title: Main-memory hash joins on multi-core CPUs: Tuning to the underlying hardware

abstract: The architectural changes introduced with multi-core CPUs have triggered a redesign of main-memory join algorithms. In the last few years, two diverging views have appeared. One approach advocates careful tailoring of the algorithm to the architectural parameters (cache sizes, TLB, and memory bandwidth). The other approach argues that modern hardware is good enough at hiding cache and TLB miss latencies and, consequently, the careful tailoring can be omitted without sacrificing performance. In this paper we demonstrate through experimental analysis of different algorithms and architectures that hardware still matters. Join algorithms that are hardware conscious perform better than hardware-oblivious approaches. The analysis and comparisons in the paper show that many of the claims regarding the behavior of join algorithms that have appeared in literature are due to selection effects (relative table sizes, tuple sizes, the underlying architecture, using sorted data, etc.) and are not supported by experiments run under different parameters settings. Through the analysis, we shed light on how modern hardware affects the implementation of data operators and provide the fastest implementation of radix join to date, reaching close to 200 million tuples per second.


title: Computer Games: A Double-Edged Sword?

abstract: Abstract Excessive computer game playing (ECGP) has already become a serious social problem. However, limited data from experimental lab studies are available about the negative consequences of ECGP on players' cognitive characteristics. In the present study, we compared three groups of participants (current ECGP participants, previous ECGP participants, and control participants) on a Multiple Object Tracking (MOT) task. The previous ECGP participants performed significantly better than the control participants, which suggested a facilitation effect of computer games on visuospatial abilities. Moreover, the current ECGP participants performed significantly worse than the previous ECGP participants. This more important finding indicates that ECGP may be related to cognitive deficits. Implications of this study are discussed.


NDE of Steel Bridges: Fatigue Crack Detection and Monitoring


title: Design and Optimization of Printed Spiral Coils for Efficient Transcutaneous Inductive Power Transmission

abstract: The next generation of implantable high-power neuroprosthetic devices such as visual prostheses and brain computer interfaces are going to be powered by transcutaneous inductive power links formed between a pair of printed spiral coils (PSC) that are batch-fabricated using micromachining technology. Optimizing the power efficiency of the wireless link is imperative to minimize the size of the external energy source, heating dissipation in the tissue, and interference with other devices. Previous design methodologies for coils made of 1-D filaments are not comprehensive and accurate enough to consider all geometrical aspects of PSCs with planar 3-D conductors as well as design constraints imposed by implantable device application and fabrication technology. We have outlined the theoretical foundation of optimal power transmission efficiency in an inductive link, and combined it with semi-empirical models to predict parasitic components in PSCs. We have used this foundation to devise an iterative PSC design methodology that starts with a set of realistic design constraints and ends with the optimal PSC pair geometries. We have executed this procedure on two design examples at 1 and 5 MHz achieving power transmission efficiencies of 41.2% and 85.8%, respectively, at 10-mm spacing. All results are verified with simulations using a commercial field solver (HFSS) as well as measurements using PSCs fabricated on printed circuit boards.


title: The AUV design based on component modeling and simulation

abstract: In the present work, design procedure and computer simulation of an AUV are documented briefly. The design procedure containing the design of propulsion system and CFD simulation of hydrodynamics behavior of the hull leads to achieve an optimum mechanical performance of AUV system. After designing, a comprehensive one dimensional model including motor, propeller, and AUV hull behavior simulates the whole dynamics of AUV system. In this design, to select the optimum AUV hull, several noses and tails are examined by CFD tools and the brushless motor is selected based on the first order model of DC electrical motor. By calculating thrust and velocity in functional point, OpenProp as a tool to select the optimum propeller is applied and the characteristics of appropriate propeller are determined. Finally, a computer program is developed to simulate the interaction between different components of AUV. The simulation leads to determine the initial acceleration, final velocity, and angular velocity of electrical motor and propeller. Results show the final AUV performance point is in the maximum efficiency regions of DC electrical motor and propeller.


title: Electrical impedance spectroscopy of the breast: clinical imaging results in 26 subjects

abstract: Electrical impedance spectroscopy (EIS) is a potential, noninvasive technique to image women for breast cancer. Studies have shown characteristic frequency dispersions in the electrical conductivity and permittivity of malignant versus normal tissue. Using a multifrequency EIS system, we imaged the breasts of 26 women. All patients had mammograms ranked using the American College of Radiology (ACR) BIRADS system. Of the 51 individual breasts imaged, 38 were ACR 1 negative, six had ACR 4-5 suspicious lesions, and seven had ACR 2 benign findings such as fibroadenomas or calcifications. A radially translatable circular array of 16 Ag/AgCl electrodes was placed around the breast while the patient lay prone. We applied trigonometric voltage patterns at ten frequencies between 10 and 950 kHz. Anatomically coronal images were reconstructed from this data using nonlinear partial differential equation methods. Typically, ACR 1-rated breasts were interrogated in a single central plane whereas ACR 2-5-rated breasts were imaged in multiple planes covering the region of suspicion. In general, a characteristic homogeneous image emerged for mammographically normal cases while focal inhomogeneities were observed in images from women with malignancies. Using a specific visual criterion, EIS images identified 83% of the ACR 4-5 lesions while 67% were detected using a numerical criterion. Overall, multifrequency electrical impedance imaging appears promising for detecting breast malignancies, but improvements must be made before the method reaches its full potential.


title: NDDR-CNN: Layerwise Feature Fusing in Multi-Task CNNs by Neural Discriminative Dimensionality Reduction

abstract: In this paper, we propose a novel Convolutional Neural Network (CNN) structure for general-purpose multi-task learning (MTL), which enables automatic feature fusing at every layer from different tasks. This is in contrast with the most widely used MTL CNN structures which empirically or heuristically share features on some specific layers (e.g., share all the features except the last convolutional layer). The proposed layerwise feature fusing scheme is formulated by combining existing CNN components in a novel way, with clear mathematical interpretability as discriminative dimensionality reduction, which is referred to as Neural Discriminative Dimensionality Reduction (NDDR). Specifically, we first concatenate features with the same spatial resolution from different tasks according to their channel dimension. Then, we show that the discriminative dimensionality reduction can be fulfilled by 1x1 Convolution, Batch Normalization, and Weight Decay in one CNN. The use of existing CNN components ensures the end-to-end training and the extensibility of the proposed NDDR layer to various state-of-the-art CNN architectures in a "plug-and-play" manner. The detailed ablation analysis shows that the proposed NDDR layer is easy to train and also robust to different hyperparameters. Experiments on different task sets with various base network architectures demonstrate the promising performance and desirable generalizability of our proposed method. The code of our paper is available at https://github.com/ethanygao/NDDR-CNN.


The operation of hidden prejudice in pre-court procedures: The case of Australian Aboriginal youth


Reduced-Gate Convolutional LSTM Design Using Predictive Coding for Next-Frame Video Prediction


title: Evolution towards smart home environments: empirical evaluation of three user interfaces

abstract: Smart home environments have evolved to the point where everyday objects and devices at home can be networked to give the inhabitants new means to control them. Familiar information appliances can be used as user interfaces (UIs) to home functions to achieve a more convenient user experience. This paper reports an ethnographic study of smart home usability and living experience. The purpose of the research was to evaluate three UIs—a PC, a media terminal, and a mobile phone—for smart home environments. The results show two main types of activity patterns, pattern control and instant control, which require different UI solutions. The results suggest that a PC can act as a central unit to control functions for activity patterns that can be planned and determined in advance. The mobile phone, on the other hand, is well suited for instant control. The mobile phone turned out to be the primary and most frequently used UI during the 6-month trial period in the smart apartment.


Night-Time Traffic Light Detection Based On SVM with Geometric Moment Features


title: Design of a Multi-Sensor Cooperation Travel Environment Perception System for Autonomous Vehicle

abstract: This paper describes the environment perception system designed for intelligent vehicle SmartV-II, which won the 2010 Future Challenge. This system utilizes the cooperation of multiple lasers and cameras to realize several necessary functions of autonomous navigation: road curb detection, lane detection and traffic sign recognition. Multiple single scan lasers are integrated to detect the road curb based on Z-variance method. Vision based lane detection is realized by two scans method combining with image model. Haar-like feature based method is applied for traffic sign detection and SURF matching method is used for sign classification. The results of experiments validate the effectiveness of the proposed algorithms and the whole system.


title: Encoded Sensing for Energy Efficient Wireless Sensor Networks

abstract: Energy efficient communication is a fundamental design problem in wireless networks significantly affecting network performance and the lifetime of wireless sensor networks (WSNs). We introduce encoded sensing—an approach for collaborative encoding and transmission of sensors data—that drastically reduces communication energy expenditure in WSN. Encoded sensing exploits the inherent spatial structure in sensed data to adaptively partition a WSN into groups of sensor nodes, so that nodes in each group sense highly correlated values. Each group encodes all individual measurements sensed by its nodes at time $t$ into a single binary sparse codeword via novel minimum distance combinatorial encoding local algorithm. When the codeword’s Hamming weight equals $w$ , a subset of $w$ nodes in the group cooperatively transmits a single binary symbol each. Upon receiving the $w$ bits, the sink has enough information to decode a measurement estimate, which is within a small error from each of the group nodes’ individual measurements. The error is bounded and guaranteed to satisfy a priori QoS accuracy requirements. We compare encoded sensing to non-cooperative state-of-the-art transmission protocols and demonstrate at least a factor of two in energy savings, without significant loss of measurement quality. Encoded sensing achieves at least 80% the energy savings of theoretically optimal cooperative transmission distributed beamforming architectures. We show by simulations and theoretical derivations that as the size of a node group grows the performance of encoded sensing converges to the optimal transmission energy efficiency.


title: Time series forecasting using neural networks

abstract: Recent studies have shown the classification and prediction power of the Neural Networks. It has been demonstrated that a NN can approximate any continuous function. Neural networks have been successfully used for forecasting of financial data series. The classical methods used for time series prediction like Box-Jenkins or ARIMA assumes that there is a linear relationship between inputs and outputs. Neural Networks have the advantage that can approximate nonlinear functions. In this paper we compared the performances of different feed forward and recurrent neural networks and training algorithms for predicting the exchange rate EUR/RON and USD/RON. We used data series with daily exchange rates starting from 2005 until 2013.


title: Accurate garment prototyping and simulation

abstract: AbstractDespite numerous methods available for cloth simulation, virtual garment prototyping has yet to find its way toward the garment industry, the main issues being simulation accuracy and the potentiality for reproducing the complex behavior of complex garment models. These goals can only be reached through an optimal combination of modeling techniques and numerical methods that combines high computation efficiency with the versatility required for simulating intricate garment designs. We here describe optimal choices illustrated by their integration in a design and simulation tool that allow interactive prototyping of garments along drape motion and comfortability tests on animated postures.


title: Wearable technology for patients with brain and spinal cord injuries

abstract: Abstract Studies have shown that patients who practice functional movements at home in conjunction with outpatient therapy show higher improvement in motor recovery. However, patients are not qualified to monitor or assess their own condition that must be reported back to the clinician. Therefore, there is a need to transmit physiological data to clinicians from patients in their home environment. This paper presents a review of wearable technology for in-home health monitoring, assessment, and rehabilitation of patients with brain and spinal cord injuries.


title: Modelling IT Projects Success with Fuzzy Cognitive Maps ABSTRACT

abstract: Abstract IT projects have certain features that make them different from other engineering projects. These include increased complexity and higher chances of project failure. To increase the chances of an IT project to be perceived as successful by all the parties involved in the project from its conception, development and implementation, it is necessary to identify at the outset of the project what the important factors influencing that success are. Current methodologies and tools used for identifying, classifying and evaluating the indicators of success in IT projects have several limitations that can be overcome by employing the new methodology presented in this paper. This methodology is based on using Fuzzy Cognitive Maps (FCMs) for mapping success, modelling Critical Success Factors (CSFs) perceptions and the relations between them. This is an area where FCM has never been applied before. The applicability of the FCM methodology is demonstrated through a case study based on a new project idea, the Mobile Payment System (MPS) project, related to the fast evolving world of mobile telecommunications.


title: Anxiety and depression in transgender individuals: The roles of transition status, loss, social support, and coping.

abstract: OBJECTIVE: The purpose of the current study was to examine facilitative and avoidant coping as mediators between distress and transition status, social support, and loss. METHOD: A total of 351 transgender individuals (n = 226 transgender women and n = 125 transgender men) participated in this study. Participants completed measures on transgender identity, family history of mental health concerns, perceptions of loss, coping, depression, and anxiety. RESULTS: The rates of depressive symptoms (51.4% for transgender women; 48.3% for transgender men) and anxiety (40.4% for transgender women; 47.5% for transgender men) within the current study far surpass the rates of those for the general population. Structural equation modeling (SEM) was used to analyze the data-2 separate models were hypothesized, based on reports of anxiety or depression. The SEM results suggest that the processes for transgender women and transgender men are primarily similar for depression and anxiety; avoidant coping served as a mediator between transition status and both distress variables. Social support was directly related to distress variables, as well as indirectly related through avoidant coping. CONCLUSION: Results suggest the need for practitioners to focus on interventions that reduce avoidant coping strategies, while simultaneously increasing social support, in order to improve mental health for transgender individuals. Individuals who are in the beginning stages of their transition will use different coping strategies than those who are in later stages; interventions should be adjusted on the basis of the transition status of transgender clients.


title: The impact of polycystic ovary syndrome on the health-related quality of life: A systematic review and meta-analysis

abstract: BACKGROUND ::: Polycystic ovary syndrome (PCOS) has been shown to cause a reduction in health-related quality of life (HRQOL). However, the relative degree of impairment in each domain differed among samples, and it was not clear which aspect of disease-specific HRQOL (modified polycystic ovary syndrome health-related quality of life questionnaire) was most negatively affected. ::: ::: ::: OBJECTIVE ::: To systematically review the effects of PCOS on specific domains of HRQOL. ::: ::: ::: MATERIALS AND METHODS ::: Literature search using search engine of database (PubMed, PsychInfo, CINAHL, CENTRAL, and Scopus) between 1998 to December 2013 yields 6 relevant publications. Pairs of raters used structural tools to analyze these articles, through critical appraisal and data extraction. The scores of each domain of polycystic ovarian syndrome questionnaire (PCOSQ) or modified version (MPCOSQ) of 1140 women with PCOS were used in meta-analysis. ::: ::: ::: RESULTS ::: The combine mean of emotional (4.40; 95% CI 3.77-5.04), infertility (4.13; 95% CI 3.81-4.45) and weight (3.88; 95% CI 2.33-5.42) dimensions were better, but menstruation (3.84; 95% CI 3.63-4.04) and hirsutism (3.81; 95% CI 3.26-4.35) domains were lower than the mean score of PCOSQ/MPCOSQ in related dimension. ::: ::: ::: CONCLUSION ::: The meta-analysis showed that the most affected domains in specific HRQOL were hirsutism and menstruation. Based on these findings, we recommend healthcare providers to be made aware that HRQOL impairment of PCOS is mainly caused by their hirsutism and menstruation, which requires appropriate management.


title: Quick Detection of Brain Tumors and Edemas: A Bounding Box Method Using Symmetry 1

abstract: Abstract A significant medical informatics task is indexing patient databases according to size, location, and other characteristics of brain tumors and edemas, possibly based on magnetic resonance (MR) imagery. This requires segmenting tumors and edemas within images from different MR modalities. To date, automated brain tumor or edema segmentation from MR modalities remains a challenging, computationally intensive task. In this paper, we propose a novel automated, fast, and approximate segmentation technique. The input is a patient study consisting of a set of MR slices, and its output is a subset of the slices that include axis-parallel boxes that circumscribe the tumors. Our approach is based on an unsupervised change detection method that searches for the most dissimilar region (axis-parallel bounding boxes) between the left and the right halves of a brain in an axial view MR slice. This change detection process uses a novel score function based on Bhattacharya coefficient computed with gray level intensity histograms. We prove that this score function admits a very fast (linear in image height and width) search to locate the bounding box. The average dice coefficients for localizing brain tumors and edemas, over ten patient studies, are 0.57 and 0.52, respectively, which significantly exceeds the scores for two other competitive region-based bounding box techniques.


title: Influence of variable supply and load flexibility on Demand-Side Management

abstract: Demand-Side Management (DSM) refers to the shaping of the electrical load to improve the fit with the supply side [1]. One way to achieve this is to confront the user with a dynamic financial incentive to influence the demand. In previous work (see [2]) a crude reactive pricing algorithm has been shown to shave the peak of the aggregated load by over 25%. In this paper this approach is extended and evaluated in more detail. The algorithm is extended to cover scenarios where the supply is no longer fixed; its performance is investigated under varying conditions such as the percentage of the flexible load as well as the extent of the load's flexibility. The expected benefit of applying the algorithm to real world scenarios is predicted, allowing for an informed decision on whether or not to employ it in specific situations and under which pricing conditions.


title: Skew Detection and Correction in Document Images Based on Straight-Line Fitting

abstract: During document scanning, skew is inevitably introduced into the incoming document image. Since the algorithms for layout analysis and character recognition are generally very sensitive to the page skew, skew detection and correction in document images are the critical steps before layout analysis. In this paper, a novel skew detection method based on straight-line fitting is proposed. And a concept of Eigen-point is introduced. After the relations between the successive Eigen-points in every text line within a suitable sub-region were analyzed, the Eigen-points most possibly laid on the baselines are selected as samples for the straight-line fitting. The average of these baseline directions is computed, which corresponds to the degree of skew of the whole document image. Then a fast skew correction method based on the scanning line model is also presented. Experiments prove that the proposed approaches are fast and accurate.


title: High-Performance Design of YARN MapReduce on Modern HPC Clusters with Lustre and RDMA

abstract: The viability and benefits of running MapReduce over modern High Performance Computing (HPC) clusters, with high performance interconnects and parallel file systems, have attracted much attention in recent times due to its uniqueness of solving data analytics problems with a combination of Big Data and HPC technologies. Most HPC clusters follow the traditional Beowulf architecture with a separate parallel storage system (e.g. Lustre) and either no, or very limited, local storage. Since the MapReduce architecture relies heavily on the availability of local storage media, the Lustre-based global storage system in HPC clusters poses many new opportunities and challenges. In this paper, we propose a novel high-performance design for running YARN MapReduce on such HPC clusters by utilizing Lustre as the storage provider for intermediate data. We identify two different shuffle strategies, RDMA and Lustre Read, for this architecture and provide modules to dynamically detect the best strategy for a given scenario. Our results indicate that due to the performance characteristics of the underlying Lustre setup, one shuffle strategy may outperform another in different HPC environments, and our dynamic detection mechanism can deliver best performance based on the performance characteristics obtained during runtime of job execution. Through this design, we can achieve 44% performance benefit for shuffle-intensive workloads in leadership-class HPC systems. To the best of our knowledge, this is the first attempt to exploit performance characteristics of alternate shuffle strategies for YARN MapReduce with Lustre and RDMA.


title: A Smart Work Performance Measurement System for Police Officers

abstract: With rapid economic growth, the management of employees working outdoors gradually becomes more intensive. The traditional attendance systems typically focus on collecting employee attendance information and are not suited to the quantification of work performance. As a remedy to this issue, a smart work performance measurement system is proposed in this paper. The proposed system consists of three components: 1) a smartphone-based APP to collect employee attendance, work, and location information; 2) a data warehouse to preprocess and store the data; and 3) a smart data analysis center to make a comprehensive and systematic evaluation of employee work performance. Management can obtain the key information quickly and adjust work assignments based on performance. New solutions and algorithms for indoor and outdoor location, GPS deviation improvement, and work performance measurement are put forward. This system is being used by over 1100 police officers in one city traffic management bureau, and the observed results are encouraging, demonstrating the efficiency and accuracy of our system, and helping governments to better regulate the traffic operation and reduce associated costs.


title: A 15-kV class intelligent universal transformer for utility applications

abstract: This paper discloses the development of 15-kV class intelligent universal transformer (IUT) to show system level design, circuit topology, and prototype test results. The complete system is split into two stages: (1) high-voltage ac to low-voltage dc and (2) low-voltage dc to low-voltage ac. With the adoption of silicon carbide (SiC) devices, the high-voltage front-end ac to low voltage dc conversion stage achieves 98.4% efficiency, and the complete power stage can be naturally cooled. With the adoption of auxiliary resonant soft-switching inverter, the second stage achieves 99.2% efficiency. Overall the system has been demonstrated at 97.5% efficiency without forced-air cooling. Extended 8-hour testing was conducted to ensure long-term operation reliability. Overall efficiency and voltage regulation were compared with that of a conventional transformer for justification of IUT adoption.


title: ORIGINAL PAPER DialogDesigner: tools support for dialogue model design and evaluation

abstract: DialogDesigner is an integrated design and development environment that supports dialogue designers in creating an electronic dialogue model, writing dialogue snippets, running and analysing simulation sessions, getting graphical views of the model, making automatic evaluation regarding dialogue model well-formedness, compiling the model into run-time code, and extracting different presentations. DialogDesigner has been used for research purposes as well as in commercial projects. Its primary focus is on providing support for the development process. We explain underlying ideas, illustrate the functionality of DialogDesigner and discuss its strengths.


Bag-of-Temporal-SIFT-Words for Time Series Classification


title: Explaining Recommendations Using Contexts

abstract: Recommender systems support user decision-making, and explanations of recommendations further facilitate their usefulness. Previous explanation styles are based on similar users, similar items, demographics of users, and contents of items. Contexts, such as usage scenarios and accompanying persons, have not been used for explanations, although they influence user decisions. In this paper, we propose a context style explanation method, presenting contexts suitable for consuming recommended items. The expected impacts of context style explanations are 1) persuasiveness: recognition of suitable context for usage motivates users to consume items, and 2) usefulness: envisioning context helps users to make right choices because the values of items depend on contexts. We evaluate context style persuasiveness and usefulness by a crowdsourcing-based user study in a restaurant recommendation setting. The context style explanation is compared to demographic and content style explanations. We also combine context style and other explanation styles, confirming that hybrid styles improve persuasiveness and usefulness of explanation.


title: Design of a broadband multilayer coupler for UWB beamforming applications

abstract: In this paper, the design of a new 3 dB-compact microstrip coupler which works over an ultra wide-band from 3 to 10.6 GHz is presented. The proposed directional is a multilayer slot-coupled directional coupler composed of two lozenge microstrip lines coupled through a hexagonal-shaped slot etched on a common ground plane. The design exhibits a coupling of 3 dB in a band of 3 to 10.6 GHz, the return loss and isolation are less than 22 dB over the operating frequency band.


title: Variability Bugs in Highly Configurable Systems: A Qualitative Analysis

abstract: Variability-sensitive verification pursues effective analysis of the exponentially many variants of a program family. Several variability-aware techniques have been proposed, but researchers still lack examples of concrete bugs induced by variability, occurring in real large-scale systems. A collection of real world bugs is needed to evaluate tool implementations of variability-sensitive analyses by testing them on real bugs. We present a qualitative study of 98 diverse variability bugs (i.e., bugs that occur in some variants and not in others) collected from bug-fixing commits in the Linux, Apache, BusyBox, and Marlin repositories. We analyze each of the bugs, and record the results in a database. For each bug, we create a self-contained simplified version and a simplified patch, in order to help researchers who are not experts on these subject studies to understand them, so that they can use these bugs for evaluation of their tools. In addition, we provide single-function versions of the bugs, which are useful for evaluating intra-procedural analyses. A web-based user interface for the database allows to conveniently browse and visualize the collection of bugs. Our study provides insights into the nature and occurrence of variability bugs in four highly-configurable systems implemented in C/C++, and shows in what ways variability hinders comprehension and the uncovering of software bugs.


title: Semi-supervised Learning in Gigantic Image Collections

abstract: With the advent of the Internet it is now possible to collect hundreds of millions of images. These images come with varying degrees of label information. "Clean labels" can be manually obtained on a small fraction, "noisy labels" may be extracted automatically from surrounding text, while for most images there are no labels at all. Semi-supervised learning is a principled framework for combining these different label sources. However, it scales polynomially with the number of images, making it impractical for use on gigantic collections with hundreds of millions of images and thousands of classes. In this paper we show how to utilize recent results in machine learning to obtain highly efficient approximations for semi-supervised learning that are linear in the number of images. Specifically, we use the convergence of the eigenvectors of the normalized graph Laplacian to eigenfunctions of weighted Laplace-Beltrami operators. Our algorithm enables us to apply semi-supervised learning to a database of 80 million images gathered from the Internet.


title: Data Breaches, Phishing, or Malware?: Understanding the Risks of Stolen Credentials

abstract: In this paper, we present the first longitudinal measurement study of the underground ecosystem fueling credential theft and assess the risk it poses to millions of users. Over the course of March, 2016--March, 2017, we identify 788,000 potential victims of off-the-shelf keyloggers; 12.4 million potential victims of phishing kits; and 1.9 billion usernames and passwords exposed via data breaches and traded on blackmarket forums. Using this dataset, we explore to what degree the stolen passwords---which originate from thousands of online services---enable an attacker to obtain a victim's valid email credentials---and thus complete control of their online identity due to transitive trust. Drawing upon Google as a case study, we find 7--25% of exposed passwords match a victim's Google account. For these accounts, we show how hardening authentication mechanisms to include additional risk signals such as a user's historical geolocations and device profiles helps to mitigate the risk of hijacking. Beyond these risk metrics, we delve into the global reach of the miscreants involved in credential theft and the blackhat tools they rely on. We observe a remarkable lack of external pressure on bad actors, with phishing kit playbooks and keylogger capabilities remaining largely unchanged since the mid-2000s.


C4.5, Class Imbalance, and Cost Sensitivity: Why Under-sampling beats Over-sampling


title: Rail defect detection with real time image processing technique

abstract: Rail defect detection has a great importance for railway transportation. Because faults on the rails cause to problems such as the cost, the disruption of transportation and the safety. In this study, based real-time video processing algorithm used Morphological feature extraction is recommended to detection defects on railroad. The rail is detected by applying Hough Transform and image processing techniques to rail images obtained from the real time camera. Features of the detected rail images are extracted with applying morphological operations and defected regions are identified. Headcheck, fracture, scour and undulation failures are detected through proposed method. In this study, results with low uptime and high success rate are acquired by performing all steps of proposed algorithm to the images of rail taken under different lighting and direction.


title: Violent flows: Real-time detection of violent crowd behavior

abstract: Although surveillance video cameras are now widely used, their effectiveness is questionable. Here, we focus on the challenging task of monitoring crowded events for outbreaks of violence. Such scenes require a human surveyor to monitor multiple video screens, presenting crowds of people in a constantly changing sea of activity, and to identify signs of breaking violence early enough to alert help. With this in mind, we propose the following contributions: (1) We describe a novel approach to real-time detection of breaking violence in crowded scenes. Our method considers statistics of how flow-vector magnitudes change over time. These statistics, collected for short frame sequences, are represented using the VIolent Flows (ViF) descriptor. ViF descriptors are then classified as either violent or non-violent using linear SVM. (2) We present a unique data set of real-world surveillance videos, along with standard benchmarks designed to test both violent/non-violent classification, as well as real-time detection accuracy. Finally, (3) we provide empirical tests, comparing our method to state-of-the-art techniques, and demonstrating its effectiveness.


title: An in-depth characterisation of Bots and Humans on Twitter

abstract: Recent research has shown a substantial active presence of bots in online social networks (OSNs). In this paper we utilise our past work on studying bots (Stweeler) to comparatively analyse the usage and impact of bots and humans on Twitter, one of the largest OSNs in the world. We collect a large-scale Twitter dataset and define various metrics based on tweet metadata. We divide and filter the dataset in four popularity groups in terms of number of followers. Using a human annotation task we assign 'bot' and 'human' ground-truth labels to the dataset, and compare the annotations against an online bot detection tool for evaluation. We then ask a series of questions to discern important behavioural bot and human characteristics using metrics within and among four popularity groups. From the comparative analysis we draw important differences as well as surprising similarities between the two entities, thus paving the way for reliable classification of automated political infiltration, advertisement campaigns, and general bot detection.


title: Interactions between working memory , attention and eye movements

abstract: This paper reviews the recent findings on working memory, attention and eye movements. We discuss the research that shows that many phenomena related to visual attention taking place when selecting relevant information from the environment are similar to processes needed to keep information active in working memory. We discuss new data that show that when retrieving information from working memory, people may allocate visual spatial attention to the empty location in space that used to contain the information that has to be retrieved. Moreover, we show that maintaining a location in working memory not only may involve attention rehearsal, but might also recruit the oculomotor system. Recent findings seem to suggest that remembering a location may involve attention-based rehearsal in higher brain areas, while at the same time there is inhibition of specific motor programs at lower brain areas. We discuss the possibility that working memory functions do not reside at a special area in the brain, but emerge from the selective recruitment of brain areas that are typically involved in spatial attention and motor control.


title: Rapid spectrotemporal plasticity in primary auditory cortex during behavior

abstract: Complex natural and environmental sounds, such as speech and music, convey information along both spectral and temporal dimensions. The cortical representation of such stimuli rapidly adapts when animals become actively engaged in discriminating them. In this study, we examine the nature of these changes using simplified spectrotemporal versions (upward vs downward shifting tone sequences) with domestic ferrets (Mustela putorius). Cortical processing rapidly adapted to enhance the contrast between the two discriminated stimulus categories, by changing spectrotemporal receptive field properties to encode both the spectral and temporal structure of the tone sequences. Furthermore, the valence of the changes was closely linked to the task reward structure: stimuli associated with negative reward became enhanced relative to those associated with positive reward. These task- and-stimulus-related spectrotemporal receptive field changes occurred only in trained animals during, and immediately following, behavior. This plasticity was independently confirmed by parallel changes in a directionality function measured from the responses to the transition of tone sequences during task performance. The results demonstrate that induced patterns of rapid plasticity reflect closely the spectrotemporal structure of the task stimuli, thus extending the functional relevance of rapid task-related plasticity to the perception and learning of natural sounds such speech and animal vocalizations.


No more beating about the bush : A Step towards Idiom Handling for Indian Language NLP.


title: Applying a new hybrid model of embedded system development methodology on a flood detection system

abstract: The embedded system development methodology has not been as well established as the development methodology in software engineering. Involving inter-disciplinary activities in the development of hardware and software need to be further considered when developing an embedded system. This paper presents a new development model for an embedded system by the hybridization of selected development methodologies in software engineering and systems engineering, considering that they are ::: both essential in embedded system. The model is harmonized with embedded system design vital tasks and also non-functional properties following ::: the ISO/IEC 9126 standard.The model is then applied to a flood detection system for verification purposes.With the phases and steps in the new hybrid model for embedded systems development methodology carefully followed, ::: the system was built in a more systematic and structured manner addressing the every needs of an embedded system’s requirements.


title: Longitudinal Erythronychia: Retrospective Single-Center Study Evaluating Differential Diagnosis and the Likelihood of Malignancy

abstract: BACKGROUND ::: Longitudinal erythronychia (LE) is an underappreciated and understudied clinical diagnosis. The rates of malignancy and prevalence within the differential diagnosis are not well-established. ::: ::: ::: OBJECTIVE ::: To examine the prevalence of biopsy-proven diagnoses in patients presenting with LE. ::: ::: ::: METHODS ::: Retrospective single-center study of 65 consecutive patients undergoing biopsy of LE. ::: ::: ::: RESULTS ::: Malignancy was identified in only 3 of 65 cases (in situ carcinoma in 2 and melanoma in 1). Onychopapilloma was the most common diagnosis in cases of localized LE. Lichen planus was the most common diagnosis in cases of polydactylous (generalized) LE. ::: ::: ::: CONCLUSION ::: Longitudinal erythronychia is not a rare clinical finding. The differential diagnosis includes neoplasms, inflammatory conditions, infectious conditions, and scar. Clinicopathologic correlation is required to make the diagnosis in many cases. Malignancy is uncommon, but not rare, with squamous cell carcinoma in situ representing the most common malignancy.


title: 3D-printed upper limb prostheses: a review

abstract: AbstractGoal: This paper aims to provide an overview with quantitative information of existing 3D-printed upper limb prostheses. We will identify the benefits and drawbacks of 3D-printed devices to enable improvement of current devices based on the demands of prostheses users.Methods: A review was performed using Scopus, Web of Science and websites related to 3D-printing. Quantitative information on the mechanical and kinematic specifications and 3D-printing technology used was extracted from the papers and websites.Results: The overview (58 devices) provides the general specifications, the mechanical and kinematic specifications of the devices and information regarding the 3D-printing technology used for hands. The overview shows prostheses for all different upper limb amputation levels with different types of control and a maximum material cost of $500.Conclusion: A large range of various prostheses have been 3D-printed, of which the majority are used by children. Evidence with respect to the user accep...


1 Distance Measures for Detecting Geo-Social Similarity


title: Attention gated networks: Learning to leverage salient regions in medical images

abstract: Graphical abstract Figure. No Caption available. ABSTRACT We propose a novel attention gate (AG) model for medical image analysis that automatically learns to focus on target structures of varying shapes and sizes. Models trained with AGs implicitly learn to suppress irrelevant regions in an input image while highlighting salient features useful for a specific task. This enables us to eliminate the necessity of using explicit external tissue/organ localisation modules when using convolutional neural networks (CNNs). AGs can be easily integrated into standard CNN models such as VGG or U‐Net architectures with minimal computational overhead while increasing the model sensitivity and prediction accuracy. The proposed AG models are evaluated on a variety of tasks, including medical image classification and segmentation. For classification, we demonstrate the use case of AGs in scan plane detection for fetal ultrasound screening. We show that the proposed attention mechanism can provide efficient object localisation while improving the overall prediction performance by reducing false positives. For segmentation, the proposed architecture is evaluated on two large 3D CT abdominal datasets with manual annotations for multiple organs. Experimental results show that AG models consistently improve the prediction performance of the base architectures across different datasets and training sizes while preserving computational efficiency. Moreover, AGs guide the model activations to be focused around salient regions, which provides better insights into how model predictions are made. The source code for the proposed AG models is publicly available.


title: Treatment Outcome of Severe Acute Malnutrition and Its Determinants among Pediatric Patients in West Ethiopia

abstract: Background ::: Malnutrition is a silent killer that is underreported, underaddressed, and as a result underprioritized. It is reported that severe acute malnutrition is the commonest reason for pediatrics hospital admission in many poor countries; 25 to 30% of children with severe malnutrition die during hospital admissions. ::: ::: ::: Objective ::: To determine treatment outcome of severe acute malnutrition and identify its determinants among pediatric patients in pediatrics ward of Nekemte Referral Hospital. ::: ::: ::: Methods ::: A retrospective hospital-based cross-sectional study was done from November 2015 to April 2017. Data had been collected by using checklist for recording information from patient card and register book. Association between independent variables and depend variable was assessed using bivariate and stepwise multivariable logistic regression, respectively. Level of statistical significance was declared at p value < 0.05. ::: ::: ::: Results ::: Out of 205 admitted children with severe acute malnutrition, 137 (66.8%) cases were cured from SAM, 9 (4.4%) cases were died because of SAM, and (16.6%) cases were defaulter from SAM management, and 25 (12.2%) cases were transferred out. Multivariable logistic regression showed that children admitted with both edema and wasting AOR = 8.30, 95% CI (1.72, 40.09) P=0.008, children without hypothermia AOR = 2.91, 95%CI (1.10, 7.69) P=0.031, children who stay 8-14 days AOR = 3.86, 95%CI (1.01, 14.75) P=0.048, children without pneumonia AOR = 7.82, 95%CI (2.74, 222.29) P=0.001, children without anemia AOR = 3.22, 95%CI (1.04, 9.97) P=0.042, and children without HIV AOR = 9.21, 95% CI (2.20, 38.54) P=0.002 were more likely to be cured from severe acute malnutrition. ::: ::: ::: Conclusion ::: Treatment outcome of severe acute malnutrition in this study is good. It shows that around three-fourths of the children were cured. Factors such as admission criteria, hypothermia, length of stay, pneumonia, anemia, and presence of HIV were associated with treatment response.


title: Vision-Based SLAM and Moving Objects Tracking for the Perceptual Support of a Smart Walker Platform

abstract: The problems of vision-based detection and tracking of independently moving objects, localization and map construction are highly interrelated, in the sense that the solution of any of them provides valuable information to the solution of the others. In this paper, rather than trying to solve each of them in isolation, we propose a method that treats all of them simultaneously. More specifically, given visual input acquired by a moving RGBD camera, the method detects independently moving objects and tracks them in time. Additionally, the method estimates the camera (ego)motion and the motion of the tracked objects in a coordinate system that is attached to the static environment, a map of which is progressively built from scratch. The loose assumptions that the method adopts with respect to the problem parameters make it a valuable component for any robotic platform that moves in a dynamic environment and requires simultaneous tracking of moving objects, egomotion estimation and map construction. The usability of the method is further enhanced by its robustness and its low computational requirements that permit real time execution even on low-end CPUs.


title: Portable wireless neurofeedback system of EEG alpha rhythm enhances memory

abstract: BackgroundEffect of neurofeedback training (NFT) on enhancement of cognitive function or amelioration of clinical symptoms is inconclusive. The trainability of brain rhythm using a neurofeedback system is uncertainty because various experimental designs are used in previous studies. The current study aimed to develop a portable wireless NFT system for alpha rhythm and to validate effect of the NFT system on memory with a sham-controlled group.MethodsThe proposed system contained an EEG signal analysis device and a smartphone with wireless Bluetooth low-energy technology. Instantaneous 1-s EEG power and contiguous 5-min EEG power throughout the training were developed as feedback information. The training performance and its progression were kept to boost usability of our device. Participants were blinded and randomly assigned into either the control group receiving random 4-Hz power or Alpha group receiving 8–12-Hz power. Working memory and episodic memory were assessed by the backward digital span task and word-pair task, respectively.ResultsThe portable neurofeedback system had advantages of a tiny size and long-term recording and demonstrated trainability of alpha rhythm in terms of significant increase of power and duration of 8–12 Hz. Moreover, accuracies of the backward digital span task and word-pair task showed significant enhancement in the Alpha group after training compared to the control group.ConclusionsOur tiny portable device demonstrated success trainability of alpha rhythm and enhanced two kinds of memories. The present study suggest that the portable neurofeedback system provides an alternative intervention for memory enhancement.


title: Review of power curve modelling for wind turbines

abstract: Currently, variable speed wind turbine generators (VSWTs) are the type of wind turbines most widely installed. For wind energy studies, they are usually modelled by means the approximation of the manufacturer power curve using a generic equation. In literature, several expressions to do this approximation can be found; nevertheless, there is not much information about which is the most appropriate to represent the energy produced by a VSWT. For this reason, in this paper, it is carried out a review of the equations commonly used to represent the power curves of VSWTs: polynomial power curve, exponential power curve, cubic power curve and approximate cubic power curve. They have been compared to manufacturer power curves by using the coefficients of determination, as fitness indicators, and by using the estimation of energy production. Data gathered from nearly 200 commercial VSWTs, ranging from 225 to 7500kW, has been used for this analysis. Results of the analysis presented in the paper show that exponential and cubic approximations give the higher R2 values and the lower error in energy estimation. With the approximate cubic power curve quite high values of R2 and low errors in energy estimation are achieved, which makes this kind of approximation very interesting due to its simplicity. Finally, the polynomial power curve shows the worst results mainly due to its sensitivity to the data given by the manufacturer.


title: Comparing contextual and flat representations ofknowledge: a concrete case about football data

abstract: The capability of dealing with context sensitive knowledge is recognized as a crucial aspect in the management of massive amounts of Semantic Web (SW) data. Contextual knowledge can be modelled either by adopting the primitives from RDF/OWL based SW languages or by extending such languages with new specific constructs for context representation. In this paper, we show the benefits of the context-based solution by comparing modelling and reasoning in the two approaches on the paradigmatic use case of FIFA World Cup. The comparison considers the three key aspects of engineering and exploiting knowledge: (i) simplicity and expressivity of the (formal) language; (ii) compactness of the representation; and (iii) efficiency of reasoning. As for (i), we show that the context-based language enables the construction of simpler and more intuitive models while the RDF/OWL "flat" model presents practical limitations in modelling cross-contextual knowledge. For (ii), we show that the contextualized model is more compact than the OWL based model. Finally for (iii), query answering in the context-based model outperforms in most of the cases performances on the flat model.


Ac ce pt ed M an us cr ip t Towards Energy-Efficient Scheduling for Real-Time Tasks under Uncertain Cloud Computing Environment


Efficient Exploration for Dialogue Policy Learning with BBQ Networks & Replay Buffer Spiking


title: Fast Image Recovery Using Variable Splitting and Constrained Optimization

abstract: We propose a new fast algorithm for solving one of the standard formulations of image restoration and reconstruction which consists of an unconstrained optimization problem where the objective includes an $\ell_2$ data-fidelity term and a non-smooth regularizer. This formulation allows both wavelet-based (with orthogonal or frame-based representations) regularization or total-variation regularization. Our approach is based on a variable splitting to obtain an equivalent constrained optimization formulation, which is then addressed with an augmented Lagrangian method. The proposed algorithm is an instance of the so-called"alternating direction method of multipliers", for which convergence has been proved. Experiments on a set of image restoration and reconstruction benchmark problems show that the proposed algorithm is faster than the current state of the art methods.


The Impact of Prediction Uncertainty in Recommendations for Health-Related Behavior


title: Is time spent playing video games associated with mental health, cognitive and social skills in young children?

abstract: Background ::: Video games are one of the favourite leisure activities of children; the influence on child health is usually perceived to be negative. The present study assessed the association between the amount of time spent playing video games and children mental health as well as cognitive and social skills.


title: On the prospects of full-duplex military radios

abstract: In-band full-duplex (FD) operation can be regarded as one of the greatest discoveries in civilian/commercial wireless communications so far in this century. The concept is significant because it can as much as double the spectral efficiency of wireless data transmission by exploiting the new-found capability for simultaneous transmission and reception (STAR) that is facilitated by advanced self-interference cancellation (SIC) techniques. As the first of its kind, this paper surveys the prospects of exploiting the emerging FD radio technology in military communication applications as well. In addition to spectrally efficient two-way data transmission, the STAR capability could give a major technical advantage for armed forces by allowing their radio transceivers to conduct electronic warfare at the same time when they are also receiving or transmitting information signals at the same frequency band. After providing a detailed introduction to FD transceiver architectures and SIC requirements in military communications, this paper outlines and analyzes some potential defensive and offensive applications of the STAR capability.


title: Basic Concepts on AUTOSAR Development

abstract: AUTOSAR(AUTomotiveOpen System ARchitecture) is an open and standardized automotive software architecture, jointly developed by automobile manufacturers, suppliers and tool developers. The AUTOSAR standard will serve as a platform upon which future vehicle applications will be implemented and will also serve to minimize the current barriers between functional domains. To improve cost-efficiency and reusability, AUTOSAR separates application software from the associated hardware. This stage, AUTOSAR is still in the promotion phase, and many countries are committed to its applications, including Japan and South Korea. It will widespread in automotive industry. This paper showed the basic concepts of AUTOSAR and comparisons of some confusing concepts for future in-depth study.


title: Solving Difficult LR Parsing Conflicts by Postponing Them

abstract: Though yacc-like LR parser generators provide ways to solve shift-reduce ::: conflicts using token precedences, no mechanisms are provided for the ::: resolution of difficult shift-reduce or reduce-reduce conflicts. To solve ::: this kind of conflicts the language designer has to modify the grammar. All ::: the solutions for dealing with these difficult conflicts branch at each ::: alternative, leading to the exploration of the whole search tree. These ::: strategies differ in the way the tree is explored: GLR, Backtracking LR, ::: Backtracking LR with priorities, etc. This paper explores an entirely ::: different path: to extend the yacc conflict resolution sublanguage with new ::: constructs allowing the programmers to explicit the way the conflict must be ::: solved. These extensions supply ways to resolve any kind of conflicts, ::: including those that can not be solved using static precedences. The method ::: makes also feasible the parsing of grammars whose ambiguity must be solved in ::: terms of the semantic context. Besides, it brings to LR-parsing a common ::: LL-parsing feature: the advantage of providing full control over the specific ::: trees the user wants to build.


title: Voltage flicker mitigation using PWM-based distribution STATCOM

abstract: Voltage flicker, a phenomenon of annoying fight intensity fluctuation, caused by large rapid industrial load changes, has been a major concern for both power companies and customers in the area of power quality. The fast response of the distribution static compensator (DSTATCOM) makes it the efficient solution for improving power quality in distribution systems. In this paper, a voltage flicker phenomena in a 69/13.8 kV distribution system Is modeled and simulated using MATLAB/Simulink Power System Blockset (PSB). Voltage flicker mitigation studies with a current controlled PWM-based DSTATCOM are also performed and discussed.


title: A specifier's introduction to formal methods

abstract: Formal methods used in developing computer systems (i.e. mathematically based techniques for describing system properties) are defined, and their role is delineated. Formal specification languages, which provide the formal method's mathematical basis, are examined. Certain pragmatic concerns about formal methods and their users, uses, and characteristics are discussed. Six well-known or commonly used formal methods are illustrated by simple examples. They are Z, VDM, Larch, temporal logic, CSP, and transition axioms. >


title: LCARS: a location-content-aware recommender system

abstract: Newly emerging location-based and event-based social network services provide us with a new platform to understand users' preferences based on their activity history. A user can only visit a limited number of venues/events and most of them are within a limited distance range, so the user-item matrix is very sparse, which creates a big challenge for traditional collaborative filtering-based recommender systems. The problem becomes more challenging when people travel to a new city where they have no activity history. In this paper, we propose LCARS, a location-content-aware recommender system that offers a particular user a set of venues (e.g., restaurants) or events (e.g., concerts and exhibitions) by giving consideration to both personal interest and local preference. This recommender system can facilitate people's travel not only near the area in which they live, but also in a city that is new to them. Specifically, LCARS consists of two components: offline modeling and online recommendation. The offline modeling part, called LCA-LDA, is designed to learn the interest of each individual user and the local preference of each individual city by capturing item co-occurrence patterns and exploiting item contents. The online recommendation part automatically combines the learnt interest of the querying user and the local preference of the querying city to produce the top-k recommendations. To speed up this online process, a scalable query processing technique is developed by extending the classic Threshold Algorithm (TA). We evaluate the performance of our recommender system on two large-scale real data sets, DoubanEvent and Foursquare. The results show the superiority of LCARS in recommending spatial items for users, especially when traveling to new cities, in terms of both effectiveness and efficiency.


title: EMPHASIS: An Emotional Phoneme-based Acoustic Model for Speech Synthesis System

abstract: We present EMPHASIS, an emotional phoneme-based acoustic model for speech synthesis system. EMPHASIS includes a phoneme duration prediction model and an acoustic parameter prediction model. It uses a CBHG-based regression network to model the dependencies between linguistic features and acoustic features. We modify the input and output layer structures of the network to improve the performance. For the linguistic features, we apply a feature grouping strategy to enhance emotional and prosodic features. The acoustic parameters are designed to be suitable for the regression task and waveform reconstruction. EMPHASIS can synthesize speech in real-time and generate expressive interrogative and exclamatory speech with high audio quality. EMPHASIS is designed to be a multi-lingual model and can synthesize Mandarin-English speech for now. In the experiment of emotional speech synthesis, it achieves better subjective results than other real-time speech synthesis systems.


title: Automating the process of dividing a map image into sections : Using Tesseract OCR and pixel traversing

abstract: This paper presents an algorithm with the purpose of automatically dividing a simple floor plan into sections. Sections include names, size and location on the image, all of which will be automatic ...


title: Multiple Hypothesis Tracking Revisited

abstract: This paper revisits the classical multiple hypotheses tracking (MHT) algorithm in a tracking-by-detection framework. The success of MHT largely depends on the ability to maintain a small list of potential hypotheses, which can be facilitated with the accurate object detectors that are currently available. We demonstrate that a classical MHT implementation from the 90's can come surprisingly close to the performance of state-of-the-art methods on standard benchmark datasets. In order to further utilize the strength of MHT in exploiting higher-order information, we introduce a method for training online appearance models for each track hypothesis. We show that appearance models can be learned efficiently via a regularized least squares framework, requiring only a few extra operations for each hypothesis branch. We obtain state-of-the-art results on popular tracking-by-detection datasets such as PETS and the recent MOT challenge.


title: Lattice reduction-aided regularized block diagonalization for multiuser MIMO systems

abstract: By employing the regularized block diagonalization (RBD) preprocessing technique, the multi-user multi-input multi-output (MU-MIMO) broadcast channel is decomposed into multiple parallel independent single user multi-input multi-output (SU-MIMO) channels and achieves the maximum diversity order at high data rates. The computational complexity of RBD, however, is relatively high due to two singular value decomposition (SVD) operations. In this paper, a low-complexity lattice reduction aided RBD is proposed. The first SVD is replaced by a QR decomposition, and the orthogonalization procedure provided by the second SVD is substituted by a lattice reduction whose complexity is mainly contributed by a QR decomposition. Simulation results show that the proposed algorithm can achieve almost the same sum-rate as RBD while offering a lower complexity and substantial BER gains with perfect as well as imperfect channel state information at the transmit side.


Deep Learning Powered In-Session Contextual Ranking using Clickthrough Data


title: A simulation framework for service-oriented computing systems

abstract: An SOA-compliant DEVS (SOAD) simulation framework is proposed for modeling service-oriented computing systems. A set of novel abstract component models that conform to the SOA principles and are grounded in the DEVS formalism is developed. The approach supports construction of hierarchical composition of service models with feedback relationships. A SOAD Simulator (SOADS) is designed and implemented. An exemplar model of a basic service-oriented computing system is described. A representative experiment capturing throughput and timeliness QoS attributes for the exemplar model is devised, simulated, and described. The paper concludes with the concept of community-based development of the SOAD framework and tools.


title: Chronological Citation Recommendation with Information-Need Shifting

abstract: As the volume of publications has increased dramatically, an urgent need has developed to assist researchers in locating high-quality, candidate-cited papers from a research repository. Traditional scholarly-recommendation approaches ignore the chronological nature of citation recommendations. In this study, we propose a novel method called "Chronological Citation Recommendation" which assumes initial user information needs could shift while users are searching for papers in different time slices. We model the information-need shifts with two-level modeling: dynamic time-related ranking feature construction and dynamic evolving feature weight training. In more detail, we employed a supervised document influence model to characterize the content "time-varying" dynamics and constructed a novel heterogeneous graph that encapsulates dynamic topic-based information, time-decay paper/topic citation information, and word-based information. We applied multiple meta-paths for different ranking hypotheses which carried different types of information for citation recommendation in various time slices, along with information-need shifting. We also used multiple learning-to-rank models to optimize the feature weights for different time slices to generate the final "Chronological Citation Recommendation" rankings. The use of Chronological Citation Recommendation suggests time-series ranking lists based on initial user textual information need and characterizes the information-need shifting. Experiments on the ACM corpus show that Chronological Citation Recommendation can significantly enhance citation recommendation performance.


Image to LaTeX via Neural Networks 1 IMAGE TO LATEX VIA NEURAL NETWORKS CS 297 Report


title: Islanding detection of active distribution system with parallel inverters

abstract: Average absolute frequency deviation value (AFDVavg) based active islanding detection technique is recently introduced for islanding detection of active distribution system with a single active DG. This paper implements, evaluates and analyze the performance of AFDVavg technique for active distribution system with parallel inverter. The main focus is on islanding detection of an ADN with parallel inverters including the worst case i.e. zero power imbalance condition. The case study is done on an ADN which is energized by two parallel inverter based DGs. Worst case of islanding has been simulated successfully by adjusting the equivalent load of ADN to match the total generation. The resultant quality factor of the equivalent load is kept 1 for testing islanding. Computer simulations are done in MATLAB.


title: Urdu Word Segmentation

abstract: Word Segmentation is the foremost obligatory task in almost all the NLP applications where the initial phase requires tokenization of input into words. Urdu is amongst the Asian languages that face word segmentation challenge. However, unlike other Asian languages, word segmentation in Urdu not only has space omission errors but also space insertion errors. This paper discusses how orthographic and linguistic features in Urdu trigger these two problems. It also discusses the work that has been done to tokenize input text. We employ a hybrid solution that performs an n-gram ranking on top of rule based maximum matching heuristic. Our best technique gives an error detection of 85.8% and overall accuracy of 95.8%. Further issues and possible future directions are also discussed.


title: Deep Recurrent Neural Networks for Hyperspectral Image Classification

abstract: In recent years, vector-based machine learning algorithms, such as random forests, support vector machines, and 1-D convolutional neural networks, have shown promising results in hyperspectral image classification. Such methodologies, nevertheless, can lead to information loss in representing hyperspectral pixels, which intrinsically have a sequence-based data structure. A recurrent neural network (RNN), an important branch of the deep learning family, is mainly designed to handle sequential data. Can sequence-based RNN be an effective method of hyperspectral image classification? In this paper, we propose a novel RNN model that can effectively analyze hyperspectral pixels as sequential data and then determine information categories via network reasoning. As far as we know, this is the first time that an RNN framework has been proposed for hyperspectral image classification. Specifically, our RNN makes use of a newly proposed activation function, parametric rectified tanh (PRetanh), for hyperspectral sequential data analysis instead of the popular tanh or rectified linear unit. The proposed activation function makes it possible to use fairly high learning rates without the risk of divergence during the training procedure. Moreover, a modified gated recurrent unit, which uses PRetanh for hidden representation, is adopted to construct the recurrent layer in our network to efficiently process hyperspectral data and reduce the total number of parameters. Experimental results on three airborne hyperspectral images suggest competitive performance in the proposed mode. In addition, the proposed network architecture opens a new window for future research, showcasing the huge potential of deep recurrent networks for hyperspectral data analysis.


title: Named data networking for IoT: An architectural perspective

abstract: The Named Data Networking (NDN) project is emerging as one of the most promising information-centric future Internet architectures. Besides NDN recognized potential as a content retrieval solution in wired and wireless domains, its innovative concepts, such as named content, name-based routing and in-network caching, particularly suit the requirements of Internet of Things (IoT) interconnecting billions of heterogeneous objects. IoT highly differs from today's Internet due to resource-constrained devices, massive volumes of small exchanged data, and traffic type diversity. The study in this paper addresses the design of a high-level NDN architecture, whose main components are overhauled to specifically meet the IoT challenges.


title: Terrorism Event Classification Using Fuzzy Inference Systems

abstract: Terrorism has led to many problems in Thai societies, not only property damage but also civilian casualties. Predicting terrorism activities in advance can help prepare and manage risk from sabotage by these activities. This paper proposes a framework focusing on event classification in terrorism domain using fuzzy inference systems (FISs). Each FIS is a decision-making model combining fuzzy logic and approximate reasoning. It is generated in five main parts: the input interface, the fuzzification interface, knowledge base unit, decision making unit and output defuzzification interface. Adaptive neuro-fuzzy inference system (ANFIS) is a FIS model adapted by combining the fuzzy logic and neural network. The ANFIS utilizes automatic identification of fuzzy logic rules and adjustment of membership function (MF). Moreover, neural network can directly learn from data set to construct fuzzy logic rules and MF implemented in various applications. FIS settings are evaluated based on two comparisons. The first evaluation is the comparison between unstructured and structured events using the same FIS setting. The second comparison is the model settings between FIS and ANFIS for classifying structured events. The data set consists of news articles related to terrorism events in three southern provinces of Thailand. The experimental results show that the classification performance of the FIS resulting from structured events achieves satisfactory accuracy and is better than the unstructured events. In addition, the classification of structured events using ANFIS gives higher performance than the events using only FIS in the prediction of terrorism events.


title: Benefits of a Population: Five Mechanisms That Advantage Population-Based Algorithms

abstract: This paper identifies five distinct mechanisms by which a population-based algorithm might have an advantage over a solo-search algorithm in classical optimization. These mechanisms are illustrated through a number of toy problems. Simulations are presented comparing different search algorithms on these problems. The plausibility of these mechanisms occurring in classical optimization problems is discussed. The first mechanism we consider relies on putting together building blocks from different solutions. This is extended to include problems containing critical variables. The second mechanism is the result of focusing of the search caused by crossover. Also discussed in this context is strong focusing produced by averaging many solutions. The next mechanism to be examined is the ability of a population to act as a low-pass filter of the landscape, ignoring local distractions. The fourth mechanism is a population's ability to search different parts of the fitness landscape, thus hedging against bad luck in the initial position or the decisions it makes. The final mechanism is the opportunity of learning useful parameter values to balance exploration against exploitation.


title: Large-Scale Taxonomy Mapping for Restructuring and Integrating Wikipedia

abstract: We present a knowledge-rich methodology for disambiguating Wikipedia categories with WordNet synsets and using this semantic information to restructure a taxonomy automatically generated from the Wikipedia system of categories. We evaluate against a manual gold standard and show that both category disambiguation and taxonomy restructuring perform with high accuracy. Besides, we assess these methods on automatically generated datasets and show that we are able to effectively enrich WordNet with a large number of instances from Wikipedia. Our approach produces an integrated resource, thus bringing together the fine-grained classification of instances in Wikipedia and a well-structured top-level taxonomy from WordNet.


title: Language identification in the limit

abstract: Language learnability has been investigated. This refers to the following situation: A class of possible languages is specified, together with a method of presenting information to the learner about an unknown language, which is to be chosen from the class. The question is now asked, “Is the information sufficient to determine which of the possible languages is the unknown language?” Many definitions of learnability are possible, but only the following is considered here: Time is quantized and has a finite starting time. At each time the learner receives a unit of information and is to make a guess as to the identity of the unknown language on the basis of the information received so far. This process continues forever. The class of languages will be considered learnable with respect to the specified method of information presentation if there is an algorithm that the learner can use to make his guesses, the algorithm having the following property: Given any language of the class, there is some finite time after which the guesses will all be the same and they will be correct. In this preliminary investigation, a language is taken to be a set of strings on some finite alphabet. The alphabet is the same for all languages of the class. Several variations of each of the following two basic methods of information presentation are investigated: A text for a language generates the strings of the language in any order such that every string of the language occurs at least once. An informant for a language tells whether a string is in the language, and chooses the strings in some order such that every string occurs at least once. It was found that the class of context-sensitive languages is learnable from an informant, but that not even the class of regular languages is learnable from a text.


title: Ball tracking in sports: a survey

abstract: Increase in the number of sport lovers in games like football, cricket, etc. has created a need for digging, analyzing and presenting more and more multidimensional information to them. Different classes of people require different kinds of information and this expands the space and scale of the required information. Tracking of ball movement is of utmost importance for extracting any information from the ball based sports video sequences. Based on the literature survey, we have initially proposed a block diagram depicting different steps and flow of a general tracking process. The paper further follows the same flow throughout. Detection is the first step of tracking. Dynamic and unpredictable nature of ball appearance, movement and continuously changing background make the detection and tracking processes challenging. Due to these challenges, many researchers have been attracted to this problem and have produced good results under specific conditions. However, generalization of the published work and algorithms to different sports is a distant dream. This paper is an effort to present an exhaustive survey of all the published research works on ball tracking in a categorical manner. The work also reviews the used techniques, their performance, advantages, limitations and their suitability for a particular sport. Finally, we present discussions on the published work so far and our views and opinions followed by a modified block diagram of the tracking process. The paper concludes with the final observations and suggestions on scope of future work.


title: Evidence based medicine: what it is and what it isn't.

abstract: It's about integrating individual clinical expertise and the best external evidence ::: ::: Evidence based medicine, whose philosophical origins extend back to mid-19th century Paris and earlier, remains a hot topic for clinicians, public health practitioners, purchasers, planners, and the public. There are now frequent workshops in how to practice and teach it (one sponsored by the BMJ will be held in London on 24 April); undergraduate1 and postgraduate2 training programmes are incorporating it3 (or pondering how to do so); British centres for evidence based practice have been established or planned in adult medicine, child health, surgery, pathology, pharmacotherapy, nursing, general practice, and dentistry; the Cochrane Collaboration and Britain's Centre for Review and Dissemination in York are providing systematic reviews of the effects of health care; new evidence based practice journals are being launched; and it has become a common topic in the lay media. But enthusiasm has been mixed with some negative reaction.4 5 6 Criticism has ranged from evidence based medicine being old hat to it being a dangerous innovation, perpetrated by the arrogant to serve cost cutters and suppress clinical freedom. As evidence based medicine continues to evolve and adapt, now is a useful time to refine the discussion of what it is and what it is not. ::: ::: Evidence based medicine is the conscientious, explicit, and judicious use of current best evidence in making decisions about the care of individual patients. The …


title: Learning variable length units for SMT between related languages via Byte Pair Encoding

abstract: We explore the use of segments learnt using Byte Pair Encoding (referred to as BPE units) as basic units for statistical machine translation between related languages and compare it with orthographic syllables, which are currently the best performing basic units for this translation task. BPE identifies the most frequent character sequences as basic units, while orthographic syllables are linguistically motivated pseudo-syllables. We show that BPE units outperform orthographic syllables as units of translation, showing up to 11% increase in BLEU scores. In addition, BPE can be applied to any writing system, while orthographic syllables can be used only for languages whose writing systems use vowel representations. We show that BPE units outperform word and morpheme level units for translation involving languages like Urdu, Japanese whose writing systems do not use vowels (either completely or partially). Across many language pairs, spanning multiple language families and types of writing systems, we show that translation with BPE segments outperforms orthographic syllables, especially for morphologically rich languages.


title: A formal theory for conceptualizing artefacts and tool manipulations.

abstract: Artefacts (physical and institutional) are ubiquitous of our social environment. We live in a tight network of socio-technical systems, which are systems where agents interact with created objects. There is an increasing need for rigorous methods to model, specify, and reason about socio-technical systems in general, and about artefacts and their functions in particular. We propose a formal theory that serves at the conceptualization of artefacts and their manipulations: design, implementation, existence, use, and persistence.


title: On Distributed Fuzzy Decision Trees for Big Data

abstract: Fuzzy decision trees (FDTs) have shown to be an effective solution in the framework of fuzzy classification. The approaches proposed so far to FDT learning, however, have generally neglected time and space requirements. In this paper, we propose a distributed FDT learning scheme shaped according to the MapReduce programming model for generating both binary and multiway FDTs from big data. The scheme relies on a novel distributed fuzzy discretizer that generates a strong fuzzy partition for each continuous attribute based on fuzzy information entropy. The fuzzy partitions are, therefore, used as an input to the FDT learning algorithm, which employs fuzzy information gain for selecting the attributes at the decision nodes. We have implemented the FDT learning scheme on the Apache Spark framework. We have used ten real-world publicly available big datasets for evaluating the behavior of the scheme along three dimensions: 1) performance in terms of classification accuracy, model complexity, and execution time; 2) scalability varying the number of computing units; and 3) ability to efficiently accommodate an increasing dataset size. We have demonstrated that the proposed scheme turns out to be suitable for managing big datasets even with a modest commodity hardware support. Finally, we have used the distributed decision tree learning algorithm implemented in the MLLib library and the Chi-FRBCS-BigData algorithm, a MapReduce distributed fuzzy rule-based classification system, for comparative analysis.


title: HEGEMONIC MASCULINITY Rethinking the Concept

abstract: The concept of hegemonic masculinity has influenced gender studies across many academic fields but has also attracted serious criticism. The authors trace the origin of the concept in a convergence of ideas in the early 1980s and map the ways it was applied when research on men and masculinities expanded. Evaluating the principal criticisms, the authors defend the underlying concept of masculinity, which in most research use is neither reified nor essentialist. However, the criticism of trait models of gender and rigid typologies is sound. The treatment of the subject in research on hegemonic masculinity can be improved with the aid of recent psychological models, although limits to discursive flexibility must be recognized. The concept of hegemonic masculinity does not equate to a model of social reproduction; we need to recognize social struggles in which subordinated masculinities influence dominant forms. Finally, the authors review what has been confirmed from early formulations (the idea of multiple...


title: A scientific theory of Ars Memoriae: Spatial view cells in a continuous attractor network with linked items.

abstract: The art of memory (ars memoriae) used since classical times includes using a well-known scene to associate each view or part of the scene with a different item in a speech. This memory technique is also known as the “method of loci.” The new theory is proposed that this type of memory is implemented in the CA3 region of the hippocampus where there are spatial view cells in primates that allow a particular view to be associated with a particular object in an event or episodic memory. Given that the CA3 cells with their extensive recurrent collateral system connecting different CA3 cells, and associative synaptic modifiability, form an autoassociation or attractor network, the spatial view cells with their approximately Gaussian view fields become linked in a continuous attractor network. As the view space is traversed continuously (e.g., by self-motion or imagined self-motion across the scene), the views are therefore successively recalled in the correct order, with no view missing, and with low interference between the items to be recalled. Given that each spatial view has been associated with a different discrete item, the items are recalled in the correct order, with none missing. This is the first neuroscience theory of ars memoriae. The theory provides a foundation for understanding how a key feature of ars memoriae, the ability to use a spatial scene to encode a sequence of items to be remembered, is implemented.


title: Phase II study of carboxyamidotriazole in patients with advanced renal cell carcinoma refractory to immunotherapy

abstract: BACKGROUND ::: ::: The current study evaluated the response rate and 6-month time to disease progression of the antiangiogenesis agent carboxyamidotriazole (CAI) in patients with metastatic renal cell carcinoma (RCC). ::: ::: ::: ::: METHODS ::: ::: Fifty-seven patients with histologically confirmed metastatic RCC that progressed after biologic therapy (interferon or interleukin-2) were enrolled. Four patients were ineligible. CAI was administered orally as a 28-day cycle. Response and time to disease progression were evaluated. ::: ::: ::: ::: RESULTS ::: ::: Fifteen of 53 eligible patients received > 5 cycles, but 13 patients eventually discontinued treatment because of progressive disease. The majority of toxicities were Grade 1. However, Grade 3/4 toxicities did occur, the majority of which were gastrointestinal in nature. One of 47 patients evaluable achieved a partial response (1.9%) lasting 172 days. Six of 53 patients were alive and disease progression free at 6 months from the start of treatment (11.3%). The median overall survival was 12.5 months. The survival periods in the low-risk, intermediate-risk, and poor-risk groups were 16.2 months, 20.9 months, and 5.8 months, respectively. ::: ::: ::: ::: CONCLUSIONS ::: ::: Patients in trials of second-line therapy appear to have a better prognosis than previously considered, in part because they are eligible for another clinical trial. CAI was found to have little to no effect on the natural history of progressive RCC. Cancer 2005. © 2005 American Cancer Society.


URIS : Underwater Robotic Intelligent System


title: Sustained Competitive Advantage in Agribusiness: Applying the Resource-Based Theory to Human Resources

abstract: An Agribusiness boom is underway in India, and it needs big talent to drive its growth. Growth in agricultural productivity will build incomes and assets, and although somewhat cyclical, at the macro level, agriculture in India will inherently be recession proof over the long term. The greatest achievement for India will come from sustained self-sufficiency in food. Food consumption is leading the boom in agriculture. India is expected to overtake China as the world’s largest nation. With the world’s youngest population, food and Agri sector is set to triple by 2020 from US $328 to US $895 Billion in just six years from now. Huge opportunities in Agribusiness are attracting investments in primary production as well as on the output side in food processing and distribution. Organized retail is growing at 35% CAGR and so are many input industries, all of whom are demanding more capable people to manage growth. This is resulting in a shortage of trained agribusiness talent. Recognizing the opportunities that will continue to grow in the future, leading corporates are reinventing their induction, retention and leadership plans for expanding their Agribusiness talent pool in India. The article provides insights and opportunities that are attracting talent to agribusiness.


title: Phish Phactors: Offensive and Defensive Strategies

abstract: Abstract Phishing attacks attempt to fraudulently solicit sensitive information from a user by masquerading as a known trustworthy agent. They commonly use spoofed emails in association with fake websites in order to coerce a user into revealing personal financial data. Phishing is now a serious problem with criminals adopting the well-developed and well-known techniques to exploit Internet users with sophisticated attacks. Phishers are known to have successfully attacked an estimated 1.2 million users and stolen an estimated US$929 million in the twelve months to May 2005. This chapter aims to provide the current status of phishing attack techniques and defense methods. We first provide an overview of the fundamental phishing techniques for delivering a successful attack, such as bulk emailing, fake websites and detection avoidance using a variety of obfuscation techniques. We then survey more sophisticated methods that may deceive even knowledgeable and vigilant users. These techniques do not rely on naive email users and simple websites, but use highly realistic fake websites, generic hacking techniques (such as DNS poisoning or cross site scripting) or actively exploit browser vulnerabilities. For example, a Man-In-The-Middle attack or the use of DNS poisoning can easily fool even an advanced user who may be aware of phishing attacks. Quite a few defensive methods have been developed, although many are still in the early stage of development. URL obfuscation can be rather reliably detected using analysis algorithms. Fake websites can also be detected automatically with a low false positive ratio by comparing them with the real websites. Clients can utilize anti-phishing-capable devices or software such as anti-virus, anti-spam, anti-spyware, or IDS. Web browsers can be armed with anti-phishing plug-ins such as Spoofstick or SpoofGuard. Given the damage that can potentially done by a phishing attack, a diverse range of efforts are being made to protect ordinary users (such as in user education, reporting and response and legal protection). The outlook is not entirely bleak against phishing given the technical and social remedies being pursued. If organizations prepare well, remain vigilant and follow attack trends carefully, they can respond quickly and effectively with a range of techniques to defend their customers' data. If individuals take a responsibility for their protection and adopt a defense-in-depth approach, they can shield themselves against the most sophisticated attacks. Although there is no simple solution, active and aware users and organizations have the ability to form a strangle-hold on this ever-growing threat.


title: Fast, high-contrast imaging of animal development with scanned light sheet–based structured-illumination microscopy

abstract: The combination of digital scanned laser light sheet microscopy and incoherent structured illumination allows intrinsic removal of scattered background fluorescence from the desired fluorescent signal. This provides substantial advantages for imaging nontransparent organisms and allowed reconstruction of a fly digital embryo from a developing Drosophila embryo.


title: Causal Inference Using the Algorithmic Markov Condition

abstract: Inferring the causal structure that links n observables is usually based upon detecting statistical dependences and choosing simple graphs that make the joint measure Markovian. Here we argue why causal inference is also possible when the sample size is one. We develop a theory how to generate causal graphs explaining similarities between single objects. To this end, we replace the notion of conditional stochastic independence in the causal Markov condition with the vanishing of conditional algorithmic mutual information and describe the corresponding causal inference rules. We explain why a consistent reformulation of causal inference in terms of algorithmic complexity implies a new inference principle that takes into account also the complexity of conditional probability densities, making it possible to select among Markov equivalent causal graphs. This insight provides a theoretical foundation of a heuristic principle proposed in earlier work. We also sketch some ideas on how to replace Kolmogorov complexity with decidable complexity criteria. This can be seen as an algorithmic analog of replacing the empirically undecidable question of statistical independence with practical independence tests that are based on implicit or explicit assumptions on the underlying distribution.


title: Sambia succinica, a crown group tenthredinid from Eocene Baltic amber (Hymenoptera: Tenthredinidae)

abstract: Sambia succinica gen. et sp.n. from Eocene Baltic amber is described and illustrated. It is apparently the first amber fossil that can be definitively assigned to Tenthredininae. It displays two diagnostic forewing characters for this subfamily: having a bend distally in vein R and the junctions of veins M and Rs + M with vein R being some distance from each other. The variance and possible transitions between the anal vein configurations among the genera in Tenthredininae is briefly discussed.


title: Stereoscopic Neural Style Transfer

abstract: This paper presents the first attempt at stereoscopic neural style transfer, which responds to the emerging demand for 3D movies or AR/VR. We start with a careful examination of applying existing monocular style transfer methods to left and right views of stereoscopic images separately. This reveals that the original disparity consistency cannot be well preserved in the final stylization results, which causes 3D fatigue to the viewers. To address this issue, we incorporate a new disparity loss into the widely adopted style loss function by enforcing the bidirectional disparity constraint in non-occluded regions. For a practical real-time solution, we propose the first feed-forward network by jointly training a stylization sub-network and a disparity sub-network, and integrate them in a feature level middle domain. Our disparity sub-network is also the first end-to-end network for simultaneous bidirectional disparity and occlusion mask estimation. Finally, our network is effectively extended to stereoscopic videos, by considering both temporal coherence and disparity consistency. We will show that the proposed method clearly outperforms the baseline algorithms both quantitatively and qualitatively.


title: Machine Learning-Based Coding Unit Depth Decisions for Flexible Complexity Allocation in High Efficiency Video Coding

abstract: In this paper, we propose a machine learning-based fast coding unit (CU) depth decision method for High Efficiency Video Coding (HEVC), which optimizes the complexity allocation at CU level with given rate-distortion (RD) cost constraints. First, we analyze quad-tree CU depth decision process in HEVC and model it as a three-level of hierarchical binary decision problem. Second, a flexible CU depth decision structure is presented, which allows the performances of each CU depth decision be smoothly transferred between the coding complexity and RD performance. Then, a three-output joint classifier consists of multiple binary classifiers with different parameters is designed to control the risk of false prediction. Finally, a sophisticated RD-complexity model is derived to determine the optimal parameters for the joint classifier, which is capable of minimizing the complexity in each CU depth at given RD degradation constraints. Comparative experiments over various sequences show that the proposed CU depth decision algorithm can reduce the computational complexity from 28.82% to 70.93%, and 51.45% on average when compared with the original HEVC test model. The Bjontegaard delta peak signal-to-noise ratio and Bjontegaard delta bit rate are −0.061 dB and 1.98% on average, which is negligible. The overall performance of the proposed algorithm outperforms those of the state-of-the-art schemes.


title: Deep filter banks for texture recognition and segmentation

abstract: Research in texture recognition often concentrates on the problem of material recognition in uncluttered conditions, an assumption rarely met by applications. In this work we conduct a first study of material and describable texture attributes recognition in clutter, using a new dataset derived from the OpenSurface texture repository. Motivated by the challenge posed by this problem, we propose a new texture descriptor, FV-CNN, obtained by Fisher Vector pooling of a Convolutional Neural Network (CNN) filter bank. FV-CNN substantially improves the state-of-the-art in texture, material and scene recognition. Our approach achieves 79.8% accuracy on Flickr material dataset and 81% accuracy on MIT indoor scenes, providing absolute gains of more than 10% over existing approaches. FV-CNN easily transfers across domains without requiring feature adaptation as for methods that build on the fully-connected layers of CNNs. Furthermore, FV-CNN can seamlessly incorporate multi-scale information and describe regions of arbitrary shapes and sizes. Our approach is particularly suited at localizing “stuff” categories and obtains state-of-the-art results on MSRC segmentation dataset, as well as promising results on recognizing materials and surface attributes in clutter on the OpenSurfaces dataset.