SMD) machine sound, which is very complex, as experimental data, and state-of-the-art performance is successfully achieved for anomaly detection.


title: Assessing anosognosia: a critical review

abstract: Anosognosia is a multi-factorial syndrome whose clinical manifestations can vary considerably from patient to patient. Considering the complexity of this syndrome, its assessment represents a major challenge for the diagnostic process, and presents various methodological complications. Lack of agreement about diagnostic criteria, high exclusion rates of specific patient categories, assessment limited to certain aspects of unawareness, and the lack of normative data and methodologically robust tools may have led to contrasting, or apparently contrasting, interpretations of this syndrome. Despite the apparently greater sensitivity of the last generation of anosognosia studies to many of these issues, many aspects are still overlooked. In this manuscript, we critically review the literature on the assessment of anosognosia, mainly focusing on motor and language deficits, with a note on the issue of awareness for memory deficits.


Towards hybrid NER : an extended study of content and crowdsourcing-related performance factors


title: Crater Detection via Convolutional Neural Networks

abstract: Craters are among the most studied geomorphic features in the Solar System because they yield important information about the past and present geological processes and provide information about the relative ages of observed geologic formations. We present a method for automatic crater detection using advanced machine learning to deal with the large amount of satellite imagery collected. The challenge of automatically detecting craters comes from their is complex surface because their shape erodes over time to blend into the surface. Bandeira provided a seminal dataset that embodied this challenge that is still an unsolved pattern recognition problem to this day. There has been work to solve this challenge based on extracting shape and contrast features and then applying classification models on those features. The limiting factor in this existing work is the use of hand crafted filters on the image such as Gabor or Sobel filters or Haar features. These hand crafted methods rely on domain knowledge to construct. We would like to learn the optimal filters and features based on training examples. In order to dynamically learn filters and features we look to Convolutional Neural Networks (CNNs) which have shown their dominance in computer vision. The power of CNNs is that they can learn image filters which generate features for high accuracy classification.


title: Method and developer characteristics for effective agile method tailoring: A study of XP expert opinion

abstract: It has long been acknowledged that software methods should be tailored if they are to achieve optimum effect. However comparatively little research has been carried out to date on this topic in general, and more notably, on agile methods in particular. This dearth of evidence in the case of agile methods is especially significant in that it is reasonable to expect that such methods would particularly lend themselves to tailoring. In this research, we present a framework based on interviews with 20 senior software development researchers and a review of the extant literature. The framework is comprised of two sets of factors—characteristics of the method, and developer practices—that can improve method tailoring effectiveness. Drawing on the framework, we then interviewed 16 expert XP practitioners to examine the current state and effectiveness of XP tailoring efforts, and to shed light on issues the framework identified as being important. The article concludes with a set of recommendations for research and practice that would advance our understanding of the method tailoring area.


title: Topic Extraction and Sentiment Classification by using Latent Dirichlet Markov Allocation and SentiWordNet

abstract: Now days, the power of internet is having an immense impact on human life and helps one to make important decisions. Since plenty of knowledge and valuable information is available on the internet therefore many users read review information given on web to take decisions such as buying products, watching movies, going to restaurants etc. Reviews contain user opinion about the product, service, event or topic. It is difficult for web users to read and understand the contents from large number of reviews. Whenever any detail is required in the document, this can be achieved by many probabilistic topic models. A topic model provides a generative model for documents and it defines a probabilistic scheme by which documents can be achieved. Topic model is an Integration of acquaintance and these acquaintances are blended with theme, where a theme is a fusion of terms. We describe Latent Dirichlet Markov Allocation 4 level hierarchical Bayesian Model (LDMA), planted on Latent Dirichlet Allocation (LDA) and Hidden Markov Model (HMM), which highlights on extracting multiword topics from text data. To retrieve the sentiment of the reviews, along with LDMA we will be using SentiWordNet and will compare our result to LDMA with feature extraction of baseline method of sentiment analysis.


title: A Multi-Oriented Scene Text Detector with Position-Sensitive Segmentation

abstract: Scene text detection has been studied for a long time and lots of approaches have achieved promising performances. Most approaches regard text as a specific object and utilize the popular frameworks of object detection to detect scene text. However, scene text is different from general objects in terms of orientations, sizes and aspect ratios. In this paper, we present an end-to-end multi-oriented scene text detection approach, which combines the object detection framework with the position-sensitive segmentation. For a given image, features are extracted through a fully convolutional network. Then they are input into text detection branch and position-sensitive segmentation branch simultaneously, where text detection branch is used for generating candidates and position-sensitive segmentation branch is used for generating segmentation maps. Finally the candidates generated by text detection branch are projected onto the position-sensitive segmentation maps for filtering. The proposed approach utilizes the merits of position-sensitive segmentation to improve the expressiveness of the proposed network. Additionally, the approach uses position-sensitive segmentation maps to further filter the candidates so as to highly improve the precision rate. Experiments on datasets ICDAR2015 and COCO-Text demonstrate that the proposed method outperforms previous state-of-the-art methods. For ICDAR2015 dataset, the proposed method achieves an F-score of 0.83 and a precision rate of 0.87.


title: Correction of Systematic Odometry Errors in Mobile Robots

abstract: Odometry is the most widely used method for determining the momentary position of a mobile robot. This paper introduces practical methods for measuring and reducing odometry errors that are caused by the two dominant error sources in differential-drive mobile robots: 1) uncertainty about the effective wheelbase; and 2) unequal wheel diameters. These errors stay almost constant over prolonged periods of time. Performing an occasional calibration as proposed here will increase the odometric accuracy of the robot and reduce operation cost because an accurate mobile robot requires fewer absolute positioning updates. Many manufacturers or end-users calibrate their robots, usually in a time-consuming and nonsystematic trial and error approach. By contrast, the method described in this paper is systematic, provides near-optimal results, and it can be performed easily and without complicated equipment. Experimental results are presented that show a consistent improvement of at least one order of magnitude in odometric accuracy (with respect to systematic errors) for a mobile robot calibrated with our method.


title: The State of the Art in Flow Visualization: Dense and Texture-Based Techniques

abstract: Flow visualization has been a very attractive component of scientific visualization research for a long time. Usually very large multivariate datasets require processing. These datasets often consist of a large number of sample locations and several time steps. The steadily increasing performance of computers has recently become a driving factor for a reemergence in flow visualization research, especially in texture-based techniques. In this paper, dense, texture-based flow visualization techniques are discussed. This class of techniques attempts to provide a complete, dense representation of the flow field with high spatio-temporal coherency. An attempt of categorizing closely related solutions is incorporated and presented. Fundamentals are shortly addressed as well as advantages and disadvantages of the methods.


title: Temporal Graph Traversals: Definitions, Algorithms, and Applications

abstract: A temporal graph is a graph in which connections between vertices are active at specific times, and such temporal information leads to completely new patterns and knowledge that are not present in a non-temporal graph. In this paper, we study traversal problems in a temporal graph. Graph traversals, such as DFS and BFS, are basic operations for processing and studying a graph. While both DFS and BFS are well-known simple concepts, it is non-trivial to adopt the same notions from a non-temporal graph to a temporal graph. We analyze the difficulties of defining temporal graph traversals and propose new definitions of DFS and BFS for a temporal graph. We investigate the properties of temporal DFS and BFS, and propose efficient algorithms with optimal complexity. In particular, we also study important applications of temporal DFS and BFS. We verify the efficiency and importance of our graph traversal algorithms in real world temporal graphs.


title: A French clinical corpus with comprehensive semantic annotations: development of the Medical Entity and Relation LIMSI annOtated Text corpus (MERLOT)

abstract: Quality annotated resources are essential for Natural Language Processing. The objective of this work is to present a corpus of clinical narratives in French annotated for linguistic, semantic and structural information, aimed at clinical information extraction. Six annotators contributed to the corpus annotation, using a comprehensive annotation scheme covering 21 entities, 11 attributes and 37 relations. All annotators trained on a small, common portion of the corpus before proceeding independently. An automatic tool was used to produce entity and attribute pre-annotations. About a tenth of the corpus was doubly annotated and annotation differences were resolved in consensus meetings. To ensure annotation consistency throughout the corpus, we devised harmonization tools to automatically identify annotation differences to be addressed to improve the overall corpus quality. The annotation project spanned over 24 months and resulted in a corpus comprising 500 documents (148,476 tokens) annotated with 44,740 entities and 26,478 relations. The average inter-annotator agreement is 0.793 F-measure for entities and 0.789 for relations. The performance of the pre-annotation tool for entities reached 0.814 F-measure when sufficient training data was available. The performance of our entity pre-annotation tool shows the value of the corpus to build and evaluate information extraction methods. In addition, we introduced harmonization methods that further improved the quality of annotations in the corpus.


title: Serverless Computing: An Investigation of Factors Influencing Microservice Performance

abstract: Serverless computing platforms provide function(s)-as-a-Service (FaaS) to end users while promising reduced hosting costs, high availability, fault tolerance, and dynamic elasticity for hosting individual functions known as microservices. Serverless Computing environments, unlike Infrastructure-as-a-Service (IaaS) cloud platforms, abstract infrastructure management including creation of virtual machines (VMs), operating system containers, and request load balancing from users. To conserve cloud server capacity and energy, cloud providers allow hosting infrastructure to go COLD, deprovisioning containers when service demand is low freeing infrastructure to be harnessed by others. In this paper, we present results from our comprehensive investigation into the factors which influence microservice performance afforded by serverless computing. We examine hosting implications related to infrastructure elasticity, load balancing, provisioning variation, infrastructure retention, and memory reservation size. We identify four states of serverless infrastructure including: provider cold, VM cold, container cold, and warm and demonstrate how microservice performance varies up to 15x based on these states.


title: Informed Blending of Databases for Emotional Speech Synthesis

abstract: The goal of this project was to build a unit selection voice that could portray emotions with varying intensities. A suitable definition of an emotion was developed along with a descriptive framework that supported the work carried out. A single speaker was recorded portraying happy and angry speaking styles. Additionally a neutral database was also recorded. A target cost function was implemented that chose units according to emotion mark-up in the database. The Dictionary of Affect supported the emotional target cost function by providing an emotion rating for words in the target utterance. If a word was particularly ’emotional’, units from that emotion were favoured. In addition intensity could be varied which resulted in a bias to select a greater number emotional units. A perceptual evaluation was carried out and subjects were able to recognise reliably emotions with varying amounts of emotional units present in the target utterance.


Combining FFT and Spectral-Pooling for Efficient Convolution Neural Network Model


title: Optimizing Base Rankers Using Clicks

abstract: We study the problem of optimizing an individual base ranker using clicks. Surprisingly, while there has been considerable attention for using clicks to optimize linear combinations of base rankers, the problem of optimizing an individual base ranker using clicks has been ignored. The problem is different from the problem of optimizing linear combinations of base rankers as the scoring function of a base ranker may be highly non-linear. For the sake of concreteness, we focus on the optimization of a specific base ranker, viz. BM25. We start by showing that significant improvements in performance can be obtained when optimizing the parameters of BM25 for individual datasets. We also show that it is possible to optimize these parameters from clicks, i.e., without the use of manually annotated data, reaching or even beating manually tuned parameters.


title: Temporal Logic Planning and Control of Robotic Swarms by Hierarchical Abstractions

abstract: We develop a hierarchical framework for planning and control of arbitrarily large groups (swarms) of fully actuated robots with polyhedral velocity bounds moving in polygonal environments with polygonal obstacles. At the first level of hierarchy, we aggregate the high-dimensional control system of the swarm into a small-dimensional control system capturing its essential features. These features describe the position of the swarm in the world and its size. At the second level, we reduce the problem of controlling the essential features of the swarm to a model-checking problem. In the obtained hierarchical framework, high-level specifications given in natural language, such as linear temporal logic formulas over linear predicates in the essential features, are automatically mapped to provably correct robot control laws. For the particular case of an abstraction based on centroid and variance, we show that swarm cohesion, interrobot collision avoidance, and environment containment can also be specified and automatically guaranteed in our framework. The obtained communication architecture is centralized


title: High performance clustering based on the similarity join

abstract: A broad class of algorithms for knowledge discovery in databases (KDD) relies heavily on similarity queries, i.e. range queries or nearest neighbor queries, in multidimensional feature spaces. Many KDD algorithms perform a similarity query for each point stored in the database. This approach causes serious performance degenerations if the considered data set does not fit into main memory. Usual cache strategies such as LRU fail because the locality of KDD algorithms is typically not high enough. In this paper, we propose to replace repeated similarity queries by the similarity join, a database primitive prevalent in multimedia database systems. We present a schema to transform query intensive KDD algorithms into a representation using the similarity join as a basic operation without affecting the correctness of the result of the considered algorithm. In order to perform a comprehensive experimental evaluation of our approach, we apply the proposed transformation to the clustering algorithm DBSCAN and to the hierarchical cluster structure analysis method OPTICS. Our technique allows the application of any similarity join algorithm, which may be based on index structures or not. In our experiments, we use a similarity join algorithm based on a variant of the X-tree. The experiments yield substantial performance improvements of our technique over the original algorithms. The traditional techniques are outperformed by factors of up to 33 for the X-tree and 54 for the R*-tree.


title: Test case prioritization using ant colony optimization

abstract: Regression testing is primarily a maintenance activity that is performed frequently to ensure the validity of the modified software. In such cases, due to time and cost constraints, the entire test suite cannot be run. Thus, it becomes essential to prioritize the tests in order to cover maximum faults in minimum time. In this paper, ant colony optimization is used, which is a new way to solve time constraint prioritization problem. This paper presents the regression test prioritization technique to reorder test suites in time constraint environment along with an algorithm that implements the technique.


title: Allele-specific Characterization of Alanine: Glyoxylate Aminotransferase Variants Associated with Primary Hyperoxaluria

abstract: Primary Hyperoxaluria Type 1 (PH1) is a rare autosomal recessive kidney stone disease caused by deficiency of the peroxisomal enzyme alanine: glyoxylate aminotransferase (AGT), which is involved in glyoxylate detoxification. Over 75 different missense mutations in AGT have been found associated with PH1. While some of the mutations have been found to affect enzyme activity, stability, and/or localization, approximately half of these mutations are completely uncharacterized. In this study, we sought to systematically characterize AGT missense mutations associated with PH1. To facilitate analysis, we used two high-throughput yeast-based assays: one that assesses AGT specific activity, and one that assesses protein stability. Approximately 30% of PH1-associated missense mutations are found in conjunction with a minor allele polymorphic variant, which can interact to elicit complex effects on protein stability and trafficking. To better understand this allele interaction, we functionally characterized each of 34 mutants on both the major (wild-type) and minor allele backgrounds, identifying mutations that synergize with the minor allele. We classify these mutants into four distinct categories depending on activity/stability results in the different alleles. Twelve mutants were found to display reduced activity in combination with the minor allele, compared with the major allele background. When mapped on the AGT dimer structure, these mutants reveal localized regions of the protein that appear particularly sensitive to interactions with the minor allele variant. While the majority of the deleterious effects on activity in the minor allele can be attributed to synergistic interaction affecting protein stability, we identify one mutation, E274D, that appears to specifically affect activity when in combination with the minor allele.


title: IC Design and Measurement of an Inductorless 48 V DC/DC Converter in Low-Cost CMOS Technology Facing Harsh Environments

abstract: With reference to 48-V systems, used in electric and hybrid vehicles and for telecom and networking power supply, this paper presents the circuit/transistor-level design, implementation, and test of an inductorless dc/dc converter. Its architecture is a cascade of three switched-capacitor converters with step-up/down capabilities plus linear converters, working in parallel at the end of the cascade. It provides multiple regulated voltages, which are isolated from input failures. The design also includes on-chip control unit and a serial interface toward an external host. Experimental tests carried out on the chip, fabricated in 0.35- $\mu \text{m}$ CMOS technology, proves that the design is suitable to face harsh operating environments, including the 48-V automotive one. Compared with the state-of-art, the proposed design stands for its wide input voltage regulation range, load/line regulation, power supply rejection ratio, and low electromagnetic interference emissions.


title: Differential Social Perception and Attribution of Intergroup Violence: Testing the Lower Limits of Stereotyping of Blacks

abstract: In a modified 4X 4 factorial design with race (black-white) of the harm-doer and race (black-white) of the victim as the major factors, the phenomenon of differential social perception of intergroup violence was established. White subjects, observing a videotape of purported ongoing interaction occuring in another room, labeled an act (ambiguous shove) as more violent when it was performed by a black than when the same act was perpetrated by a white. That is, the concept of violence was more accessible when viewing a black than when viewing a white committing the same act. Causal attributions were also found to be divergent. Situation attributions were preferred when the harm-doer was white, and person (dispositional) attributions were preferred in the black-protagonist conditions. The results are discussed in terms of perceptual threshold, stereotypy, and attributional biases. It is now generally accepted that systems of values and beliefs are important determinants of individual behavior. However, we know little "about the lower limits of these influences, about their capacity to reach toward the regions of human experience which appear as immediately given, as the incontrovertible evidence of our senses" (Tajfel, 1969, p. 322). The many studies related to the "new look" in perception have adequately demonstrated that perception is not necessarily "veridical" and that an individual's perception of a stimulus object or event is influenced by his particular needs, wishes, and expectations of the moment—we tend to perceive what we wish or expect to perceive (Merton, 1957). A classic experiment in the "new look" tradition was performed by Bruner and Goodman (1947) who found that the perceiver tended to accentuate the size of valued objects (i.e., coins) and that this tendency was especially


title: A Convergence Theorem for the Fuzzy ISODATA Clustering Algorithms

abstract: In this paper the convergence of a class of clustering procedures, popularly known as the fuzzy ISODATA algorithms, is established. The theory of Zangwill is used to prove that arbitrary sequences generated by these (Picard iteration) procedures always terminates at a local minimum, or at worst, always contains a subsequence which converges to a local minimum of the generalized least squares objective functional which defines the problem.


title: Behavioural Computer Science: an agenda for combining modelling of human and system behaviours

abstract: The rapidly increasing pervasiveness and integration of computers in human society calls for a broad discipline under which this development can be studied. We argue that to design and use technology one needs to develop and use models of humans and machines in all their aspects, including cognitive and memory models, but also social influence and (artificial) emotions. We call this wider discipline Behavioural Computer Science (BCS), and argue in this paper for why BCS models should unify (models of) the behaviour of humans and machines when designing information and communication technology systems. Thus, one main point to be addressed is the incorporation of empirical evidence for actual human behaviour, instead of making inferences about behaviour based on the rational agent model. Empirical studies can be one effective way to constantly update the behavioural models. We are motivated by the future advancements in artificial intelligence which will give machines capabilities that from many perspectives will be indistinguishable from those of humans. Such machine behaviour would be studied using BCS models, looking at questions about machine trust like "Can a self driving car trust its passengers?", or artificial influence like "Can the user interface adapt to the user's behaviour, and thus influence this behaviour?". We provide a few directions for approaching BCS, focusing on modelling of human and machine behaviour, as well as their interaction.


title: A generalized iterative LQG method for locally-optimal feedback control of constrained nonlinear stochastic systems

abstract: We present an iterative linear-quadratic-Gaussian method for locally-optimal feedback control of nonlinear stochastic systems subject to control constraints. Previously, similar methods have been restricted to deterministic unconstrained problems with quadratic costs. The new method constructs an affine feedback control law, obtained by minimizing a novel quadratic approximation to the optimal cost-to-go function. Global convergence is guaranteed through a Levenberg-Marquardt method; convergence in the vicinity of a local minimum is quadratic. Performance is illustrated on a limited-torque inverted pendulum problem, as well as a complex biomechanical control problem involving a stochastic model of the human arm, with 10 state dimensions and 6 muscle actuators. A Matlab implementation of the new algorithm is availabe at www.cogsci.ucsd.edu//spl sim/todorov.


title: Perceptions of trustworthiness online: the role of visual and textual information

abstract: People increasingly rely on social networking websites to initiate personal and professional relationships. This requires that a considerable amount of trust be placed in strangers solely on the basis of their online profiles. This paper examines how the nature of online information affects how trustworthy online daters are perceived. Visual (i.e., photographs) and textual (i.e., "about me" section) information is considered. Results show that textual information elicits the highest ratings of trustworthiness, and that the addition of a photograph decreases daters' perceived trustworthiness. However, the accuracy of trustworthiness impressions is low regardless of the type of information available, because of a truth bias. Results are discussed in terms of (1) hyperpersonal impression formation and the nature of truth bias; and (2) practical implications for building trustworthiness online.


title: Automated Failure Diagnosis in Aviation Maintenance Using Explainable Artificial Intelligence (XAI)

abstract: A repair card is used in aviation maintenance to report a failure or anomaly and register it in the maintenance management system. An incorrect or incomplete repair card may result in incorrect maintenance and make it very hard to analyse the maintenance data. There are several reasons for this incomplete reporting. Firstly, (part of) the information is often unknown at the moment the maintenance crew fills in the card. Also, the findings on repair cards are generally filled in as free-form text, making it difficult to automatically interpret the findings. An automatically assessed failure description will lead to more complete and consistent repair cards. This will also improve the efficiency of troubleshooting since this failure diagnosis can add information which would otherwise not be at the disposal of the maintenance crew at that time. With this research, a model is developed which is able to automatically diagnose a failure. The model utilises a data driven approach, combining maintenance data and usage data. The model is based on Artificial Intelligence (AI) such that it is no longer necessary to completely understand the physics of a (sub)system or component. A newly proposed XAI (eXplainable AI) methodology, Failure Diagnosis Explainability (FDE), is added to the model to provide transparency and interpretability of the assessed diagnosis. The assessed diagnosis is explained by checking whether a new failure matches the expected values of a certain diagnosis (class). A failure is from class (diagnosis) A, because the features have similar values as class A. Contrary, this failure is not from classes (diagnosis) B and C, because the features have dissimilar values as class B and C. Two techniques are used to check whether a failure matches the expectations: visual representation and the proposed χ-factor. The proposed model and XAI methodology FDE are applied to a case study with a main wheel of the RNLAF (Royal Netherlands Air Force) F-16. This feasibility study already showed the value of this automated failure diagnosis model with an achieved accuracy of 81% of classifying a diagnosis. The proposed XAI methodology FDE was able to explain the diagnosis assessed by the failure diagnosis model both with visual representation and the χ-factor. Thereby the feasibility of this model is proved. This model will also support a repair shop to repair NFF (No Failure Found) components based on their historical usage.


title: Comprehensive Locomotion Performance Evaluation of All-Terrain Robots

abstract: Information about the locomotion performance of known rovers is sparse. A comprehensive evaluation of wheeled passive systems is presented in this work. It is based on a static 2D approach that includes optimization of the wheel torques in order to minimize the required friction which is an important performance metric. The evaluation comprises well known rover concepts and new suspension systems. The performance of the systems is compared and interesting effects of some concepts are discussed in more detail including torques and load distribution. The rovers MER (NASA) and CRAB (EPFL) show good performance which is topped only by the eight wheeled DoubleSpring system.


title: Attractiveness of facial averageness and symmetry in non-Western cultures: In search of biologically based standards of beauty

abstract: Averageness and symmetry are attractive in Western faces and are good candidates for biologically based standards of beauty. A hallmark of such standards is that they are shared across cultures. We examined whether facial averageness and symmetry are attractive in non-Western cultures. Increasing the averageness of individual faces, by warping those faces towards an averaged composite of the same race and sex, increased the attractiveness of both Chinese (experiment 1) and Japanese (experiment 2) faces, for Chinese and Japanese participants, respectively. Decreasing averageness by moving the faces away from an average shape decreased attractiveness. We also manipulated the symmetry of Japanese faces by blending each original face with its mirror image to create perfectly symmetric versions. Japanese raters preferred the perfectly symmetric versions to the original faces (experiment 2). These findings show that preferences for facial averageness and symmetry are not restricted to Western cultures, consistent with the view that they are biologically based. Interestingly, it made little difference whether averageness was manipulated by using own-race or other-race averaged composites and there was no preference for own-race averaged composites over other-race or mixed-race composites (experiment 1). We discuss the implications of these results for understanding what makes average faces attractive. We also discuss some limitations of our studies, and consider other lines of converging evidence that may help determine whether preferences for average and symmetric faces are biologically based.


title: The tear trough and lid/cheek junction: anatomy and implications for surgical correction.

abstract: BACKGROUND ::: The tear trough and the lid/cheek junction become more visible with age. These landmarks are adjacent, forming in some patients a continuous indentation or groove below the infraorbital rim. Numerous, often conflicting procedures have been described to improve the appearance of the region. The purpose of this study was to evaluate the anatomy underlying the tear trough and the lid/cheek junction and to evaluate the procedures designed to correct them. ::: ::: ::: METHODS ::: Twelve fresh cadaver lower lid and midface dissections were performed (six heads). The orbital regions were dissected in layers, and medical photography was performed. ::: ::: ::: RESULTS ::: In the subcutaneous plane, the tear trough and lid/cheek junction overlie the junction of the palpebral and orbital portions of the orbicularis oculi muscle and the cephalic border of the malar fat pad. In the submuscular plane, these landmarks differ. Along the tear trough, the orbicularis muscle is attached directly to the bone. Along the lid/cheek junction, the attachment is ligamentous by means of the orbicularis retaining ligament. ::: ::: ::: CONCLUSIONS ::: The tear trough and lid/cheek junction are primarily explained by superficial (subcutaneous) anatomical features. Atrophy of skin and fat is the most likely explanation for age-related visibility of these landmarks. "Descent" of this region with age is unlikely (the structures are fixed to bone). Bulging orbital fat accentuates these landmarks. Interventions must extend significantly below the infraorbital rim. Fat or synthetic filler may be best placed in the intraorbicularis plane (tear trough) and in the suborbicularis plane (lid/cheek junction).


title: An Analysis of Data Mining Applications in Crime Domain

abstract: In this paper, we give comprehensive surveys of efficient and effective methods/techniques on data mining for crime data analysis. These techniques aim at finding the illegal activities of professional identity fraudsters based on knowledge discovered from their own histories. We also raise some problems of applied data mining in crime control and criminal suppression. It is known that detecting crime from data analysis can be difficult because daily activities of criminal generate large amounts of data and stem from various formats. In addition, the quality of data analysis depends greatly on background knowledge of analyst. A criminal can range from civil infraction such as illegal driving to terrorism mass murder such as the 9/11 attacks, therefore it is difficult to model the perfect algorithm to cover all of them. Finally, this paper proposes a guideline to overcome the problem.


title: A survey of product recognition in shelf images

abstract: Nowadays, merchandising is one of the significant method which allows to increase the sales. Therefore, activities such as monitoring the number of products on the shelves, completing the missing products and matching the planogram continuously have become important. An autonomous system is needed to automate operations such as product or brand recognition, stock tracking and planogram matching. In the literature, it is seen that many studies have been carried out in order to address this issue. This survey classifies and compares all existing works with the aim to guide researchers working on merchandising.


title: BASE: using abstraction to improve fault tolerance

abstract: Software errors are a major cause of outages and they are increasingly exploited in malicious attacks. Byzantine fault tolerance allows replicated systems to mask some software errors but it is expensive to deploy. This paper describes a replication technique, BASE, which uses abstraction to reduce the cost of Byzantine fault tolerance and to improve its ability to mask software errors. BASE reduces cost because it enables reuse of off-the-shelf service implementations. It improves availability because each replica can be repaired periodically using an abstract view of the state stored by correct replicas, and because each replica can run distinct or non-deterministic service implementations, which reduces the probability of common mode failures. We built an NFS service where each replica can run a different off-the-shelf file system implementation, and an object-oriented database where the replicas ran the same, non-deterministic implementation. These examples suggest that our technique can be used in practice --- in both cases, the implementation required only a modest amount of new code, and our performance results indicate that the replicated services perform comparably to the implementations that they reuse.


title: Adaptive Speed Control for Permanent-Magnet Synchronous Motor System With Variations of Load Inertia

abstract: Considering the variations of inertia in real applications, an adaptive control scheme for the permanent-magnet synchronous motor speed-regulation system is proposed in this paper. First, a composite control method, i.e., the extended-state-observer (ESO)-based control method, is employed to ensure the performance of the closed-loop system. The ESO can estimate both the states and the disturbances simultaneously so that the composite speed controller can have a corresponding part to compensate for the disturbances. Then, considering the case of variations of load inertia, an adaptive control scheme is developed by analyzing the control performance relationship between the feedforward compensation gain and the system inertia. By using inertia identification techniques, a fuzzy-inferencer-based supervisor is designed to automatically tune the feedforward compensation gain according to the identified inertia. Simulation and experimental results both show that the proposed method achieves a better speed response in the presence of inertia variations.


title: Real-time part-based visual tracking via adaptive correlation filters

abstract: Robust object tracking is a challenging task in computer vision. To better solve the partial occlusion issue, part-based methods are widely used in visual object trackers. However, due to the complicated online training and updating process, most of these part-based trackers cannot run in real-time. Correlation filters have been used in tracking tasks recently because of the high efficiency. However, the conventional correlation filter based trackers cannot deal with occlusion. Furthermore, most correlation filter based trackers fix the scale and rotation of the target which makes the trackers unreliable in long-term tracking tasks. In this paper, we propose a novel tracking method which track objects based on parts with multiple correlation filters. Our method can run in real-time. Additionally, the Bayesian inference framework and a structural constraint mask are adopted to enable our tracker to be robust to various appearance changes. Extensive experiments have been done to prove the effectiveness of our method.


title: A Practical MRI Grading System for Lumbar Foraminal Stenosis

abstract: OBJECTIVE ::: This study aimed to evaluate the reproducibility of a new grading system for lumbar foraminal stenosis. ::: ::: ::: MATERIALS AND METHODS ::: Four grades were developed for lumbar foraminal stenosis on the basis of sagittal MRI. Grade 0 refers to the absence of foraminal stenosis; grade 1 refers to mild foraminal stenosis showing perineural fat obliteration in the two opposing directions, vertical or transverse; grade 2 refers to moderate foraminal stenosis showing perineural fat obliteration in the four directions without morphologic change, both vertical and transverse directions; and grade 3 refers to severe foraminal stenosis showing nerve root collapse or morphologic change. A total of 576 foramina in 96 patients were analyzed (from L3-L4 to L5-S1). Two experienced radiologists independently assessed the sagittal MR images. Interobserver agreement between the two radiologists and intraobserver agreement by one reader were analyzed using kappa statistics. ::: ::: ::: RESULTS ::: According to reader 1, grade 1 foraminal stenosis was found in 33 foramina, grade 2 in six, and grade 3 in seven. According to reader 2, grade 1 foraminal stenosis was found in 32 foramina, grade 2 in six, and grade 3 in eight. Interobserver agreement in the grading of foraminal stenosis between the two readers was found to be nearly perfect (kappa value: right L3-L4, 1.0; left L3-L4, 0.905; right L4-L5, 0.929; left L4-L5, 0.942; right L5-S1, 0.919; and left L5-S1, 0.909). In intraobserver agreement by reader 1, grade 1 foraminal stenosis was found in 34 foramina, grade 2 in eight, and grade 3 in seven. Intraobserver agreement in the grading of foraminal stenosis was also found to be nearly perfect (kappa value: right L3-L4, 0.883; left L3-L4, 1.00; right L4-L5, 0.957; left L4-L5, 0.885; right L5-S1, 0.800; and left L5-S1, 0.905). ::: ::: ::: CONCLUSION ::: The new grading system for foraminal stenosis of the lumbar spine showed nearly perfect interobserver and intraobserver agreement and would be helpful for clinical study and routine practice.


title: The Hidden Information Statemodel : A practical framework for POMDP-based spoken dialogue management

abstract: This paper explains how Partially Observable Markov Decision Processes (POMDPs) can provide a principled mathematical framework for modelling the inherent uncertainty in spoken dialogue systems. It briefly summarises the basic mathematics and explains why exact optimisation is intractable. It then describes in some detail a form of approximation called the Hidden Information State model which does scale and which can be used to build practical systems. A prototype HIS system for the tourist information domain is evaluated and compared with a baseline MDP system using both user simulations and a live user trial. The results give strong support to the central contention that the POMDP-based framework is both a tractable and powerful approach to building more robust spoken dialogue systems.


title: Data-Driven Human-Robot Coordination Based Walking State Monitoring With Cane-Type Robot

abstract: The walking state monitoring is indispensable during the robot-aided walking of people with lower limb dysfunctions. In this paper, the existence of human–robot coordination state is first statistically verified in the process of using a walking-aid cane-type robot during walking. Based on this coordination, a new walking state monitoring method is proposed by using the principal component analysis (PCA). The abnormal or emergency walking state is promptly detected if the new sample data are found to deviate from an off-line PCA model, which is generated from plentiful normal walking data of different subjects. Furthermore, a state diagnosis algorithm based on the contribution plot is also developed for the walking state recognition and diagnosis. In this way, typical abnormal states like the leg restrictions can be distinguished from the emergency states including falls and the stumbling. Moreover, the human–robot coordination analysis can be performed using less sensors built-in the robot without needing the posture information of full human body. The effectiveness of the proposed method is proven by experiments. Better recognition rate and real-time performance of the method are also verified by comparing with conventional center of pressure based monitoring method.


title: A review of recent developments in vision-based vehicle detection

abstract: This document provides a review of the past decade's literature in on-road vision-based vehicle detection. Over the past decade, vision-based surround perception has matured significantly from its infancy. We detail advances in vehicle detection, discussing representative works from the monocular and stereo-vision domains. We provide discussion on the state-of-the-art, and provide perspective on future research directions in the field.


title: SPARTA: Sparse phase retrieval via Truncated Amplitude flow

abstract: A linear-time algorithm termed SPARse Truncated Amplitude flow (SPARTA) is developed for the phase retrieval (PR) of sparse signals. Upon formulating the sparse PR as a non-convex empirical loss minimization task, SPARTA emerges as an iterative solver consisting of two components: s1) a sparse orthogonality-promoting initialization leveraging support recovery and principal component analysis; and, s2) a series of refinements by hard thresholding based truncated gradient iterations. SPARTA is simple, scalable, and fast. It recovers any k-sparse n-dimensional signal (k ≪ n) of large enough minimum (in modulus) nonzero entries from about k2 log n measurements with high probability; this is achieved at computational complexity of order k2n log n, improving upon the state-of-the-art by at least a factor of k. SPARTA is robust against bounded additive noise. Simulated tests corroborate the merits of SPARTA relative to existing alternatives.


title: Fast human segmentation using color and depth

abstract: Accurate segmentation of humans from live videos is an important problem to be solved in developing immersive video experience. We propose to extract the human segmentation information from color and depth cues in a video using multiple modeling techniques. The prior information from human skeleton data is also fused along with the depth and color models to obtain the final segmentation inside a graph-cut framework. The proposed method runs real time on live videos using single CPU and is shown to be quantitatively outperforming the methods that directly fuse color and depth data.


title: Social Vehicle Swarms: A Novel Perspective on Social-aware Vehicular Communication Architecture

abstract: Internet of vehicles is a promising area related to D2D communication and the Internet of Things. We present a novel perspective on vehicular communications and social vehicle swarms, to study and analyze a socially aware Internet of vehicles with the assistance of an agent-based model intended to reveal hidden patterns behind superficial data. After discussing its components (its agents, environments, and rules), we introduce supportive technology and methods, deep reinforcement learning, privacy preserving data mining, and sub-cloud computing in order to detect the most significant and interesting information for each individual effectively, which is the key desire. Finally, several relevant research topics and challenges are discussed.


title: A Neural Local Coherence Model

abstract: We propose a local coherence model based on a convolutional neural network that operates over the entity grid representation of a text. The model captures long range entity transitions along with entity-specific features without loosing generalization, thanks to the power of distributed representation. We present a pairwise ranking method to train the model in an end-to-end fashion on a task and learn task-specific high level features. Our evaluation on three different coherence assessment tasks demonstrates that our model achieves state of the art results outperforming existing models by a good margin.


title: Ultra-Large Gain Step-Up Switched-Capacitor DC-DC Converter With Coupled Inductor for Alternative Sources of Energy

abstract: An ultra-large voltage conversion ratio converter is proposed by integrating a switched-capacitor circuit with a coupled inductor technology. The proposed converter can be seen as an equivalent parallel connection to the load of a basic boost converter and a number of forward converters, each one containing a switched-capacitor circuit. All the stages are activated by the boost switch. A single active switch is required, with no need of extreme duty-ratio values. The leakage energy of the coupled inductor is recycled to the load. The inrush current problem of switched capacitors is restrained by the leakage inductance of the coupled-inductor. The above features are the reason for the high efficiency performance. The operating principles and steady state analyses of continuous, discontinuous and boundary conduction modes are discussed in detail. To verify the performance of the proposed converter, a 200 W/20 V to 400 V prototype was implemented. The maximum measured efficiency is 96.4%. The full load efficiency is 95.1%.


Mixing Confidential Transactions: Comprehensive Transaction Privacy for Bitcoin.


title: Model-Based Clustering and Visualization of Navigation Patterns on a Web Site

abstract: We present a new methodology for exploring and analyzing navigation patterns on a web site. The patterns that can be analyzed consist of sequences of URL categories traversed by users. In our approach, we first partition site users into clusters such that users with similar navigation paths through the site are placed into the same cluster. Then, for each cluster, we display these paths for users within that cluster. The clustering approach we employ is model-based (as opposed to distance-based) and partitions users according to the order in which they request web pages. In particular, we cluster users by learning a mixture of first-order Markov models using the Expectation-Maximization algorithm. The runtime of our algorithm scales linearly with the number of clusters and with the size of the datas and our implementation easily handles hundreds of thousands of user sessions in memory. In the paper, we describe the details of our method and a visualization tool based on it called WebCANVAS. We illustrate the use of our approach on user-traffic data from msnbc.com.


title: Data mining to predict and prevent errors in health insurance claims processing

abstract: Health insurance costs across the world have increased alarmingly in recent years. A major cause of this increase are payment errors made by the insurance companies while processing claims. These errors often result in extra administrative effort to re-process (or rework) the claim which accounts for up to 30% of the administrative staff in a typical health insurer. We describe a system that helps reduce these errors using machine learning techniques by predicting claims that will need to be reworked, generating explanations to help the auditors correct these claims, and experiment with feature selection, concept drift, and active learning to collect feedback from the auditors to improve over time. We describe our framework, problem formulation, evaluation metrics, and experimental results on claims data from a large US health insurer. We show that our system results in an order of magnitude better precision (hit rate) over existing approaches which is accurate enough to potentially result in over $15-25 million in savings for a typical insurer. We also describe interesting research problems in this domain as well as design choices made to make the system easily deployable across health insurance companies.


title: Implementation og Web-based Information Systems in Distributed Organizations: A Change Management Approach

abstract: This article presents results elicited from studies conducted in rela- tion to implementing a web-based information system throughout a large dis- tributed organization. We demonstrate the kind of expectations and conditions for change that management face in relation to open-ended, configurable, and context specific web-based information systems like Lotus QuickPlace. Our synthesis from the empirical findings is related to two recent models, the improvisational change management model suggested by Orlikowski and Hofman (1997), and Gallivan's (2001) model for organizational adoption and assimilation. In line with comparable approaches from the knowledge man- agement area (Dixon 2000; Markus 2001), we relate to, refine, and operation- alize the models from an overall organizational view by identifying and characterizing four different and general implementation contexts.


title: The International General Game Playing Competition

abstract: Games have played a prominent role as a test-bed for advancements in the field of Artificial Intelligence ever since its foundation over half a century ago, resulting in highly specialized world-class game-playing systems being developed for various games. The establishment of the International General Game Playing Competition in 2005, however, resulted in a renewed interest in more general problem solving approaches to game playing. In general game playing (GGP) the goal is to create game-playing systems that autonomously learn how to skillfully play a wide variety of games, given only the descriptions of the game rules. In this paper we review the history of the competition, discuss progress made so far, and list outstanding research challenges.


title: A microcontroller-based multi-function solar tracking system

abstract: Renewable energy solutions are becoming increasingly popular. Photovoltaic (solar) systems are but one example. Maximizing power output from a solar system is desirable to increase efficiency. In order to maximize power output from solar panels, one needs to keep the panels aligned with the sun. As such, a means of tracking the sun is required. This is definitely a more cost effective solution than purchasing additional solar panels. It has been estimated that the yield from solar panels can be increased by 30 to 60 percent by utilizing a tracking system instead of a stationary array. In this paper, a prototype for a microcontroller-based multi-function solar tracking system is described, which will keep the solar panels aligned with the sun in order to maximize efficiency. The maximum power point tracking (MPPT) data can be transmitted in real time to other solar systems in need of this data.


title: QOM - Quick Ontology Mapping

abstract: (Semi-)automatic mapping - also called (semi-) automatic alignment - of ontologies is a core task to achieve interoperability when two agents or services use different ontologies. In the existing literature, the focus has so far been on improving the quality of mapping results. We here consider QOM, Quick Ontology Mapping, as a way to trade off between effectiveness (i.e. quality) and efficiency of the mapping generation algorithms. We show that QOM has lower run-time complexity than existing prominent approaches. Then, we show in experiments that this theoretical investigation translates into practical benefits. While QOM gives up some of the possibilities for producing high-quality results in favor of efficiency, our experiments show that this loss of quality is marginal.


title: Vehicle-to-vehicle-to-infrastructure (V2V2I) intelligent transportation system architecture

abstract: In this paper, I describe the vehicle-to-vehicle-to-infrastructure (V2V2I) architecture, which is a hybrid of the vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) architectures. The V2V2I architecture leverages the benefits of fast queries and responses from the V2I architecture, but with the advantage of a distributed architecture not having a single point-of-failure from the V2V architecture. In the V2V2I architecture, the transportation network is broken into zones in which a single vehicle is known as the super vehicle. Only super vehicles are able to communicate with the central infrastructure or with other Super Vehicles, and all other vehicles can only communicate with the super vehicle responsible for the zone in which they are currently traversing. I describe the super vehicle detection (SVD) algorithm for how a vehicle can find or become a super vehicle of a zone and how super vehicles can aggregate the speed and location data from all of the vehicles within their zone to still ensure an accurate representation of the network. I perform an analysis using FreeSim to determine the trade-offs experienced based on the size and number of zones within a transportation network and describe the benefits of the V2V2I architecture over the pure V2I or V2V architectures.


title: DNS to the rescue: discerning content and services in a tangled web

abstract: A careful perusal of the Internet evolution reveals two major trends - explosion of cloud-based services and video streaming applications. In both of the above cases, the owner (e.g., CNN, YouTube, or Zynga) of the content and the organization serving it (e.g., Akamai, Limelight, or Amazon EC2) are decoupled, thus making it harder to understand the association between the content, owner, and the host where the content resides. This has created a tangled world wide web that is very hard to unwind, impairing ISPs' and network administrators' capabilities to control the traffic flowing in their networks. In this paper, we present DN-Hunter, a system that leverages the information provided by DNS traffic to discern the tangle. Parsing through DNS queries, DN-Hunter tags traffic flows with the associated domain name. This association has several applications and reveals a large amount of useful information: (i) Provides a fine-grained traffic visibility even when the traffic is encrypted (i.e., TLS/SSL flows), thus enabling more effective policy controls,(ii) Identifies flows even before the flows begin, thus providing superior network management capabilities to administrators, $(iii)$ Understand and track (over time) different CDNs and cloud providers that host content for a particular resource, (iv) Discern all the services/content hosted by a given CDN or cloud provider in a particular geography and time interval, and (v) Provides insights into all applications/services running on any given layer-4 port number. We conduct extensive experimental analysis and show results from real traffic traces (including FTTH and 4G ISPs) that support our hypothesis. Simply put, the information provided by DNS traffic is one of the key components required for understanding the tangled web, and bringing the ability to effectively manage network traffic back to the operators.


title: Back to Basics: The Role of Agile Principles in Success with an Distributed Scrum Team

abstract: Agile processes rely on feedback and communication to work and they often work best with co-located teams for this reason. Sometimes agile makes sense because of project requirements and a distributed team makes sense because of resource constraints. A distributed team can be productive and fun to work on if the team takes an active role in overcoming the barriers that distribution causes. This is the story of how one product team used creativity, communications tools, and basic good engineering practices to build a successful product.


title: PATTY: A Taxonomy of Relational Patterns with Semantic Types

abstract: This paper presents PATTY: a large resource for textual patterns that denote binary relations between entities. The patterns are semantically typed and organized into a subsumption taxonomy. The PATTY system is based on efficient algorithms for frequent itemset mining and can process Web-scale corpora. It harnesses the rich type system and entity population of large knowledge bases. The PATTY taxonomy comprises 350,569 pattern synsets. Random-sampling-based evaluation shows a pattern accuracy of 84.7%. PATTY has 8,162 subsumptions, with a random-sampling-based precision of 75%. The PATTY resource is freely available for interactive access and download.


title: Soft robotics : Biological inspiration , state of the art , and future research

abstract: Traditional robots have rigid underlying structures that limit their ability to interact with their environment. For example, conventional robot manipulators have rigid links and can manipulate objects using only their specialised end effectors. These robots often encounter difficulties operating in unstructured and highly congested environments. A variety of animals and plants exhibit complex movement with soft structures devoid of rigid components. Muscular hydrostats e.g. octopus arms and elephant trunks are almost entirely composed of muscle and connective tissue and plant cells can change shape when pressurised by osmosis. Researchers have been inspired by biology to design and build soft robots. With a soft structure and redundant degrees of freedom, these robots can be used for delicate tasks in cluttered and/or unstructured environments. This paper discusses the novel capabilities of soft robots, describes examples from nature that provide biological inspiration, surveys the state of the art and outlines existing challenges in soft robot design, modelling, fabrication and control.


title: Weighted K-Nearest Neighbor Classification Algorithm Based on Genetic Algorithm

abstract: K-Nearest Neighbor (KNN) is one of the most popular algorithms for data classification. Many researchers have found that the KNN algorithm accomplishes very good performance in their experiments on different datasets. The traditional KNN text classification algorithm has limitations: calculation complexity, the performance is solely dependent on the training set, and so on. To overcome these limitations, an improved version of KNN is proposed in this paper, we use genetic algorithm combined with weighted KNN to improve its classification performance. and the experiment results shown that our proposed algorithm outperforms the KNN with greater accuracy. DOI: http://dx.doi.org/10.11591/telkomnika.v11i10.2534 Full Text: PDF


Deep Neural Architecture for News Recommendation


title: Sound and Music Recommendation with Knowledge Graphs

abstract: The Web has moved, slowly but steadily, from a collection of documents towards a collection of structured data. Knowledge graphs have then emerged as a way of representing the knowledge encoded in such data as well as a tool to reason on them in order to extract new and implicit information. Knowledge graphs are currently used, for example, to explain search results, to explore knowledge spaces, to semantically enrich textual documents, or to feed knowledge-intensive applications such as recommender systems. In this work, we describe how to create and exploit a knowledge graph to supply a hybrid recommendation engine with information that builds on top of a collections of documents describing musical and sound items. Tags and textual descriptions are exploited to extract and link entities to external graphs such as WordNet and DBpedia, which are in turn used to semantically enrich the initial data. By means of the knowledge graph we build, recommendations are computed using a feature combination hybrid approach. Two explicit graph feature mappings are formulated to obtain meaningful item feature representations able to catch the knowledge embedded in the graph. Those content features are further combined with additional collaborative information deriving from implicit user feedback. An extensive evaluation on historical data is performed over two different datasets: a dataset of sounds composed of tags, textual descriptions, and user’s download information gathered from Freesound.org and a dataset of songs that mixes song textual descriptions with tags and user’s listening habits extracted from Songfacts.com and Last.fm, respectively. Results show significant improvements with respect to state-of-the-art collaborative algorithms in both datasets. In addition, we show how the semantic expansion of the initial descriptions helps in achieving much better recommendation quality in terms of aggregated diversity and novelty.


title: Misresponse to Reversed and Negated Items in Surveys: A Review

abstract: Abstract There are important advantages to including reversed items in questionnaires (e.g., control of acquiescence, disruption of nonsubstantive responding, better coverage of the domain of content of a construct), but reversed items can also lead to measurement problems (e.g., low measure reliability, complex factor structures). The authors advocate the continued use of reversed items in measurement instruments but also argue that they should be used with caution. To help researchers improve their scale construction practices, the authors provide a comprehensive review of the literature on reversed and negated items and offer recommendations about their use in questionnaires. The theoretical discussion is supplemented with data on 1330 items from measurement scales that have appeared in Journal of Marketing Research and Journal of Consumer Research.


title: SEAL Pack versatile, portable, and rapidly deployable SEa, air, and land vehicle

abstract: There are thirteen categories in the Robotic USAR ontology covering land, air, and sea vehicles. We present a robot system that is capable of four of those categories including aerial, terrestrial, and marine locomotion in a single package that is man-portable and low cost. Each mode of locomotion has useful capabilities that the others do not. The land vehicle can travel over 5km with a 500g load. The boat can travel 0.5 km over water or loiter for 140 minutes. The flyer can traverse any terrain for short periods of time. The system packs into a small 33 × 20 × 14 cm package weighing 1 kg. We present design issues and experimental verification.


Employee Turnover Analysis with Application of Data Mining Methods


title: Multipurpose reservoir operation using particle swarm optimization

abstract: This paper presents an efficient and reliable swarm intelligence-based approach, namely elitist-mutated particle swarm opti- mization EMPSO technique, to derive reservoir operation policies for multipurpose reservoir systems. Particle swarm optimizers are inherently distributed algorithms, in which the solution for a problem emerges from the interactions between many simple individuals called particles. In this study the standard particle swarm optimization PSO algorithm is further improved by incorporating a new strategic mechanism called elitist-mutation to improve its performance. The proposed approach is first tested on a hypothetical multireservoir system, used by earlier researchers. EMPSO showed promising results, when compared with other techniques. To show practical utility, EMPSO is then applied to a realistic case study, the Bhadra reservoir system in India, which serves multiple purposes, namely irrigation and hydropower generation. To handle multiple objectives of the problem, a weighted approach is adopted. The results obtained demonstrate that EMPSO is consistently performing better than the standard PSO and genetic algorithm techniques. It is seen that EMPSO is yielding better quality solutions with less number of function evaluations.


title: Machine learning methods from group to crowd behaviour analysis

abstract: The human behaviour analysis has been a subject of study in various fields of science (e.g. sociology, psychology, computer science). Specifically, the automated understanding of the behaviour of both individuals and groups remains a very challenging problem from the sensor systems to artificial intelligence techniques. Being aware of the extent of the topic, the objective of this paper is to review the state of the art focusing on machine learning techniques and computer vision as sensor system to the artificial intelligence techniques. Moreover, a lack of review comparing the level of abstraction in terms of activities duration is found in the literature. In this paper, a review of the methods and techniques based on machine learning to classify group behaviour in sequence of images is presented. The review take into account the different levels of understanding and the number of people in the group.


title: Ingestion, Indexing and Retrieval of High-Velocity Multidimensional Sensor Data on a Single Node

abstract: Multidimensional data are becoming more prevalent, partly due to the rise of the Internet of Things (IoT), and with that the need to ingest and analyze data streams at rates higher than before. Some industrial IoT applications require ingesting millions of records per second, while processing queries on recently ingested and historical data. Unfortunately, existing database systems suited to multidimensional data exhibit low per-node ingestion performance, and even if they can scale horizontally in distributed settings, they require large number of nodes to meet such ingest demands. For this reason, in this paper we evaluate a single-node multidimensional data store for high-velocity sensor data. Its design centers around a two-level indexing structure, wherein the global index is an in-memory R*-tree and the local indices are serialized kd-trees. This study is confined to records with numerical indexing fields and range queries, and covers ingest throughput, query response time, and storage footprint. We show that the adopted design streamlines data ingestion and offers ingress rates two orders of magnitude higher than those of Percona Server, SQLite, and Druid. Our prototype also reports query response times comparable to or better than those of Percona Server and Druid, and compares favorably in terms of storage footprint. In addition, we evaluate a kd-tree partitioning based scheme for grouping incoming streamed data records. Compared to a random scheme, this scheme produces less overlap between groups of streamed records, but contrary to what we expected, such reduced overlap does not translate into better query performance. By contrast, the local indices prove much more beneficial to query performance. We believe the experience reported in this paper is valuable to practitioners and researchers alike interested in building database systems for high-velocity multidimensional data.


title: A component model for dynamic correlations

abstract: We propose a model of dynamic correlations with a short- and long-run component specification, by extending the idea of component models for volatility. We call this class of models DCC-MIDAS. The key ingredients are the Engle (2002) DCC model, the Engle and Lee (1999) component GARCH model replacing the original DCC dynamics with a component specification and the Engle etÂ al. (2006) GARCH-MIDAS specification that allows us to extract a long-run correlation component via mixed data sampling. We provide a comprehensive econometric analysis of the new class of models, and provide extensive empirical evidence that supports the model's specification.


title: FAST - An Automatic Generation System For Grammar Tests

abstract: This paper introduces a method for the semi-automatic generation of grammar test items by applying Natural Language Processing (NLP) techniques. Based on manually-designed patterns, sentences gathered from the Web are transformed into tests on grammaticality. The method involves representing test writing knowledge as test patterns, acquiring authentic sentences on the Web, and applying generation strategies to transform sentences into items. At runtime, sentences are converted into two types of TOEFL-style question: multiple-choice and error detection. We also describe a prototype system FAST (Free Assessment of Structural Tests). Evaluation on a set of generated questions indicates that the proposed method performs satisfactory quality. Our methodology provides a promising approach and offers significant potential for computer assisted language learning and assessment.


title: Working principles, behavior, and performance of MOEAs on MNK-landscapes

abstract: This work studies the working principles, behavior, and performance of multiobjective evolutionary algorithms (MOEAs) on multiobjective epistatic fitness functions with discrete binary search spaces by using MNK-landscapes. First, we analyze the structure and some of the properties of MNK-landscapes under a multiobjective perspective by using enumeration on small landscapes. Then, we focus on the performance and behavior of MOEAs on large landscapes. We organize our study around selection, drift, mutation, and recombination, the four major and intertwined processes that drive adaptive evolution over fitness landscapes. This work clearly shows pros and cons of the main features of MOEAs, gives a valuable guide for the practitioner on how to set up his/her algorithm, enhance MOEAs, and presents useful insights on how to design more robust and efficient MOEAs.