title: An approach for selecting seed URLs of focused crawler based on user-interest ontology

abstract: Seed URLs selection for focused Web crawler intends to guide related and valuable information that meets a user's personal information requirement and provide more effective information retrieval. In this paper, we propose a seed URLs selection approach based on user-interest ontology. In order to enrich semantic query, we first intend to apply Formal Concept Analysis to construct user-interest concept lattice with user log profile. By using concept lattice merger, we construct the user-interest ontology which can describe the implicit concepts and relationships between them more appropriately for semantic representation and query match. On the other hand, we make full use of the user-interest ontology for extracting the user interest topic area and expanding user queries to receive the most related pages as seed URLs, which is an entrance of the focused crawler. In particular, we focus on how to refine the user topic area using the bipartite directed graph. The experiment proves that the user-interest ontology can be achieved effectively by merging concept lattices and that our proposed approach can select high quality seed URLs collection and improve the average precision of focused Web crawler.


title: The Cinderella of psychology: the neglect of motor control in the science of mental life and behavior.

abstract: One would expect psychology--the science of mental life and behavior--to place great emphasis on the means by which mental life is behaviorally expressed. Surprisingly, however, the study of how decisions are enacted--the focus of motor control research--has received little attention in psychology. This article documents the neglect and considers possible reasons for it. The hypotheses considered include three that are raised and then rejected: (a) no famous psychologists have studied motor control, (b) cognitive psychologists are mainly interested in uniquely human functions, and (c) motor control is simply too hard to study. Three other hypotheses are more viable: (d) cognitive psychologists have been more interested in epistemology than in action, (e) psychologists have disfavored motor control because overt responses were the only admissible measure in behaviorism, and (f) psychologists have felt that neuroscientists have the market cornered when it comes to motor control research. There are signs that motor control's Cinderella status is changing.


title: Big Data and Specific Analysis Methods for Insurance Fraud Detection

abstract: Analytics is the future of big data because only transforming data into information gives them value and can turn data in business in competitive advantage. Large data volumes, their variety and the increasing speed their growth, stretch the boundaries of traditional data warehouses and ETL tools. This paper investigates the benefits of Big Data technology and main methods of analysis that can be applied to the particular case of fraud detection in public health insurance system in Romania.


title: Wireless M-Bus sensor nodes in smart water grids: The energy issue

abstract: Smart Metering is one of the key issues in modern energy efficiency technologies. Several efforts have been recently made in developing suitable communication protocols for metering data management and transmission, and the Metering-Bus (M-Bus) is a relevant standard example, with a wide diffusion in the European market. This paper deals with its wireless evolution, namely Wireless M-Bus (WM-Bus), and in particular looks at it from the energy consumption perspective. Indeed, specially in those applicative scenarios where the grid powering is not available, like in water and gas metering settings, it is fundamental to guarantee the sustainability of the meter itself, by means of long-life batteries or suitable energy harvesting technologies. The present work analyzes all these aspects directly referring to a specific HW/SW implementation of the WM-Bus variants, providing some useful guidelines for its application in the smart water grid context.


title: Cross-Domain Face Verification: Matching ID Document and Self-Portrait Photographs

abstract: Cross-domain biometrics has been emerging as a new necessity, which poses several additional challenges, including harsh illumination changes, noise, pose variation, among others. In this paper, we explore approaches to cross-domain face verification, comparing self-portrait photographs ("selfies") to ID documents. We approach the problem with proper image photometric adjustment and data standardization techniques, along with deep learning methods to extract the most prominent features from the data, reducing the effects of domain shift in this problem. We validate the methods using a novel dataset comprising 50 individuals. The obtained results are promising and indicate that the adopted path is worth further investigation.


title: Crystalline methamphetamine use and methamphetamine‐related harms in Australia

abstract: INTRODUCTION AND AIMS ::: Concerns about crystal methamphetamine use and harm have increased in multiple countries. This paper describes how changes in the availability and use of crystal methamphetamine have impacted on methamphetamine-related harms in Australia. ::: ::: ::: DESIGN AND METHODS ::: Data on methamphetamine use were obtained from population-level surveys, health service data and surveys of drug use among sentinel groups of ecstasy users and people who inject drugs. Data were obtained on seizures, arrests, clandestine laboratory detections, hospital separations, mental health unit admissions, drug telephone helpline calls and drug treatment episodes. Segmented linear regression models were fitted to identify changes in these series using log-transformed data where appropriate. ::: ::: ::: RESULTS ::: The availability of crystal methamphetamine has increased as evidenced by increased laboratory detections, domestic seizures and purity of the seized drug. Population surveys do not report an increase in the number of people who used at least once in the past year. However, more users report using crystal methamphetamine rather than lower-purity powder methamphetamine and more regular use. Indicators of methamphetamine-related harms have increased in parallel with this change. Amphetamine-related helpline calls, drug treatment, arrests and hospital admissions for amphetamine disorders and psychosis all peaked in the mid-2000s, declined for several years and have increased steeply since 2010. ::: ::: ::: DISCUSSION AND CONCLUSIONS ::: The increased availability and use of crystal methamphetamine have been associated with increased regular use and harms. Treatment is required for those experiencing problems and the capacity of health services to provide care needs to be enhanced.[Degenhardt L, Sara G, Connor JP, McKetin R, Roxburgh A, Dobbins T, Farrell M, Burns L, Hall WD. Crystalline methamphetamine use and methamphetamine-related harms in Australia. Drug Alcohol Rev 2017;36:160-170].


A System to Verify Network Behavior of Known Cryptographic Clients.


title: Constructing and Analyzing Criminal Networks

abstract: Analysis of criminal social graph structures can enable us to gain valuable insights into how these communities are organized. Such as, how large scale and centralized these criminal communities are currently? While these types of analysis have been completed in the past, we wanted to explore how to construct a large scale social graph from a smaller set of leaked data that included only the criminal's email addresses. We begin our analysis by constructing a 43 thousand node social graph from one thousand publicly leaked criminals' email addresses. This is done by locating Facebook profiles that are linked to these same email addresses and scraping the public social graph from these profiles. We then perform a large scale analysis of this social graph to identify profiles of high rank criminals, criminal organizations and large scale communities of criminals. Finally, we perform a manual analysis of these profiles that results in the identification of many criminally focused public groups on Facebook. This analysis demonstrates the amount of information that can be gathered by using limited data leaks.


title: Paleolimnological assessment of human-induced impacts on Walden Pond (Massachusetts, USA) using diatoms and stable isotopes

abstract: Abstract Multi-proxy analysis of a sediment core spanning 1600 years from Walden Pond, Massachusetts (USA), reveals substantial changes in the nutrient status over the past ∼250 years resulting from anthropogenic impacts on the lake and watershed. Following a period of environmental stability from about 430 AD to 1750 AD, the abundance of the diatom Cyclotella stelligera increased, the chrysophyte cyst to diatom ratio decreased, organic content declined, bulk organic δ13C decreased, and bulk organic δ15N increased. These changes coincided with logging in the watershed, and are mainly attributed to an increase in detrital input of inorganic sediment and delivery of dissolved soil decomposition products from the watershed. With the beginning of intensive recreational development of Walden Pond in the early 20th century, oligotrophic diatom species were largely replaced by disturbance indicators and the diatom-inferred lake pH increased by 0.5 units, while the bulk organic carbon and nitrogen stable isotope ...


title: The Impact of Computer Self-Efficacy and Technology Dependence on Computer-Related Technostress: A Social Cognitive Theory Perspective

abstract: Professionals and end users of computers often experience being constantly surrounded by modern technology. One side effect of modern technology is termed technostress, which refers to the “negative impact on attitudes, thoughts, behaviors, or body physiology that is caused either directly or indirectly by technology” (Well and Rosen, 1997). Based on social cognitive theory, this study developed a conceptual model in which computer-related technostress was studied as consequences of computer self-efficacy and technology dependence. Results show that (a) employees with higher level of computer self-efficacy have lower level of computer-related technostress, (b) employees with higher level of technology dependence have higher level of computer-related technostress, and (c) employees under different individual situations may perceive different levels of technostress. Contributions of this research and implications for theory and managerial practice are also discussed.


© Asia-Pacific Society for Computers in Education SUBSTANTIVE VALIDITY OF A SIMULATION-BASED GAME


title: From experimental design to validated hits a comprehensive walk-through of fragment lead identification using surface plasmon resonance.

abstract: The detection and characterization of fragment binding requires the use of technologies with extreme sensitivity to observe the binding interactions of low-affinity and low-molecular weight compounds to proteins. A number of methods have emerged capable of providing fragment hits to project teams including, but certainly not limited to, NMR, X-ray crystallography, and surface plasmon resonance (SPR). SPR-based biosensors are sufficiently sensitive and high throughput to provide complete fragment screens on libraries of several thousand compounds in just a few weeks per target. Biosensors provide quantitative binding information for ranking fragments by affinity and ligand efficiency and can support ongoing quantitative structure-activity efforts during fragment hit-to-lead development. The combination of speed and binding quantitation makes SPR a valuable technology in pharmaceutical fragment-based drug discovery and development. Successful implementation of SPR biosensors in fragment efforts requires specialized methods for instrument preparation, assay development, primary compound handling, primary screening, confirmation testing, and data analysis. In this chapter, each of these topics is discussed in detail with general best practices for maintaining the highest throughput while maximizing data quality.


title: FastSLAM 2.0: An Improved Particle Filtering Algorithm for Simultaneous Localization and Mapping that Provably Converges

abstract: Proceedings of IJCAI 2003 In [15], Montemerlo et al. proposed an algorithm called FastSLAM as an efficient and robust solution to the simultaneous localization and mapping problem. This paper describes a modified version of FastSLAM which overcomes important deficiencies of the original algorithm. We prove convergence of this new algorithm for linear SLAM problems and provide real-world experimental results that illustrate an order of magnitude improvement in accuracy over the original FastSLAM algorithm.


title: X-Stream: edge-centric graph processing using streaming partitions

abstract: X-Stream is a system for processing both in-memory and out-of-core graphs on a single shared-memory machine. While retaining the scatter-gather programming model with state stored in the vertices, X-Stream is novel in (i) using an edge-centric rather than a vertex-centric implementation of this model, and (ii) streaming completely unordered edge lists rather than performing random access. This design is motivated by the fact that sequential bandwidth for all storage media (main memory, SSD, and magnetic disk) is substantially larger than random access bandwidth. We demonstrate that a large number of graph algorithms can be expressed using the edge-centric scatter-gather model. The resulting implementations scale well in terms of number of cores, in terms of number of I/O devices, and across different storage media. X-Stream competes favorably with existing systems for graph processing. Besides sequential access, we identify as one of the main contributors to better performance the fact that X-Stream does not need to sort edge lists during preprocessing.


title: Broadcasting safety information in vehicular networks: issues and approaches

abstract: A primary goal of intelligent transportation systems is to improve road safety. The ability of vehicles to communicate is a promising way to alleviate traffic accidents by reducing the response time associated with human reaction to nearby drivers. Vehicle mobility patterns caused by varying traffic dynamics and travel behavior lead to considerable complexity in the efficiency and reliability of vehicular communication networks. This causes two major routing issues: the broadcast storm problem and the network disconnection problem. In this article we review broadcast communication in vehicular communication networks and mechanisms to alleviate the broadcast storm problem. Moreover, we introduce vehicular safety applications, discuss network design considerations, and characterize broadcast protocols in vehicular networks.


title: Computing Productivity: Firm-Level Evidence

abstract: We explore the effect of computerization on productivity and output growth using data from 527 large U.S. firms over 19871994. We find that computerization makes a contribution to measured productivity and output growth in the short term (using 1-year differences) that is consistent with normal returns to computer investments. However, the productivity and output contributions associated with computerization are up to 5 times greater over long periods (using 5- to 7-year differences). The results suggest that the observed contribution of computerization is accompanied by relatively large and time-consuming investments in complementary inputs, such as organizational capital, that may be omitted in conventional calculations of productivity. The large long-run contribution of computers and their associated complements that we uncover may partially explain the subsequent investment surge in computers in the late 1990s.


Humans and Automation : Use , Misuse , Disuse ,


title: Intelligent refrigerator using ARTIFICIAL INTELLIGENCE

abstract: The task to discover useful systems (electronic home appliances) to simplify our lives, is one of the main purpose of intelligent systems. Intelligent appliances with multimedia capability have been emerging into our daily life. Thanks to the fast advance of computing technology. Smart home is one of the most prominent areas of intelligent appliances. Kitchen is one of the places where such intelligent appliances have been used. At the same time it has been a practice for all of us to use the junk foods, or too old vegetables and fruits in our daily life due to the busy scheduled life that we lead. Or some possibilities where we do not remember the vegetables/fruits we have not been eating for a long time. This in turn leads to lack of nutrition or, some hazardous infections or might disturb the imunity power in each of us. Why don't we come across an attempt to have a device to indicate what we have (food/vegetables/fruits) and how old is it. Of course it is not possible for us to determine the exact age of the vegetable, since it is to be calculated from the day it is grown, but yet we could come across one small effort to at-least have an eye on how old is the vegetable from the day it was fetched from the vendor. Existing systems used barcode or RFID scanning to keep track of the stock. The products currently available are expensive as the user has to purchase the whole refrigerator. The Intelligent Refrigerator module is designed to convert any existing refrigerator into an intelligent cost effective appliance using ARTIFICIAL INTELLIGENCE. The intelligent refrigerator is capable of sensing and monitoring its contents and counts the age of the contents. The smart refrigerator is also able to remotely notify the user about old products via SMS (Short Message Service). Additional functionality includes indication of items we have not been eating for long time. The core functionality of the intelligent refrigerator is to maintain, with minimum effort, an inventory list of food items which might want to be purchased as soon as they turn too old. As a result, the user is notified every day about the count and age of vegetables, and the unloaded vegetables.


title: Expert model for detection of epileptic activity in EEG signature

abstract: Seizure detection and classification using signal processing methods has been an important issue of research for the last two decades. In the present study, a novel scheme was presented to detect epileptic seizure activity with very fast and high accuracy from background electro encephalogram (EEG) data recorded from epileptic and normal subjects. The proposed scheme is based on discrete wavelet transform (DWT) and energy estimation at each node of the decomposition tree followed by application of probabilistic neural network (PNN) for classification. Normal as well as epileptic EEG epochs were decomposed into approximation and details coefficients till the sixth-level using DWT. Approximate energy (EDA) values of the wavelet coefficients at all nodes of the down sampled tree were used as a feature vector to characterize the predictability of the epileptic activity within the records of EEG data. In order to demonstrate the classification accuracy of the proposed probabilistic neural network, tenfold cross-validation was implemented in the expert model. Clinical EEG data recorded from normal as well as epileptic subjects were used to test the performance of this new scheme. It was found that with the proposed scheme, the detection is 99.33% accurate with sensitivity and specificity as 99.6% and 99%, respectively. The proposed model can be widely used in developing countries where there is an acute shortage of trained neurologist.


title: OMWEdit - The Integrated Open Multilingual Wordnet Editing System

abstract: Wordnets play a central role in many natural language processing tasks. This paper introduces a multilingual editing system for the Open Multilingual Wordnet (OMW: Bond and Foster, 2013). Wordnet development, like most lexicographic tasks, is slow and expensive. Moving away from the original Princeton Wordnet (Fellbaum, 1998) development workflow, wordnet creation and expansion has increasingly been shifting towards an automated and/or interactive system facilitated task. In the particular case of human edition/expansion of wordnets, a few systems have been developed to aid the lexicographers’ work. Unfortunately, most of these tools have either restricted licenses, or have been designed with a particular language in mind. We present a webbased system that is capable of multilingual browsing and editing for any of the hundreds of languages made available by the OMW. All tools and guidelines are freely available under an open license.


title: Graph Based Over-Segmentation Methods for 3D Point Clouds

abstract: Abstract Over-segmentation, or super-pixel generation, is a common preliminary stage for many computer vision applications. New acquisition technologies enable the capturing of 3D point clouds that contain color and geometrical information. This 3D information can be utilized to improve the results of over-segmentation, which uses mainly color information, and to generate clusters of points we call super-points. We consider a variety of possible 3D extensions of the Local Variation (LV) graph based over-segmentation algorithms, and compare them thoroughly. We consider different alternatives for constructing the connectivity graph, for assigning the edge weights, and for defining the merge criterion, which must now account for the geometric information and not only color. Following this evaluation, we derive a new generic algorithm for over-segmentation of 3D point clouds. We call this new algorithm Point Cloud Local Variation (PCLV). The advantages of the new over-segmentation algorithm are demonstrated on both outdoor and cluttered indoor scenes. Performance analysis of the proposed approach compared to state-of-the-art 2D and 3D over-segmentation algorithms shows significant improvement according to the common performance measures.


title: Vision-Based Driver-Assistance Systems

abstract: This chapter outlines the general context of the book. Autonomous driving is still at a stage where drivers are expected to be in control of the vehicle at all times, but provided automated control features of the vehicle (based on input data generated by different sensors) already enhance safety and driver comfort. We especially consider automated control features possible by using camera data.


title: A meta-learning prediction model of algorithm performance for continuous optimization problems

abstract: Algorithm selection and configuration is a challenging problem in the continuous optimization domain. An approach to tackle this problem is to develop a model that links landscape analysis measures and algorithm parameters to performance. This model can be then used to predict algorithm performance when a new optimization problem is presented. In this paper, we investigate the use of a machine learning framework to build such a model. We demonstrate the effectiveness of our technique using CMA-ES as a representative algorithm and a feed-forward backpropagation neural network as the learning strategy. Our experimental results show that we can build sufficiently accurate predictions of an algorithm's expected performance. This information is used to rank the algorithm parameter settings based on the current problem instance, hence increasing the probability of selecting the best configuration for a new problem.


title: Touch-less fingerphoto feature extraction, analysis and matching using monogenic wavelets

abstract: Despite the wide use and success of touch-based fingerprint identification, elastic deformation is one of the primary reasons for false non-matching in most of the minutiae based automatic fingerprint identification systems. The main cause behind the large variation of the intra-class matching score is due to the non-uniform pressure applied during acquisition of fingerprint image. To avail deformation-free acquisition and other advantages of hygiene and user convenience, touch-less 2D fingerprint (fingerphoto) matching systems have been gaining popularity. The already existing fingerprint matchers are not suitable for extracting reliable minutiae features from fingerphoto images due to varying illumination, contrast, and magnification which occur while capturing the fingerphoto. In order to make fingerphoto compatible for such matchers, effective fingerphoto enhancement is crucial. In this paper, we propose a fingerphoto based recognition system which comprises a novel monogenic wavelet based fingerphoto enhancement technique that uses phase congruency features. For the performance verification of our system, we created a fingerphoto database using a smartphone camera in an unconstrained environmental condition along with its corresponding live-scan images. The experimental results on this database, as well as on other standard fingerphoto databases show a significant improvement in Equal Error Rates (EER) achieved using the proposed system.


title: Joint head pose and facial landmark regression from depth images

abstract: This paper presents a joint head pose and facial landmark regression method with input from depth images for realtime application. Our main contributions are: firstly, a joint optimization method to estimate head pose and facial landmarks, i.e., the pose regression result provides supervised initialization for cascaded facial landmark regression, while the regression result for the facial landmarks can also help to further refine the head pose at each stage. Secondly, we classify the head pose space into 9 sub-spaces, and then use a cascaded random forest with a global shape constraint for training facial landmarks in each specific space. This classification-guided method can effectively handle the problem of large pose changes and occlusion. Lastly, we have built a 3D face database containing 73 subjects, each with 14 expressions in various head poses. Experiments on challenging databases show our method achieves state-of-the-art performance on both head pose estimation and facial landmark regression.


title: A Review of Statistical Approaches to Level Set Segmentation: Integrating Color, Texture, Motion and Shape

abstract: Since their introduction as a means of front propagation and their first application to edge-based segmentation in the early 90's, level set methods have become increasingly popular as a general framework for image segmentation. In this paper, we present a survey of a specific class of region-based level set segmentation methods and clarify how they can all be derived from a common statistical framework. ::: ::: Region-based segmentation schemes aim at partitioning the image domain by progressively fitting statistical models to the intensity, color, texture or motion in each of a set of regions. In contrast to edge-based schemes such as the classical Snakes, region-based methods tend to be less sensitive to noise. For typical images, the respective cost functionals tend to have less local minima which makes them particularly well-suited for local optimization methods such as the level set method. ::: ::: We detail a general statistical formulation for level set segmentation. Subsequently, we clarify how the integration of various low level criteria leads to a set of cost functionals. We point out relations between the different segmentation schemes. In experimental results, we demonstrate how the level set function is driven to partition the image plane into domains of coherent color, texture, dynamic texture or motion. Moreover, the Bayesian formulation allows to introduce prior shape knowledge into the level set method. We briefly review a number of advances in this domain.


title: Compiler-assisted data distribution for chip multiprocessors

abstract: Data access latency, a limiting factor in the performance of chip multiprocessors, grows significantly with the number of cores in non-uniform cache architectures with distributed cache banks. To mitigate this effect, it is necessary to leverage the data access locality and choose an optimum data placement. Achieving this is especially challenging when other constraints such as cache capacity, coherence messages and runtime overhead need to be considered. This paper presents a compiler-based approach used for analyzing data access behavior in multi-threaded applications. The proposed experimental compiler framework employs novel compilation techniques to discover and represent multi-threaded memory access patterns (MMAPs). At run time, symbolic MMAPs are resolved and used by a partitioning algorithm to choose a partition of allocated memory blocks among the forked threads in the analyzed application. This partition is used to enforce data ownership by associating the data with the core that executes the thread owning the data. We demonstrate how this information can be used in an experimental architecture to accelerate applications. In particular, our compiler assisted approach shows a 20% speedup over shared caching and 5% speedup over the closest runtime approximation, "first touch".


Understanding and Predicting the Usefulness of Yelp Reviews


title: Task-based Authorization Controls (TBAC): A Family of Models for Active and Enterprise-oriented Authorization Management

abstract: In this paper, we develop a new paradigm for access control and authorization management, called task-based authorization controls (TBAC). TBAC models access controls from a task-oriented perspective than the traditional subject-object one. Access mediation now involves authorizations at various points during the completion of tasks in accordance with some application logic. By taking a task-oriented view of access control and authorizations, TBAC lays the foundation for research into a new breed of “active” security models that are required for agent-based distributed computing and workflow management.


title: Cross-Modal Retrieval With CNN Visual Features: A New Baseline

abstract: Recently, convolutional neural network (CNN) visual features have demonstrated their powerful ability as a universal representation for various recognition tasks. In this paper, cross-modal retrieval with CNN visual features is implemented with several classic methods. Specifically, off-the-shelf CNN visual features are extracted from the CNN model, which is pretrained on ImageNet with more than one million images from 1000 object categories, as a generic image representation to tackle cross-modal retrieval. To further enhance the representational ability of CNN visual features, based on the pretrained CNN model on ImageNet, a fine-tuning step is performed by using the open source Caffe CNN library for each target data set. Besides, we propose a deep semantic matching method to address the cross-modal retrieval problem with respect to samples which are annotated with one or multiple labels. Extensive experiments on five popular publicly available data sets well demonstrate the superiority of CNN visual features for cross-modal retrieval.


title: A GW Level High-Power Radial Line Helical Array Antenna

abstract: High-power radial line helical array antenna is a novel antenna developed to realize directional radiation of high-power microwaves. This antenna directly accepts an azimuthally symmetric output of a high-power microwave source and radiates circularly polarized microwave with a boresight peak. It has a high efficiency, a compact structure, and a notable power-handling capacity. Starting from its fundamental design theory, an antenna prototype consisting of 48 elements is proposed. Numerical simulation and experimental measurement are accomplished for the prototype. In the range of 3.7-4.1 GHz, the experimental result shows an antenna VSWR of below 1.2, an antenna gain of over 23.2 dB, an aperture efficiency of more than 65%, and an antenna axial ratio of below 1.6 dB. In addition, the high-power experiment proves that this antenna has a power-handling capacity of more than 1 GW.


title: Im2Sketch: Sketch Generation by Unconflicted Perceptual Grouping

abstract: Abstract Effectively solving the problem of sketch generation, which aims to produce human-drawing-like sketches from real photographs, opens the door for many vision applications such as sketch-based image retrieval and non-photorealistic rendering. In this paper, we approach automatic sketch generation from a human visual perception perspective. Instead of gathering insights from photographs, for the first time, we extract information from a large pool of human sketches. In particular, we study how multiple Gestalt rules can be encapsulated into a unified perceptual grouping framework for sketch generation. We further show that by solving the problem of Gestalt confliction, i.e., encoding the relative importance of each rule, more similar to human-made sketches can be generated. For that, we release a manually labeled sketch dataset of 96 object categories and 7680 sketches. A novel evaluation framework is proposed to quantify human likeness of machine-generated sketches by examining how well they can be classified using models trained from human data. Finally, we demonstrate the superiority of our sketches under the practical application of sketch-based image retrieval.


title: Training Constrained Deconvolutional Networks for Road Scene Semantic Segmentation

abstract: In this work we investigate the problem of road scene semantic segmentation using Deconvolutional Networks (DNs). Several constraints limit the practical performance of DNs in this context: firstly, the paucity of existing pixel-wise labelled training data, and secondly, the memory constraints of embedded hardware, which rule out the practical use of state-of-the-art DN architectures such as fully convolutional networks (FCN). To address the first constraint, we introduce a Multi-Domain Road Scene Semantic Segmentation (MDRS3) dataset, aggregating data from six existing densely and sparsely labelled datasets for training our models, and two existing, separate datasets for testing their generalisation performance. We show that, while MDRS3 offers a greater volume and variety of data, end-to-end training of a memory efficient DN does not yield satisfactory performance. We propose a new training strategy to overcome this, based on (i) the creation of a best-possible source network (S-Net) from the aggregated data, ignoring time and memory constraints; and (ii) the transfer of knowledge from S-Net to the memory-efficient target network (T-Net). We evaluate different techniques for S-Net creation and T-Net transferral, and demonstrate that training a constrained deconvolutional network in this manner can unlock better performance than existing training approaches. Specifically, we show that a target network can be trained to achieve improved accuracy versus an FCN despite using less than 1\% of the memory. We believe that our approach can be useful beyond automotive scenarios where labelled data is similarly scarce or fragmented and where practical constraints exist on the desired model size. We make available our network models and aggregated multi-domain dataset for reproducibility.


title: WaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Images

abstract: This letter reports on WaterGAN, a generative adversarial network (GAN) for generating realistic underwater images from in-air image and depth pairings in an unsupervised pipeline used for color correction of monocular underwater images. Cameras onboard autonomous and remotely operated vehicles can capture high-resolution images to map the seafloor; however, underwater image formation is subject to the complex process of light propagation through the water column. The raw images retrieved are characteristically different than images taken in air due to effects, such as absorption and scattering, which cause attenuation of light at different rates for different wavelengths. While this physical process is well described theoretically, the model depends on many parameters intrinsic to the water column as well as the structure of the scene. These factors make recovery of these parameters difficult without simplifying assumptions or field calibration; hence, restoration of underwater images is a nontrivial problem. Deep learning has demonstrated great success in modeling complex nonlinear systems but requires a large amount of training data, which is difficult to compile in deep sea environments. Using WaterGAN, we generate a large training dataset of corresponding depth, in-air color images, and realistic underwater images. These data serve as input to a two-stage network for color correction of monocular underwater images. Our proposed pipeline is validated with testing on real data collected from both a pure water test tank and from underwater surveys collected in the field. Source code, sample datasets, and pretrained models are made publicly available.


title: MEMS based IMU for tilting measurement: Comparison of complementary and kalman filter based data fusion

abstract: This research investigates real time tilting measurement using Micro-Electro-Mechanical-system (MEMS) based inertial measurement unit (IMU). Accelerometers suffer from errors caused by external accelerations that sums to gravity and make accelerometers based tilting sensing unreliable and inaccurate. Gyroscopes can offset such drawbacks but have data drifting problems. This paper presents a study on complementary and Kalman filter for tilting measurement using MEMS based IMU. The complementary filter algorithm uses low-pass filter and high-pass filter to deal with the data from accelerometer and gyroscope while Kalman filter takes the tilting angle and gyroscope bias as system states, combining the angle derived from the accelerometer to estimate the tilting angle. The study carried out both static and dynamic experiments. The results showed that both Complementary and Kalman filter were less sensitive to variations and almost no signal coupling phenomenon and able to obtain smooth and accurate results.


title: CT Imaging Findings of Ruptured Ovarian Endometriotic Cysts: Emphasis on the Differential Diagnosis with Ruptured Ovarian Functional Cysts

abstract: Objective: The purpose of this study is to assess the prevalence of abnormal CT fi ndings in patients with surgically proven ruptured endometriotic cysts, as compared with those abnormal CT fi ndings of ruptured ovarian functional cysts. Materials and Methods: This study included 13 retrospectively identified patients with surgically confirmed ruptured ovarian endometriotic cysts and who had also undergone preoperative CT scanning during the previous seven years. As a comparative group, 25 cases of surgically confi rmed ruptured ovarian functional cysts were included. We assessed the morphologic features of the cysts and the ancillary fi ndings based on CT. Results: For the endometriotic cysts, the mean maximum cyst diameter was signifi cantly larger than that of the functional cysts (70.1 mm versus 36.4 mm, respectively, p < 0.05). The endometriotic cysts frequently had a multilocular shape and a thicker cyst wall, as compared to that of functional cysts, and these differences were statistically signifi cant. Among the ancillary fi ndings, endometriotic cysts showed a signifi cantly higher prevalence of loculated ascites, ascites confi ned to the pelvic cavity without extension to the upper abdomen, and peritoneal strandings and infi ltrations (p < 0.05). Although 11 of the 25 cases of functional cysts showed active extravasation of contrast material at the ovarian bleeding site, only one of 13 cases of endometriotic cysts showed active extravasation. Conclusion: The diagnosis of ruptured endometriotic cyst should be suspected for a woman in whom CT reveals the presence of multilocular or bilateral ovarian cysts with a thick wall and loculated ascites confi ned to the pelvic cavity with pelvic fat infi ltrations. Index terms: Endometriosis; Ovary; Cysts; Computed tomograpy (CT); Comparative studies; Pelvis


title: Wind farm wake modeling using NWTC design codes

abstract: Wind turbines located in a wind farm are subject to a wind field that is substantially modified when compared to the ambient wind field due to wake effects. It is clear that in addition to the powe...


title: The Past, Present, and Future of Artificial Life

abstract: For millennia people have wondered what makes the living different from the non-living. Beginning in the mid-1980s, artificial life has studied living systems using a synthetic approach: build life in order to understand it better, be it by means of software, hardware, or wetware. This review provides a summary of the advances that led to the development of artificial life, its current research topics, and open problems and opportunities. We classify artificial life research into fourteen themes: origins of life, autonomy, self-organization, adaptation (including evolution, development, and learning), ecology, artificial societies, behavior, computational biology, artificial chemistries, information, living technology, art, and philosophy. Being interdisciplinary, artificial life seems to be losing its boundaries and merging with other fields.


title: Tet-Mediated Formation of 5-Hydroxymethylcytosine in RNA

abstract: Oxidation of 5-methylcytosine in DNA by ten-eleven translocation (Tet) family of enzymes has been demonstrated to play a significant role in epigenetic regulation in mammals. We found that Tet enzymes also possess the activity of catalyzing the formation of 5-hydroxymethylcytidine (5-hmrC) in RNA in vitro. In addition, the catalytic domains of all three Tet enzymes as well as full-length Tet3 could induce the formation of 5-hmrC in human cells. Moreover, 5-hmrC was present at appreciable levels (∼1 per 5000 5-methylcytidine) in RNA of mammalian cells and tissues. Our results suggest the involvement of this oxidation in RNA biology.


title: Learning to Optimize Tensor Programs

abstract: We introduce a learning-based framework to optimize tensor programs for deep learning workloads. Efficient implementations of tensor operators, such as matrix multiplication and high dimensional convolution, are key enablers of effective deep learning systems. However, existing systems rely on manually optimized libraries such as cuDNN where only a narrow range of server class GPUs are well-supported. The reliance on hardware-specific operator libraries limits the applicability of high-level graph optimizations and incurs significant engineering costs when deploying to new hardware targets. We use learning to remove this engineering burden. We learn domain-specific statistical cost models to guide the search of tensor operator implementations over billions of possible program variants. We further accelerate the search by effective model transfer across workloads. Experimental results show that our framework delivers performance competitive with state-of-the-art hand-tuned libraries for low-power CPU, mobile GPU, and server-class GPU.


title: Security Vulnerabilities Against Fingerprint Biometric System

abstract: The biometric system is an automatic identification and authentication system that uses unique biological traits, such as fingerprint, face, iris, voice, retina, etc. of an individual. Of all these systems, fingerprint biometric system is the most widely used because of its low cost, high matching speed, and relatively high matching accuracy. Due to the high efficiency of fingerprint biometric system in verifying a legitimate user, numerous government and private organizations are using this system for security purpose. This paper provides an overview of the fingerprint biometric system and gives details about various current security aspects related to the system. The security concerns that we address include multiple attacks on the system, associated threat models, biometric cryptosystems, current issues, challenges, opportunities, and open problems that exist in present day fingerprint biometric systems


title: Sentence Modeling via Multiple Word Embeddings and Multi-level Comparison for Semantic Textual Similarity

abstract: Abstract Recently, using a pretrained word embedding to represent words achieves success in many natural language processing tasks. According to objective functions, different word embedding models capture different aspects of linguistic properties. However, the Semantic Textual Similarity task, which evaluates similarity/relation between two sentences, requires to take into account of these linguistic aspects. Therefore, this research aims to encode various characteristics from multiple sets of word embeddings into one embedding and then learn similarity/relation between sentences via this novel embedding. Representing each word by multiple word embeddings, the proposed MaxLSTM-CNN encoder generates a novel sentence embedding. We then learn the similarity/relation between our sentence embeddings via Multi-level comparison. Our method M-MaxLSTM-CNN consistently shows strong performances in several tasks (i.e., measure textual similarity, identify paraphrase, recognize textual entailment). Our model does not use hand-crafted features (e.g., alignment features, Ngram overlaps, dependency features) as well as does not require pre-trained word embeddings to have the same dimension.


title: On Learning High Dimensional Structured Single Index Models

abstract: Single Index Models (SIMs) are simple yet flexible semi-parametric models for classification and regression, where response variables are modeled as a nonlinear, monotonic function of a linear combination of features. Estimation in this context requires learning both the feature weights and the nonlinear function that relates features to observations. While methods have been described to learn SIMs in the low dimensional regime, a method that can efficiently learn SIMs in high dimensions, and under general structural assumptions, has not been forthcoming. In this paper, we propose computationally efficient algorithms for SIM inference in high dimensions using atomic norm regularization. This general approach to imposing structure in high-dimensional modeling specializes to sparsity, group sparsity, and low-rank assumptions among others. We also provide a scalable, stochastic version of the method. Experiments show that the method we propose enjoys superior predictive performance when compared to generalized linear models such as logistic regression, on several real-world datasets.


title: Method engineering: engineering of information systems development methods and tools

abstract: This paper proposes the term method engineering for the research field of the construction of information systems development methods and tools. Some research issues in method engineering are identified. One major research topic in method engineering is discussed in depth: situational methods, i.e. the configuration of a project approach that is tuned to the project at hand. A language and support tool for the engineering of situational methods are discussed.


title: Complex Embeddings for Simple Link Prediction

abstract: In statistical relational learning, the link prediction problem is key to automatically understand the structure of large knowledge bases. As in previous studies, we propose to solve this problem through latent factorization. However, here we make use of complex valued embeddings. The composition of complex embeddings can handle a large variety of binary relations, among them symmetric and antisymmetric relations. Compared to state-of-the-art models such as Neural Tensor Network and Holographic Embeddings, our approach based on complex embeddings is arguably simpler, as it only uses the Hermitian dot product, the complex counterpart of the standard dot product between real vectors. Our approach is scalable to large datasets as it remains linear in both space and time, while consistently outperforming alternative approaches on standard link prediction benchmarks.


title: Transformational Leadership and Organizational Innovation: The Roles of Internal and External Support for Innovation

abstract: Leadership has been suggested to be an important factor affecting innovation. A number of studies have shown that transformational leadership positively influences organizational innovation. However, there is a lack of studies examining the contextual conditions under which this effect occurs or is augmented. Therefore, this study aimed to investigate the impact of transformational leadership on organizational innovation, and to determine whether internal and external support for innovation as contextual conditions influence this effect. Organizational innovation was conceptualized as the tendency of the organization to develop new or improved products or services and its success in bringing those products or services to the market. Transformational leadership was hypothesized to have a positive influence on organizational innovation. Furthermore, this effect was proposed to be moderated by internal support for innovation, which refers to an innovation supporting climate and adequate resources allocated to innovation. Support received from external organizations for the purposes of knowledge and resource acquisition was also proposed to moderate the relationship between transformational leadership and organizational innovation. In order to test these hypotheses data were collected from 163 RD whereas, managers' questionnaires included questions about product innovations of their companies, and the degree of support they received from external institutions. Organizational innovation was measured with a market-oriented criterion developed specifically for developing countries and newly developing industries. Hierarchical regression analysis was used to test the hypothesized effects. The results of the analysis provided support for the positive influence of transformational leadership on organizational innovation. This finding is significant because this positive effect was identified in micro- and smallsized companies while previous research focused mainly on large companies. In addition, external support for innovation was found to significantly moderate this effect. Specifically, the relationship between transformational leadership and organizational innovation was stronger when external support was at high levels than when there was no external support. This study is the first to investigate and empirically show the importance of this contextual condition for organizational innovation. The moderating effect of internal support for innovation, however, was not significant. This study shows that transformational leadership is an important determinant of organizational innovation and encourages managers to engage in transformational leadership behaviors in order to promote organizational innovation. In line with this, transformational leadership, which is heavily suggested to be a subject of management training and development in developed countries, should also be incorporated into such programs in developing countries. Moreover, this study highlights the importance of external support in the organizational innovation process. The results suggest that technical and financial support received from outside the organization can be a more important contextual influence in boosting up innovation than an innovation-supporting internal climate. Therefore, managers, particularly of micro- and small-sized companies, should play external roles such as boundary spanning and should build relationships with external institutions that provide technical and financial support. The findings of this study are especially important for managers of companies that plan to or currently operate in countries with developing economies.


TDD reciprocity calibration for multi-user large-scale (massive) MIMO systems with iterative coordinate descent


title: Enforcing k-anonymity in Web Mail Auditing

abstract: We study the problem of k-anonymization of mail messages in the realistic scenario of auditing mail traffic in a major commercial Web mail service. Mail auditing is necessary in various Web mail debugging and quality assurance activities, such as anti-spam or the qualitative evaluation of novel mail features. It is conducted by trained professionals, often referred to as "auditors", who are shown messages that could expose personally identifiable information. We address here the challenge of k-anonymizing such messages, focusing on machine generated mail messages that represent more than 90% of today's mail traffic. We introduce a novel message signature Mail-Hash, specifically tailored to identifying structurally-similar messages, which allows us to put such messages in a same equivalence class. We then define a process that generates, for each class, masked mail samples that can be shown to auditors, while guaranteeing the k-anonymity of users. The productivity of auditors is measured by the amount of non-hidden mail content they can see every day, while considering normal working conditions, which set a limit to the number of mail samples they can review. In addition, we consider k-anonymity over time since, by definition of k-anonymity, every new release places additional constraints on the assignment of samples. We describe in details the results we obtained over actual Yahoo mail traffic, and thus demonstrate that our methods are feasible at Web mail scale. Given the constantly growing concern of users over their email being scanned by others, we argue that it is critical to devise such algorithms that guarantee k-anonymity, and implement associated processes in order to restore the trust of mail users.


title: Crime Analysis Using K-Means Clustering

abstract: Analysis of crime is essential for providing safety and security to the civilian population. Using data mining, we can discover critical information which can help local authorities detect crime and areas of importance. The main purpose of this paper is to analyze the crime which entails theft, homicide and various drug offences which also include suspicious activities, noise complaints and burglar alarm by using qualitative and quantitative approach. Using K-means clustering data mining approach on a crime dataset from New South Wales region of Australia, crime rates of each type of crimes and cities with high crime rates have been found.


title: National frameworks for marine conservation — a hierarchical geophysical approach

abstract: 1. Development of environmental protected areas has been driven ‘more by opportunity than design, scenery rather than science’ (Hackman A. 1993. Preface. A protected areas gap analysis methodology: planning for the conservation of biodiversity. World Wildlife Fund Canada Discussion Paper; i–ii). If marine environments are to be protected from the adverse effects of human activities, then identification of types of marine habitats and delineation of their boundaries in a consistent classification is required. Without such a classification system, the extent and significance of representative or distinctive habitats cannot be recognized. Such recognition is a fundamental prerequisite to the determination of location and size of marine areas to be protected. ::: ::: ::: ::: 2. A hierarchical classification has been developed based on enduring/recurrent geophysical (oceanographic and physiographic) features of the marine environment, which identifies habitat types that reflect changes in biological composition. Important oceanographic features include temperature, stratification and exposure; physiographic features include bottom relief and substrate type. ::: ::: ::: ::: 3. Classifications based only on biological data are generally prohibited at larger scales, due to lack of information. Therefore, we are generally obliged to classify habitat types as surrogates for community types. The data necessary for this classification are available from mapped sources and from remote sensing. It is believed they can be used to identify representative and distinctive marine habitats supporting different communities, and will provide an ecological framework for marine conservation planning at the national level. Copyright © 2000 John Wiley & Sons, Ltd.


title: The Use of Remote Sensing and Landscape Metrics to Describe Structures and Changes in Urban Land Uses

abstract: Remote sensing technology has great potential for acquisition of detailed and accurate land-use information for management and planning of urban regions. However, the determination of land-use data with high geometric and thematic accuracy is generally limited by the availability of adequate remote sensing data, in terms of spatial and temporal resolution, and digital image analysis techniques. This study introduces a methodology using information on image spatial form -- landscape metrics -- to describe urban land-use structures and land-cover changes that result from urban growth. The analysis is based on spatial analysis of land-cover structures mapped from digitally classified aerial photographs of the urban region Santa Barbara, CA. Landscape metrics were calculated for segmented areas of homogeneous urban land use to allow a further characterization of the land use of these areas. The results show a useful separation and characterization of three urban land-use types: commercial development, high-density residential, and low-density residential. Several important structural land-cover features were identified for this study. These were: the dominant general land cover (built up or vegetation), the housing density, the mean structure and plot size, and the spatial aggregation of built-up areas. For two test areas in the Santa Barbara region, changes (urban growth) in the urban spatial land-use structure can be described and quantified with landscape metrics. In order to discriminate more accurately between the three land-cover types of interest, the landscape metrics were further refined into what are termed 'landscape metric signatures' for the land-use categories. The analysis shows the importance of the spatial measurements as second-order image information that can contribute to more detailed mapping of urban areas and towards a more accurate characterization of spatial urban growth pattern.


title: Journal of Computer-Mediated Communication Cultural Cognitive Style and Web Design: Beyond a Behavioral Inquiry into Computer-Mediated Communication

abstract: Numerous studies have identified links among culture, user preferences, and Web site usability. Most of these studies were reports of findings from a behavioral perspective in explaining how cultural factors affect processes of Web-related content design and use. Based on the research of Vygotsky and Nisbett, the authors propose a broader model, referred to as ‘‘cultural cognition theory,’’ by which Web design, like other types of information production, is seen as being shaped by cultural cognitive processes that impact the designers’ cognitive style. This study explores issues related to Web designers’ cultural cognitive styles and their impact on user responses. The results of an online experiment that exposed American 1 and Chinese users to sites created by both Chinese and American designers indicate that users perform information-seeking tasks faster when using Web content created by designers from their own cultures.


title: Genital findings in prepubertal girls selected for nonabuse: a descriptive study.

abstract: This project was designed to collect normative data on the genital anatomy from a sample of nonabused prepubertal girls. A total of 114 girls between the ages of 10 months and 10 years were examined and photographed with a colposcope. After screening for the onset of puberty and the possibility of undetected abuse, 93 subjects remained. Examination techniques included a supine labial separation approach, a supine labial traction method, and a prone knee-chest position. Common genital finding included erythema of the vestibule (56%), periurethral bands (50.6%), labial adhesions (38.9%), lymphoid follicles on the fossa navicularis (33.7%), posterior fourchette midline avascular areas (25.6%), and urethral dilation with labial traction (14.9%). The hymenal orifice opened more frequently during the knee-chest (95.2%) and the labial traction (90.5%) methods than with the supine separation (79.3%) approach. Hymenal findings included mounds (33.8%), projections (33.3%), and "septal remnants"/midline hymenal tags (18.5%). Intravaginal findings of vaginal ridges (90.2%) and rugae (88.7%) were found primarily through the labial traction approach. The cervix was visualized without the use of speculum in 69% of the children during the knee-chest examination. Unusual findings included posterior fourchette friability (4.7%), anterior hymenal clefts (1.2%), and notches of the hymen (6%). One child had an imperforate hymen (1.2%) and 2 (2.5%) had hymenal septa. A vaginal discharge was detected in 2 (2.6%) and a foreign body in 1 (1.3%). Tables that include the vertical and horizontal hymenal orifice diameters by age group and by method are presented.


title: Human-robot embodied interaction in hallway settings: a pilot user study

abstract: This paper explores the problem of embodied interaction between a service robot and a person in a hallway setting. For operation in environments with people that have limited experience with robots, a behaviour that signals awareness of the persons and safety of motion is essential. A control strategy based on human spatial behaviour studies is presented that adopts human-robot interaction patterns similar to those used in person-person encounters. The results of a pilot study with human subjects are presented in which the users have evaluated the acceptability of the robot behaviour patterns during passage with respect to three basic parameters: the robot speed, the signaling distance at which the robot starts the maneuver and the lateral distance from the person for safe passage. The study has shown a good overall user response and has provided some useful indications on how to design a hallway passage behaviour that could be most acceptable to human users.


title: An upper-body rehabilitation exoskeleton Harmony with an anatomical shoulder mechanism: Design, modeling, control, and performance evaluation

abstract: We present an upper-body exoskeleton for rehabilitation, called Harmony, that provides natural coordinated motions on the shoulder with a wide range of motion, and force and impedance controllability. The exoskeleton consists of an anatomical shoulder mechanism with five active degrees of freedom, and one degree of freedom elbow and wrist mechanisms powered by series elastic actuators. The dynamic model of the exoskeleton is formulated using a recursive Newton–Euler algorithm with spatial dynamics representation. A baseline control algorithm is developed to achieve dynamic transparency and scapulohumeral rhythm assistance, and the coupled stability of the robot–human system at the baseline control is investigated. Experiments were conducted to evaluate the kinematic and dynamic characteristics of the exoskeleton. The results show that the exoskeleton exhibits good kinematic compatibility to the human body with a wide range of motion and performs task-space force and impedance control behaviors reliably.


title: Ontology and the Lexicon

abstract: Ontologies and lexicons enjoy a complex relationship. Although words denote concepts and concepts make up ontologies, a lexicon is at best an ersatz ontology: there is no clear mapping between the words and word relationships that it contains and the concepts and concept relationships in an ontology. The reasons for this include the following: Word senses overlap in complex ways; many concepts are not lexicalized in some or all languages; and languages make semantic distinctions that are not ontological. Nonetheless, a lexicon can sometimes be the basis for the development of a practical ontology.


title: Global health learning outcomes for pharmacy students in the UK

abstract: Context : The pharmacy world is a highly globalised environment. Pharmacy graduates must therefore be adequately prepared. The question for educators is, how best to prepare our students. We propose that integrating global health learning outcomes within the core curriculum would ensure that all students – irrespective of their personal interest and intended work sector - are engaged with global issues. / Description: Internal discussions were held with representatives from all disciplines within University College London (UCL) School of Pharmacy and existing curriculum documents were reviewed. A draft set of learning outcomes was proposed and then revised following consultation with representatives from the United Kingdom (UK) pharmacy profession and others with an interest in global health. / Evaluation: Analysis of the consultation data led to the derivation of a revised set of global health learning outcomes for pharmacy. / Future plan: Embedding and assessing global health learning outcomes within the UCL School of Pharmacy’s undergraduate curriculum is now our focus.


title: Comparison of Self organizing maps and Sammon's mapping on agricultural datasets for precision agriculture

abstract: Over the ages technology has been occupying every field including the agriculture. Precision agriculture and Visual data mining uses technology to apply specific principles of data to interpret details like when and how much fertilizers to be used in a particular area (land). Data mining is the process of detecting patterns in a clustering form (certain chunk of information) to get more precise and accurate information. Not only it provides facilitating results but also improves the efficiency of farmer's productivity, it has also helped in qualitative improvement in the overall quality of life by providing timely and data inputs for decision making. Raw data collected from the statistics analysis has helped in determining the data to its full extent. This paper shows the techniques applied to real and till date agriculture data to reduce the high dimensional input data to much smaller size. We are using Self-organizing maps and multi-dimensional scaling techniques (Sammon's mapping) to reduce the data.


title: Improved binary artificial fish swarm algorithm for the 0-1 multidimensional knapsack problems

abstract: The 0–1 multidimensional knapsack problem (MKP) arises in many fields of optimization and is NP-hard. Several exact as well as heuristic methods exist. Recently, an artificial fish swarm algorithm has been developed in continuous global optimization. The algorithm uses a population of points in space to represent the position of fish in the school. In this paper, a binary version of the artificial fish swarm algorithm is proposed for solving the 0–1 MKP. In the proposed method, a point is represented by a binary string of 0/1 bits. Each bit of a trial point is generated by copying the corresponding bit from the current point or from some other specified point, with equal probability. Occasionally, some randomly chosen bits of a selected point are changed from 0 to 1, or 1 to 0, with an user defined probability. The infeasible solutions are made feasible by a decoding algorithm. A simple heuristic add_item is implemented to each feasible point aiming to improve the quality of that solution. A periodic reinitialization of the population greatly improves the quality of the solutions obtained by the algorithm. The proposed method is tested on a set of benchmark instances and a comparison with other methods available in literature is shown. The comparison shows that the proposed method gives a competitive performance when solving this kind of problems.


title: Convex co-embedding

abstract: We present a general framework for association learning, where entities are embedded in a common latent space to express relatedness via geometry--an approach that underlies the state of the art for link prediction, relation learning, multi-label tagging, relevance retrieval and ranking. Although current approaches rely on local training methods applied to non-convex formulations, we demonstrate how general convex formulations can be achieved for entity embedding, both for standard multi-linear and prototype-distance models. We investigate an efficient optimization strategy that allows scaling. An experimental evaluation reveals the advantages of global training in different case studies.


title: Possible Association Between Dental Sealants and Urinary Bisphenol A Levels in Children Warrants Additional Biomonitoring and Safety Research

abstract: Article Title and Bibliographic Information Dental sealants and restorations and urinary bisphenol A concentrations in children in the 2003-2004 National Health and Nutrition Examination Survey. McKinney C, Rue T, Sathyanarayana S, Martin M, Seminario AL, DeRouen T. J Am Dent Assoc 2014;145(7):745-750. Reviewer Nancy Maserejian, ScD Purpose/Question Is there an association between dental sealants or restorations and urinary bisphenol A levels in school-aged children in the United States? Source of Funding None reported Type of Study/Design Cross-sectional study Level of Evidence Level 3: Other evidence Strength of Recommendation Grade Not applicable


title: Semantic-driven Generation of Hyperlapse from $360^\circ$ Video

abstract: We present a system for converting a fully panoramic ($360^\circ$) video into a normal field-of-view (NFOV) hyperlapse for an optimal viewing experience. Our system exploits visual saliency and semantics to non-uniformly sample in space and time for generating hyperlapses. In addition, users can optionally choose objects of interest for customizing the hyperlapses. We first stabilize an input $360^\circ$ video by smoothing the rotation between adjacent frames and then compute regions of interest and saliency scores. An initial hyperlapse is generated by optimizing the saliency and motion smoothness followed by the saliency-aware frame selection. We further smooth the result using an efficient 2D video stabilization approach that adaptively selects the motion model to generate the final hyperlapse. We validate the design of our system by showing results for a variety of scenes and comparing against the state-of-the-art method through a user study.


title: Saturating Auto-Encoders

abstract: We introduce a simple new regularizer for auto-encoders whose hidden-unit activation functions contain at least one zero-gradient (saturated) region. This regularizer explicitly encourages activations in the saturated region(s) of the corresponding activation function. We call these Saturating Auto-Encoders (SATAE). We show that the saturation regularizer explicitly limits the SATAE's ability to reconstruct inputs which are not near the data manifold. Furthermore, we show that a wide variety of features can be learned when different activation functions are used. Finally, connections are established with the Contractive and Sparse Auto-Encoders.


title: A requirements taxonomy for reducing Web site privacy vulnerabilities

abstract: The increasing use of personal information on Web-based applications can result in unexpected disclosures. Consumers often have only the stated Web site policies as a guide to how their information is used, and thus on which to base their browsing and transaction decisions. However, each policy is different, and it is difficult—if not impossible—for the average user to compare and comprehend these policies. This paper presents a taxonomy of privacy requirements for Web sites. Using goal-mining, the extraction of pre-requirements goals from post-requirements text artefacts, we analysed an initial set of Internet privacy policies to develop the taxonomy. This taxonomy was then validated during a second goal extraction exercise, involving privacy policies from a range of health care related Web sites. This validation effort enabled further refinement to the taxonomy, culminating in two classes of privacy requirements: protection goals and vulnerabilities. Protection goals express the desired protection of consumer privacy rights, whereas vulnerabilities describe requirements that potentially threaten consumer privacy. The identified taxonomy categories are useful for analysing implicit internal conflicts within privacy policies, the corresponding Web sites, and their manner of operation. These categories can be used by Web site designers to reduce Web site privacy vulnerabilities and ensure that their stated and actual policies are consistent with each other. The same categories can be used by customers to evaluate and understand policies and their limitations. Additionally, the policies have potential use by third-party evaluators of site policies and conflicts.


title: Techniques for modeling large-scale HPC I/O workloads

abstract: Accurate analysis of HPC storage system designs is contingent on the use of I/O workloads that are truly representative of expected use. However, I/O analyses are generally bound to specific workload modeling techniques such as synthetic benchmarks or trace replay mechanisms, despite the fact that no single workload modeling technique is appropriate for all use cases. In this work, we present the design of IOWA, a novel I/O workload abstraction that allows arbitrary workload consumer components to obtain I/O workloads from a range of diverse input sources. Thus, researchers can choose specific I/O workload generators based on the resources they have available and the type of evaluation they wish to perform. As part of this research, we also outline the design of three distinct workload generation methods, based on I/O traces, synthetic I/O kernels, and I/O characterizations. We analyze and contrast each of these workload generation techniques in the context of storage system simulation models as well as production storage system measurements. We found that each generator mechanism offers varying levels of accuracy, flexibility, and breadth of use that should be considered before performing I/O analyses. We also recommend a set of best practices for HPC I/O workload modeling based on challenges that we encountered while performing our evaluation.


Rising longevity , education , savings , and growth Jie


title: DEVELOPING A PARAMETRIC URBAN DESIGN TOOL. SOME STRUCTURAL CHALLENGES AND POSSIBLE WAYS TO OVERCOME THEM

abstract: Parametric urban design is a potentially powerful tool for collaborative urban design processes. Rather than making oneoff designs which need to be redesigned from the ground up in case of changes, parametric design tools make it possible keep the design open while at the same time allowing for a level of detailing which is high enough to facilitate an understanding of the generic qualities of proposed designs. Starting from a brief overview of parametric design, this paper presents initial findings from the development of a parametric urban design tool with regard to developing a structural logic which is flexible and expandable. It then moves on to outline and discuss further development work. Finally, it offers a brief reflection on the potentials and shortcomings of the software ‐ CityEngine ‐ which is used for developing the parametric urban design tool.


title: Feature representation for statistical-learning-based object detection

abstract: Statistical-learning-based object detection is an important topic in computer vision. It learns visual representation from annotated exemplars to identify semantic defined objects in images. High-performance object detection is usually carried out in feature space and effective feature representation can improve the performance significantly. Feature representation is the encoding process which maps raw image pixels inside local regions into discriminant feature space. The motivation of this paper is to present a review on feature representation in recent object detection methods. Visual features applied in object detection are categorized according to the differences in computation and visual properties. The most valued features are introduced and discussed in detail. Representative extensions are introduced briefly for comparison. Descriptive power, robustness, compactness as well as computational efficiency are viewed as important properties. According to these properties, discussions are presented on the advantages and drawbacks of features. Besides, generic techniques such as dimension reduction and combination are introduced. Through this review, we would like to draw the feature sketch and provide new insights for feature utilization, in order to tackle future challenges of object detection. HighlightsWe review the feature representation in statistical learning based object detection.We categorize and introduce features based on visual properties.The pros/cons on feature properties (e.g., descriptiveness, robustness) are discussed.Generic techniques such as dimension reduction and combination are introduced.We put some emphasis on future challenges in feature design through this review.


title: Design, analysis and simulation of biped running robot

abstract: Legged systems are potentially expected to have outstanding mobility. However, they have shown only modest levels of capability to traverse on flat ground. This paper introduces a biped robot inspired by the hind limbs of cats. We present the characteristics of the system and conduct a position analysis based on vector loop equations. Afterwards, the ground clearance and parallel movement of the robotic leg are shown. We present a speed equation in an effort to verify how the major parameters affect the speed. In addition, we explore control strategies for ground speed matching, acceleration, slip, and speed control. A dynamic simulation shows that the biped robot reached 13.31 leg lengths per second (9.3km/h). This biped robot with the speed equation and its control strategies allow us to understand legged locomotion and can show us how to improve the speed.


title: Safe Partial Diagnosis from Normal Observations

abstract: Model-based diagnosis (MBD) is difficult to use in practice because it requires a model of the diagnosed system, which is often very hard to obtain. We explore theoretically how observing the system when it is in a normal state can provide information about the system that is sufficient to learn a partial system model that allows automated diagnosis. We analyze the number of observations needed to learn a model capable of finding faulty components in most cases. Then, we explore how knowing the system topology can help us to learn a useful model from the normal observations for settings in which many of the internal system variables cannot be observed. Unlike other data-driven methods, our learned model is safe, in the sense that subsystems identified as faulty are guaranteed to truly be faulty.


title: Object-Oriented Dynamics Predictor

abstract: Generalization has been one of the major challenges for learning dynamics models in model-based reinforcement learning. However, previous work on action-conditioned dynamics prediction focuses on learning the pixel-level motion and thus does not generalize well to novel environments with different object layouts. In this paper, we present a novel object-oriented framework, called object-oriented dynamics predictor (OODP), which decomposes the environment into objects and predicts the dynamics of objects conditioned on both actions and object-to-object relations. It is an end-to-end neural network and can be trained in an unsupervised manner. To enable the generalization ability of dynamics learning, we design a novel CNN-based relation mechanism that is class-specific (rather than object-specific) and exploits the locality principle. Empirical results show that OODP significantly outperforms previous methods in terms of generalization over novel environments with various object layouts. OODP is able to learn from very few environments and accurately predict dynamics in a large number of unseen environments. In addition, OODP learns semantically and visually interpretable dynamics models.


title: Learning Conditioned Graph Structures for Interpretable Visual Question Answering

abstract: Visual Question answering is a challenging problem requiring a combination of concepts from Computer Vision and Natural Language Processing. Most existing approaches use a two streams strategy, computing image and question features that are consequently merged using a variety of techniques. Nonetheless, very few rely on higher level image representations, which allow to capture semantic and spatial relationships. In this paper, we propose a novel graph-based approach for Visual Question Answering. Our method combines a graph learner module, which learns a question specific graph representation of the input image, with the recent concept of graph convolutions, aiming to learn image representations that capture question specific interactions. We test our approach on the VQA v2 dataset using a simple baseline architecture enhanced by the proposed graph learner module. We obtain state of the art results with 65.77% accuracy and demonstrate the interpretability of the proposed method.


title: Key drivers of internet banking services use

abstract: Purpose – The purpose of this paper is to analyse the determinants of internet banking use, paying special attention to the role of product involvement, perceived risk and trust.Design/methodology/approach – The impact of trust, perceived risks, product involvement and TAM beliefs (ease of use and usefulness) on internet banking adoption is tested through structural equation modelling techniques. The sample consists of 511 Spanish internet banking services users and the data are collected through an internet survey. Risk is measured as a formative construct.Findings – Data analysis shows that TAM beliefs and perceived risks (security, privacy, performance and social) have a direct influence on e‐banking adoption. Trust appears as a key variable that reduces perceived risk. Involvement plays an important role in increasing perceived ease of use.Practical implications – This research provides banks with knowledge of what aspects to highlight in their communications strategies to increase their internet bank...


title: Down will Come Baby, Cradle and All: Diagnostic and Therapeutic Implications of Chronic Trauma on Child Development

abstract: Objective: This review examines the clinical outcomes associated with exposure to chronic intrafamilial trauma and explores the treatment of the psychological, biological and cognitive sequelae. Method: The existing research literature on the subject was collected, using Index Medicus/MEDLINE, Psychological Abstracts and the PILOTS database. The research findings were supplemented with clinical observations by the authors and other clinical writings on this topic. Results: Children with histories of exposure to multiple traumatic experiences within their families or in medical settings usually meet criteria for numerous clinical diagnoses, none of which capture the complexity of their biological, emotional and cognitive problems. These are expressed in a multitude of psychological, cognitive, somatic and behavioural problems, ranging from learning disabilities to aggression against self and others. Conclusions: Exposure to intrafamilial violence and other chronic trauma results in pervasive psychological and biological deficits. Treatment needs to address issues of safety, stabilise impulsive aggression against self and others, promote mastery experiences, compensate for specific developmental deficits, and judiciously process both the traumatic memories and trauma-related expectations.


title: Nonlinear modeling for a class of nano-robotic systems using piezoelectric stick-slip actuators

abstract: This paper addresses modeling issues for a class of nano-robotic systems using piezoelectric stick-slip actuators. The work focuses on the friction force modeling to describe the dynamics of a stick-slip actuator in a wide operating range needed in nano-robotics. Based on the theory of the single state elasto-plastic model and on an experimental analysis, necessary conditions on presiding modeling are highlighted. The conditions allow describing the dynamics of stick-slip type actuators for both scanning mode and stepping mode in the time and the frequency domains and for backward and forward directions of the motion. The proposed dynamic model opens new perspective for closed loop control of nano-robotic system.


Embeddings via Recurrent Language Models


title: Online Gaming Addiction: The Role of Sensation Seeking, Self-Control, Neuroticism, Aggression, State Anxiety, and Trait Anxiety

abstract: Abstract Research into online gaming has steadily increased over the last decade, although relatively little research has examined the relationship between online gaming addiction and personality factors. This study examined the relationship between a number of personality traits (sensation seeking, self-control, aggression, neuroticism, state anxiety, and trait anxiety) and online gaming addiction. Data were collected over a 1-month period using an opportunity sample of 123 university students at an East Midlands university in the United Kingdom. Gamers completed all the online questionnaires. Results of a multiple linear regression indicated that five traits (neuroticism, sensation seeking, trait anxiety, state anxiety, and aggression) displayed significant associations with online gaming addiction. The study suggests that certain personality traits may be important in the acquisition, development, and maintenance of online gaming addiction, although further research is needed to replicate the findings ...


title: A comparison of dosimetric parameters in PET-based active bone marrow volume and total bone volume in prediction of hematologic toxicity in cervical cancer patients treated with chemoradiation

abstract: Objective ::: The purpose of this study is to evaluate whether dose to PET-defined active regions of the bone marrow is a better predictor of hematologic toxicity than dose to the total bone as a surrogate of the bone marrow.


title: Reinforcement Learning under Threats

abstract: In several reinforcement learning (RL) scenarios, mainly in security settings, there may be adversaries trying to interfere with the reward generating process. However, when non-stationary environments as such are considered, Q-learning leads to suboptimal results (Busoniu, Babuska, and De Schutter 2010). Previous game-theoretical approaches to this problem have focused on modeling the whole multi-agent system as a game. Instead, we shall face the problem of prescribing decisions to a single agent (the supported decision maker, DM) against a potential threat model (the adversary). We augment the MDP to account for this threat, introducing Threatened Markov Decision Processes (TMDPs). Furthermore, we propose a level-k thinking scheme resulting in a new learning framework to deal with TMDPs. We empirically test our framework, showing the benefits of opponent modeling.


title: Providing Context-Aware Security for IoT Environments Through Context Sharing Feature

abstract: Nowadays security and privacy in Internet of Things (IoT) environments is a real issue. Traditional security mechanisms use a non-aware approach, in which static parameters are used to provide secure decisions. IoT is a dynamic environment. Thus a non-static approach for security provision becomes mandatory. Context-aware security appears as a viable choice for this kind of processing. It uses the context information of IoT environments thus providing dynamic security. When together with context sharing feature, it can add new dimensions to the IoT security. Context sharing allows the use of off-domain context information to the security provision. This paper defines an EdgeCentric Context Sharing Architecture that provides context-aware security by using shared context information. Moreover, we discuss the challenges in the context-aware security area.


title: A coplanar Vivaldi antenna with wide band balun proposal for the low frequency band of the SKA: approach to the FPA solution

abstract: A 17:1 band coplanar Vivaldi antenna with wide band balun for the low frequencies of the SKA (square kilometre array) is designed, simulated and measured. Looking for best capabilities in the widest possible band, keeping in mind the low cost and weight desired for the application and the requirement for a later activation of the antenna, a close study to the feeding method was developed.


- 1-Risk Neutral is Best for Risky Decision Making


title: Dealing with the Inventory Risk

abstract: Market makers have to continuously set bid and ask quotes for the stocks they have under consideration. Hence they face a complex optimization problem in which their return, based on the bid-ask spread they quote and the frequency they indeed provide liquidity, is challenged by the price risk they bear due to their inventory. In this paper, we provide optimal bid and ask quotes and closed-form approximations are derived using spectral arguments.


title: Pervasive social context: Taxonomy and survey

abstract: As pervasive computing meets social networks, there is a fast growing research field called pervasive social computing. Applications in this area exploit the richness of information arising out of people using sensor-equipped pervasive devices in their everyday life combined with intense use of different social networking services. We call this set of information pervasive social context. We provide a taxonomy to classify pervasive social context along the dimensions space, time, people, and information source (STiPI) as well as commenting on the type and reason for creating such context. A survey of recent research shows the applicability and usefulness of the taxonomy in classifying and assessing applications and systems in the area of pervasive social computing. Finally, we present some research challenges in this area and illustrate how they affect the systems being surveyed.


title: A Metadata Catalog Service for Data Intensive Applications

abstract: Advances in computational, storage and network technologies as well as middle ware such as the Globus Toolkit allow scientists to expand the sophistication and scope of data-intensive applications. These applications produce and analyze terabytes and petabytes of data that are distributed in millions of files or objects. To manage these large data sets efficiently, metadata or descriptive information about the data needs to be managed. There are various types of metadata, and it is likely that a range of metadata services will exist in Grid environments that are specialized for particular types of metadata cataloguing and discovery. In this paper, we present the design of a Metadata Catalog Service (MCS) that provides a mechanism for storing and accessing descriptive metadata and allows users to query for data items based on desired attributes. We describe our experience in using the MCS with several applications and present a scalability study of the service.


title: Shared Autonomy via Deep Reinforcement Learning

abstract: In shared autonomy, user input is combined with semi-autonomous control to achieve a common goal. The goal is often unknown ex-ante, so prior work enables agents to infer the goal from user input and assist with the task. Such methods tend to assume some combination of knowledge of the dynamics of the environment, the user's policy given their goal, and the set of possible goals the user might target, which limits their application to real-world scenarios. We propose a deep reinforcement learning framework for model-free shared autonomy that lifts these assumptions. We use human-in-the-loop reinforcement learning with neural network function approximation to learn an end-to-end mapping from environmental observation and user input to agent action values, with task reward as the only form of supervision. This approach poses the challenge of following user commands closely enough to provide the user with real-time action feedback and thereby ensure high-quality user input, but also deviating from the user's actions when they are suboptimal. We balance these two needs by discarding actions whose values fall below some threshold, then selecting the remaining action closest to the user's input. Controlled studies with users (n = 12) and synthetic pilots playing a video game, and a pilot study with users (n = 4) flying a real quadrotor, demonstrate the ability of our algorithm to assist users with real-time control tasks in which the agent cannot directly access the user's private information through observations, but receives a reward signal and user input that both depend on the user's intent. The agent learns to assist the user without access to this private information, implicitly inferring it from the user's input. This paper is a proof of concept that illustrates the potential for deep reinforcement learning to enable flexible and practical assistive systems.


title: Unsupervised Ranking using Graph Structures and Node Attributes

abstract: PageRank has been the signature unsupervised ranking model for ranking node importance in a graph. One potential drawback of PageRank is that its computation depends only on input graph structures, not considering external information such as the attributes of nodes. This work proposes AttriRank, an unsupervised ranking model that considers not only graph structure but also the attributes of nodes. AttriRank is unsupervised and domain-independent, which is different from most of the existing works requiring either ground-truth labels or specific domain knowledge. Combining two reasonable assumptions about PageRank and node attributes, AttriRank transfers extra node information into a Markov chain model to obtain the ranking. We further develop approximation for AttriRank and reduce its complexity to be linear to the number of nodes or links in the graph, which makes it feasible for large network data. The experiments show that AttriRank outperforms competing models in diverse graph ranking applications.


title: IoT Devices Recognition Through Network Traffic Analysis

abstract: The growing Internet of Things (IoT) market introduces new challenges for network activity monitoring. Legacy network monitoring is not tailored to cope with the huge diversity of smart devices. New network discovery techniques are necessary in order to find out what IoT devices are connected to the network. In this context, data analysis techniques can be leveraged to find out specific patterns that can help to recognize device types. Indeed, contrary to desktop computers, IoT devices perform very specific tasks making their networking behavior very predictable. In this paper, we present a machine learning based approach in order to recognize the type of IoT devices connected to the network by analyzing streams of packets sent and received. We built an experimental smart home network to generate network traffic data. From the generated data, we have designed a model to describe IoT device network behaviors. By leveraging the t-SNE technique to visualize our data, we are able to differentiate the network traffic generated by different IoT devices. The data describing the network behaviors are then used to train six different machine learning classifiers to predict the IoT device that generated the network traffic. The results are promising with an overall accuracy as high as 99.9% on our test set achieved by Random Forest classifier.


title: A Bayesian LSTM Model to Evaluate the Effects of Air Pollution Control Regulations in China

abstract: Rapid socio-economic development and urbanization have resulted in serious deterioration in air-quality in many world cities, including Beijing, China. This preliminary study is the first attempt to examine the effectiveness of air pollution control regulations implemented in Beijing during 2013 – 2017 through a data-driven regulatory intervention analysis. Our proposed machine-learning model utilizes proxy data including Aerosol Optical Depth (AOD) and meteorology; it can explain 80% of the PM 2.5 variability. Our preliminary results show that air pollution control regulatory measures introduced in China and Beijing have reduced PM 2.5 pollution in Beijing by 23% on average.


title: A Recent Survey on Colon Cancer Detection Techniques

abstract: Colon cancer causes deaths of about half a million people every year. Common method of its detection is histopathological tissue analysis, which, though leads to vital diagnosis, is significantly correlated to the tiredness, experience, and workload of the pathologist. Researchers have been working since decades to get rid of manual inspection, and to develop trustworthy systems for detecting colon cancer. Several techniques, based on spectral/spatial analysis of colon biopsy images, and serum and gene analysis of colon samples, have been proposed in this regard. Due to rapid evolution of colon cancer detection techniques, a latest review of recent research in this field is highly desirable. The aim of this paper is to discuss various colon cancer detection techniques. In this survey, we categorize the techniques on the basis of the adopted methodology and underlying data set, and provide detailed description of techniques in each category. Additionally, this study provides an extensive comparison of various colon cancer detection categories, and of multiple techniques within each category. Further, most of the techniques have been evaluated on similar data set to provide a fair performance comparison. Analysis reveals that neither of the techniques is perfect; however, research community is progressively inching toward the finest possible solution.


title: Shadow Detection and Compensation in High Resolution Satellite Image Based on Retinex

abstract: In this paper, shadow detection and compensation are treated as image enhancement tasks. The principal components analysis (PCA) and luminance based multi-scale Retinex (LMSR) algorithm are explored to detect and compensate shadow in high resolution satellite image. PCA provides orthogonally channels, thus allow the color to remain stable despite the modification of luminance. Firstly, the PCA transform is used to obtain the luminance channel, which enables us to detect shadow regions using histogram threshold technique. After detection, the LMSR technique is used to enhance the image only in luminance channel to compensate for shadows. Then the enhanced image is obtained by inverse transform of PCA. The final shadow compensation image is obtained by comparison of the original image, the enhanced image and the shadow detection image. Experiment results show the effectiveness of the proposed method.